Understanding Racial Portrayals on ESPN's

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Why is Michael Vick so fast and Peyton Manning so smart? Understanding Racial Portrayals on ESPN’s Sportscenter

by Daniel Coogan

B.A. in Government and Politics, May 2004, University of Maryland MPP in Program Evaluation and Analysis, December 2008, George Washington University

A Dissertation submitted to

The Faculty of Columbian College of Arts and Sciences of The George Washington University in partial fulfillment of the requirements for the degree of Doctor of Philosophy

August 31, 2013

Dissertation directed by

Gregory D. Squires Professor of Sociology

The Columbian College of Arts and Sciences of The George Washington University certifies that Daniel Coogan has passed the Final Examination for the degree of Doctor of

Philosophy as of April 10, 2013. This is the final and approved form of the dissertation.

Why is Michael Vick so fast and Peyton Manning so smart? Understanding Racial Portrayals on ESPN’s Sportscenter

Daniel Coogan

Dissertation Research Committee:

Gregory D. Squires, Professor of Sociology and Public Policy & Public Administration, Dissertation Director

Steven A. Tuch, Professor of Sociology and Public Policy & Public Administration, Committee Member

Kimberly Gross, Professor of Media and Public Affairs, Committee Member

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Acknowledgments

I wish to thank everyone directly or indirectly involved for their support and encouragement throughout this process. First and foremost, I want to thank my committee for their collective interest and participation in the project. I want to thank

Michael Wenger for joining the project toward the end of the process and offering helpful and considerate comments on what this research means from a broader perspective and why it matters. I want to thank Richard Zamoff for his careful review of the dissertation over the last few years and insightful suggestions on historical patterns and anecdotes of racial discrimination in sports. His advice certainly bolstered the project and offered context that I believe made it much more enjoyable to read.

Greg Squires and Steven Tuch were invaluable over the last two years. I wish to thank

Greg Squires for taking on the dissertation, for making sense of my early ideas and helping me formulate an actual research plan based on those ideas, and for serving as an advocate throughout the process. As anyone who has gone through this process will appreciate, Greg Squires recognized that if not the most important quality—certainly one of—a dissertation is that it be completed. For that I am grateful. Steven Tuch was instrumental in helping me understand how best to conduct analysis. Early on he pushed for more advanced methods, which greatly benefitted the research. His careful review of my analysis and many office meetings not only strengthened the paper, but also contributed to me developing a genuine interest in quantitative methods, which prior to this I was tepid about at best. Besides their guidance and support throughout the project,

Greg and Steven’s interest in the project made the dissertation fun.

None of this project would have even begun without my fifth committee member, Kim

Gross, who when she had a little less than no time on her schedule took me on for an independent study. Over the course of less than a year she alone provided me with a graduate education on media studies and psychology as it relates to race. Over the last few years, her exceptionally thoughtful and careful review of dozens of dozens of sections, chapters, and drafts have strengthened my dissertation immensely. And for someone, who coming into the process claimed to have little to no familiarity with sports, she frequently asked perceptive questions related to a sport that even the most ardent sport junkie might never consider.

Finally, I wish to thank those who had a less direct influence on the project but whom provided the support (and drinks) necessary to make this a virtually stress-free dissertation. To my mom and dad, Mercy and Bob, for too many things to list, but a family trip to Ireland served to perfectly re-energize me after completing comps. For my brother, Mick, whose interest in the project was unparalleled but, admittedly, I could have researched accounting standards or the use of nitrogen in fertilizer and he would have held the same genuine interest. His adherence to the philosophy that with work there must be revelry functioned to significantly inhibit dissertation stress and anxiety. For

Katie, my wife, whose ability to manage a chronically fastidious, compulsive, neurotic individual, like myself, is worthy of case studies, dissertations, and maybe even a discipline in itself. She is living evidence of the merits of marrying-up. For Nann and

Jim, my in-laws, who have provided years of Sunday dinners that functioned perfectly to

v rejuvenate me for a week of research and work. For Killarney without whom I would not have been able to complete the final stages of the dissertation with an 80 pound

Rottweiler sitting on my lap—naturally, in an office chair for one—or becoming irrationally jealous whenever I ‘pet’ my computer keyboard and mouse and not her. For my bros, who despite their aversion to LeBron James and any attempts at discussing race, served as an excellent outlet to email with whenever I needed a break. There were countless other friends and families—most notably the rest of the Lutz/ Keaton family— who made the last few years and the dissertation such an enjoyable experience and learning process. Thank you.

Finally, it should go without saying—all errors in this document are my own.

Abstract of Dissertation

Why is Michael Vick so fast and Peyton Manning so smart? Understanding Racial Portrayals on ESPN’s Sportscenter

Research examining racial portrayals in the sports media show that a physical- psychological racial dichotomy exists where black athletes receive praise for physical attributes and white athletes garner acclaim for cognitive attributes. But, many of these studies are limited because they focus on programming of only one sport, perceive race in binary terms, or fail to control for certain characteristics of athletes and commentators that could grant necessary insight. To add to the literature this study uses content analysis to analyze racial and gender portrayals over three months of Sportscenter programming in

2011 and 2012.

Logistic regression analysis revealed that while the black-white racial dichotomy found in the literature appeared to hold at an aggregate level, however analyzing content for individual sports and level of competition presented conflicting results. In particular, the findings from the literature were only supported in professional football and in college football and professional basketball the patterns of stereotyping were actually the opposite. The virtual absence of commentary on women’s athletics represented the other significant finding.

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Table of Contents

Acknowledgments….…...... iv

Abstract of Dissertation ...... vii

Table of Contents...... viii

List of Tables…………………………...... ix

Chapter 1: Introduction ...... 1

Chapter 2: Literature Review ...... 17

Chapter 3: Data Collection & Methods...... 45

Chapter 4: Analysis & Results...... 63

Chapter 5: Discussion & Conclusion...... 109

References...... 131

Appendices...... 146

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List of Tables

Table 1………………………………………………………………………………….12

Table 2………………………………………………………………………………….13

Table 3………………………………………………………………………………….56

Table 4………………………………………………………………………………….66

Table 5………………………………………………………………………………….68

Table 6………………………………………………………………………………….78

Table 7………………………………………………………………………………….82

Table 8………………………………………………………………………………….85

Table 9………………………………………………………………………………….87

Table 10..……………………………………………………………………………….88

Table 11..……………………………………………………………………………….94

Table 12..……………………………………………………………………………….98

Table 13..……………………………………………………………………………….99

Chapter 1: Introduction

“[Clogston] added that ‘it is true that we cannot house teams that have colored boys in the down town hotels,’ where teams with all white rosters were lodged. But he assured Mileham that [NC] State College had special facilities near its campus that were ‘ideal for athletic teams’.”

. The response from Roy Clogston, Athletic Director at North Carolina State University in 1958, to M. Charles Mileham, the director of physical education and athletics at the University of Cincinnati, when Mileham inquired about housing for Cincinnati’s visiting basketball team, which was led by African American superstar Oscar Robertson (Walker, 2011).

Sports represent a valuable platform for understanding racial dynamics in

America. In contrast to other institutions where historically entrenched racial discrimination has inhibited progress, black Americans, in particular, have achieved phenomenal success in athletics. In light of this success it is worth considering how the media portray minority achievement because of its ability to shape public discourse and to influence perceptions of an out-group’s behaviors and traits (Gamson & Modigliani,

1987; Newman & Uleman, 1989). Using the mainstream media as a barometer for racial portrayals proves worrisome as evidence shows that news programs present racial minorities unfavorably in stories on crime (Dixon & Linz, 2002; Entman & Rojecki,

2001), welfare and social policy (Gilens, 1999), classifying race and ethnicity in the political process (Squires & Jackson, 2010), and campaign advertisements (Mendelberg,

2001). But in none of these areas have racial minorities achieved the success that they have in athletics. Thus, does athletic merit mitigate racial stereotyping in the sports media? This paper seeks to address this question, and to gain a fuller understanding of stereotyping in the news media by focusing exclusively on sports news. In this first chapter I identify the problem, limitations of the literature, and the purpose of the study. I

1 then justify the study’s significance and my selection of the particular content used to measure racial portrayals.

Problem Statement

In light of stereotyping in the mainstream media, have the athletic achievements of African Americans over the past 70 years garnered them fairer treatment in the sports media than in other news focusing on relatively less significant achievements? Does athletic meritocracy supersede racial stereotyping? Entman and Rojecki (2001) found that blacks appear between three and four times as often in stories on sports as do whites (p.

66), which suggests an answer in the affirmative. But prior research on race and the sports media indicates a racial dichotomy where praise is bestowed upon black athletes for their physicality and white athletes for their psychological attributes (Buffington,

2005; McCarthy & Jones, 1997; Murrell & Curtis, 1994; Rainville & McCormick, 1977).

Emphasizing physicality implies that blacks’ achievements result from inherent and, thus, unearned attributes. Whites, on the other hand, are often portrayed as benefiting from qualities like discipline and effort that suggest earned achievement. While focusing on media portrayals may seem myopic when discussing broad social phenomena, like racial discrimination, evidence suggests that we understand other groups’ behavior based on traits and stereotypes (Newman & Uleman, 1989) and media coverage contributes to these stereotyped perceptions. Considering that media-driven discourse enables select groups to construct and define race and allows them to prohibit other groups from contributing to the discussion (Hall, 1997), stereotyping in the media may reflect majority

2 whites’ subtle efforts aimed at maintaining power and privilege over minorities (Pieterse,

1995).

That being said, the literature identifying the physical-psychological racial dichotomy suffers from four primary limitations. Researchers tend to focus on in-game commentary of individual sports (Billings, 2004; Eastman & Billings, 2001; McCarthy &

Jones, 1997; Rainville & McCormick, 1977), or of a single position within one sport

(Billings, 2004; Buffington, 2005; Mercurio & Filak, 2010; Murrell & Curtis, 1994). If these commentators possess beliefs unique to their sport then the culture of the sport might influence portrayals. In this study, I focused on a program with commentators that address all sports thus accounting for biases unique to any individual sport. Second, several studies evaluate racial portrayals within a binary black-white dichotomy (Billings,

2004; Buffington & Fraley, 2008; Denham, Billings, & Halone, 2002; Eastman &

Billings, 2001; McCarthy & Jones, 1997; Rada, 1996). Because I measure race categorically I can expand on simple black-white comparisons. Third, researchers have largely ignored the relationship between the race of the commentator and the racial portrayal. I controlled for race, gender and role of the commentator to gain insight on this relationship. Finally, most research reports only univariate and bivariate statistics

(Billings, 2004; Eastman & Billings, 2001; McCarthy & Jones, 1997; Rainville &

McCormick, 1977). I used multivariate analysis to better account for the simultaneous relationship of multiple independent variables.

Purpose of the Study

This study looks at how race and gender were portrayed on ESPN’s Sportscenter.

I used content analysis to measure descriptions from 91 episodes which occurred in

December 2011, February 2012, and May 2012. Logistic and multinomial logistic regression were the primary quantitative techniques used to predict comment types and value based on the independent variables, which included the athlete’s race, sex, sport, and level of competition and the commentator’s race, sex, and role. There are six main research questions. While race remains the primary focus, I included gender with the intention of understanding gender portrayals and accounting for the intersection of race and gender.

1. How likely are commentators to portray physical and psychological attributes

of athletes of different race?

2. How likely are commentators to praise or criticize athletes of different race

and gender?

3. How likely are commentators to offer praise or criticism in their physical and

psychological descriptions of athletes of different race?

4. How likely are commentators to praise or criticize athletes of different race

and gender in off-the-field scenarios?

5. How do these portrayals vary among different sports?

6. How do the race, gender, and role of the commentator influence the likelihood

that commentators will offer praise or criticism in their physical and

psychological descriptions of athletes of different race and gender?

Significance of the Study

This paper adds to the literature on racial portrayals in sports coverage because it includes new controls, like sport, level of competition and commentator role, it uses multivariate in addition to bivariate analysis, it expands on the black-white dichotomy by including categories for Asian and Latino/a, and it accounts for the race and gender of the commentator. For policymakers, this study grants insight on factors in the media that contribute to racial stereotyping, which could influence minority youths’ perceptions of professional and human capital development. The paper also presents evidence on the merits of promoting racial and gender diversity in news programming.

More broadly, racial portrayals on Sportscenter can influence a range of policy decisions if commentators undermine racial stereotypes. Viewers with limited interactions with individuals outside their racial and ethnic group, rely on the media to form opinions of those other racial and ethnic groups. As I will document later in the paper, on issues of crime and politics, we know the media are more critical of minorities than whites. Sports represent an institution where black Americans, in particular, have achieved phenomenal success, and Sportscenter, with its massive audience and cultural influence possesses a unique opportunity to undermine stereotypes and portrayals that the media promotes in other institutions, like crime and politics. If commentators provide a narrative that conflicts with the conventional portrayals and stereotypes found in the media, then Sportscenter may transform perception on race and achievement in a manner that mutes or reverses entrenched racial perceptions. As a result, the program could serve to undermine beliefs on race as they relate to broader social, political and economic arenas.

Hypotheses

Over the last thirty years, research shows that commentators disproportionately praise black athletes’ physicality in contrast to white athletes’ psychological acumen.

Because this paper controls for additional characteristics of athletes and commentators and because patterns may have changed since the bulk of the research was completed, I expect some new patterns to emerge and some to hold true. Below are seven hypotheses related to the research questions.

Hypothesis 1: In line with the literature, I expect that commentators will emphasize physicality for nonwhite athletes and psychological attributes for white athletes. Support for this comes from Gonzales, Jackson, and Regoli’s (2006) survey in which respondents stated that whites benefitted more from factors, like access to facilities and coaching, and that black athletes benefitted more from natural physical abilities1 (p. 52), which suggests that individuals may assign such attributions to athletes based on race.

Hypothesis 2: Commentators will portray black male athletes most negatively in “off-the- field” issues relative to non-black athletes (Rada & Wulfemeyer, 2005; Rainville &

McCormick, 1977). There is anecdotal evidence showing that athletes in basketball and football—the two professional sports in which black males represent the majority

(Lapchick, Kaiser, Russell, & Welch, 2010; Lapchick, Kitnurse, & Moss II, 2010)—tend to receive more attention for their off-the-field crimes in comparison to the coverage

1 The authors surveyed college students and not members of the sports media, who might possess different views on the relationship between race and ability.

6 white athletes receive for similar behavior (Eitzen, 2009). A survey and interviews of 104

NFL athletes (61.5% black) also discovered high-levels of law breaking and deviance

(Carter & Carter, 2007). I expect white and nonwhite commentators to stereotype equally in such scenarios because off-the-field descriptions are typically presented in segments that producers and commentators contribute to, which complicates efforts aimed at discerning the true progenitor of a portrayal. In other words, an off-camera producer, whose race the viewer has no knowledge of, determines what off-the-field content qualifies and, thus, practices the stereotyping rather than the on-air anchor or analyst, who is simply reporting the scheduled off-the-field content.

Hypothesis 3: I anticipate that the physical-intellectual racial dichotomy will be most prevalent in basketball and football, the two major professional sports in which black athletes are most represented (Lapchick, Kaiser, Russell, & Welch, 2010; Lapchick,

Kitnurse, & Moss II, 2010). Farred (2006) and Shields (1999) have argued that there exists an inherent “blackness” in professional basketball, and if perceptions of a sport can prime thoughts on race then racial stereotyping might be magnified for those sports.

However the nature of the sport and not the demographics of its participants might explain racial portrayals. Because no two sports share an identical culture and level of physicality and contact, this proposition is difficult to assess2. For example, the physicality for basketball is close to that of soccer, which being a more international sport has witnessed more ethnic than racial stereotyping. While hockey possesses a similar physical contact to football, the low number of minorities in hockey compromises

2 Not to mention, there is no research supporting my assumption that the level of physicality in a sport affects the likelihood of a given description and that likelihood holds across sports with similar levels of physicality.

7 comparison. I’ve encountered no research suggesting that a sport primes stereotyping.

The best analogy might be research showing that language and images can frame non- racial government programs in a racial context (Bonilla-Silva, 2003; Schuman et al.,

1997).

Hypothesis 4: Male athletes will be subjected to the physical-intellectual racial dichotomy more so than female athletes (Denham, Billings, & Halone, 2002; Eastman &

Billings, 2001; Kian, Vincent, & Mondello, 2008; Toohey & Veal, 2000; Van

Sterkenburg & Knoppers, 2004). Billings and Eastman (2007) suggest that these findings may reflect the misperception that female athletes are less athletic and physical than their male counterparts (p. 366). Among female athletes I anticipate that the physical- intellectual racial dichotomy will persist most in basketball where the relationships of race, class, and gender appear most complex (Hanis-Martin, 2011), and where less- concealing uniforms better signal race. Denham, Billings, and Halone’s (2002) analysis of the 2000 Men’s and Women’s NCAA Final Four basketball tournament found that black athletes as a whole garnered more physical description, however the study lacked the sample size to draw conclusions about female athletes. While the research measuring the interaction of race, gender and portrayal is limited, evidence shows that shifting attention on the social group membership of black women from race to gender elicits more positive automatic responses (Mitchell, Nosek, & Banaji, 2003). Therefore, a commentator that identifies a basketball player first as black and then as a woman may lead to stereotyping based on race but if the commentator identifies her primarily as a

8 woman who also happens to be black then gender stereotypes may be more likely and stereotypes based on race may be less prominent.

Hypothesis 5: Non-white hosts and analysts3 will be more likely than white hosts to recognize black athletes’ psychological abilities. I assume this because their lived- experience as minorities allows non-white commentators to better recognize stereotyping and to appreciate the qualities necessary for blacks’ success. I also anticipate that white analysts will be more likely than white anchors to acknowledge black athletes’ psychological abilities because analysts are more likely to have been athletes themselves.

(Of the three months I sampled none of the 19 hosts were former professional coaches or athletes in contrast to 43% of 127 analysts.) As former athletes, analysts possess personal relationships or a familiarity with current athletes that grant them more intimate knowledge of the attributes that contribute to achievement, which I assume will mitigate the likelihood that an analyst would accept and practice conventional racial stereotypes found in the sports media. Literature shows that interracial group interactions and the formation of personal relationships mitigate racial stereotyping. Amodio (2010) cites van

Banel’s research on brain activity of whites that showed that negative associations with blacks was overwritten once whites learned that blacks were ‘on the same team’ and friendly (p. 51). This supports Gaertner and Dovidio’s (2000) ingroup identity model, which claims that automatic prejudices diminish once people believe they share interests with another group. To inhibit racial stereotyping whites’ negative attitudes of blacks must be weakened and nonprejudiced attitudes must be made stronger and more

3 As I discuss in Chapter 3 hosts are the two individuals who introduce the show and the segments, and analysts present in-depth information on a topic or player. Analysts are every commentator that’s not one of an episode’s two anchors.

9 conscious, and individuals must purposefully activate beliefs that conflict with the stereotype (Devine, 1989). Personal relationships represent one such opportunity to achieve this (Amodio, 2010).

Hypothesis 6: Commentators will identify physicality the most for whites in hockey4. I base this assumption on the perception that hockey—the one professional sport that condones in-game fighting—is inherently violent and physical, which I assume will prime physicality for commentators as an attribute of hockey players. Because this sport is far whiter in terms of player demographics, this should lead to a greater likelihood of physical descriptions than for whites in all other sports5. The only research I encountered that analyzed hockey and race and ethnicity compared descriptions of white

Francophones to that of white Anglophones (Grenier & Lovoie, 1988; Lavoie, Grenier, &

Coulombe, 1987), thus this hypothesis is largely untested in the literature.

Hypothesis 7: In baseball, Latino players will garner more criticism and less praise for on-the-field descriptions than will white baseball players. In analysis of the media’s coverage of Mark McGwire and Sammy Sosa’s chase to break Roger Maris’s home run record in 1998, Butterworth (2007) claims that the media portrayed McGwire as the exceptional American and Sosa as a dehumanized, comic-relief, side-kick. Similarly,

Regalado (1998) documented a history of racial animosity directed at Latino MLB

4 The most recent racial and gender report card capturing data on the National Hockey League (NHL) showed that as of the 2001-02 season the league is far whiter (98%) than Major League Baseball (MLB) (60%), Major League Soccer (MLS) (60%), NFL (33%), and the NBA (20%) (Lapchick, 2003). The 1% of African Americans in the NHL is one-tenth of the percentage of the next major league sport (MLB) with the lowest percentage of African American athletes. 5 I should acknowledge that the near absence of non-white hockey players inhibits any comparison between physicality of white-dominated hockey with physical stereotypes of black athletes in sports outside of hockey.

10 athletes in Viva Baseball. While anecdotally interesting, these works lack the rigor to confirm that such patterns were common throughout the broader MLB community.

Why Study Sportscenter?

Before delving into the literature on race and the sports media, it is necessary that

I justify the content I will analyze in this study. Because of the effects that stereotyping may have on minorities as well as the mainstream white audience I wanted a platform that was not just popular among whites but also non-white audiences. This meant finding a sports news with high ratings and broad appeal. A nationally representative survey indicated that in comparison to whites, blacks had a higher level of comfort with television, fewer positive sentiments toward the internet, and watched more sports than did whites and Latinos (Albert & Jacobs, 2008). Thus, television and sports news seemed like an appropriate platform for analysis. I selected ESPN’s Sportscenter for four reasons.

First, it covers all major sports, which allowed me to control for cultural characteristics unique to individual sports. Second, it regularly includes minority commentators, which enabled me to understand the relationship between portrayals and commentator race.

Third, Sportscenter is a social phenomenon. Miller and Shales (2011) and Olberman and

Patrick (1997) detail the cultural force of a program that regularly draws millions of viewers per episode per day (Evey & Broughton, 2004; Smith & Hollihan, 2009; “TV by the Numbers Ravens/Browns, Sportscenter, WWE RAW, Project Runway, and Sons of

Anarchy Lead Cable Viewing with adults 18-49,” 2009) and averages up to 115 million viewers per month (“ESPN Fact Sheet”, 2010). Finally, Sportscenter represents a hard- test of the level of racial stereotypes on sports news because it is marketed as hip and

11 progressive and possesses a racially diverse cast—characteristics ideal for repudiating stereotypes. Therefore, if stereotyping does occur on Sportscenter then I would expect it to be magnified on less progressive newscasts.

While having a high number of minority commentators makes Sportscenter a worthy case study, illustrating its diversity in comparison to other news programming presents a challenge. Where most nightly news programs may have no more than a half- dozen anchors and a few more analysts (i.e., sports reporters, meteorologists, etc),

Sportscenter has dozens of anchors and hundreds of analysts. This presents an issue because adding a single minority commentator can make programs with a smaller newscast corps appear racially diverse. For a comparative baseline then I needed a larger sample of newscasters. Table 1, the most recent Radio Television Digital News

Association’s (RTDNA) Women and Minorities Survey, lists such statistics by showing the overall diversity of TV and radio news sports programming.

Table 1: Results from the 2008 RTDNA Newsroom Racial Demographics Survey African Asian Native Caucasian American Hispanic American American News 76.3% 10.1% 10.3% 2.7% 0.5% Television Workforce News Anchors 75.3% 11.9% 9.1% 3.5% 0.2% News Reporter 70.8% 12.4% 10.8% 5.6% 0.6% Sports Anchor 86.0% 7.6% 5.8% 0.6% <0.1% Sports Reporter 83.8% 5.2% 6.4% 3.2% 1.3% Racial identifiers are those used by RTDNA.

Because Sportscenter’s cast varies depending upon the timeslot and because of inconsistencies in online repositories it is difficult to get an accurate picture of the true racial make-up of those who appear on the show. For example, as of July 13, 2011 of

Wikipedia’s 44 listed ESPN Sportscenter “personalities” 79% were white, 14% black, and 7% Hispanic (“List of ESPN Personalities”, 2011). TV Guide (same date) lists only

36 Sportscenter cast members of which 77% were white, 11% black, 6% Hispanic, and

6% Asian (“Sportscenter: Cast and Details”, 2011)6. Therefore RTDNA’s, albeit dated, survey provides the best source for comparing racial demographics. In Table 2 I provide the count and percentages of commentators that contributed at least one comment in my sample. In comparison to the RTDNA survey, Sportscenter is far more diverse at anchor and has a far greater percentage of black analysts. Latino/a and Asian analysts, however, were underrepresented. The percentages in Table 2 are close to Messner, Cooky and

Hextrum’s (2010) analysis of Sportscenters in March, July, and November of 2009, which reported that commentators were 72% white, 22% black, 3% Latino/a, and 3% other, and 89% male (p. 21).

Table 2: Sportscenter Commentators’ Racial, Gender, and Role Statistics Black White Latino/a Asian Male Anchor 4 (24%) 10 (59%) 1 (6%) 2 (11%) Female Anchor 1 (14%) 6 (86%) 0 (0%) 0 (0%) Male Analyst 34 (30%) 78 (69%) 1 (1%) 0 (0%) Female Analyst 1 (11%) 7 (78%) 1 (11%) 0 (0%) Total 40 (27%) 101 (69%) 3 (2%) 2 (2%) Out of 146 total commentators in my sample 89% were male and 16% were anchors.

Sports’ News & Automatic Responses

Most of the prior research on stereotyping in sports focuses on in-game commentary, and not sports news programming. Thus I wish to address two concerns specific to Sportscenter. First, an issue arises if the audience that watches sporting events differs from Sportscenter’s audience. I’ve only encountered one piece of undated market research (Research: Sports Landscape) that compares Sportscenter viewership with that of college football and it showed that two-thirds of college football viewers watched

6 These online repositories fail to identify commentator role and I found errors, such as Wikepedia’s failure to associate Herm Edwards, Trent Dilfer, Doug Gotlieb, and Jay Bilas with Sportscenter.

Sportscenter daily, which suggests that the audiences are likely quite similar. The other issue is that some of Sportscenter—like the nightly news—is scripted, which deviates from more reflexive in-game commentary. Thus, differences might emerge between research focusing on in-game commentary and Sportscenter if in-game commentators stereotype more because they are more likely to rely on automatic processes. However in their book ESPN: The Company, Smith and Hollins (2009) state that “anchors were encouraged to talk sometimes at great length, making up narrative occasionally as they went along (p. 112).” In fact, the authors argue that this approach of emphasizing

“unscripted, personality-driven conversation” served as a framework for future cable television news programming. Thus, despite being scripted, Sportscenter commentators provide instant, unrehearsed reactions to content.

Unscripted content limits commentators’ ability to filter stereotypes, which makes analyzing live television preferable to that of the print media, which thoroughly filter content in the editorial review process. Here I analyze a live program that is less edited than print but not as unscripted as live in-game commentary. Analyzing unscripted, unfiltered content is necessary because views on race and sensitive social issues can conflict with reported beliefs (Fazio & Olsen, 2003; Greenwald, McGhee, & Schwartz,

1998; McConnell & Leibold, 2001), which means stereotyping should be more prominent in live, unfiltered programming. To measure racial attitudes psychologists often rely on self-report measures, like the modern racism scale (Henry & Sears, 2002; McConahay,

1986) that consist of a series of questions aimed at unmasking subtle or hidden stereotypes. But recent research has challenged self-report surveys for ignoring social desirability factors (Devine, Brodish, & Vance, 2005), for confounding racial feelings

14 with political perspectives (Sniderman, Crosby, & Howell, 2000), and for failing to account for automatic responses when individuals are motivated to suppress their actual beliefs (Fazio, Jackson, Dunton, & Williams, 1995). Automatic responses differ from controlled, self-reported responses in that the former reflect unintentional and spontaneous reactions of associations established through repeated events7 (Devine,

1989). This is important because implicit attitudes and stereotypes that arise in automatic responses—such as in unscripted, live TV—better predict behavior in racial interactions than do controlled responses (Akalis, Banaji & Kosslyn, 2008).

Because Sportscenter is partially-scripted and relies on Teleprompters, its automatic responses might differ from those of controlled psychological research where study participants typically must respond to content of which they have no prior knowledge. As a result scripted-segments and Teleprompters may serve as a buffer that mitigates commentators’ automatic responses or that enables them to better moderate stereotypes. While this represents a concern, evidence shows that scripting and the use of teleprompters in television news has failed to eliminate racial stereotypes during pretrial criminal coverage (Dixon & Linz, 2002) and in portrayals of women on social welfare

(Maura, 2010). Further, automatic responses may arise even if individuals attempt to ignore or consciously avoid them (Neely, 1977). Not to mention, inherently reactionary commentary, like that of unscripted, in-game commentary, may lead audiences to view

7 In her distinction between prejudiced and non-prejudiced individuals, Devine argues that where prejudiced individuals must actively suppress automatic stereotypes because of their distaste for them, truly non-prejudiced individuals actually possess no pressure to moderate such automatic responses because those beliefs do not exist. Further, the greater the effort that individuals that actively suppress stereotypes place on rejecting those automatic responses that they view with such distaste signifies progress toward non-prejudice.

15 stereotyping with less alarm than if it occurred on a format, like the sports’ news, where there is opportunity for deliberation8.

Organization of the Chapters

The exceptional athletic achievements of African Americans, in particular, seem to justify fairer treatment for them in the sports media than in news focusing on other social arenas where entrenched racism has resulted in relatively less significant achievement. Sportscenter represents an ideal platform to study because of its high ratings and broad appeal across racial groups. In the following chapter I identify my theoretical perspectives and address the literature on race and the sports media with a focus on language, images, names, and the intersection of race and gender. This chapter details past findings and elaborates on the research that underlies my hypotheses and expectations. Chapter three details the sampling technique, the data collection and research method, the construction and measurement of the dependent and independent variables, and the analytical technique used in the study. Chapter 4 presents the findings of my analysis of Sportscenter, and Chapter 5 summarizes the findings, discusses their importance, and identifies limitations and policy implications.

8 Fortunately, it is worth noting that the ingrained stereotypes that emerge in automatic responses can be corrected. The in-group identity model states that shared interests with out-group members mitigates prejudice, and allows groups to ‘overwrite’ negative associations with others through camaraderie (Amodio, 2010). Weakening previous associations with purposefully activated personal beliefs that challenge automatic stereotypes can further mitigate prejudice (Devine, 1989). Therefore, policy makers interested in mitigating racial stereotyping in the media should recognize ingrained social biases that contribute to stereotyping and consider initiatives that promote the mutual interests of different racial groups.

Chapter 2: Literature Review

This chapter is divided into four sections. First, I identify the three theoretical perspectives that help explain the problem of racial stereotyping in the sports media.

Second, I provide background literature on racial discrimination in athletics. Third, I review research on race and the media and identify potential media effects associated with racial stereotyping. Finally, I cover literature that focuses on stereotyping in the sports media.

Theoretical Perspectives

The media reinforces—and has the potential to undermine—stereotypes through its news coverage. As I discussed in the first chapter the sports media reflects a worthy medium to understand if and how such stereotypes emanate9. I used three theoretical perspectives to develop my research questions. Each perspective also serves to explain a distinct component of stereotyping in the sports media.

Framing theory identifies how the media can focus attention on specific attributes of an event to influence perceptions on it. Therefore the media’s practice of couching black success in terms of physicality in contrast to whites’ intellect and mental discipline affects perceptions on blacks’ and whites’ natural abilities. Because I’m interested in how the media frame racial and gender issues, social constructivism is also relevant to framing theory. Second, social dominance theory contends that racially- and gender-biased

9 This theoretical discussion focuses primarily on the physical-psychological racial dichotomy and it is limiting if audiences believe—regardless of the presence of stereotyping— that psychological qualities are integral to and coterminous with physical success. Research measuring media effects in sports is conflicting as there is evidence that a viewer’s race affects how he negotiates racial portrayals (Van Sterkenburg & Knoppers, 2004) but that contextual factors, like personal experiences, and ethnic identification, also play a role (Dubrow & Adams, 2010). Stereotyping proves most damaging if it influences broader, negative perceptions of minorities.

17 language reflects dominant groups’ efforts to maintain hegemony over minority groups.

This perspective expands on the rationale for including framing theory because social dominance theory indicates that white commentators inadvertently stereotype black athletes in a manner that qualifies their success by basing it not on accepted Western norms, like effort and intelligence, but on natural ability, which reinforces a racial hierarchy that identifies whites as the most deserved and legitimate racial group. Third, social learning theory addresses how institutions and environment can influence and reinforce behavior, and it explains how generations of a predominantly white, male media corps contribute to a cyclical culture that portrays minority athletes in a pejorative manner.

According to Scheufele and Tewksbury (2007) framing in the news reflects how an issue is characterized and how audiences interpret the depiction (p. 11). The response to the US’s drug policy in the 1980’s presents an example from public policy. As the crack cocaine epidemic plagued inner cities, conservative policymakers promoted a ‘war on drugs’ that criminalized use and severely punished individuals tied to the drug trade, which caused a swell of incarcerated young black men. Policymakers began framing the socioeconomic degradation of America’s cities as either an issue of victims’ rights

(Kennedy, 1998), which tended to prefer punitive sentencing measures, or of regressive policies that used incarceration (instead of drug treatment) to create a socially disruptive criminal justice system (Tonry, 1996). Over the following decades these two conflicting perspectives greatly influenced the debate over drug policies, like the powder-to-crack sentencing ratio and mandatory sentencing laws.

Scheufele and Tewksbury (2007) explain that framing functions as a macroconstruct in the way the media present information so it resonates with viewers’ schemas, and as a microconstruct in the way viewers use the information to form impressions (p. 12). Research shows that the latter—the way frames are used by individuals and how frames influence opinions—help to understand information and frames presented in the media. This paper focuses on the former because to understand it as the latter would require evaluating viewers’ reactions to stereotyping; such empirical analysis falls outside the scope of my research model. It is also necessary to understand how viewers make inferences based on the organization and interpretation of information

(Pan & Kosicki, 1993). Scheufele (1999) identifies frame building as the media’s organizational or structural factors that influence the frames present in media texts. In the sports media, frame building focuses on portrayals, such as: Do the media associate a black athlete’s success with his size, work ethic, or a combination of qualities?

The nature of content matters because the media’s selection of content can affect viewers’ perceptions (Goffman, 1974). Thus, if the media dedicate more content to black athletes’ criminality than to their charity, viewers may prioritize their criminality as a defining trait. Considering that evidence indicates that individuals understand others’ behavior based on traits (Gamson & Modigliani, 1987; Newman & Uleman, 1989), identifying black athletes as particularly criminal or physical may affect perceptions on race in venues exclusive of sports.

Implicit framing occurs when content reflects cognitively accessible thoughts that give structure to racial predispositions, or ‘schemas’ (Price & Tewksbury, 1997). Winter

(2008) states that racial schemas divide the world into in- and out-group, identify

19 qualities for each group, are often based in a zero-sum competition of “us” versus

“them”, and include ingrained beliefs about work, success and discrimination. The physical-intellectual racial dichotomy present in the sports’ media is a racial schema that tends to identify physicality as a quality specific to black athletes and categorizes them as an out-group. Inherent in the description is an implicit disregard for traits like intellect and work-ethic which are presumably monopolized by the white in-group. Further, disregarding blacks’ work ethic as a means to their success delegitimizes their achievements and prevents them from sharing the prestige of benefitting from that preferred American norm.

In the sports’ media, Billings (2004) argues that commentators do not explicitly state that a black athlete is inferior; rather they describe the athlete so the viewer is uncertain if he possesses inherent qualities different from other athletes (p. 203). Praising physicality and ignoring equally critical psychological attributes proves damaging because it qualifies black achievement as inherent rather than earned, and because it may contribute to schemas that imply that blacks possess a deficiency of effort or intellect. In other words, sports function similar to non-racial policy programs in that the two are ostensibly non-racial but can be viewed through a racial lens.

Social constructivism is the concept that groups can create a social knowledge and reality influenced by factors like culture and history (Scheler, 1980). This theory suggests that media content is relevant because it can influence audience opinions through how the media constructs reality. The media helps manufacture such social realities (Cohen &

Young, 1981), and in societies, like the US, where the media’s breadth makes institutions dependent upon it, its power and influence grows exponentially (Tuchman, 1978). Fisk

(1987) states that the media construct reality in a syntagmatic fashion through how it presents content. Reality can often be constructed syntagmaticaly through language and stereotypes, which enable news programs to facilitate the story-telling process (Dowler,

2003). Thus, media-based socially constructed narratives on race, such as that of the physically superior black athlete, may unintentionally serve to reinforce broader social narratives aimed at qualifying blacks’ achievements.

Social dominance theory states that dominant groups seek to establish hierarchies and perpetuate inequality to maintain power over other groups (Nederveen Pieterse,

1995; Sidanius & Pratto, 1999). A social dominance perspective provides an understanding of why the dominant, majority group (i.e., whites) might seek to construct a reality that diminishes black success in order to maintain their own position. Van

Sterkenburg and Knoppers (2004) argue that the sports media represent an ideal medium to create and reinforce socially constructed negative stereotypes of subordinate groups because it is a “site of struggle” where different groups influence the discussion of social power structures (p. 302). Where social dominance theory in many cases may imply intent on the part of a dominant group (in this case whites), in the sports media I believe the practices of most white commentators reflect an ingrained culture of sports journalism and not deliberate, conscious attempts at establishing racial bifurcation. Also, considering the large contracts awarded to professional athletes it is inaccurate to equate racial stereotyping in athletics with discrimination outside of it where in most professions whites earn greater income and possess more wealth than blacks. In football and basketball, two of the more lucrative professional sports, black athletes make-up well over half the playing corps and are among the highest performing and, thus, highest paid.

But stereotyping can reinforce the belief that whites’ success and playing income is earned and that blacks’ is unearned and undeserved. It also associates whites with cognitive qualities, which are perceived as more important than physical ones in Western culture. Therefore white commentators inadvertently portray athletes based on racial stereotype in a manner that implicitly reinforces racial hierarchies.

The culture of media organizations also helps explain why stereotyping occurs and how it is perpetuated. The news influences journalists just as it does those that read and view it (Fishman, 1980), which enables the news media to create and perpetuate its own cultural perspectives. Grounded in critical theory, the cultural studies perspective holds that the sports media functions to develop perceptions on race, which are then practiced in society (Van Sterkenburg, Knoppers, & De Leeuw, 2010). As a result, a select few in the media construct a discourse on race (Hall, 1997), which results in dominant groups stereotyping non-dominant groups and repressing their point of view

(Dyer, 1997; Van Sterkenburg & Knoppers, 2004). In the sports media this emanates in the form of discourse aimed at maintaining the status quo that contrasts the intelligent and diligent white athlete with the cognitively-limited, undeserving black athlete (Van

Sterkenburg, Knoppers, & De Leeuw, 2010, p. 822). Overtime journalists become continuously subjected to the same stereotypes to the extent that they may then reproduce those stereotypes themselves.

Endorsing racial difference and hierarchy allows the media to subtly reinforce prejudice built into human cognition (Entman & Rojecki, 2001). Eastman and Billings

(2001) contend that because sports’ commentary exists in an “emotionally laden context, and its messages are repeated hundreds of times, hidden biases are likely to be stored in

22 long-term memory without attachment to any particular source (p. 185).” Because individuals focus more on negative attributes and on behaviors that confirm stereotypes

(Rothbart & John, 1993), the majority white news corps may be more inclined than minority newscasters to reinforce negative racial stereotypes. Social learning theory holds that over time this can result in white commentators imitating culturally acceptable behavior and language that qualifies the abilities of black athletes (Bandura, 1971), and can lead to one generation of journalists establishing standards that ensuing generations adopt and practice (Mercurio & Filak, 2010). As a result, a cyclical culture of stereotyping arises as the stereotyping of one generation of journalists begets others.

Racial Discrimination and Athletics

First, it is important to recognize the discrimination that black athletes, in particular, have encountered despite their athletic success to illustrate the ingrained and pervasive nature of racial stereotyping. Many of the studies on the topic rely on data that are almost twenty years old, which suggests either that researchers are less interested in studying racial discrimination in sports or that there is declining evidence of its practice.

With the exception of the discrepancy between the high participation rate of minority athletes in basketball, football and baseball, and the relatively low number of minority coaches, athletic directors, and owners I expect the latter to be the case despite an absence of empirical analysis supporting its decline. Evidence of racial discrimination in player compensation in the 1980s and 1990s is well-documented in the NBA (Hamilton,

1997; Kahn, 1991; Kahn & Shah, 2005), MLB (Lapchick, 1991), and the NFL (Kahn,

1992). At the college level Edwards (1980) identifies the irony of black student athletes

23 receiving scholarships for high revenue sports, like football and basketball that support low revenue sports dominated by white student athletes.

Evidence of stacking, or assigning players to race-specific positions, existed in

MLB (Margolis & Piliavin, 1999; Smith & Seff, 1989; Sack, Singh, & Thiel, 2005), college football (Hawkins, 2002), English Soccer (Melnick, 1988), and the NFL (Eitzen

& Sanford, 1975). Although coaches and managers—and not the media—practice stacking, it and stereotyping are based on a similar rational, which supports the argument that the media may propagate such perceptions. In football whites tend to be stacked at the quarterback position, which demands poise, intellect, and leadership, and blacks tend to be stacked at running back, which demands speed, strength, and durability (Hawkins,

2002; Eitzen and Sanford, 1975). Such stereotypes become so ingrained in sports’ culture that when blacks play quarterback commentators often emphasize the physical attributes ideal for a running back and ignore the psychological qualities necessary for success at quarterback. If stacking occurs then an athlete’s position would confound the relationship between race and description. In other words, if the quarterback position is associated with intelligence and leadership and if quarterbacks are overwhelmingly white then who’s to say that the descriptions of a quarterback are reflective of race and not of the position? While this is a legitimate concern, the stacking studies cited above rely on much older data, which is likely the case because of its declining practice as evidenced by the high number of minority athletes that now play ‘cognitive’ positions, like quarterback or point guard.

There is also evidence of structural discrimination, such as professional sports’ leagues implementing rules that penalize black culture and suppress black athletes

(Leonard, 2006; Simons; 2003). In the NBA, Price and Wolfers’ (2010) showed that black players received more fouls and scored fewer points when white referees called the game. At the professional and college levels Coakley (2004) and Eitzen (2006) illustrated the stark underrepresentation of blacks in coaching and management positions in contrast to the large number of black athletes playing the sports10. This is a problem because some white coaches stereotype black players as lacking motivation, discipline and, intelligence

(Lapchick, 1991). In comparison to white athletes, black athletes have reported greater feelings of exploitation (Leonard II, 1986), and experienced greater scrutiny over their families and backgrounds from agents and team personnel in preparation for the NFL draft (Dufur & Feinberg, 2009). Black athletes and their families also experienced discrimination during competition (Lawrence, 2005; Spencer, 2004). A recent example of this occurred in September 2011 when a white hockey fan threw a banana peel onto the ice in the direction of the Flyer’s Afro-Canadian Wayne Simmonds.

There is also evidence (that is again, quite dated) that sports’ customers discriminate. In a survey of Philadelphia 76er fans in 1980-81, white fans reported that they preferred to watch white athletes and would not want to watch black athletes

(Lapchick, 1991). Researchers have estimated that replacing a black player with an identical white player increased attendance between 8,000 and 13,000 fans (Kahn &

Sherer, 1988), and that fans watch more games of teams with white players, even if the white players are on the bench (Kanazawa & Funk, 2001). The latter study, which analyzed Nielsen ratings, projected that a white player increased a team’s commercial advertising revenue anywhere from $2,600 to $27,200 per game.

10 Cunningham, Bruening, and Straub (2006) showed that in comparison to white coaches, black coaches’ perceived race to represent more of a limiting factor in gaining a coaching position; and black football coaches believed they would have greater occupational turnover than their white counterparts.

Studies on MLB attendance show that black pitchers drew fewer fans than white pitchers in 1967 (Sully, 1974). White players positively affected attendance in the predominantly black NBA (Kahn & Sherer, 1988; Koch & Vander Hill, 1988; Brown,

Spiro & Kennan, 1991). Conversely, there is evidence that black athletes raised attendance in 1950’s MLB (Gwartney & Haworth, 1974), had an insignificant effect on mid-1970’s MLB team revenue (Sommers & Quinton, 1982), and significantly increased

NBA team revenue from 1978-1981 (Scott, Long & Somppi, 1985). Further, the addition of a foreign-born player to an MLB roster increased stadium revenue by almost $600,000 in 2000 (Tainsky & Winfree, 2010). Karabel and Karen (1982), and Burdekin and Idson

(1991) find a positive relationship between a team’s racial composition reflecting that of their franchise city and attendance, but Leonard (1997) and Schollaert and Smith (1987) showed no effect on attendance. These conflicting results likely reflect the seasons and controls, such as the type of sport, measured in each study. There is, however, anecdotal evidence suggesting that in the NBA discriminatory hiring was practiced until the early

1980s. Ted Stepien, former owner of the Cleveland Cavaliers, claimed in 1980 that, “I think the Cavs have too many blacks, ten of eleven. You need a blend of white and black.

I think that draws and I think that’s a better team (as cited in Lapchick, 1991, p. 281).”

The research above relies largely on data from the 1960s to 1990s, which are antiquated considering current racial dynamics. Further, factors that mitigate discrimination have emerged, such as the NBA’s 2011 collective bargaining agreement, which structured players’ salaries, so discrimination is unlikely. Newer research shows a decline in customer discrimination. Where Nardinelli and Simon (1990) found that baseball cards of black players sold for 10% and 13% less, respectively, than similar

26 white players, more recent analysis of basketball (Broyles & Keen, 2010), baseball

(Findlay & Santos, 2012; Hewitt et al, 2005), and football (Primm et al, 2010; Regoli,

Primm, & Hewitt, 2007) cards finds no relationship between race and card value11. With respect to achievement, racial discrimination adversely affected success in MLB

(Gwartney & Haworth, 1974; Leonard, Pine & Rice, 1988) and English soccer

(Szymanski, 2000). In the NBA, diversity exhibited no effect on a team’s winning percentage (Weiss & Sommers, 2009), which makes sense considering the high percentage of black players. Intuitively, discrimination hinders performance because diverse teams benefit from gaining access to an untouched talent pool (Goff, McCormick,

& Tollison, 2002).

Race & the Sports Newsroom

Now I will provide context on diversity in the sports newsroom and on sports’ media effects, which address potential results of stereotyping. The absence of minority commentators may explain the media’s pejorative portrayals of black athletes (Eitzen,

2006; Lapchick, 1991; Messner & Sabo, 1994)12. Eitzen and Sage (2003) cite statistics collected between 1995 and 2002 that show that minorities accounted for 12% of newspaper journalists, that 44% of newspapers had no nonwhite reporters, that Sports

Illustrated had no African American department heads or editors, and that the National

Association of African American Sportswriters and Broadcasters reported a membership

11 Of course these findings reflect only those factors that the researchers coded for. An important factor that is often omitted in these studies is one that looks at the status of superstars. For example, New York Yankee great Mickey Mantle’s memorabilia have maintained great value over the year. It would be interesting to contrast the worth of his memorabilia with that of a great African American player, like Willie Mays or Hank Aaron. 12 This argument holds with the non-sports, mainstream media as well.

27 of less than 5% of total professionals in print and electronic sports’ journalism (p. 262-

263). The dearth of black media members has inhibited quantitative analysis on the relationship between commentator race and stereotyping on account of an insufficient number of black sportswriters (Murrell & Curtis, 1994). Thus, despite a lack of evidence

Rada and Wulfemeyer (2005) speculate that “the combination of predominantly white reporting and announcing corps . . . [in combination with a] large number of African

American athletes . . . can create an environment that is ripe for the reproduction of racialized representations (p. 67).”

Having few minorities in the sports’ media presents a problem because commentators’ framing power allows a majority white media corps to establish a white- dominated power structure that creates and reinforces stereotypes (Hoberman, 1997;

Lavelle, 2010; Staples & Jones, 1985; Van Sterkenburg & Knoppers, 2004). Hall (1995) argues that stereotyping makes certain racial issues more salient and shapes perceptions on race. If this is true then increasing diversity in the sports newsroom may enable minority commentators to restructure the dialogue on race and sports, and to influence broader racial perceptions. In one of the few studies with a sufficient number of comments from black commentators, for statistical analysis, Eastman and Billings (2001) reported that minority announcers “somewhat mitigate the impact of racially loaded speech coming from white announcers . . . [and] are part of the solution, while white broadcasters are the primary problem (p. 198).” But these findings should be qualified because the authors only analyzed basketball, an overwhelmingly black sport.

How then does the race of commentators from sports, like baseball, with different racial dynamics affect portrayals? Although African American newspapers from the first

28 half of the 20th century offer little comparative value to 21st century sports media, Reisler

(1994) argues that black sportswriters that covered the Negro Leagues enhanced perceptions of black athletes13:

“Jackie Robinson said that he could never have made it to the major leagues without [Negro League writer Wendell Smith’s] help. Indeed, [black sports writers’] lasting collective contribution may have been an eloquent, persistent and occasionally bitter demonstration of words designed to urge the white baseball establishment to integrate . . . Arguably, their campaign was what finally pushed big league owners to question and finally end the color ban (p. 2).”

But this favorable press was likely less reflective of the nature of baseball than it was the role of the black press. And, fortunately since Eitzen and Sage (2003) collected their statistics on minorities in sports journalism the presence of minority sports media members appears on the rise. According to the 2010 Racial Report Cards, nonwhites make up 28%, 27%, and 22%, of radio and TV announcers in the NBA, NFL, and MLB, respectively (Lapchick, Kaiser, Russell, & Welch, 2010; Lapchick, Kitnurse, & Moss II,

2010; Lapchick, Kaiser, Caudy, & Wang, 2010). Research on the role of black media— or, black-run platforms dedicated to issues in the black community (Wolseley, 1990)— suggests that an increase in minority participation may mitigate stereotyping.

In comparison to the mainstream media, the black media focuses more attention on how issues adversely affect African Americans and other minority groups (Clawson,

Strine & Waltenburg, 2003; Grose, 2006). Dedicating more content to minority groups says nothing of a decline in or less caustic stereotyping however measuring perceptions of the black media may fill this void. Black viewers rate the black media more favorably

13 The efforts and actions of white media members in facilitating Robinson’s transition into MLB should not be ignored. Perhaps none were more integral than Brooklyn Dodgers’ play-by-play broadcaster, Red Barber, whose praise of Robinson’s skill and support for further integration in baseball benefitted many black MLB players in the middle of the 20th century (Barber, 1982).

29 and as more believable than the mainstream media (Vercellotti & Brewer, 2006) 14 and report that it increases self-reliance and a willingness to form coalitions with nonwhites

(Harris-Lacewell, 2004). Further, surveys of black viewers show that the black media decreases support for negative stereotypes while enhancing racial consciousness (Allen &

Thornton, 1992), which may occur because black media organizations repute stereotypical portrayals. Therefore increasing the presence of minority newscasters may result in more balanced reporting. That being said, a black individual working for a major media organization, like ESPN, likely possesses less influence over agenda-setting and framing than does one working for a black-owned and –managed organization.

Race, the Media, and Media Effects

While the presence of stereotyping is concerning, it is also important to understand if and how it affects perceptions on race. If audiences recognize bias and reject the stereotype then the media effects are mollified. The literature, however, suggests this is not the case. In his paper on young men of color in the media, Entman

(2006) argues that “the media are among the most powerful sources of mental impressions that people form of categories of out-groups (p. 5).” This is troubling considering that television entertainment programming tends to portray minorities as more violent, deviant or antisocial than whites (Entman, 2006; Entman & Rojecki, 2001;

Oliver, 1994; Rich et al., 1998).

14 In a sample of 386 African American respondents in North Carolina Vercellotti and Brewer (2006) found there to be evidence of the strongest relationship between political alienation and preference for black media (p. 247). Interestingly, with respect to gender Baiocchi-Wagner and Behm-Morawitz (2010) surveyed 347 undergraduate students’ perceptions on the credibility and persuasiveness of reporters and found that gender exhibited no difference for male and female respondents. In another somewhat related study, a controlled experiment aimed at understanding black journalism students’ perceptions on subjects of varying race involved in ethical dilemmas, Coleman (2011) reported that race had no effect on the students’ ethical reasoning related to the subjects.

Studies of the news show that blacks are twice as likely as whites to experience unfavorable pretrial publicity (Dixon & Linz, 2002), and that they are significantly more likely to be shown as perpetrators than as victims of crime (Romer, Jamieson, & De

Coteau, 1998)15. In fact, Rada and Wulfemeyer (2005) contend that black athletes are

“typecast into the same stereotype-ridden portrayals that have been found in other venues of television programming (p. 81).” Such stereotyping in the news media may explain studies showing that whites perceive blacks as a whole as being more dangerous

(Gilliam, Iyengar, Simon, & Wright, 1996; Mastro, Lapinski, Kopacz, & Behm-

Morawitz, 2009). It also may explain Johnson et al’s (1997) analysis that consisted of showing violent and non-violent content to participants and found that in comparison to white defendants, respondents identified the behavior of black defendants as being more related to their disposition (p. 86).

But are these issues reflective of all news content or just stories focusing on crime? The literature suggests that similar patterns might be prevalent in the sports media as well (Eitzen, 2009; Rada & Wulfemeyer, 2005; Rainville & McCormick, 1977).

Although personal experiences influence discourse on race and athletes (Van Sterkenburg

& Knoppers, 2004), evidence shows that exposure to television also affects views on the physical and cognitive abilities of black and white athletes (Buffington & Fraley, 2008).

Sailes (1991) argues that excessive praise for blacks’ physical attributes is troublesome because in the absence of other praise it implies intellectual inferiority16, and qualifies

15 Ter Wal, D’Haenens, and Koeman (2005) show that this phenomenon also exists in the EU. 16 An important distinction to keep in mind is that altering how the sports media portrays athletes might prove insufficient if the sports media simply reinforces broader social stereotypes. As cited throughout this chapter there is evidence that suggests that it’s not just the sports media that practices stereotyping. Therefore to rectify this problem the sports and non-sports media must improve upon how they portray race if the media is to undermine broad social stereotypes. At the same time, the sports media may also represent an ideal platform to undermine pre-existing stereotypes.

31 their achievements as resulting from predisposed, natural gifts rather than effort.

Buffington (2005) echoes these concerns and says that the emphasis on physicality shifts attention from “the intellectual, management, and emotional tools of black quarterbacks—the very skills that have the most potential to challenge dominant ideas about race (p. 33).” In one of the few empirical studies aimed at understanding how viewers interpret portrayals, McCarthy, Jones, and Potrac (2003) coded images of black and white English soccer players and led same-race focus groups after participants viewed clips. While the authors did not measure broader social perceptions on factors, such as deviance or intellect, they did find that where black focus groups noticed racial stereotypes, white focus groups exhibited uncertainty17.

In one of the few studies looking at the relationship between race, crime and sports’ media effects Seate, Harwood and Blecha (2010) show that although athletes were framed in an accusatory manner, the framing exhibited no influence on racial perceptions, which challenges Rada and Wulfemeyer’s (2005) finding that off the playing field black athletes were portrayed “as being at odds with society (p. 81).” The fact that Seate,

Harwood and Blecha (2010) experimented only using newspapers, however limits the generalizability of their study. While the type of criminal featured on the nightly news typically comes from a lower social stratum, Niven (2004) showed that even the minority elite were susceptible to harsher media treatment. In his study on Congressional members

17 I should also identify some tangentially related research that while not focusing on the media offers insight. Kingsbury and Tauer’s (2009) analysis of youth basketball players, who watched same-race players perform skills the youths could achieve, led to optimism about their careers in professional athletics as opposed to watching different-race players or skills the youths could not achieve (p. 30). With respect to gender perceptions, Jones and Greer (2011) used an experimental design on 267 undergraduates, who were provided one of four versions of an online news article. The authors found that men exhibited more interest in sports (i.e., volleyball) and images that appeared more feminine. Women, on the other hand, exhibited no preference with respect to sport and preferred images of more masculine female athletes. These findings somewhat conflict with Klomsten, Marsh and Skaalvik’s (2005) study of 357 secondary-school students, which found that girls prefer more feminine-looking athletes.

32 implicated in the 1992 House banking scandal, he found that black members received more coverage and greater scrutiny than did white members. This finding reflects

Entman’s (2006) argument that the media ignore positive high profile minority role models and focus instead on minority celebrities involved in scandal.

The literature suggests that stereotyping affects viewers differently depending upon their race but there is no empirical evidence identifying the effects of the stereotyping found in the sports media on broader social perceptions. One way to indirectly understand the effect is to look at the role that sports play in black communities. In a survey of 234 male college athletes Sailes (1984) found that sports represented a fundamental component of the socialization for black males, and their interest and participation in sports was influenced more so by friends than were white males, who were influenced most by their fathers (p. 91). Wilson and Sparks (1996) also found that black youths were more inclined than non-black youths to perceive athletes as positive role models. Thus, if stereotyping affects black viewers then the socialization process could continually reinforce misconceptions of youths’ beliefs about their inherent abilities and professional opportunities.

The prevalence of stereotyping in the media may also explain surveys—albeit qualified by their lack of rigor—suggesting that black children and their parents develop expectations about careers as professional athletes at rates greater than that of white children and their parents18 (Eitzen, 2006; Lapchick, 1991; Sabo & Jensen, 1994). Former

18 Considering the few positions available in major professional athletics in comparison to the number of practicing athletes, surveys suggest that both black and white parents and children develop highly unrealistic expectations of the possibility of the child achieving a career. It should also be noted that there exist two important limitations related to these surveys. First, they are dated so we cannot be confident that these views exist currently. Second, the methodological rigor with which they were conducted should be questioned. For example, Lapchick combines findings from a 1989-90 National Federation of State High School Associations survey, which presents data on the number of athletes by sport but fails to control for

NBA great Charles Barkley claimed that “sports are a detriment to blacks, not a positive.

You have a society now where every black kid in the country thinks the only way he can be successful is through athletics (as cited in Eitzen, 2009, p. 205).” Continual media content associating black success with physicality likely contributes to black children pursuing endeavors that reflect their supposed natural gifts. But besides the fact that earning a career as a professional athlete is extremely unlikely, the absence of content praising black athlete’s cognitive acumen could dissuade these youths from intellectual pursuits. Murrell and Curtis (1994) offer keen insight on this perspective:

“Young people may be at risk of internalizing these views primarily because of their lack of experience of media images and their own developing sense of self- concept and racial identity . . . Perhaps communicating these messages about highly visible sport figures has a dramatic impact on the attributions that these youth will make concerning their own performance in both athletic and performance situations. It may be that internalizing internal, stable, and uncontrollable attributions encourages these youths to overemphasize these dimensions within their own behavior and thus leads to a preference for alternative activities that are less controllable such as risky behavior (e.g., participation in aggressive or violent behavior) (p. 231).”

There also may be a connection between the emphasis on physicality in the sports media and the emphasis on physicality in disadvantaged black communities. In his ethnographic study of deprived black communities in Philadelphia, Anderson (1999) illustrates that for black youth physicality and play-fighting becomes the ‘code of the street’ (p. 25). Over time physicality defines a young man’s status in the community (p.

70, 91, 314). In the sports media, Tamburro et al. (2004) illustrate that children who watch sporting events are exposed to a significant amount of violent and unsafe behavior.

But just because physicality represents a dominant theme among disadvantaged, young

race, with citations from sports’ sociologist’s Harry Edwards, who estimates that approximately 3 million black youths over the age of twelve place a higher priority on an athletic career than do white youths. Therefore, we should consider this assertion on the relationship between race and a professional athletic career anecdotally rather than as if it reflected the findings of a methodologically rigorous social survey.

34 black men does not mean the media are responsible for creating it as obvious structural factors, like poverty and inadequate security, play a role. And while it’s inaccurate to assume portrayals in the sports media and socio-cultural adaptations are related, the media make no effort to rebuke such patterns. The question then becomes: Do the media perpetuate a black culture rooted in physicality and assign the stereotype to all blacks? If this occurs then racial stereotyping in the sports media may impose considerable costs on the self-image of black youth.

Race and the Sports Media Overview

Before addressing the literature on race and the sports media I wish to summarize the important themes from this chapter. In comparison to the percentage of minority athletes in professional sports’ leagues African Americans, in particular, are underrepresented in the sports media. There is reason to believe that increased minority participation may mitigate stereotyping, which matters because stereotyping may prove deleterious to minorities if it contributes to broader, social perceptions and expectations of them.

Research on race and the sports media is relatively limited. A common theme shows that commentators emphasize the physical attributes of black athletes in contrast to the psychological attributes of white athletes. Research using content from the last 10-15 years finds that racial stereotyping is declining but methodological concerns exist with many of these studies. For example, researchers may focus on one sport or one position in a sport, which limits generalizability. In other cases, researchers rely on insufficient sample sizes (i.e., a few games), or use only univariate and bivariate statistics. Below I

35 review the literature on race in the sports media based on two categories—i) language, and ii) images, names and gender. I chose these categories because they best fit the two most common approaches to studying racial and gender portrayals in the sports media.

While the focus of this paper is race, understanding the role and interaction of gender with race could add depth to my analysis.

Race and the Sports’ Media: Language

Anecdotal evidence of traditional and subtle racism exists in the sports’ media.

On Nightline Al Campanis argued that blacks lack inherent managerial qualities necessary for baseball (Aldridge, 1987); sportscaster Jimmy “the Greek” Snyder claimed that blacks possessed a biological superiority to white athletes because of forced breeding patterns during slavery (Solomon, 1988); commentator Billy Packard compared black basketball star Allen Iverson to a “tough monkey” (Weiss, 1996); and, then ESPN- commentator Rush Limbaugh claimed that the media exhibited undue preference toward black athletes because the media wanted to see them succeed (Sandomir, 2003)19.

Beliefs, like Snyder’s, are rooted in traditional racism and were galvanized by a 1971

Sports Illustrated article, in which Martin Kane argued that black athletes possess anatomical advantages over whites, like denser bones, larger adrenal glands, and

“hyperextensibility” in joints. In addition to physical superiority, Kane claimed that blacks also benefitted from psychological advantages, like a naturally care-free attitude that allowed them to handle pressure better than whites. Kane did not create this thesis as the history of research investigating the myth of black physical supremacy is well

19 In response to Limbaugh’s comment, Niven (2005) reviewed more than 10,000 newspaper articles and compared coverage on the National Football League’s (NFL) seven starting black quarterbacks, and seven white quarterbacks and found no support for Limbaugh’s accusation.

36 documented (Sailes, 1991). But endorsing it in a popular venue, like Sports Illustrated, stigmatized black athletes to a greater audience and associated their success with inherent gifts, not with the work ethic and cognitive acumen that are also so necessary for athletic achievement.

Rainville and McCormick (1977) were the first to uncover the racially-based physical-psychological dichotomy in the sports media. They found that black athletes were praised for physicality20 and that white athletes garnered acclaim for psychological qualities, and that the media was more critical of black players in off-the-field scenarios.

Over the next two decades across different sports and levels of competition the literature re-enforced these findings.

In the NFL Murrell and Curtis (1994) analyzed portrayals of black and white quarterbacks, and found that the print media assigned black quarterbacks’ performance to internal, stable, and uncontrollable factors (i.e., innate ability), and white quarterbacks’ performance to internal, unstable, and controllable factors (i.e., effort). Dimensions were measured based on whether the description was due to the player or the environment

(locus), was volatile or consistent (stability), and was under the influence of the player or some other agent (controllable). The combination of stable and uncontrollable attributes suggests that black quarterbacks succeeded because of natural ability; the combination of unstable and controllable factors, on the other hand, indicates that white quarterbacks succeeded because of work ethic. Rada and Wulfemeyer (2005) found that announcers

20 In the latter 19th century and early 20th century some black athletes were stereotyped for their inherently diminutive size, as opposed to contemporary stereotypes based on their immense physicality. At the time, black jockeys were discriminated against for being naturally slight in size, which gave them an unfair advantage as jockeys (Sailes, 1991). Interestingly, in 1875, at the first Kentucky Derby 13 out of the 15 jockeys were black and 15 of the first 28 winning jockeys were black. By 1921 there were no black jockeys in the Derby.

37 also created negative images of black athletes when describing them as people. In their analysis of college football and basketball, the authors could correctly predict that if a comment was made about a player’s physical attributes, his character, or was a negative personal interest story that the player was black.

In basketball, Davis and Harris (1998) found that when white players made a mistake, commentators associated it with factors outside the player’s control and rarely associated the mistake with poor work ethic. In their analysis of men’s and women’s college basketball, Eastman and Billings (2001) report that while gender stereotypes have declined, commentators continued to praise black players for physicality and white players for their work effort and acumen. Focusing exclusively on black NBA players,

Wilson (1997) viewed game tapes and promotional shows of the Toronto Raptors, and identified the media’s efforts to create ideologies of ‘good’ and ‘bad’ black players. In a piece for The Boston Globe, Jackson (1989) studied seven NFL playoff games and five

National Collegiate Athletic Association (NCAA) basketball games in 1988 and 1989 and determined that commentators tended to recognize white players for mental aptitude, black players for athleticism, and that the majority of “dumb” plays were attributed to black athletes. Jackson’s findings should be qualified as the study lacked some of the methodological rigor, such as a sufficient sample size, to warrant broader generalization.

In international soccer, McCarthy and Jones (1997) compared commentators’ language on black and white soccer players, and determined that black players received excessive positive description for physicality, and that white players received disproportionate praise for psychological characteristics. Van Sterkenburg, Knoppers, and De Leeuw

(2010) cite Dekker’s (2007) study of coverage of African soccer players playing in the

Netherlands, which had findings that mirrored those of McCarthy and Jones (1997).

According to Buffington (2005) the racial physical-psychological dichotomy’s

“overemphasis on athletic skills re-marks race in a more nuanced or subtle manner, making it increasingly difficult to both see and articulate the connection between the recognized, overt forms of racism and the much more subtle practices (p. 33).” Such subtle stereotyping can prove more damaging than overt racism because the conspicuous nature of the later enables the viewer to easily identify and negotiate it. The nuanced approach that Buffington identifies avoids easy conscious detection because audiences may ignore the presence of stereotyping when practiced in a positive context. As a result, audiences gradually develop an understanding of inherent racial attributes based on stereotypical portrayals. Buffington’s claim also suggests that the media’s emphasis on black physicality as a means to success reinforces stereotypes about the black body, and implicitly qualifies blacks’ intellect21.

More recent research identifies improvement in the media’s portrayals. Denham,

Billings, and Halone (2002) analyzed commentary from the 2000 NCAA Men’s and

Women’s Final Four Basketball Tournament and discovered that although black athletes received more praise than whites for their athleticism, commentators also recognized leadership and cognitive qualities in black athletes, which suggests increased attention to cognitive qualities in comments on black athletes. In his study of black and white

21 The obsession with black physicality contributes to suspicious behavior in athletics. Oates and Durham (2004) argue that even in seemingly innocuous circumstances, like the NFL draft, the intersection of physicality and race contributes to larger cultural, racial and gender-based hierarchies. They cite how predominantly white owners’ and team managers’ construct majority black players’ bodies based on numerical rankings (p. 320). Thus, by defining an athlete by his height, weight, strength, and speed, owners and managers dehumanize prospective employees. The practice of quantifying athletes is applicable to amateur athletes of all racial groups.

39 quarterbacks in college and professional football, Billings (2004) found that the stereotypes reported in prior studies have been muted or eliminated altogether with the exception that blacks’ success continues to be attributed to athletic skill, and whites’ failures to a deficiency of innate ability. Similarly, Byrd and Ulster (2007) found no difference in the way black and white NFL quarterbacks’ intelligence was described in

Sports Illustrated and only a minor difference in physical descriptions. Despite evidence of progress in his study, Billings (2004) warns the reader from assuming that the sports media has reached a point of racial comity as the progress might be exclusive to football22.

In summary, early research on race and the sports media shows that the media emphasized black athletes’ physicality in contrast to white athletes’ intelligence and cognitive acumen (Davis & Harris, 1998; McCarthy & Jones, 1997; Mercurio & Filak,

2010; Rada, 1996; Rainville & McCormick, 1977). More recent research has produced mixed results with some studies showing a decline in the presence of stereotyping and others indicating that certain patterns persist. In the following section I turn to the relationship between race and images, names, and gender in sports media. These studies illustrate patterns of racial stereotyping that mirror the findings cited above suggesting that culturally ingrained biases that contribute to stereotyping exist in a variety of ways in the sports media.

Race and the Sports’ Media: Images, Names, and Gender

22 Also, Mercurio and Filak’s (2010) analysis of depictions of NFL quarterbacks on the Sports Illustrated website from 1998-2007 identified evidence of the physical-psychological racial dichotomy (which contrasts with Byrd and Ulster’s findings, which were based on data only from 2002-2004).

In her analysis of Sports Illustrated issues from 1985 and 1994, Dufur (1998) found that success for blacks was attributed to physical ability and for whites it was attributed to intelligence, and that advertisements aggrandized stereotypes of violence and sexuality among black athletes23. In another study on Sports Illustrated covers from

1954 to 2004, Primm, DuBois, and Regoli (2007) found that the likelihood of black athletes appearing on the cover increased over time but controlling for participation rates they were still underrepresented in some sports (p. 247-248). Curry, Arriagda, and

Cornwell’s (2002) research on images in non-sport magazines showed low incidence of interracial images containing black and white athletes, and that black athletes were more likely to be portrayed as powerful and in performance than in pleasure. These findings may come as little surprise since publications with little connection to athletics may be expected to perpetuate stereotypes commonly practiced in the sports’ media.

Other studies focusing on the significance of names also suggest a racial disparity in coverage. Bruce (2004) showed that commentators were more likely to call minority athletes by their first names with the exception of elite athletes, like Michael Jordan. In analysis of Los Angeles KNBC sports’ news focusing on the 1989 Men’s and Women’s

NCAA Basketball Final Four and the 1989 US Open tennis tournament, Duncan,

Messner, and Williams (1990) exposed a naming hierarchy where commentators identified black players by their first name and white players by their full name.

According to Henley (1977) this echoes a dominant-subordinate naming dichotomy based on last names reflecting formality and first names informality. Giglioli (1972) recognized that the pattern of identifying whites by their last name and blacks by their first mirrors

23 These findings echo Schultz’s (2002) critical analysis of the media’s commentary on Serena Williams’ 2002 US Open outfit, which she argued illustrated that the media based its descriptions of Williams in hegemonic, racialized language.

41 practices of the racially oppressive South’s past where blacks were expected to identify the dominant white class by their last name preceded with a prefix, like Mister. In recognizing whites by their full or last names and blacks by only their first names, Bruce

(2004) and Duncan, Messner, and Williams’ (1990) present evidence that commentators may be reinforcing racial hierarchy.

While this paper focuses primarily on race, it is also necessary to understand portrayals of gender and its interaction with race. The literature on gender and the sports media illustrates that female athletes receive less coverage and poorer quality coverage than do male athletes (Billings & Angelini, 2007; Billings & Eastman, 2002; Crossman,

Vincent, & Speed, 2007; Duncan, 2006; Eastman & Billings, 1999; Kian, 2007; Koivula,

1999; Messner, Cooky & Hextrum, 2010; Pederson, 2002; Shifflett & Revelle, 1994), and that female athletes’ attributes tend to be defined in terms of physical attractiveness

(Blinde, Greendorfer, & Shanker, 1991; Billings & Angelini, 2007; Billings & Eastman,

2002; Duncan, 2006; Eastman & Billings, 1999; Jones, Murrell, & Jackson, 1999;

Koivula, 1999; Messner, Cooky & Hextrum, 2010). Several of these studies also measure for race; however there is limited discussion of the intersection between race and gender.

With respect to the physical-psychological racial dichotomy, research focusing on men’s and women’s sports largely shows that it is most prevalent among male athletes (Eastman

& Billings, 2001; Kian, Vincent, & Mondello, 2008; Toohey & Veal, 2000; Van

Sterkenburg & Knoppers, 2004).

In digital media, black college basketball players, regardless of gender, garnered greater attention than whites relative to their participation rates (Jordan, Maxwell &

LaVoi, 2009), which conflicts with Eastman and Billings (2001), who showed that in

42 women’s college basketball white players received a disproportionate amount of commentary considering the percentage of white female players. Denham, Billings, and

Halone (2002) found that black athletes as a whole continue to garner more comments than white athletes for physicality, but the study lacked the sample size to draw conclusions about female athletes. Thus, the existing literature is limited in explaining how the sports media portray gender while taking race into account. But it is worth noting that Mitchell, Nosek, and Banaji (2003) showed that shifting attention on the social group membership of black women from race to gender elicits more positive automatic responses. Therefore, whether a commentator identifies a basketball player as a black woman or a woman who is black may influence stereotyping. In other words, commentators may be more inclined to stereotype male athletes.

Conclusion

In general, minority athletes are portrayed pejoratively in the sports media. Black athletes, in particular, tend to be described as being more physical and less intelligent.

Because assigned psychological gifts reflect earned rather than inherent attributes, white athletes are assumed to be more worthy of their success and achievement than are minority athletes. Research relying on more recent data shows that racial stereotyping is declining, but methodological issues, such as limited sample sizes, failure to control for omitted variables, and reliance on univariate and bivariate statistics qualify findings.

Moreover it is important to continue to update the research.

My research seeks to fill a gap in the literature. Prior research has focused on in- game commentary of a single sport. I expand on this research with analysis of

43 commentators that address multiple sports and, thus, are less likely to follow patterns reflective of the culture of a single sport24. Because I am controlling for more variables I hope to expose previously unstudied relationships. It is also important in an increasingly diverse country to examine race and ethnicity beyond the black-white dichotomy so my study takes into account Latino, Latina, and Asian athletes. By controlling for characteristics of the commentator I will better understand if the findings described in the literature reflect the sports media in general or the patterns of certain subgroups25. In addition, the use of multivariate analysis may expand on findings from univariate and bivariate analysis, techniques common in the existing literature. In the following chapter I address my sampling technique, data collection method, and the analytic technique.

24 Conversely the argument can be made that looking exclusively at one sport controls for the various factors that might arouse physical and psychological descriptors for different sports. So, because football is an overwhelmingly physical game and since most football players are black should not we expect a disproportionate number of physical comments about black football players? But this counterargument ignores that some sports might prime racial cues that lead commentators to emphasize certain characteristics. Analyzing individuals, who provide commentary on different sports mitigates this concern. Not to mention, some of the analysts on Sportscenter cover only one sport. Because I analyze sports individually within the program, I can also account for patterns unique to the culture of a specific sport. 25 Focusing on commentators will also hopefully explain if older studies like Rainville and McCormick (1977) that exhibit a high prevalence of stereotyping simply reflect the practices of a formerly white- dominated sports media.

Chapter 3: Data Collection & Methods

In this chapter I outline my research method and analytical approach. I begin by explaining the sampling. I then describe the research method (content analysis), and my intercoder reliability, which represents a major concern for studies using this research method. Next, I describe the dependent and independent variables and how I measured them, and I close with a discussion of my analytical technique.

Sampling

I collected data from Sportscenter programming from December 2011, February

2012, and May 2012. The months were purposively selected to capture content from the seven sports that were the focus of the study26. An obvious limitation exists if content from this period is unique to those three months. During these three months there were a few anomalous events—after a protracted labor negotiation between owners and players the NBA started its season almost three months late in December; Tim Tebow’s unconventional play and success garnered him significant scrutiny and coverage; the

Superbowl was played in February (as it is every year); and Jeremy Lin emerged as the first Asian American professional basketball star27. I discuss Tebow and Lin in greater detail in the following chapters but for the most part the programming should be consistent with other Sportscenter’s aired over the last few years. I used stratified random sampling with day of the week as the stratification variable to select an episode each day.

26 This is the case because sports are played seasonally. For example, where baseball runs from March to October, football is played from August to February. 27 It would have taken a truly epochal event that occurred outside the sports’ world and dominated coverage, however, to have made these three months dissimilar to the rest of Sportscenter’s programming in 2011 and 2012. While the Superbowl absorbed much of the attention in early February that type of coverage occurs every year.

Stratified random sampling is a process where “the sampling frame is segmented according to categories on some variable(s) of prime interest to the researcher

(Neuendorf, 2002).” This method was beneficial because it ensured appropriate representation of weekend days that run fewer episodes and because it reduced sampling error for the stratifying variable (Neuendorf, p. 86).

I sampled one episode per day. Based on extensive, prior viewing I determined that content remains relatively constant throughout a day because with the exception of anomalous events, like the firing of a coach, most sports news occurs in the evening when sporting events take place. On the weekend, events happen throughout the day.

However, Sportscenter is only consistently run late at night or early in the morning and those episodes tend to capture the prior day’s news in its entirety. Time slots tend to have the same hosts each day so it was necessary that I randomly selected episodes rather than code the same episode Monday through Friday. Failure to account for this would have limited how representative the sample would have been of the entire program. For each episode on each day I assigned a number and then used Microsoft Excel 2010’s random number generator to select an episode. This technique accounted for the possibility that commentary fundamentally differs across episodes as it decreased the likelihood of selecting from the same time slots, though I had little reason to believe that content would vary fundamentally across episodes.

It was necessary to understand how content varied within an episode. In nightly newscasts networks typically lead with a few main stories that will garner the most attention from viewers and the networks anticipate a slight drop-off in viewership for the remaining programming. As a result, researchers studying the nightly news are more

46 interested in the leading, highly-viewed stories. Sportscenter follows a similar pattern in that it has lead stories but it differs in a fundamental way from network news in that on the left-hand side of the screen ESPN runs a ticker of the story currently being addressed as well as the next three to six stories that will be covered. This allows viewers, who do not plan on following the entire episode to pick those segments that are of interest. In some cases Sportscenter highlights the main story of the hour at the top of the ticker and leaves it there for viewers to notice at any point over the hour. If ESPN anticipates breaking news related to the topic, it will include the time when they expect to report that news. In addition, throughout the episode there is a second ticker at the bottom of the screen that continually runs scores and reports major news. As a result, leading and top stories can occur throughout the telecast. Based on preliminary viewing I found no evidence that commentators portrayed athletes differently during leading or top stories in comparison to the rest of the episode therefore I did not control for the type of segment.

Before sampling episodes I had to first determine the daily programming schedule for Sportscenter. TV Guide provides online program schedules for a period of two weeks, which I examined from Tuesday, September 27, 2011 to Monday, October 10, 2011.

While ESPN may run as many as 15 hour-long episodes of Sportscenter in a given day, the number of new episodes varied from two to eleven. Since I’m sampling one episode for each day of the week it was necessary that I sampled from a population of only new episodes. Sampling from all episodes (recorded and live) runs the risk of disproportionately selecting the 2am episode, which most days ESPN repeats hourly until the next new episode at 9am.

There were further issues associated with selecting time slots based on a given week of programming. For example, the network uses Sportscenter to fill time gaps in the schedule after live sporting events. On Thursday and Friday nights in the fall ESPN airs college football that end between 11pm and 12am depending upon the length of the game. If the game ends at 11pm then the network runs an hour-long segment. If, however, the game runs into overtime and does not finish until 11:30pm then the network might run a half-hour or an hour-and-a-half segment. There is also the issue that as seasonal programming changes so to do the times when ESPN airs Sportscenter. On

Wednesday, September 28, ESPN ran its last Sportscenter at 6pm because it aired an

MLB game and Baseball Tonight for the rest of the night. However the MLB regular season ended that day and so too did ESPN’s rights to airing MLB games. (Only TBS and Fox retained the rights to air MLB playoff games.) As a result, on the following

Wednesday (October 5) the network aired a new 6pm and 11pm Sportscenter because it no longer scheduled MLB content on Wednesday nights. Because of inconsistencies in programming schedules I only included those episode times that regularly appeared in the uninterrupted hour-long format. While some of those eliminated timeslots may have included new episodes, their inconsistency precluded selection to the episode population28.

Appendix B shows the ‘new’ Sportscenter schedule for Tuesday, September 27,

2011 through Monday, October 10, 201129. The episodes highlighted in yellow reflect

28 An alternative to this problem would be to code ESPN for 24 hours a day over the three months, and then select the population after reviewing the entire network’s content. Unfortunately, data storage limitations on video recording devices prevent this option. As a result, I had to sample beforehand based on my best judgment of new episodes. 29 Times are collected based on the day the programming commenced. For example, if ESPN runs an episode from 11:30pm on Tuesday to 12:30am on Wednesday it would be considered a Tuesday episode because it began airing on Tuesday.

48 those times that I believe were episodes ESPN used to fill time slots when uncertainty existed about the ending time of a scheduled sporting event. The episodes highlighted in green were those times that I believe were anomalous programming based on prior weeks’ scheduling. Those episodes encapsulated in the blue bars compromised what I considered the population of ‘new’ Sportscenter episodes each day for the purposes of my study. Because I needed a set schedule of times when Sportscenter would always air to conduct stratified random sampling there had to be a standard for qualifying episodes.

That being said, based on viewing decades of Sportscenter across time slots I do not expect that eliminating “filler” and “anomalous” episodes adversely affected the data.

There are 50 ‘new’ regularly scheduled episodes per week. This resulted in 220 episodes in December 2011, 210 episodes in February 2012, and 227 episodes in May 2012, which make a population of 657 episodes eligible for inclusion in the analysis. From this population I sampled 91 episodes. (Appendix C lists the recording time of episodes for each day.)

Research Method: Content Analysis

Content analysis is the “systematic, objective, quantitative analysis of message characteristics (Neuendorf, 2002).” Often used to analyze film, newspapers and literature, it is also applicable to television news (Dixon & Linz, 2002; Entman & Rojecki, 2001).

In this study, I focused only on verbal content and coded it in manifest and latent form.

Manifest content is that information that is “physically present and countable”, and latent content reflects unobserved concepts, such as what an author meant to say in a text

(Neuendorf, 2002). Latent content is more difficult to define than manifest content, and

49 demands predetermined, precisely defined categories. For the most part, I experienced no trouble coding superficial characteristics, like the athlete’s race, sex, sport, and other manifest content, like “he’s such a powerful runner”. Categorizing latent content, on the other hand, like “what a poor read by Peyton Manning”, demanded subjective appraisal and, thus, possessed less certainty. The codebook served to minimize this uncertainty for the researcher and the coder.

Variable Construction & Measurement – Dependent Variables

A comment on an athlete represents the unit of analysis and the nature of the comment represents the dependent variable. A comment occurs every time a member of

ESPN personnel mentions an active athlete’s name and ends the moment a different commentator mentions that athlete’s or another athlete’s name. Commercials or the language of other commentators not mentioning the name of an athlete do not end a comment. Only qualified commentators stating the name of a different qualifying athlete end a comment. Descriptions associated with the comment could come before or after the commentator mentioned the athlete’s name as long as there was no interruption of another commentator mentioning any qualifying athlete’s name. A commentator may have continuously referred to an athlete with a pronoun and as long as the comment was not interrupted by another commentator mentioning an athlete’s name, all descriptions associated with the pronoun were included once the commentator mentioned the athlete’s name. Similarly, in cases where the commentator identified a player by name and in the same comment later by a pronoun the comment and its descriptions of the pronoun qualified.

In cases where more than one player was mentioned in a comment all players were included with the description as long as they were the subject of the statement. For example, “LeBron James and Dwight Howard are two of the most powerful players in the

NBA” is coded as positive physical ability for each player. The comment must address an athlete or the athlete’s play. So a statement like “player A recognizes that great plays win championships” is not coded as positive. A positive comment would be “player A makes great plays, which will lead to championships”. Finally, in cases where a commentator contrasted the past and current state of the player the description of the current state received preference. So, “player A was fast in his youth, but now he’s lost a step and is slow” would be coded negatively.

Only ESPN employees qualified as commentators. Therefore, comments from post-game recordings or live news conferences of athletes, coaches, executives or any other non-ESPN personnel were excluded from the analysis. Billings believes that this approach allows the researcher to “focus on network employees . . . to gauge agenda- setting and gatekeeping functions that the network may implicitly or overtly use within its coverage (p. 205).”

To qualify as an athlete the individual must have been actively participating in one of the seven designated sports and have been playing at the college (any division) or professional level (including minor or developmental leagues). Commentary on top high school athletes declaring their college was not coded as they were not college athletes at that time. The athlete must have been identified by her or his first, last or both names.

Referring to the athlete by number, nickname or a pronoun did not qualify for coding with the exception of nicknames that include the first or last name. To recap—a

51 qualifying comment for a player: a) lasted from the point another commentator mentions any qualifying athlete’s name until another commentator mentions any qualifying athlete’s name; b) addressed an athlete or the play an athlete was involved in; c) was made by a member of ESPN personnel; d) was of a current professional or collegiate athlete of the seven qualifying sports; and, e) mentioned the athlete’s first, last, or full name.

All comments fall into two tiers—type and quality. The type of a comment falls under the broad categories of physical, psychological, or general but within physical there are two subcategories, within psychological there are four subcategories, and within general there are three subcategories, one of which is neutral. The quality of a comment could be positive or negative with the exception of the general-neutral subcategory, which are neither positive nor negative and only neutral. This tiered-system reflects an accepted approach for content analysis of the sports media (Denham, Billings, & Halone,

2002). All on-the-field comments were coded as positive, negative, or neutral, and were coded for whether they provide a physical or psychological description. Off-the-field comments were also coded separately as positive, negative, or neutral.

Descriptions could be adjectival, like “strong” or “smart”, or more extensive (i.e.,

“his power allows him to break through the line”), but the majority of comments qualified as neutral (i.e., “player B was traded to a new team”). For a comment to qualify as positive or negative or to be categorized as physical or psychological—which in this research model are all either positive or negative—the comment must have possessed some degree of subjectivity. For example, the comment “player A is a record-breaker” while presumably positive is an objective statement assuming player A broke some

52 record. But “player A is a great record-breaker” qualifies as a positive description because adding “great” indicates the commentator’s subjective assessment of player A.

The same comment can have multiple descriptions. If a commentator states that “player

A’s speed and leadership in the locker room make player A an all-star” then the comment would be coded as positive for physical ability and for psychological leadership. The codebook in Appendix A provides extensive detail on comments and descriptions for coders.

Below I address the subcategories of the three major comment types. It is important to recognize that comments in all of these subcategories can only qualify as positive or negative with the exception of the final subcategory neutral, which is the only subcategory that is not positive or negative and is strictly neutral.

Physical – Ability: These comments identify the performance of some physical act, and rely on descriptors, like strength, speed, power, or quickness. An example is: “LeBron’s power enabled him to split the defense.”

Physical – Appearance: These comments identify the appearance of an athlete, and emphasize characteristics, like height, weight, or mass. An example is: “Brees is too short to see over his linemen, which limits his passing window.”

Psychological – Leadership: These comments identify a player’s ability to lead or set an example on the playing field. Common descriptors include leader, captain, guide, and

53 manage. An example is: “Haynesworth’s failure to attend team meetings set a poor example for the rookies.”

Psychological – Focus/ Demeanor: These comments identify a player’s ability to maintain her or his disposition and to manage stress. Common descriptors include focus, demeanor, attitude, conduct, and poise. An example is: “Holdsclaw’s focus at the free throw line down the stretch secured the win.” Comments can also be related to non- playing in game behavior. An example is: “Curry lost his composure when he began fighting with the other team.”

Psychological – Effort: These comments identify a player’s effort and dedication to success in athletics. Common descriptors include effort, strive, training, “putting in time”, discipline, and battle (verb). An example is: “Bradshaw’s effort earned him that last extra yard to the goal line.”

Psychological – Intelligence: These comments identify a player’s cognitive or mental capacity. Common descriptors include smart, dumb, intelligent, and brainy. An example is: “Brady reads the secondary and knows immediately that he’ll have man-to-man coverage on the outside.”

General – On-the-field: This category includes on-field, value-based description that did not qualify for any of the above categories. It includes broad descriptions, such as great, terrible, good, bad, helped or hurt, where the coder cannot discern if the comment is

54 related to physical or psychological attributes. An example is: “Jordan made a great play to win the game,” or “What a terrible shot.” In both cases the viewer does not know if the commentator believes that the play (shot) was great (terrible) because of the athlete’s physical or psychological abilities.

General – Off-the-field30: This category includes commentary on an athlete off of the court, field, or rink. Qualifying comments come in entire segments and adjectival descriptors. Examples include a segment on a player’s volunteer work in the community, acquisition from another team, or suspension for using performance-enhancing substances.

General – Neutral: This category includes neutral comments that possess no value and do not qualify for any of the comment types listed above. It represents the default category and encompasses most coded comments. Examples include objective statements, like

“Lebron passes to Wade” or “The Wizards traded McGee.”

Table 3 presents three examples of each quality and type of comment. Because of the complexity associated with appropriately categorizing latent content the codebook provides far greater detail and many more examples to minimize uncertainty for coders.

30 Understanding the relationship between race and off-the-field content is important as research suggests that the media constructs—rather than discovers—‘truths’ of minority athletes involved in crime (Blackshaw & Crabbe, 2005).

Table 3: Operationalization of Comments Example Category Sub-Category Positive Negative Ability “she is such a strong rebounder” “she’s too slow to get to “he’s one of the quickest guards” the net“ “what a powerful swing” “he’s too weak to maintain possession” “his release isn’t quick enough” Appearance “her long arms allow her to “she’s too short to play Physicality deflect passes” center” “his height proves to be a “he doesn’t have the girth problem for opposing” to box out opposing “he’s put on an extra 15 lbs of rebounders” muscle so he’s in great shape” “he looks like he put on too much weight this offseason” Leadership “leads the team . . .” “she sets a poor example “her work in film study sets an for the rookies with her example . . .” insubordination . . .” “he called together a team “he attacked his team to meeting to establish team unity . the media after the loss . . . .” .” “she doesn’t have the trust of her teammates . . .” Focus/ “never gets distracted . . .” “lost his composure and Demeanor “she showed great perseverance yelled at the fans . . .” recovering from that injury . . .” “she’s easily frazzled at “maintains composure at the free the free throw line . . .” throw line . . .” “he blows up and assaults Psychological the ref . . .” Effort “put in time at the gym during “gave up . . . “ the off-season . . .” “he quit on the team . . .” “he’s the first one in, last one out “he didn’t want to fight at practice . . .” for those final three yards “she kept fighting on every serve . . .” . . .” Intelligence “a brilliant decision to take the “a poor read of the shot . . .” defense . . .” “she out-witted the defender . . “ “that was a bone-headed “she just sees two plays ahead of decision . . .” everyone else on the field . . “ “what a rookie mistake to not pick up the blitzing linebacker . . .” “what a great pass . . .” “what a pathetic play . . .” “she’s a fantastic driver . . .” “she plays poor defense . . On-the-Field “a good, good defender . . .” .” “that was a bad pass . . .” A story on a player’s . . . A story on a player’s . . . volunteer work in the criminal arrest, offensive community, philanthropy, comments on a social Off-the-Field success in a business venture, networking site, financial ability to overcome adversity, troubles . . . such as childhood poverty . . .

Neutral “she is 6’1” . . .” “she makes the basket . . “ (the absence of a modifier, like Neutral “good” basket makes this comment neutral) “she’s from Salt Lake City . . .”

Across 13 episodes the coder and the researcher agreed on 96.7% of all coded comments. Within comment types the coder and the researcher agreed that a comment was positive or negative from a low of 86.7% for psychological-effort up to 100.0% for physical-ability and psychological-intelligence1. The Cohen’s kappa measuring the reliability on all variables was 93.8, which exceeds Popping’s (1988) criterion that sufficient intercoder reliability is achieved with a Cohen’s kappa score in excess of 0.80.

In the case of discrepancies in coding the researcher and coder discussed the disagreements and based on the codebook determined the appropriate coding, which was used in the final analysis31.

Variable Construction & Measurement – Independent Variable

For each comment I also code the following variables related to athletes and commentators. These become the independent variables in my analysis. The independent variables represent manifest content for the most part, which means they presented less uncertainty than did the dependent variables. The researcher assigned all values for the independent variables. The coder coded only the time of the comment, the full name of the athlete and the commentator, and the dependent variable. For athletes, the independent variables are race, sex, level of competition, and sport. For commentators, they are race, sex, and role. I address each variable in further detail below.

31 Additional information on the sample, data recording and intercoder reliability is presented in Appendix D.

Race is a categorical variable consisting of black, white, Latino/a, and Asian32.

Race is determined based on appearance, and information from media guides and internet sources. In cases where there exists some uncertainty, I relied on country of origin to assign race. In the case of MLB’s Afro-Dominicans or the NBA’s Afro-Brazilians those athletes were assigned to Latino rather than black. Similarly, Argentinean athletes, regardless of complexion, were assigned to Latino/a. In cases of uncertainty and where country of origin was not indicative of race, I relied on surnames. Therefore, an athlete, like Alex Rodriguez, was assigned to Latino. With respect to mixed race athletes, I assigned them to the minority group that their physical appearance suggested if country of origin or surnames failed to offer necessary insight. My reasoning is that phenotypic qualities that associate an individual with a minority group would be more poignant to commentators than would the individual’s European phenotypic qualities. In addition, determining who is mixed race depends upon personal and family information specific to that player. So while for popular mixed race athletes, like Tiger Woods and Blake

Griffin, this is not an issue, for less prominent ones, like Delonte West, or prominent ones whose lineage is less well-known, like Marc Bulger or Tony Romo, it is arbitrary to classify an individual as mixed race because classification is dependent upon the researcher’s knowledge of that athlete’s family history33.

I recognize that this approach ignores the issue that race—at least according to the

US Census—is self-identified. Therefore, one would have to survey every athlete and

32 There were no athletes for whom I could not identify their race and ethnicity and none fell out of these four constructed racial groups. 33 There is one final distinction related specifically to Tiger Woods. Should Tiger be defined as i) black, ii) black and Asian, iii) black, Asian, and white, or iv) black, Asian, white, and other (‘Native American’)? If I were to define every racial group of which he has ancestry then iv would be correct. I will code him as black however because that is how he is most often identified. I justify this because of the media’s reticence to acknowledge him as an Asian golfer (and even much less so as a golfer of European descent).

58 commentator to be certain of the self-identified racial classification. While this is a concern, it is mitigated by two factors. First, I am interested in how the media portray certain racial groups, not how athletes self-identify their race. Thus, assuming I mis- identify an athlete based on physical appearance it might be the case that commentators similarly mis-identify the athlete. If this occurs I will still be able to measure portrayals of the racial group that the athlete is ascribed to. Second, other media studies, such as those focusing on portrayals of criminals in local television news, do not survey subjects on their racial identification. Therefore just because researchers cannot confirm the racial self-identification of individuals, it should not proscribe a model that allows the researcher to assign race.

While racial classifications are necessary for quantitative analysis, defining race in exhaustive, exclusive categories is limiting. Van Sterkenburg, Knoppers, and De

Leeuw (2010) identify the importance of analyzing race outside of the black-white dichotomy (p. 826). Similar to Eastman and Billings (2001) my model codes for

“Latino”, and “Asian”. Although my approach grants greater insight than studies that view race only in the black-white dichotomy, it ignores the relationship among race and ethnicity and class (Cole, 2009).

In his biography of Muhammad Ali, Remnick (1998) presents an example that illustrates the notion of socially constructed power structures. In the early 1960’s before

Ali first won his heavyweight championship in boxing, Sonny Liston and Floyd Patterson were the two primary competitors battling for the title. While both men were black, the media pegged Liston, who was formerly incarcerated for robbery, as the “bad Negro” and

Patterson, who promoted integration and the Civil Rights’ movement, as the “good

Negro” (p. 17). Then when Ali challenged Liston for the title in 1964, the media continued to identify Liston as the “bad Negro” but Ali, who was becoming increasingly associated with the Nation of Islam, was deemed the foreign “Muslim” (p. 185).

Although Ali, Liston, and Patterson are all African American the media constructed identities along ethnic, political, religious, and cultural lines. Such portrayals are difficult to capture as they can be unique to the individual. The anecdote is intended to illustrate that one should not expect uniform portrayals of all individuals within a racial group.

Sex, a binary variable, is incorporated to account for the interaction of social power structures based on characteristics, like class, gender orientation, and sex (Cole,

2009; Van Sterkenburg, Knoppers, & De Leeuw, 2010). I use ‘sex’ rather than ‘gender’ because where the former is typically believed to reflect biological assignment. Gender identity can expand into many more categories and is difficult to measure without consulting each individual athlete and commentator. Further, most sports separate competition by sex, which makes athlete sex a reasonable selection. Sport is a categorical variable consisting of football, soccer, basketball, baseball, hockey, golf and tennis34. It controls for qualities unique to each sport and the physical characteristics that might influence portrayals. For example, sports, like football and hockey, cover their athletes in pads, helmet and jerseys but basketball’s tank tops and shorts make racial and gender markers more conspicuous. Finally, level of competition is a binary variable that is coded for professional or collegiate. Amateur competition, such as high school basketball or the Little League World Series, was excluded.

34 Some sports are so racially and gender homogenous that they tell us little about the sports media’s racial portrayals. For example, almost all auto racers are white males. Danica Patrick, who is white, is the only female driver to receive regular attention. Thus, not only would I lack minority female drivers to compare with Patrick, but I would also be uncertain if the media’s portrayals reflect all white, female auto racers or just Patrick.

Role, a category exclusive to commentators, consists of hosts, who are the two individuals who introduce the show and the segments, and analysts, who present in-depth information on a topic or player. Everyone but an episode’s two anchors is coded as an analyst. Chapter 1 showed that analysts were more likely than anchors to have been professional athletes or coaches35, which suggests that analysts may possess unique beliefs on the qualities necessary for success. Controlling for race, sex and role among commentators was necessary to understand if stereotyping reflects commentators in general, or a subgroup of commentators.

Analytical Technique

In Chapter 4 I begin by providing a general overview of the nature of commentary and the types of athletes and sports that receive coverage. I then look at bivariate statistics to further clarify the nature of the commentary. To better understand the relationships between the independent variables and the dependent variables, I also used logistic and multinomial logistic regression to predict the probability of a comment’s value and type36. I included qualitative analysis on athletes that garnered disproportionate attention, and on findings not easily captured through quantitative analysis. In these cases I relied on a modified version of an ‘intrinsic’ case study approach, which granted me intensive

35 I’m only interested in those involved in professional athletics because I assume most anchors or analysts played high school or collegiate athletics. While some might be high-profile ex-college athletes the level of competition and the notoriety among all colleges varies so much that there is little ground for comparison between say a Division III soccer player and Division I nationally competitive basketball player. I should note however that qualifying commentator-athlete relationships based on prior profession is limiting. For example, many of the most respected analysts, who came from a career in journalism and not athletics, reached their status because of the relationships they’ve forged with professional athletes. These relationships grant analysts inside information on some of the less discussed, intricacies of professional sports. Thus, they too likely identify closely with athletes. There is also the issue of when does an ex- coach- or ex-athlete-turned-analyst become a journalist and no longer a ‘coach’ or ‘athlete’? 36 Data were entered into Microsoft Excel 2010 and then imported into STATA/ IC 12, which I used to conduct the quantitative analysis.

61 analysis of individual units (Stake, 1995). In the Appendix I provide the codebook

(Appendix A), a chart illustrating two weeks of Sportscenter programming times

(Appendix B), the randomly selected programming times (Appendix C), and additional data collection and coding information (Appendix D).

Chapter 4: Analysis & Results

In this chapter I review quantitative and qualitative analyses that speak to the nature of the racial messages that Sportscenter viewers receive to understand the types of messages received and how media coverage of sports may undermine or reinforce racial stereotypes. I introduce my results with a broad overview of the nature of coverage (using summary statistics), but the bulk of the analysis falls into two main sections. The first addresses physical, psychological and general comments (i.e. comment type) and the second addresses positive, negative and neutral comments (i.e., comment value). Both sections follow the same pattern. I introduce the section with bivariate analysis to compare the comment type with characteristics of athletes and commentators. I then conduct logistic regressions on binary dependent variables, and close with a multinomial logistic regression on a categorical dependent variable. These sections answer the questions: Are Sportscenter commentators more likely to emphasize minority athletes’ physicality and white athletes’ intellect in describing their play? Are there differences in the valence of comments by race of athlete where black and Latino athletes are portrayed more negatively? How do these dynamics vary by race of commentator and sport? After those two sections I address the nature of off-the-field commentary, and the media hysteria surrounding Denver Broncos’ quarterback Tim Tebow in December 2011 and

New York Knicks’ point guard Jeremy Lin in February 2012—two stories worthy of closer attention but insufficiently covered in the initial logistic regression analyses.

Introduction to Variables and the Nature of Commentary on Sporscenter

This chapter begins by introducing and describing the nature of the comments, athletes and commentators found on Sportscenter37. There were 11,311 codeable (i.e.,

‘qualifying’) comments on the sampled Sportscenter programming from December 2011,

February 2012, and May 2012. Table 4 provides summary statistics on all qualifying comments from the three month sample. For each comment type and category of independent variable it lists the number (left column), percentage of total comments

(middle column), and the percentage of the total comments that were positive (right column). Table 4 illustrates that most comments were neutral and that with the exception of off-the-field comments, non-neutral comments were overwhelmingly positive (as exhibited in the furthest right column for the top section). Seven out of every ten comments were neutral, which reflect those comments that possess no direction or value and often consist of commentators mentioning an athlete’s name as part of a routine play.

General on-the-field comments (approximately one-fifth of all comments) were the most common non-neutral comments. General off-the-field represents only around 4% of total comments. The portion of non-neutral, non-general portrayals as a percentage of total comments ranges from around 1% for intelligence, effort, appearance, and leadership up to 3% for ability. Of the non-general comments more are of psychological (5%) attributes than of physical (4%) attribute and together four-fifths of non-general comments are positive. This was expected as Sportscenter is a highlight show where athletes and plays tend to be portrayed positively.

37 This section and the others leading up to the discussion on multivariate analysis describe the summary statistics in the text to facilitate presentation. This discussion, however, is based on over a dozen contingency tables, which can be found in the appendix. Those tables indicate the number and percentage of comment types, as well as characteristic of athletes and commentators. I reference the relevant table in parenthesis next to the text if the results are not presented in this chapter.

Commentators tend to be white (65%), male (83%) and anchors (71%). The proportion of qualifying comments for commentator race and sex are relatively similar to statistics presented in Chapter 138. Just over half of qualifying comments focus on black athletes, two-fifths of comments are on white athletes, and almost 90% address professionals. Basketball is the most commented upon sport followed by football, baseball then hockey. Tennis and soccer are largely ignored.

The virtual absence of any commentary on female athletes (0.25%) represents a major finding of this study. Women athletes just are not featured on Sportscenter. The exceptionally few comments preclude any meaningful statistical analysis of comments about women athletes or any examination of differences in the nature of comments between male and female athletes. Thus, comments on women athletes are excluded from all subsequent analyses. (I do however include a brief qualitative discussion of comments about female athletes in a later section.)

38 The statistics in Chapter 1 measured the racial distribution of commentators that appeared on the show rather than the racial breakdown of commentators based on comments offered. We saw in Chapter 1 that the pool of commentators was 69% white, 27% black, and 2% Latino/a and Asian.

Table 4: Summary Statistics of Dependent and Independent Variables N of % of Total % of Positive Comments Comments Comments1 within Comment Type2 OVERALL DISTRIBUTION OF COMMENT TYPES Neutral Comments 8,137 (68%) Non-Neutral Comments General On-the-Field 2,272 (19%) (86%) General Off-the-Field 491 (4%) (47%) Physical by Comment Type Appearance 165 (1%) (88%) Ability 325 (3%) (94%) Psychological by Comment Type Leadership 177 (2%) (97%) Focus/ Demeanor 220 (2%) (73%) Effort 112 (1%) (91%) Intelligence 97 (1%) (79%) OVERALL DISTRIBUTION OF COMMENTS BY ATHLETE, SPORT AND COMMENTATOR Athlete Race Black 5,846 (52%) (24%) White 4,357 (39%) (22%) Latino/a 814 (7%) (16%) Asian 294 (3%) (44%) Male Athlete 11,283 >99% (23%) Sport Football 3,388 (30%) (24%) Soccer 102 (1%) (37%) Basketball 4,767 (42%) (25%) Baseball 2,026 (18%) (17%) Hockey 716 (6%) (26%) Golf 282 (2%) (17%) Tennis 30 >1% (27%) Professional Sport 9,937 88% (22%) Commentator Race Black 2,822 (25%) (24%) White 7,328 (65%) (23%) Latino/a 316 (3%) (16%) Asian 845 (7%) (21%) Male Commentator 9,432 (83%) (24%) Anchor 7,983 (71%) (19%) N: 11,311 Total Comments. 1 The percentages add to more than 100% because non neutral comments can fall into multiple categories (e.g. a comment might be about both leadership and effort). 2 Column 4 presents proportion of comments within a given comment type that were positive. Non-neutral comments could only be coded as positive or negative (N for this is from first column).

Comments on the Nature of Athletes

Table 4 shows that within athlete race over 90% of comments focus on black and white athletes combined, and that within sports approximately 90% of comments address football, basketball and baseball combined. One might expect this result considering the racial demographics of athletes and the popularity of certain sports in America. However, the disproportionate commentary for white and black athletes and a few sports affects how I approach multivariate analyses as will be noted below. In order to further clarify the nature of the comments being made, I begin by providing some information on comments by race of athlete and sport (Table 5). The important information to take from

Table 5 is that football commentary is virtually all about black and white players (97%); that basketball is over four-fifths black (84%) but commentary on Asian athletes is most represented in it of any sport (5%)39, and that Latinos are most represented in baseball commentary (31%) though most baseball comments focus on white players (55%).

While hockey receives significant coverage (716 comments), the most coverage of any sport after baseball, basketball and football, the racial homogeneity of its athletes

(99% white) is reflected in the commentary being exclusively about white players and precludes comparison by athlete race within the sport. Soccer and tennis receive too few comments (> 1%) to characterize the commentary in terms of race of athletes. The number of non-neutral comments along with the dominance of certain sports indicates that not all sports qualify for meaningful statistical analysis (football, basketball, baseball, and hockey account for 96% of non-neutral comments). As a result, in subsequent multivariate analyses, I run the models on the full data and then on those sports with

39 As I discuss in detail later, this percentage is misleading as it reflects comments only on Jeremy Lin who emerged as a star in February 2012.

67 adequate data individually—college football and basketball, and professional football, basketball, baseball, hockey, and golf. (For professional tennis and soccer and all college sports besides football and basketball, which receive little or no attention, I include them in the full model but not individual models.) Also, it is important to note that the high percentage of comments on black golfers and Asian basketball players is misleading. All comments in those two subcategories focus only on Tiger Woods and Jeremy Lin, respectively (and not a larger population of athletes). As a result, for those two groups I report findings based on Jeremy Lin rather than ‘Asian basketball players’ and on Tiger

Woods rather than ‘black golfers’40.

Table 5: Comments on Athlete by Race and Sport41 Football Soccer Basketball Baseball Hockey Golf Tennis Total N for Athlete Race Black 1,447 (24.8%) 16 (0.37%) 4,001 (68.4%) 247 (4.2%) 11 (0.2%) 121 (2.1%) 3 (0.1%) 5,846 (42.7%) (15.7%) (83.9%) (12.2%) (1.5%) (42.9%) (10.0%) (51.7%)

White 1,844 (42.3%) 56 (1.3%) 470 (10.8%) 1,108 705 (16.2%) 147 (3.4%) 27 (0.6%) 4,357 (54.4%) (54.9%) (9.9%) (25.4%) (98.5%) (52.1%) (90.0%) (38.52) (54.7%) Latino/a 85 (10.4%) 28 (3.4%) 70 (8.6%) 630 (77.4%) 0 1 (0.1%) 0 814 (2.5%) (27.5%) (1.5%) (31.1%) (0.4%) (7.2%)

Asian 12 (4.1%) 2 (0.7%) 226 (76.9%) 41 (14.0%) 0 13 (4.4%) 0 294 (0.4%) (2.0%) (4.7%) (2.0%) (4.6%) (2.6%)

Total N 3,388 102 (0.9%) 4,767 (42.1%) 2,026 716 (6.3%) 282 (2.5%) 30 (0.3%) 11,311 for Sport (30.0%) (17.9%)

Overall, there were relatively few physical (3%) and psychological (4%) portrayals. While most were neutral (72%), more comments were positive (72%) than negative (5%). With respect to portrayals by individual sport, football athletes are criticized the most for the two types of physical comments: appearance (60% of negative comments about football as a whole) and ability (48% of negative comments about

40 Lin and Woods were the only athletes that absorbed such a disproportionate amount of attention to obfuscate if findings were reflective of a subgroup or an immensely popular individual. 41 For all tables in this chapter the percentage to the right of the number captures the percentage in the row, and the percentage below the number captures the percentage in the column.

68 football as a whole). Yet hockey, a sport that demands physical contact on par with football, earns only praise for physicality (see Table A14)42. Basketball (47%) and football (39%) receive the most psychological attributions, and for general on-the-field portrayals (i.e., not physical or psychological attributions), soccer athletes garner the most praise (95%) and golfers receive the least (64%) (A15). With respect to off-the-field commentary, football (60% negative), soccer (100% negative of 3 qualifying comments) and baseball (72% negative) receive more criticism than praise (A16). Football and baseball athletes achieve this because of stories focusing on suspensions for dirty play43 and the use of performance enhancing substances.

Table 4 also shows that professional athletes receive approximately 90% of all comments. Of the 1,357 comments on college athletes, basketball and football comprise

97% of comments by sport (A4) and black and white athletes comprise 98% of comments by athlete race (A3). In other words, college athletics in my sample really consists of commentary on black and white male football and basketball athletes. Given the overwhelming emphasis on these subgroups in collegiate sports, I also run models that analyze college and professional sports separately (with the model for college athletes limited to black and white male football and basketball players) to understand if portrayals remain consistent within football and basketball at to the two levels of competition. Thus, my multivariate models proceed as follows: First, I run the models with the full dataset of all comments on male athletes. Second, I run separate models for college and professional athletes, and when analyzing college athletes focus only on

42 I reference bivariate tables from the Appendix in parenthesis next to the relevant text if the results in the text are not presented in a table in this chapter. 43 In the NFL ‘Bountygate’ where New Orleans Saints’ defensive players allegedly pooled money to entice vicious and injurious hits on opponents received regular coverage over the recording period.

69 black and white football and basketball players. Finally, I focus on the more commonly commented on sports both to eliminate potentially anomalous circumstances relative to those less popular sports (i.e., soccer, golf and tennis) and to better understand if a specific sport has an intervening effect on the relationship between portrayals and athlete race.

Overview of Comments by Commentator

Most comments come from white male commentators. Female commentators contribute 17% of qualifying comments. Within gender, white males (60%) and white females (85%) contribute the most comments (A7). Most comments for female commentators come from the anchor role (91%) (A8) as was the case for comments from

Asians (100%) and Latino/as (85%) regardless of sex (A9). Male analysts contribute 95% of all analyst commentary (A8), which likely reflects that the position tends to be filled with former professionals (and considering Sportscenter’s disregard for women’s sports it makes sense that analysts consist largely of former professional male athletes and coaches).

With respect to comment value and type, there is relatively little variance between male and female commentators except that male commentators identify physicality relative to psychological attributes at a greater rate than do female commentators

(A23:A24). In comparing commentator roles, analysts44 are slightly more critical than anchors in attributing physical qualities and in commentary on general on-the-field play but analysts are more positive than anchors in attributing psychological characteristics

44 As a reminder, analysts are every commentator that’s not one of an episode’s two anchors, who are the two individuals who introduce the show and the segments.

(A26:A28). Anchors are also most critical of off-the-field content (A28), which is often reported in segments that tend to be the domain of anchors. White commentators contribute 65% of all codeable (qualifying) comments (Table 4) and 66% of non-neutral portrayals (A20:A22). (Black commentators contribute the second most with 25% of all comments and 25% of non-neutral comments.) Because of this disparity and more importantly my research interest in understanding the influence of commentator race on racial portrayals, after qualifying data on athletes by sex, sport, and level of competition I also rerun models comparing results for white and nonwhite commentators.

Framework for Multivariate and Logistic Analysis

The preceding descriptive statistics grant limited insight on the relationships between the dependent and independent variables. Multivariate analysis is helpful because it controls for the simultaneous influence of multiple variables. It also measures the statistical significance of individual independent variables and the direction and degree of relationships between independent variables and the dependent variable. In order to assess what influences comment types, I consolidated the nine comment types into three broad categories: physical, psychological and general.45 I then created four binary dependent variables for logistic regression analysis which were coded 1 (yes) or 0

(no) based on the following: Was the comment physical? Was the comment psychological? Was the comment positive? Was the comment negative?

While there are comments that are both physical and psychological or positive and negative, I capture all comments contingent on a ‘yes/ no’ criterion regardless if the

45 Because of the relative paucity of specific types of physical and psychological comments, it was necessary to collapse them into a few broader categories (e.g., some individual comment types like “physical ability” had too few comments for statistical analysis).

71 comment is also of the other type (physical or psychological) or value (positive or negative). In other words, the logistic regression for physical comments is coded ‘1’ if a commentator assigned an athlete a physical attribute and ‘0’ if not (i.e., it was strictly psychological or neutral comment). If a commentator assigns a physical and psychological attribute the comment is still coded as ‘1’ because it includes at least one physical attribution. Similarly, in the logistic regression for positive comments a comment is coded as ‘1’ if there is a positive attribution and ‘0’ if not (i.e., it was strictly negative or neutral). A physical comment may be positive or negative but as long as it qualifies as physical then it is coded ‘1’. It is important to bear in mind that for the purposes of logistic regression, physical and psychological comments (i.e., comment types) and positive and negative comments (i.e., comment values) operate independent of each other

I also run two multinomial logistic regressions where I treat the dependent variables for type and value with separate categories that fall into both or neither category. Using a categorical dependent variable enables me to include a category for comments that are both physical and psychological (for regressions on comment type) and both positive and negative (for regressions on comment value). I generated variables for comment type where: 1=physical, 0=psychological, 2=both, 3=general; and for comment value (valence) where: 1=positive, 0=negative, 2=both, 3=neutral. Looking at the comment type variable, comments are 3% physical, 4% psychological, 1% physical and psychological, and 92% general (i.e., on-the-field, off-the-field, or neutral)46. The

46 Again, it is necessary to reiterate that for the purpose of multivariate analysis comment value and comment type operate independently of each other. While there were ‘positive physical’ or ‘negative psychological’ comments, I was only interested in understanding how athletes were portrayed within their value or type.

72 comment value categorical dependent variable shows that comments are 22% positive,

5% negative, 1% positive and negative, and 72% neutral.

The analyses proceed as follows: First, I present a contingency table that compares the dependent variable with athlete race. I also discuss results from three other contingency tables that compare the dependent variable with commentator race, and the dependent variable with athlete race but only for white then nonwhite commentators47.

Next, I identify the independent variables and conditions used to fit the models for maximum likelihood estimation. For each dependent variable, I run the same base four models and then run additional models contingent on the results from the base models.

Model 1 is the full model that examines the relationship between comment type and athlete race, sport and level of competition, and commentator race, sex and role.

Model 2 is the same as Model 1 except it removes any independent variable or category in a categorical independent variable with fewer than 10 comments for that outcome in the dependent variable48. This second iteration serves as a sensitivity analysis in the event that low comment categories distort results. Model 3 and Model 4 follow the same criteria as Model 2 except that Model 3 is only professional athletes and Model 4 is only college athletes. Model 3 (compared with Model 2) allows me to understand if the results hold upon excluding college sports. Model 4 allows me to examine whether comments on college sports look different (because, as noted above, virtually all college athletes receiving attention are black and white basketball or football players so Model 4 excludes any racial and sport categories outside these).

47 Similar to the previous sections in this chapter, I reference bivariate tables from the Appendix in parenthesis next to the relevant text if the results in the text are not presented in this chapter. 48 For example, the first logistic regression measures if a comments was physical (yes =1) or was not physical (no=0). If tennis players only had 8 physical descriptions then they would not be included in the second iteration of the model.

For professional athletes I also ran additional models limiting the data to individual, high comment sports, like football, basketball and baseball (and for black and white college football and basketball players) to understand if the direction and statistical significance of the relationship between the dependent variable and athlete race is consistent49. I also ran a final iteration of models for just professional and just college male athletes by individual, high-comment sport based on commentary from only white commentators and only nonwhite commentators to understand the influence of commentator race. The fact that Asian and Latino/a commentators contribute so few comments demanded that I lump all nonwhite commentators together to achieve meaningful results.

The tables for each regression list only the models with meaningful results. To qualify as meaningful, the results must include at least one statistically significant (p <

.05) odds ratio for a category in the athlete race independent variable. In the tables, I present the model findings of those logistic regressions with results that are relevant to past research to better illustrate the significant findings. For other findings, unrelated to the literature, I discuss them but do not include them in the tables. For these other results that I describe in the text and I reference in parentheses where in the Appendix the actual logistic regression results can be found. For each model I include the number of qualifying comments and the Pseudo R2 to give some context of model fit50. I anticipate relatively low measures of fit for the models because the primary determinant of a

49 This is only possible with the logistic regressions as the computationally intensive nature of multinomial logistic regression demands that models for those two DVs only include interactions down to the level of comparing college and professional male athletes. 50 In comparing the fit of different models Long and Freese (2006) warn that measures of fit only indicate a preference between models, and not evidence of an optimal model (p. 104). Thus, theoretical relevance, and not measure of fit, should determine the selection of independent variables.

74 portrayal will be content and not a physical marker. Sportscenter consists mainly of highlights so when it shows a clip of an athlete making a spectacular play then the content of the play is likely the main determinant for a commentator’s attribution and not some characteristic of the athlete.. My goal however is not to achieve a high degree of model fit because I do not aim to predict comments. Rather, I wish to gain insight on whether there are systematic differences in the portrayals of athletes of different races.

To facilitate interpretation, I transform coefficients from the un-exponentiated log-odds into odds ratio in the logistic regression models and into the relative risk ratio in multinomial logistic regression models and report these ratios in the tables (Cameron &

Travedi, 2010). Odds ratios are interpreted based on the likelihood of a variable or category occurring in comparison to the reference group. For example, “white athletes are

0.87 times as likely as black athletes to receive a positive comment all else equal”51.

Unlike many other forms of coefficients the direction of the relationship between the dependent variable and the independent variable(s) is based on one not zero. A value less than one means that the variable or category is less likely to occur than its reference group, a value of one means that the variable or category is equally as likely to occur as its reference group, and a value greater than one means that the variable or category is more likely to occur than its reference group. To identify statistical significance I include asterisk(s) next to the odds ratio52.

51 In the text, I used the Stata user-created listcoef, percent function to report coefficients in percentages. Percent = 100{exp(BkXS)-1} 52 Standard practices demand that I recognize two other components of post-estimation analysis. First, I used the Wald test to conduct hypothesis testing to understand if the effects of all of the independent variables on the dependent variable are real or if they occur by chance. Kleinbaum and Klein (2002) explain that the Wald test and the likelihood ratio test produce similar estimates in large samples (p. 119). In addition to measuring the statistical significance of all independent variables, I also assess the significance of individual coefficients on the dependent variable with the z-statistic when I interpret each model. Second, there is the issue of residuals and influential observations. Residuals represent the

For multinomial logistic regression I report the relative risk ration (RRR), which are found by exponentiating the multinomial logistic coefficients. They differ from the odds ratio, which compares the odds of an event occurring for two groups, in that the

RRR reflect the probability of two events occurring. As was the case with the tables for logistic regression, when there are no observations or when Stata perfectly predicts failure nothing is reported. Interpreting RRR coefficients can be burdensome for the reader because the interpretation includes a comparison of the dependent variable category to its reference group and of the independent variable to its reference group

(when the independent variable is categorical). For example, the RRR for white athletes can be interpreted as “the relative risk ratio of a psychological comment relative to a comment that is neither physical nor psychological is 1.46 times greater for white athletes than it is for black athletes, holding all other variables constant”. I prefer to avoid such onerous language therefore for my interpretations I compare categories within independent variables in relative terms (i.e., “white athletes are more likely to be portrayed psychologically than are black athletes”). To limit redundancy when discussing results on odds ratios and RRRs all interpretations are assumed to include “holding all other variables in the model constant”53.

difference between a model’s predicted and observed outcome for each observation and influential observations reflect outliers that have a large effect on estimated parameters (Long & Freese, 2006). While I believe that the rigor established in the codebook and the pre-coding data collection process identified in Chapter 3 justify that I include all observations, those categories with few comments (i.e., black tennis players) are a concern because the data will reflect portrayals of one or few athletes rather than that category as a whole. Eliminating low-comment independent variables and categories (i.e., fewer than 10 outcomes for each outcome for that dependent variable) further accommodates for potentially influential outliers. The fact that none of the variables are continuous and that each comment was coded multiple times also limits methodological concerns. 53 I should also point out that the selection of the reference group for my independent variable affects how I interpret coefficients. For athlete race, black represents the reference group. There may be a scenario where black and white athletes possess a significant difference but the likelihood is the same for blacks and Latinos. If however, whites had served as the reference group then I may have found that blacks and Latinos were significantly different than whites.

Were commentators more likely to assign physical attributes to black athletes and psychological attributes to white athletes?

Prior research suggests that a prominent feature of sports commentary was the differential use of physical and psychological attributions for athletes based on race. To understand whether this continues today, I use both logistic and multinomial logistic regression to examine the relationship between the race of the athlete and the type of the comment. I go further than prior research by also examining the relationship by sport and commentator race. As I identified in Table 4, relative to total comments there were few physical (3%) and psychological (4%) attributions. In this section I use logistic regression to address two questions: Did the comment identify physicality? Did the comment identify psychological attributes? I include a multinomial logistic regression where the dependent variable is categorical: Did the comment address physicality, psychological attributes, both, or neither?

Before turning to the multivariate analysis, first I examine the overall breakdown on comment type (i.e., physical and psychological) by athlete race. Table 6 shows that commentators portray Asian (68%) and white athletes (57%) in a psychological context in over half the qualifying comments on Sportscenter but black (49%) and Latino (47%) athletes for less than half54. Commentators identify Latino (49%) and black (46%) athletes in a strictly physical manner at a percentage that is almost 50% greater than that of the percentage of white athletes (33%) and two-and-a-half times that of Asian athletes

(19%). A chi square test indicates that the relationship between physical-psychological comments and athlete race is statistically significant at the .01 level.

54 Here ‘qualifying’ means a comment that identifies a physical, psychological or both attribute.

Table 6: Physical and Psychological Comments by Male Athlete Race Black White Latino/a Asian Total Psychological 254 178 22 25 479 (48.66%) (57.23%) (46.81%) (67.57%) (52.24%)

Physical 241 102 23 7 373 (46.17%) (32.80%) (48.94%) (18.92%) (40.68%)

Both 27 31 2 5 65 (5.17%) (9.97%) (4.26%) (13.51%) (7.09%)

Total 522 311 47 37 917

X2=27.4, p=.00

Among comments that attribute only physical and/ or psychological qualities, black (60%) and Latino/a (53%) commentators make the greatest percentage of strictly psychological portrayals, and Asian (54%) and Latino/as (47%) make the greatest percentage of strictly physical portrayals (A29). However, Asian and Latino/a commentators contribute less than 7% of all non-general (i.e., physical or psychological comments) commentary (A29). In comparing racial portrayals between white and nonwhite commentators55, white commentators emphasize strictly psychological qualities the most for Asian (59%) and white athletes (54%) and strictly physical qualities the most for Latino (54%) and black athletes (47%) however the number of qualifying comments for Latino and Asian athletes (84) are about a tenth of that of black and white athletes

(833) (A30). Similar to white commentators, nonwhite commentators identify psychological attributes the most for Asian and white athletes, and physical attributes the most for black and Latino athletes. Unlike white commentators, however over half of nonwhite commentators’ comments on Latino (53%) and black athletes (52%) address strictly psychological attributes (A31).

55 The relatively few qualifying comments made by all nonwhite commentators necessitated that I report commentator race dichotomously rather than categorically.

For the logistic regression on physical comments I began with Model 1, which consists of all the main effect independent variables. Model 2 drops those categories with fewer than 10 physical comments—soccer, golf, tennis, and Latino/a commentators.

Model 3 kept the same constraints as Model 2 except it includes only professional athletes. Model 4 follows the same pattern but includes only black and white college football and basketball athletes. The Wald test shows that the effect of all independent variables on the probability that a comment is physical is statistically significant at the

.01 level for all models. Focusing exclusively on professional athletes I ran nine more iterations of Model 3. The first three focused exclusively on football, basketball or baseball—the three sports with sufficient comments for exclusive analysis. This helped me to understand if individual sports drove the results found in Model 3. Then I reran each of those models twice including only white commentators and then only nonwhite commentators. Focusing next exclusively on college athletes I ran six more iterations of

Model 4. The first two focus exclusively on football and basketball, and then I reran each of those models twice, once including white commentators and then once with nonwhite commentators.

I only report odds ratios for models in Table 7 that I deem meaningful, which for this dependent variable are Model 1, Model 3, Model 7 (only professional male football players and nonwhite commentators), and Model 12 (only professional male baseball players and white commentators). As a reminder, to qualify as meaningful, the results must include at least one statistically significant (p < .05) odds ratio for a category in the athlete race independent variable. Model 1 in Table 7 illustrates that commentators are

61% less likely to portray professionals physically than they are college athletes and that

79 male commentators and analysts are 125% and 69%, respectively, more likely than female commentators and anchors to identify physicality in an athlete. Removing low- comment categories and college athletes in Model 3 increases male commentators’ likelihood to 187% more likely than female commentators. With respect to fit, of the reported models Model 1 has the highest Pseudo R2 of the base models, which makes sense as it includes the most categories of the independent variables.

The important finding from Model 1 is that commentators are 29% less likely to describe white athletes physically than they are black athletes and that no other category of athlete race has a statistically significant relationship with the likelihood of a physical comment. When I focus only on categories with 10 or more comments and exclude college athletes for Model 3, commentators are 37% more likely to assign physicality to black male professional athletes than they are white male professional athletes. Focusing only on college athletes and isolating just football and just basketball and then comparing each for only white commentators and only nonwhite commentators exhibited that there is no statistically significant difference between the likelihood that a comment is physical and a male college athlete’s race (B4). In other words, the relationship I found in Models

1, 2 and 3 appears to be a phenomenon exclusive to professional athletes. To further understand this relationship at the professional level I reran the Model 3 but constrained the data to just football, basketball or baseball and compared results for only white and only nonwhite commentators.

Looking only at male professional football players, I found that commentators are approximately 41% more likely to portray professional black football players physically than they are white football players (B5). In addition, commentators are 69% more likely

80 to portray professional black baseball players physically than they are white players

(B11). There is no relationship between the likelihood of a physical comment with any other athlete race in football and baseball or for any athlete race in professional basketball

(B8:B10), the one other ‘high comment’ professional sport. This suggests the racial difference in portrayal of physicality is driven by football and baseball. Taking commentator race into account showed that for white professional football commentators the pattern holds but is not statistically significant (B6). Nonwhite commentators however are 50% more likely to portray black professional football players physically than they are white players (Model 7). As for baseball white commentators are 76% were more likely to portray black baseball players in a physical context than they are white players (Model 12). Nonwhite baseball commentators are 61% more likely but the difference is not statistically significant at conventional levels56 (B13).

Thus, Table 7 answers the research question of the title of this section with a qualified affirmative—commentators are more likely to portray black athletes as compared to white athletes as physical—but this holds only for professional football and baseball. When I reran models only for white and nonwhite commentators these relationships are qualified further in that they only hold in football for nonwhite commentators and in baseball for white commentators. The fact that I only found support for the literature in professional football (I have not encountered quantitative research measuring racial portrayals of MLB players) is likely explained by two factors. First, there may be a culture unique to the NFL that makes the game and its media commentary

56 The coefficient for nonwhite commentators was only statistically significant at the .10 level.

81 ripe for assigning ‘accepted’, stereotypical attributes to black and white athletes57. It also might reflect that much of the previous research analyzed NFL content. As a result, I found support for the literature only in professional football because the literature largely only analyzed professional football.

Table 7: Logistic Regression Odds Ratios for Physical Comments on Male Athletes Model 7: Model 12: Model 1: Model 3: Pro Football & Pro Baseball & All Pros Nonwhite Comms White Comms Athlete Race1 White .71*** .63*** .50** .24*** Latino/a .86 .78 .70 Asian 1.17 1.15 1.74 Sport2 Soccer Basketball .95 1.06 Baseball 1.00 1.05 Hockey 1.11 1.25 Golf .42 Tennis Professional Athlete .39*** Commentator Race3 White 1.19 1.10 Latino/a .79 Asian 1.18 1.27 Male Commentator 2.25*** 2.87*** 3.98 2.04 Anchor .31*** .36*** .45** .40** Model 1 N: 11,161 Pseudo R2: 6.82%; Model 3 N: 9,292 Pseudo R2: 4.85%; Model 7 N: 920 Pseudo R2: 3.89%; Model 12 N: 1,232 Pseudo R2: 7.07% Reference Groups: 1Black Athletes; 2Football; 3Black Commentator * p < .10; ** p < .05; *** p < . 01

Next I ran logistic regressions for psychological comments. Similar to physical comments I began with Model 1, which consists of all the main effect independent variables, and Model 2, which drops those categories with fewer than 10 psychological

57 Note that I did not find support that white NFL athletes were portrayed one way and all black, Latino and Asian NFL athletes were portrayed another. This is really an issue of how commentators assign attributes to black and white NFL athletes.

82 comments—soccer, tennis, and Latino/a commentators. Model 3 keeps the same constraints as Model 2 except it includes only professional athletes. Model 4 follows the same pattern but includes only black and white college football and basketball athletes.

The Wald test shows that the effect of all independent variables on the probability that a comment is psychological is statistically significant at the .01 level for all models.

Focusing exclusively on college then professional athletes I ran the same iterations of models that focus on sport and commentator race that I did for physical comments. I only report odds ratios for models in Table 8 for Model 1, Model 3, Model 6 (only professional male football players and white commentators), and Model 7 (only professional male football players and nonwhite commentators). Model 1 in Table 8 illustrates that commentators are less likely to portray baseball (64%) and hockey (31%) players psychologically than they are football players, and that commentators are 25% less likely to assign psychological attributes to professional athletes than they are college athletes. Anchors and Asian commentators are also the least likely to assign psychological attributes.

Most importantly, Model 1 illustrates that commentators are more likely to assign white (34%) and Asian (158%) athletes psychological attributes than they are black athletes. Excluding categories with fewer than 10 psychological comments indicates that this difference holds when looking only at professional athletes (Model 3). Similar to the previous regression, I also reran the model separately for football, basketball, and baseball, which receive the most psychological attributions58. Focusing only on college athletes and isolating just football and just basketball and then comparing each for only

58 As previously noted, hockey had sufficient psychological attributions however they were all on white players so that sport was omitted.

83 white commentators and only nonwhite commentators, indicates that there is no difference between the likelihood that a comment is psychological and a male college athlete’s race (C4). Therefore, again the relationship I found in Model 1 appears to be a phenomenon exclusive to professional athletes.

In professional football, commentators are much more likely to portray white players (106%) and Latino players (205%) in a psychological context than they are black players (C5). Both are statistically significant at the .01 level. In professional basketball, commentators are much more likely (191%) to portray Jeremy Lin in a psychological context than they are black professional basketball players and there is no difference with white or Latino basketball players (C8). In baseball there is no difference between any category of athlete race and the likelihood of a psychological attribution (C11:C13).

Taking commentator race into account, Table 8 shows that in football white commentators are much more likely to assign psychological attributes to white (144%) and Latino (246%) athletes than they are black athletes (Model 6). Nonwhite commentators are also more likely (but to a lesser extent) to assign psychological attributes to white football players (58%) than they are black players however the coefficient is only statistically significant at the .10 level (Model 7). (There is some evidence that this dynamic holds across commentator race.)

Similar to the logistic regression on physical comments, Table 8 presents qualified findings. At an aggregate level, commentators are more likely to portray white and Asian athletes than they are black athletes in a psychological context. Upon closer analysis however, commentators regardless of race are really more likely to portray

Jeremy Lin in a psychological context than they are professional black basketball players.

The fact that all comments on professional Asian basketball players are on Jeremy Lin prevents me from assuming that this relationship is true of all Asian basketball players and not just the Harvard graduate. Further, in football, white commentators are much more likely to assign psychological attributes to white and Latino professional football players than they are black professional football players. Similar to the logistic regression on physical comments, the fact that I found support for the literature on the likelihood of commentators assigning psychological attributes to black and white NFL players, likely reflects that much of the previous research focused only on analyzing professional football. The finding on Latino professional football athletes however is not documented in the literature. While it may be an anomaly of my three month sample, the finding on

Latino professional football athletes also might reflect that Sportscenter is a much more socially progressive media program than the media for sporting events (i.e., in-game commentary), which are the content analyzed in much of the literature. Because of its progressive nature, commentators on Sportscenter may be less likely than in-game commentators to hold to the norms and accepted language that justify success for athletes of different races.

Table 8: Logistic Regression Odds Ratios for Psychological Comments on Male Athletes Model 6: Model 7: Model 1: Model 3: Pro Football & Pro Football & All Pros White Comms Nonwhite Comms Athlete Race1 White 1.34*** 1.35** 2.43*** 1.58* Latino/a 1.38 1.25 3.45*** 2.11 Asian 2.58*** 2.66*** Sport2 Soccer .65 Basketball 1.07 1.06 Baseball .36*** .34*** Hockey .69* .69* Golf 1.27 1.29

Tennis 1.06** Professional .75** Athlete Commentator

Race3 White .85* .80** Latino/a .54 Asian .63* .52** Male .83 .79 .90 .93 Commentator Anchor .39*** .42*** .46*** .25*** Model 1 N: 11,283Pseudo R2: 4.81%; Model 3 N: 9,557 Pseudo R2: 4.88%; Model 6 N: 1,888 Pseudo R2: 3.43%; Model 7 N: 956 Pseudo R2: 6.56% Reference Groups: 1Black Athletes; 2Football; 3Black Commentator * p < .10; ** p < .05; *** p < . 01

For the final table in this section I conduct multinomial logistic regression where the categorical dependent variable addressed if the comment identifies physical attributes, psychological attributes, both, or neither (i.e., general and neutral comments). I fit a single model that includes all of the main effect independent variables. Because there are four categories (physical, psychological, both, and neither) the model is even more parsimonious than the logistic regression as more independent variable categories were removed on account of failing to achieve at least ten comments in each outcome of the dependent variable. These include Latino and Asian athletes, soccer, hockey, golf, tennis, and Latino/a and Asian commentators59. I ran the model for professional and college athletes. At the college level there are no statistically significant coefficients on athlete race so the results presented in Table 9 are strictly for professional athletes.

Table 9 shows the relative risk rations (RRR) for the multinomial logistic regression where the categorical dependent variable captures if the comment identifies

59 More categories were dropped here than in the logistic regression because fewer categories achieved 10 comments in the both (i.e., physical attribution AND psychological attribution in the same comment) outcome.

86 physical attributes, psychological attributes, both, or neither60. Table 9 confirms what I learned from the logistic regression results in Tables 7 and 8—even taking both physical and psychological comments into account at an aggregate level, commentators are more likely to portray white male professional athletes psychologically and black male professional athletes physically than the other.

Table 9: Multinomial Logistic Regression Results for Physical-Psychological Comments of Professional Male Athletes Psychological RRR Physical RRR Both RRR Athlete Race1 White 1.46** .63** 1.23 Sport2 Basketball 1.19 1.15 .86 Baseball .37*** .91 Commentator Race3 White .73*** .98* 1.12 Male Commentator .91 2.93*** 3.80 Anchor .49*** .42*** .18*** N: 8,234 Pseudo R2: 4.59% Reference Group Dependent Variables: Neutral comments that were neither physical nor psychological. Reference Groups Independent Variables: 1Black Athletes; 2Football; 3Black Commentator. * p < .10; ** p < .05; *** p < . 01

Are commentators more likely to praise white athletes and criticize black athletes?

The prior literature also gave reason to expect that non-white athletes might receive more negative coverage than white athletes. Here I include general on- and off- the-field comments as well as those that mentioned physical and psychological attributes to look at comment value. For this section I used logistic regression to address two questions. Was the comment positive? Was the comment negative? The multinomial logistic regression uses a categorical dependent variable to identify if the comment was positive, negative, both, or neutral. Table 10 addresses qualifying descriptive (non-

60 The computationally intensive nature of multinomial logistic regression precludes me from qualifying my criteria further by sport and commentator race, as I did in the logistic regressions.

87 neutral) comments and presents bivariate analysis that compare positive and negative attributions of any comments type across male athlete race. It shows that as a percentage of qualifying negative and positive comments within each racial group, Asian athletes

(92%) followed by Latino (83%), white (79%) then black (78%) athletes receive the greatest percentage of strictly positive attributions. Black athletes (19%), on the other hand, are subject to the greatest percentage of strictly negative attributions followed by whites (18%), Latinos (15%) then Asians (8%). The general descriptive data suggest that

Asian athletes are the most praised and least criticized and black athletes are the most criticized and least praised. The relationship between comment value and athlete race is statistically significant at the .01 level.

Table 10: Positive and Negative Comments by Athlete Race Black White Latino/a Asian Total Negative 331 200 24 11 566 (19.12%) (17.61%) (15.29%) (7.80%) (17.88%)

Positive 1,342 899 130 129 2,500 (77.53%) (79.14%) (82.80%) (91.49%) (78.99%)

Both 58 37 3 1 99 (3.35%) (3.26%) (1.91%) (0.71%) (3.13%)

Total 1,731 1,136 157 141 3,165

X2=17.4, p=.01

Among strictly physical (i.e., appearance and ability) portrayals black athletes receive 61% of positive comments. While commentators are largely positive of Latino

(96%) and Asian (100%) athletes, the two groups account for less than 9% of all physical attributions (A11). Commentators are most critical of white athletes’ physicality (12% negative) and where black athletes receive a negative physical comment for every 13

88 positive comments, white athletes receive a negative comment for every 8 positive comments (A11)61.

Black athletes receive 51% of all psychological comments and 57% of negative attributions for psychological characteristics (A12). Within each psychological comment type black athletes also received at least half of the critical comments. With the exception of comments on effort, which might represent the psychological quality most closely associated with physicality, white athletes are commented upon positively at a higher rate than are black athletes for every psychological comment type (A12). Latino athletes garner largely positive commentary except for focus where only half of the attributions are positive (A12). Asian athletes are only praised for psychological qualities, which supports literature suggesting that they are stereotyped in psychological and intellectual terms (A12). Taking total psychological comments into account however illustrates that

Latino/a (27%) then black (17%) and white (14%) athletes are the most likely to receive criticism (A12). Finally, all groups of athlete race receive 85%-87% praise for on-the- field comments however for off-the-field scenarios approximately two-thirds of portrayals for black athletes are negative, which contrasts with whites (48% negative),

Latinos (29% negative), and Asians (0% negative) (A13).

To understand the source of the commentary it is necessary to examine who provides the most negative and positive portrayals. In comparing racial portrayals between white and nonwhite commentators, white commentators praise Asian (92%) and

Latino (82%) athletes the most and criticize black (21%) and white (18%) athletes the most (A33). Similar to white commentators, nonwhite commentators praise Asian (91%)

61 With respect to commentators, Black (60%) and Latino/a commentators (53%) contribute the greatest percentage of strictly psychological comments, and Asian (54%) and Latino/a commentators (47%) contribute the greatest percentage of strictly physical comments (A29).

89 and Latino (84%) athletes the most and criticize white (18%) and black (16%) the most however unlike white commentators they are more likely to criticize white than black athletes (A34). Latino/a commentators (24%) followed by white (19%), black (16%) and then Asian (15%) commentators provide the greatest percentage of strictly negative attributions, and Asian (85%) followed by black (81%), white (78%) and then Latino/a

(74%) commentators provide the greatest percentage of strictly positive attributions

(A32).

For the logistic regression on positive comments I started with the same four model structure as the sections on physical and psychological comments. The Wald test shows that the effect of all independent variables on the probability that a comment was physical is statistically significant at the .01 level for all models. Focusing exclusively on professional then college athletes I ran the same iterations of models that focus on sport and commentator race that I did in the previous section, and only report odds ratios in

Table 11 for Model 1, Model 3, Model 13 (only professional male basketball players and nonwhite commentators), and Model 25 (only college male football players and white commentators). Model 1 illustrates that commentators are more likely to praise soccer

(197%), basketball (11%), hockey (50%), and tennis (190%) than they are football players, and that professional athletes are 30% less likely to receive praise than are college athletes. Male commentators and anchors are the least likely to offer praise relative to female commentators and analysts, respectively.

Model 1 in Table 11 also illustrates that commentators are less likely to praise white (10%) and Latino (27%) athletes and more likely to praise Asian athletes (180%) than they are black athletes. Model 3 shows that the likelihood of praise for black and

90 white athletes disappears when looking at all professional sports. Focusing exclusively on professional football, white commentators are 21% more likely to praise white players than they are black players however the difference only holds at the .10 level (D5). The only relationship that achieves an accepted level of statistical significance in professional football shows that nonwhite commentators are 69% less likely to praise Latino players than they are black players (D7). In professional basketball, commentators are much more likely to praise Jeremy Lin (208%) but less likely to praise white (29%) and Latino

(71%) players than they are black players (D11). Further, white commentators are much more likely to praise Lin (230%) and less likely to praise Latinos (69%) than they are black professional basketball players (D12). There is no difference between white and black players for white professional basketball commentators. Interestingly, Model 13 in

Table 11 shows that nonwhite professional basketball commentators are much less likely to praise white basketball players (64%) and much more likely to praise Jeremy Lin

(182%) than they are black players.

In professional baseball commentators are most likely to praise Asian players

(D14), however after re-running the models only for white (D15) and nonwhite commentators (D16) the trend holds for both groups but fails to achieve statistical significance. Nonwhite baseball commentators however are 54% less likely to praise

Latino professional baseball players than they are black professional baseball players

(statistically significant at the .01 level) (D16). There is no statistically significant difference between the likelihood that a comment is positive and athlete race in soccer

(D8:D10). White golf commentators are much more likely to praise white golfers (108%) than they are Tiger Woods but the odds ratio is only statistically significant at the .10

91 level (D21). Focusing exclusively on college sports, commentators are 32% less likely to praise white athletes than they are black athletes (D4) and in college football the difference increases to 40% (both statistically significant at the .05 level) (D23). Model

25 in Table 11 illustrates that white commentators are 45% less likely to praise white male college football players than they are black players. The difference did not hold for nonwhite commentators (D25) nor is there any difference between the likelihood that a comment is positive with a male college basketball player’s race (D26:D28).

The balance of evidence does not support that commentators are more likely to praise white athletes than they are nonwhite athletes. For example, in professional football the only definitive relationship I find is that nonwhite commentators are less likely to praise Latino players than black players. In college football, however white commentators are almost half as likely to praise white players as they are black players.

In basketball, commentators regardless of race are most likely to praise Jeremy Lin, but nonwhite professional basketball commentators are much less likely to praise white players than they are black players. Overall, I can answer the research question in the negative—commentators are not more likely to praise white athletes than they are black athletes and in two sports the opposite occurs. There is however evidence across sports that suggest that commentators are least likely to praise Latino male athletes.

The fact that these findings conflict with some of the previous literature, which showed that commentators were more likely to praise white athletes than black athletes, may reflect Sportscenter’s socially progressive nature and its commentators’ propensity to reject conventional patterns for assigning praise to athletes of different race. In professional basketball however, I think it also likely reflects the culture of a professional

92 sport in which African Americans have sustained excellence for so many decades and have achieved vastly greater success in the coaching and executive ranks62 in comparison to that of any other major professional sport. As a result, NBA commentators—and audiences, more broadly—may be more inclined to reject stereotypes of African

American men, which results in greater racial comity than other professional sports. In other words, in the NBA African American men have such an influence over every aspect of the game that it has become increasingly difficult for whites, or any other non-African

American ethnic group, to appropriate and manipulate the NBA culture.

The findings on college football, which are not documented in the literature and conflict with patterns from the research on professional football, are more surprising.

While I would certainly expect commentators to be less critical of college football athletes than their professional counterparts because the former are amateurs, it is not clear why white commentators would be less likely to praise white than black college athletes. This is only further complicated by the fact that on a program, like Sportscenter, college football athletes are almost treated like non-human commodities in how they are constantly being quantified, assessed, and re-assessed after every Saturday’s performance to project their value in the NFL. Perhaps black college athletes are more likely to receive praise than whites because in this college football commodities’ market that Sportscenter promotes, black athletes are perceived as being superior to white athletes in the conventional measures63 used to forecast success in the NFL.

62 Michael Jordan is the only African American majority owner of a major professional sports team, the NBA’s Charlotte Bobcats. In the NBA, there are also other African American team owners but with a minority share of their team. 63 NFL scouts rely on many physical and psychological measures to project a college athletes value, but perhaps the most conventional metrics focus on speed (the time it takes to run 40 yards from a stop) and strength (the number of times one can bench press 225 pounds). Depending upon position the number of metrics can grow exponentially.

Table 11: Logistic Regression Odds Ratios for Positive Comments on Male Athletes Model 13: Model 25: Model 1: Model 3: Pro Basketball & Col Football & All Pros Nonwhite Comms White Comms Athlete Race1 White .90* .95 .36*** .55** Latino .73*** .72*** .26 Asian 2.80*** 2.82*** 2.82*** Sport2 Soccer 2.97*** 3.09*** Basketball 1.11* 1.16** Baseball .96 .95 Hockey 1.50*** 1.47*** Golf .91 .92 Tennis 2.90** Professional Athlete .70*** Commentator Race3 White .99** .97 Latino/a .76 .74* Asian 1.07 1.04 Male Commentator 1.14* 1.21** 1.25 .47 Anchor .45*** .49*** .55*** .16*** Model 1 N: 11,283 Pseudo R2: 3.57%; Model 3 N: 9,906 Pseudo R2: 3.42%; Model 13 N: 1,454 Pseudo R2: 3.78%; Model 25 N: 402 Pseudo R2: 12.71% Reference Groups: 1Black Athletes; 2Football; 3Black Commentator; 4Black Football; 5Black Professional; and, 6Black Athlete—Black Commentator * p < .10; ** p < .05; *** p < . 01

Next, I repeated the same model structure as the three previous logistic regressions but this time negative comments serve as the dependent variable. Model 2 dropped soccer and tennis, the only categories with fewer than 10 negative comments.

The Wald test shows that the effect of all independent variables on the probability that a comment is physical is statistically significant at the .01 level for all models. In Table 12

I report odds ratios for Model 1, Model 3, Model 6 (only professional male football players and nonwhite commentators), and Model 7 (only professional male football players and nonwhite commentators). Model 1 illustrates that commentators are 72%

94 more likely to criticize golfers and 26% less likely to criticize basketball players than they are football players, and that professional athletes are more likely to receive criticism (42%) than college athletes. Among commentators, whites, males and anchors are the most likely to criticize.

Most importantly, Model 1 in Table 12 shows that commentators are less likely to criticize Latino (64%), Asian (42%), and white (34%) athletes than they are black athletes. After removing soccer and tennis and rerunning the models only for professional and college male athletes, the difference only holds for professionals (Model 3). In professional football, commentators are 61% less likely to criticize white players than they are black players (E5). Odds ratios for Latino and Asian players fail to achieve statistical significance. Table 12 shows that white professional football commentators

(Model 6) are 67% less likely to criticize white players than they are black players, but nonwhite football commentators (Model 7) are only 45% less likely to criticize white players than black players.

Professional basketball commentators are actually more likely (45%) to criticize white players than they are black players (no difference for Latinos or Jeremy Lin) however the difference is only statistically significant at the .10 level (E8). In professional baseball, nonwhite commentators are much less likely to criticize Latino players (73%) than they are black and white players (statistically significant at the .05 level) (E13). There is no difference between criticism and athlete race for white professional baseball commentators (E12). White golf commentators are 97% less likely to criticize white golfers than they are Tiger Woods (E18) but the odds ratios on athlete race for nonwhite golf commentators fails to achieve statistical significance (E19).

In comparison to white athletes, commentators are most likely to criticize professional black football players and Tiger Woods. While both white and nonwhite commentators are more critical of black players in comparison to white players in professional football, white commentators are especially critical. There is also evidence that nonwhite commentators are more critical of black athletes in professional football and baseball in comparison to Latino athletes however taking the findings of Table 11 into account, which show that that Latinos are the least likely to receive praise, Latinos are simply the group most identified in a non-positive, non-negative context (i.e., neutral). Lastly, commentators, especially whites, are much more critical of Tiger Woods than they are of white professional golfers.

Tiger Woods’ circumstances are so unique from that of any other golfer or athlete that any comparison or attempt to generalize based on commentators’ portrayal of him would be inaccurate. With respect to race, Woods is often identified as black, however his father was African American, his mother is Thai, Chinese and Dutch, and Woods has also claimed that he has Native American ancestry. In other words, he is truly multi- ethnic thus it would be incorrect to assume that commentators perceive all black or Asian or white golfers as they do Woods. In addition, in the mainstream sports media Woods represents the only non-Asian minority to receive regular attention in a sport that has traditionally been played and dominated by upper-class whites. There is no other major sport with similar historical racial and class structures to that of golf. (There certainly are sports, like tennis, polo and sailing, that likely possess a similar historical racial and class make-up, but none of those receive near the attention that golf does in the mainstream sports media.) There is also the fact that no single individual dominates the media

96 attention of his or her sport, like Tiger Woods. Even if Woods was considered white one should be hesitant to generalize commentators’ portrayals to other white golfers. He receives such a disproportionate amount of attention relative to other golfers that it is almost like there are two golf sports media—the primary one for Woods and an ancillary media for all other golfers. In fact, it is common knowledge that networks and tournament hosts hope that Woods remains competitive in their tournaments into Sunday afternoon64 because viewership is significantly greater when he is in contention versus when he is out of contention. Finally, turmoil in Woods’ personal life also received regular national media attention in late 2009 when questions of infidelity arose about his marriage. As a result, no current athlete—save, perhaps Michael Vick65—has received more negative press over the last decade than did Woods. It is worth noting that Woods’ wife as well as many of the alleged mistresses were white women—another factor that complicates any comparison to other white or non-white golfers or even athletes more generally. Thus, Woods truly is unique and any attempt to generalize portrayals on him to other athletes should be met with the utmost apprehension.

With respect to the findings on professional football, the fact that I found support for the literature showing that commentators are more likely to criticize black than white

NFL athletes likely reflects that much of the previous research focused only on analyzing professional football and that the NFL possesses a unique culture where patterns of stereotyping hold unlike other popular American sports.

64 Most golf tournaments consist of four 18-hole matches a day over four days from Thursday to Sunday. As the contenders sort themselves out over the four days the tournaments arrange for the golfers with the lowest score and, thus, with the highest likelihood of winning, to tee off latest in the day on Sunday. This normally results in the winner of the tournament being determined during the prime golf viewing hours between 3 and 7pm on Sunday afternoons. 65 In 2007, Vick was convicted and incarcerated for operating a large interstate dog fighting operation that included the torture and killing of dozens of dogs.

Table 12: Logistic Regression Odds Ratios for Negative Comments on Male Athletes Model 6: Model 7: Model 1: Model 3: Pro Foottball & Pro Foottball & All Pros White Comms Nonwhite Comms Athlete Race1 White .66*** .62*** .33** .55** Latino .36*** .37*** .53 .86 Asian .58* .54* Sport2 Soccer 1.38 Basketball .74*** .69*** Baseball 1.17 1.12 Hockey .98 .96 Golf 1.72** 1.66** Tennis Professional Athlete 1.42** Commentator Race3 White 1.24** 1.25** Latino/a 1.18 1.15 Asian .83 .96 Male Commentator 1.25* 1.19 1.90** 1.62 Anchor .69*** .72*** 1.38* .86 Model 1 N: 11,261 Pseudo R2: 1.71%; Model 3 N: 9,811 Pseudo R2: 1.51%; Model 6 N: 1,888 Pseudo R2: 4.50%; Model 7 N: 956 Pseudo R2: 2.28% Reference Groups: 1Black Athletes; 2Football; 3Black Commentator * p < .10; ** p < .05; *** p < . 01

Finally, I conduct multinomial logistic regression where the categorical dependent variable addresses if the comment was positive, negative, both, or neutral. Similar to the multinomial logistic regression I remove categories that fail to achieve at least ten comments across the four outcomes of the dependent variable. These include Latino and

Asian athletes, soccer, hockey, golf, tennis, and Latino, Asian and female commentators.

I ran the model for professional and college athletes. At the college level, commentators are less likely to praise white football players (41%) than they are black players but that is the only statistically significant RRR. Table 13 includes results for professional athletes and reiterates what Table 11 showed at an aggregate level—commentators are

98 less likely to criticize white professional male athletes than they are black athletes and are less likely to criticize basketball than football.

Table 13: Multinomial Logistic Regression Results for Positive Negative Comments of Professional Male Athletes Negative RRR Positive RRR Both RRR Athlete Race1 White .57*** .92 1.07 Sport2 Basketball .59*** 1.06 1.52 Baseball .85 .83* 1.53 Commentator Race3 White 1.17 .88* .93 Anchor .78** .48*** .35*** N: 5,766 Pseudo R2: 2.12% Reference Group Dependent Variables: Comments that were neutral. Reference Groups Independent Variables: 1Black Athletes; 2Football; 3Black Commentator. * p < .10; ** p < .05; *** p < . 01

Off-the-Field Comments

One might expect the success of minority athletes on the playing field to mitigate racial stereotyping in off-the-field scenarios. Examples of off-the-field commentary range from the negative, such as an arrest for driving while intoxicated or receiving a suspension for the use of performance-enhancing drugs, to the positive, such as the hysteria in a city surrounding an emerging star or an athlete’s work with her charitable foundation. In chapter 2 I cited research showing that black athletes were portrayed in a more pejorative manner than white athletes in off-the-field scenarios (Rada &

Wulfemeyer, 2005; Rainville & McCormick, 1977). Because of the age of these two studies I’m interested in understanding if these findings hold today. Further, this study focuses on sports news broadcasting and not in-game commentary, which has been the focus of previous studies and which likely has less coverage of off-the-field events.

Sportscenter airs highlights, interviews and the occasional biopic all of which pay greater attention to (and, thus, possesses greater relevance on) off-the-field content.

Off-the-field comments are the only majority negative non-neutral comment type.

Looking at the commentary, only black athletes (70%) receive more negative than positive comments. Just over half of the comments on white athletes (52%) are positive

(A13). While off-the-field comments on Latinos are overwhelmingly positive (71%) there are few qualifying comments (A14). Perhaps most interesting, all 58 off-the-field comments on Asian athletes are positive. This finding should be qualified as all coded comments focus on Jeremy Lin so I cannot be confident that such praise is typical for all

Asian athletes or just him. White commentators (55% negative) are the most negative followed by Latino/as (50%), blacks (49%) and then Asians (33%), which likely reflects white commentators’ high presence as anchors, which are more likely than analysts to report on off-the-field content (A22).

I ran a logistic regression on comment value (1-positive; 0-negative) for off-the- field comments where athlete race is the independent variable. Stata dropped the 58 observations associated with Jeremy Lin because they perfectly predict a positive comment. Analyzing strictly athlete race shows that commentators are 154% more likely to praise white athletes and 480% more likely to praise Latino athletes than they are black athletes in off-the-field scenarios, however I am cautious of the coefficient for Latino/as because of the relatively few qualifying comments (F1). Although there is a large confidence interval for them even at the low-end it indicates that commentators are much more likely to praise Latino athletes than they are black athletes. Next, I reran the model for off-the-field comments but only for football (F2), basketball (F6) and baseball (F9)—

100 the three sports with sufficient comments. A statistically significant difference holds in football where commentators are much more likely to praise white players (1,263%) than they are black players in off-the-field scenarios. Upon closer analysis I find that this trend only occurs in professional football (both white and nonwhite commentators are much more likely to praise white professional football players than they are black players and both are statistically significant at the .01 level)66 and that there are no statistically significant differences for the categories of athlete race in other sports (F4:F5). Thus, my findings support research showing that commentators are more likely to criticize black athletes in off-the-field commentary than they are white athletes (Rada & Wulfemeyer,

2005; Rainville & McCormick, 1977), but only when comparing black and white professional football players.

Ignoring Women’s Athletics

Table 4 shows that female athletes are largely ignored to the scale of receiving only 28 out of a sample of 11,311 qualifying comments (or, 0.25%). The 28 comments broke down as follows: 1 leadership (positive); 2 focus (positive and negative); 1 intelligence (positive); 7 on-the-field (6 positive, 1 negative); and 19 neutral. (Two comments qualified under multiple types.) It’s worth noting that there are no comments on female physicality. Of the few qualifying comments on women’s athletics, all athletes are either black (61%) or white (39%) and 61% of comments focus on college athletics.

Within sports 57% are on basketball, 29% are on tennis, and golf and soccer each receive

7% (or, 2 comments). Most comments are on women’s college basketball, which likely

66 Commentators were more likely in basketball but less likely in baseball to praise white professional athletes in off-the-field scenarios than they were black athletes but in both cases there was not a statistically significant difference.

101 reflects that it is the most popular women’s sport at any level of competition. The high presence of comments on tennis reflects that the French Open, one of the four “Grand

Slam” tennis events, occurred during coding.

There was also coverage on women’s sports that fails to qualify for coding.

Sportscenter ran segments on “The Power of IX: The 40th Anniversary of Title IX”, which celebrated 40 top female athletes in honor of the 40th anniversary of the historic legislation67. However all of the athletes are retired so they are not captured in the data.

Also, Danica Patrick, who was beginning her much publicized career in NASCAR racing, receives significant attention when the program focuses on auto racing, a sport not coded in this analysis. That being said, Patrick’s marketing has consisted of her appearing in seductive commercials, which obfuscates if her appeal is based on athletic merit or if it simply reinforces gender stereotypes. Finally, Sportscenter includes occasional highlights of women’s college softball but most of the analysis is perfunctory and quickly covers a single exceptional play. The absence of attention paid to women’s athletics is a significant concern and an important finding of this study.

Tebowmania & Linsanity

ESPN and Sporstcenter excel at reporting and fueling trends in the sports world.

To achieve this they focus on a few popular stories rather than parsimoniously distribute content to all of the day’s sporting events. The day after a major event, like the

Superbowl, Sportscenter will dedicate most, if not all, of its attention to the game and ignore all other news. Even the signing of a major NFL free agent could consume a

67 A law passed by Congress that prohibited sexual discrimination in education and among other things mandated that universities provide the same opportunities to activities, like sports, to women and men.

102 majority of the coverage at the expense of the NBA, NHL, college basketball, and professional soccer. Thus, I would be remiss to ignore two of the more sensational sports stories that occurred during my study period. In one scenario a former college standout but unheralded professional quarterback took his team from futility to the playoffs, and in the other an Asian American Harvard graduate emerged from the bench to become the starting guard for a franchise located in the country’s largest media market.

In 2011 Tim Tebow became the starting quarterback for the Denver Broncos. His unconventional style of quarterbacking68 led many to relegate him to a gimmick incapable of consistently helping his team win. But with the Broncos in last place in their division—and at the behest of broad fan support—the Denver coaches appointed Tebow starter where he contributed to six straight wins and a first round playoff victory. While the success galvanized his massive popularity (dubbed “Tebowmania” by ESPN personnel), Tebow’s outspoken Christian beliefs made him a polarizing figure. His narrative consists of winning two national championships and a Heisman trophy in college, but also of being the son of Christian missionaries, who rejected doctor’s advice on abortion when an infection during his mother’s pregnancy with him threatened her well-being. During the 2010 Superbowl Tebow appeared in a pro-life commercial sponsored by the socially conservative Focus on the Family organization, which further divided opinions on the quarterback. Where some fans saw him as a symbol of work ethic, commitment, and faith—a modern day exemplar of Kingsley’s Muscular

Christianity—others believed he garnered undue attention resulting largely from his status as a symbol of white, male, Christian hegemony. Besides his spectacular

68 Tebow was as much a runner as he was a passer—anathema to conventional NFL expectations of the QB position.

103 popularity, Tebow is relevant to this study because his playing success was often defined by intangibles as objective measures, like his playing statistics, proved deficient in comparison to other quarterbacks. As a result, commentators often attributed Tebow’s

“focus”, “will to win”, or “clutch play” to his success. (Concomitantly, such subjective praise also minimized the contributions of teammates.)

Claiming that Tebow’s race, gender or religion generated his massive popularity would be overstated as other important factors, like winning, also contributed. But the media seemed more inclined to define Tebow by his religion than other equally outspoken athletes. For this reason and because of the disproportionate amount of content that Sportscenter focused on him in December 2011, he represents a worthy case study.

(In fact, on December 7, 2011 anchors regularly referred to the episode as “Tebowcenter” because he was garnering so much attention.) Of the 3,543 qualifying comments for

December 2011, Tebow received the most with 189. (Chris Paul received the second most with 123.) Of those 189 comments, there are 63 (33%) positive comments and attributions and 9 (5%) negative comments and attributions69. There are 9 (4%) physical comments and attributions and 30 (15%) psychological comments with 37 attributions

(A36:A37). Among non-neutral comments white commentators praise Tebow at a rate of over 10 to 1 negative comments (A36), which far exceeds the 3 to 1 rate of nonwhite commentators (A37). With respect to comment type, white commentators identify

69 Bear in mind that a comment can have multiple attributions. That’s why the attribution total will be greater than or equal to the comment total for a specific category. For example, the same comment may include a commentator assigning attributes associated with leadership, effort and ability. In that case there is one comment that includes three attributes.

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Tebow’s psychological to physical characteristics at a rate of 4.5 to 1, which is almost twice the 2.3 rate of nonwhite commentators70.

During the three months of coding only the emergence of New York Knick’s point guard, Jeremy Lin, surpassed the popularity of Tim Tebow in terms of the number of comments on an athlete in one month. It is fitting then that Lin’s rise to fame followed a narrative even more spectacular than Tebow’s. Born in Paolo Alto, California Lin attended Harvard after going unrecruited in high school. After graduating in 2010 and going undrafted he bounced among three NBA teams and landed at the end of the

Knicks’ bench in 2011. Rumor has it that the team was set to cut Lin—likely ending his

NBA career—when superstar Carmelo Anthony demanded that the organization give Lin one last chance. After a spectacular game off the bench, Lin received a starting position and led the Knicks to seven straight wins, gained unprecedented fan support in opposing cities and sparked a social media frenzy dubbed “Linsanity”. As the first Asian American of Chinese or Taiwanese descent (and the first Asian guard of any nationality) to play in the NBA, Lin’s meteoric rise to fame aroused such fervor because as an Asian he represented an almost non-existent minority in a league dominated by African

Americans. Similar to Tebow, Lin too is outspoken in his Christian beliefs. However unlike Tebow objective statistical measures supported Lin’s achievements.

Of the 3,534 qualifying comments in February 2012, Lin received the most for any athlete with 220. (Peyton Manning received the second most with 114.) Of those 220 comments, there are 109 (50%) positive comments with 169 attributions and 10 (5%) negative comments and attributions (45% were neutral). There are 10 (4%) physical comments with 12 total attributions and 29 (13%) psychological comments with 38 total

70 The small sample for Tebow and my use of simple bivariate analysis however limits broader inference.

105 attributions (A38:A39). Similar to Tebow, I compared the percentage of positive/ negative and physical/ psychological comments for white and nonwhite commentators. I found that taking neutral comments into account the percentages of positive and negative comments are similar between the two groups of commentators. Excluding neutral comments white commentators offer slightly more praise than nonwhite commentators

(A38) and with respect to comment type there are relatively few attributions but white commentators are slightly more likely than nonwhite commentators to identify Lin’s psychological characteristics (A39). Similar to the results on Tebow there are limitations to making inferences from this data but the interest in Lin’s psychological acumen, especially considering the stereotype of Asians as “the model minority”, is noteworthy.

ESPN illustrated its progressive approach to race while covering Lin71. When

Floyd Mayweather, Jr, a famous African American boxer, Tweeted that Lin only garnered such attention because he was Asian American and that if Lin were African

American his success would be of less consequence, Sportscenter focused on the racial implications of Lin’s emergence and related reactions in social media. On the February

14, 2012 Sportscenter analyst Rachel Nichols asked him how he perceived his role and witnessed stereotyping as an Asian American star. Interestingly and echoing the thesis of this study Lin cited portrayals from the media as the most blatant examples. He identified comments that he “looks deceptively fast” which Lin cogently asked—what about him looks slow to justify such a comment? Such innuendo reflects subtle signaling that propagates racial classification in the sports media.

Conclusion

71 ESPN even hosted Jeremy Lin viewing parties in Taiwan where the network would air Knicks’ games.

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Quantitative and qualitative analysis of Sportscenter episodes from December

2011, February 2012, and May 2012 illustrates that in certain scenarios race remains a factor in how athletes are portrayed. I found that many of the patterns from the literature that identified how black athletes were portrayed in contrast to white athletes held, however it was almost always the case that these patterns appeared only in commentary on professional football and the patterns were not found in college football or any other professional or college sport. In fact, there were some sports, like professional basketball and one instance in college football, where the patterns of stereotyping found in the literature were actually reversed. I also found that when the patterns of stereotyping identified in the literature occurred, they only held in comparing black and white athletes—it was not as if all white athletes were portrayed one way and all non-white athletes were portrayed another. This was made clear in professional football where consistent with the literature commentators were more likely to identify psychological attributes in white than black athletes, but commentators were most likely to identify these attributes in Latino athletes—more so than black and white professional football athletes. In addition, the near absence of commentary on women’s athletics is also troubling as the program dedicated so little content to them that it prevented quantitative analysis. While the literature unequivocally illustrated that men’s sports receive greater attention than do women’s, my findings granted some scope of this difference and illustrated just how significant the disparity is in media coverage of men’s and women’s athletics.

Regardless of their race commentators are much more likely to identify psychological attributes for Jeremy Lin than they are for black professional basketball

107 players, are least likely to praise Latino athletes, and are more critical of black professional football players than they are white professional football players. White professional football commentators however are especially critical of black professional football players in comparison to white players, and are more likely to identify psychological attributes for white and Latino football players than black players.

However, white commentators are also more likely to assign physical attributes to white professional baseball players than they are for black professional baseball players.

Nonwhite commentators, on the other hand, are more likely to assign physicality to black professional football players then they are white professional football players. The fact that commentators continue to criticize and to assign physical attributes to black professional football players at a greater rate than white players is cause for concern. This concern however is somewhat mitigated by the fact that the trend does not occur in college football. In the following chapter I summarize the findings and present a conclusion that addresses each research question and hypothesis. I then situate the research in the literature, present policy recommendations, and qualify my findings based on limitations to the model. I close with my reaction and recommendations for future research.

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Chapter 5: Discussion & Conclusion

Summary

This study aimed to reveal how race and gender figure in the portrayal of athletes on ESPN’s Sportscenter. Including additional control variables for athletes and commentators, using multivariate analysis, and measuring race categorically enabled me to expand on the literature. While there was support for previous research, with the exception of one instance in professional baseball the patterns from the literature only held in commentary on professional football. In professional basketball however the patterns from the literature were reversed in some cases. Further, those patterns of stereotyping from the literature only held in comparing white and black athletes and measuring athlete race categorically indicated that Asian and Latino athletes were often portrayed far differently than were black or white athletes. Sportscenter’s nearly complete disregard for female athletes other than Danica Patrick illustrated that while the program has made progress in the presence and role of female commentators, it can only improve in the quantity and quality of content it dedicates to women’s athletics. Lastly, taking commentator race into account provided evidence supporting the importance of diversity in newscasters. While I did not find evidence that nonwhite commentators ever reversed patterns of stereotyping, I did find that nonwhite commentators mitigated stereotyping and were less likely to assign stereotypical attributes to black athletes than were white commentators.

In particular, the results show that in professional basketball commentators are most likely to identify psychological attributes in Jeremy Lin, in professional football they are more critical of black players than white players, and in professional football and

109 baseball Latinos are the least likely to receive praise. Further, white commentators are especially critical of black professional football players and Tiger Woods but are more likely to praise black college football players than white players. They are also more likely to assign psychological attributes to white and Latino professional football players than they are black players, and are less likely to assign physical attributes to white professional baseball players than they are black players. Nonwhite commentators, however, are most likely to assign physicality to black professional football players, and are much less likely to praise white professional basketball players than they are black players. In off-the-field scenarios white commentators are especially critical of black professional football players in comparison to white players. Jeremy Lin only received praise from all commentators. There are no other statistically significant differences for off-the-field comments and athlete race for any other sport or at the college level. Below I summarize my main findings for each independent variable, compare them with findings from the literature, and indicate where this research offers new insight. I then assess the accuracy of my seven hypotheses, discuss policy implications, and list limitations to the study. I close with my perspective on the work and suggestions for future research.

Conclusion

This research supports the literature in some instances however some patterns existing at the aggregate level disappeared once I qualified data by athlete sex, sport, level of competition, and commentator race. In professional football white commentators are most likely to criticize black players and portray them in a physical context. White commentators are also much more critical of Tiger Woods than they are white

110 professional golfers but the fact that Woods was the only black golfer to receive comments limits generalizability. The additional measures that I included on athletes and commentators revealed that most of the differences in portrayal and athlete race are exclusive to professional football and in college football the trend that commentators are more critical of black than white athletes is actually reversed. In college basketball72 and in all other professional sports there is either no relationship or the conventional patterns of stereotyping are reversed.

Commentators were most likely to identify physicality in football and hockey, slightly less so in basketball and baseball, and least likely in golf. They were most likely to identify psychological attributes in tennis, football, golf and basketball, and least likely in hockey, soccer, and baseball. Commentators praised soccer, hockey, tennis, football, and basketball most but criticized golf most. They were more likely to criticize but less likely to praise or to identify physical and psychological attributes in professional athletes in comparison to college athletes. White and Asian commentators were most likely to identify physicality relative to black and Latino/a commentators; white and black commentators were most likely to identify psychological attributes relative to Latino/a and Asian commentators. While there was no discernible relationship between commentator race and the likelihood to praise an athlete, white and Latino/a commentators were the most likely to criticize. In comparison to female commentators, male commentators were considerably more likely to identify physicality and to praise or criticize an athlete, and about equally as likely to identify psychological attributes.

72 Of course, there were relatively fewer comments on college basketball and these other sports, thus data on them were less reliable.

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Anchors were less likely than analysts to identify physical and psychological attributes and to praise or criticize an athlete.

Next I compare my findings to the literature by comment type, value, and the interaction of type and value. In some cases I had to rely on bivariate analysis to address a topic because there were not enough observations in my data for more rigorous statistical methods. All comparisons include analysis only of comments on male athletes.

The literature shows that commentators identify ability the most for black football players and effort the most for white football players (Murrell & Curtis, 1994). Davis and Harris

(1998) report a similar trend for effort but for black and white athletes in all sports. In my study, commentators identified physical ability the most for black professional football players however this trend did not hold in any other professional sport or in college football and basketball.

Looking strictly at the percentage of total comments and including all sports,

Asian athletes, disproportionately represented by Jeremy Lin, receive the greatest percentage of comments for ability followed by black athletes. Taking the same approach to comments on effort, commentators identify effort one-and-a-half times more for black athletes (66) than they did for all other athlete race categories combined (46). Taking total comments into account black athletes also had the greatest percentage. My findings show that commentators are slightly more likely to assign ability to black professional football players than other groups of athlete race but the trend does not hold at the college level or in other sports. Combine that with the fact that commentators are most likely to identify effort in black athletes; these results undermine research implying that commentators couch black athletes’ success in terms of unearned, natural ability.

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The literature’s most consistent finding is that commentators praise black athletes for their physicality and white athletes for their psychological attributes (Buffington,

2005; Dufur, 1998; McCarthy & Jones, 1997; Murrell & Curtis, 1994; Rainville &

McCormick, 1977). In more recent research, Denham, Billings, and Halone (2002) and

Billings (2004) found that while some stereotypes had been muted, commentators continued to praise black athletes for ability and criticize white athletes for their lack of it. In my research, examining a more recent period, at an aggregate level, commentators appear most likely to praise the physicality of black professional athletes but after analyzing by level of competition and sport no statistically significant difference exists between physical praise and athlete race.

In my analysis, commentators are most likely to praise psychological attributes for Jeremy Lin and for white professional football players. Denham, Billings, and

Halone (2002) also reported that black athletes were increasingly praised for their leadership. Assessing improvements across studies is challenging because there is no accepted baseline for comparison and because each study relies on different content. That being said, I found that black athletes receive the most descriptions for leadership, and in comparison to white professional athletes, commentators are more likely to identify leadership qualities in black professional basketball players but are less likely to in black professional football players73. Thus, my findings support Denham, Billings, and Halone

(2002) in professional football but present conflicting findings in other sports and at the college level.

73 As documented in Table 4 in Chapter 4, however this finding is qualified by there being relatively few total comments focusing exclusively on leadership (177).

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In line with research from Denham, Billings, and Halone (2002), my data lacked the sample size necessary to draw conclusions about female athletes. This absence of findings supports research showing that female athletes receive disproportionately less content than male athletes (Billings & Angelini, 2007; Billings & Eastman, 2002;

Crossman, Vincent, & Speed, 2007; Duncan, 2006; Eastman & Billings, 1999; Koivula,

1999; Pederson, 2002; Shifflett & Revelle, 1994). Therefore I cannot assess if commentators designate female athletes’ physicality in terms of attractiveness (Blinde,

Greendorfer, & Shanker, 1991; Billings & Angelini, 2007; Billings & Eastman, 2002;

Duncan, 2006; Eastman & Billings, 1999; Jones, Murrell, & Jackson, 1999; Koivula,

1999). That being said—and this is qualified as I did not measure auto racing—Danika

Patrick seemed to receive disproportionate attention considering her achievements in

NASCAR, which likely reflects that she is the only female driver in the Sprint Cup or

Nationwide Series74, and her notoriety from serving as a commercial spokeswoman.

There are also singular findings from the literature that are relevant to my study.

For example, Eastman and Billings (2001) found that commentators praised black basketball players’ physicality and white players’ work effort and acumen (or

‘intelligence’). Jackson (1998) reported similar results—commentators praised white athletes’ intelligence but criticized it in black athletes. My analysis showed that although black basketball players received over five times as many positive physical descriptions as all other groups of basketball players combined, taking total comments into account

Jeremy Lin had the greatest percentage followed by white and then black players. In addition, commentators praised black basketball players the most for effort and Asian

74 These are the top series of NASCAR racing. The Sprint Cup is typically considered the elite level and Nationwide Series is considered more of a minor league that develops drivers.

114 basketball players the most for intelligence. Percentage-wise, black basketball players only surpassed whites in receiving praise for intelligence. Commentators only criticized black professional basketball players for intelligence, which likely occurred because they received over five times as many total comments as all other groups combined. The above illustrates that my research supports the literature in some instances when analyzing professional football but does not support or conflicts when accounting for college football and other professional men’s sports. I close this section with a summary and assessment of the 7 main hypotheses from Chapter 1.

Hypothesis 1: Commentators will emphasize physicality for nonwhite athletes and psychological attributes for white athletes.

Findings related to Hypothesis 1: White and nonwhite commentators are more likely to assign physicality to black professional football players than white professional football players. White commentators are also more likely to assign psychological qualities to white and Latino professional football players than they are black players. However, no relationship holds for physical or psychological comments at the college level or for any other sport.

Hypothesis 2: Commentators will criticize black male athletes the most in off-the-field scenarios.

Findings related to Hypothesis 2: This hypothesis was unequivocally supported but only in professional football. There were only a sufficient number of off-the-field comments to compare portrayals across sports in football, basketball and baseball.

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Hypothesis 3: The physical-psychological racial dichotomy will be most prevalent in basketball and football, the two major sports with the greatest percentage of black athletes.

Findings related to Hypothesis 3: This was only true in professional football among nonwhite commentators emphasizing physicality and for all commentators emphasizing psychological attributes. Further, this was only the case in comparing white with black players and not white with all nonwhite players. In fact, white commentators were more likely to identify psychological attributes in Latino professional football players than they were white players. No statistically significant difference exists for the college level or in basketball for either level of competition. Further, in professional baseball, the only sport with statistically significant results beside professional football, white commentators were more likely to identify physicality in black athletes than they were white athletes

(no statistically significant differences for Latino and Asian players). Thus, my findings largely fail to support this hypothesis.

Hypothesis 4: Male athletes will be subjected to the physical-psychological racial dichotomy more so than female athletes.

Findings related to Hypothesis 4: My study lacked a sample size of comments on female athletes necessary to analyze differences by athlete sex.

Hypothesis 5: Nonwhite commentators regardless of race will be more likely than white hosts to recognize black athletes’ psychological qualities.

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Findings related to Hypothesis 5: Nonwhite commentators described black athletes psychologically 98 times and white hosts described them psychologically 84 times.

Taking total comments into account nonwhite commentators had a slightly greater percentage of psychological descriptions of black athletes than did white hosts. Nonwhite hosts also had a slightly higher percentage of psychological attributions than did white hosts. Focusing exclusively on bivariate statistics and black commentators, they had a greater percentage of psychological comments (as a percentage of all comments) on black athletes than did white commentators but black commentators also had a greater percent of psychological comments of white athletes than did white commentators.

Hypothesis 6: Commentators will emphasize physicality the most for whites in hockey.

Findings related to Hypothesis 6: For white athletes hockey tied with basketball for second most physical descriptions behind football. Taking the total number of comments into account white hockey players trailed white basketball and football players for physical descriptions. Thus, this hypothesis was not supported.

Hypothesis 7: Non-white baseball players—especially Latinos—will garner more criticism than will white athletes.

Findings related to Hypothesis 7: White baseball athletes received more negative comments than black, Latino, and Asian baseball players combined. Taking total negative comments into account, whites had the highest percentage and Latinos had the lowest.

This hypothesis was not supported.

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The findings support only three of my seven hypotheses and of those three all were qualified as being contingent upon some subset of athletes and commentators.

Compared to the literature these findings illustrate that Sportscenter exhibits progress in how its commentators portray athletes. In few professional sports and in no college sports

(i.e. college football and basketball) are commentators likely to define athletes based on patterns where black athletes receive attention for negative and physical qualities and white athletes receive attention for positive and psychological qualities and, in fact, in some sports the opposite of those patterns occur. That being said, three concerns remain.

First, the physical-psychological racial dichotomy for black and white athletes persists in the NFL. This is something researchers should measure periodically to understand if the trend continues or if my sample period was in some way anomalous to contemporary sports media coverage75. Second, women’s sports receive virtually no attention. In the absence of some major cultural shift in the mainstream sports media, researchers will likely have to study content that explicitly focuses only on women’s sports to understand portrayals of women’s athletics. Third, in some instances, there appears to be evidence that the race of the commentator affects the likelihood of stereotyping. Although I had a large data set of 11,311 comments, after parsing the data for comment type or value and by athlete and commentator subgroup my analysis was limited because I had to lump all nonwhite commentators together for multivariate analysis. In the future, interested researchers will hopefully collect larger and more robust data sets so commentator race can be analyzed at a categorical level.

75 As mentioned in Chapter 1, I don’t expect this to be the case but potential anomalies over the three months sample include—a late start to the NBA season (December); Tim Tebow’s coverage and scrutiny; the Superbowl in February (as it is every year); and Jeremy Lin’s emergence as a star.

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Policy Recommendations

Broadly speaking, racial portrayals on Sportscenter can influence a range of policy decisions if commentators prove to undermine racial stereotypes. For audience members with limited interactions with individuals outside their racial and ethnic group the media can function as the source from which they form opinions of those other racial and ethnic groups. As previously documented, on issues of crime and politics, at least, we know the media tend to disparage minorities or cast them in a pejorative light in comparison to whites. Sports (and Sportscenter) however are not only massively popular in America but they are also an institution where black Americans, in particular, have achieved phenomenal success in contrast to other social institutions. Thus, we might see an opportunity for the media to undermine the stereotypes and portrayals that it promotes in other institutions, like politics and crime.

During my three month sample, evidence shows that racial portrayals in professional basketball run counter to the conventional portrayals and stereotypes found in the research and in professional football from my sample. This is promising as

Sportscenter and professional basketball audiences may experience a subtle transformation whereby their understanding of race and achievement is reconstructed in a manner that mutes or reverses entrenched racial perceptions. As a result, Sportscenter

(and other sports media outlets) could have a panoptic effect where audiences begin viewing other social, political and economic institutions based on the reconstructed understanding of race presented on Sportscenter.

I selected Sportscenter because I viewed it as a ‘hard-test’ case of sports news broadcasting in that the program was more progressive on social matters than

119 conventional sports broadcasting. While Sportscenter supported my assumption about its progressive nature based on findings, like the high level of praise given to black athletes in comparison to white athletes, especially for psychological attributes in professional basketball, disparities remain in professional football and to a lesser degree in professional baseball, and in the paucity of content paid to women’s athletics.

I first wish to identify alternatives that have the right intent but would be of limited efficacy. Training aimed at mitigating misperceptions of minority groups represents one such alternative. Besides the time and costs associated with educating all sports’ media outlets, one must consider how and for whom to implement such training.

Subtle stereotyping is not overtly racist nor does it violate any Federal Communications

Commission (FCC) regulations on permissible language over public airwaves. Creating programs aimed at reversing ingrained social, racial, and gender biases is commendable but mandating them arouses 1st Amendment concerns over the government’s role in regulating the media. Voluntary programs, on the other hand, would get sporadically adopted and likely fail to arouse interest from those commentators most likely to stereotype. Policymakers could also encourage networks to expand on content with a focus on social dimensions. Sportscenter is dedicated to succinct highlights that maximize news coverage, which compromises the possibility of expansion. That being said, the program exhibited its progressive nature and occasionally addressed social issues, like when Rachel Nichols asked Jeremy Lin how he thought being Asian affected perceptions on his emerging stardom.

Based on my findings, increasing the presence of a racially diverse news corps represents the best method for mitigating stereotyping. While the relatively high number

120 of nonwhite commentators did not eliminate all patterns of stereotyping, their presence appeared to at least mitigate those patterns of stereotyping found in the literature. There is ground for optimism in this recommendation as an opinion filed July 7, 2011 in the U.S.

Court of Appeals for the Third Circuit ruled that the FCC should improve its approach to increasing broadcast ownership among minorities and women (Prometheus Radio Project v. Federal Communications Commission, 2011). While the ruling focuses more on ownership than employment practices, the FCC’s Advisory Committee for Diversity in the Digital Age compliments the ruling and is dedicated to “lowering barrier to entry for historically disadvantaged men and women, exploring ways in which to ensure universal access to and adoption of broadband, and creating an environment that enables employment of a diverse workforce within the telecommunications and related industries

(Advisory Committee for Diversity in the Digital Age, 2012).”

How then does a network promote diversity without compromising quality on account of potentially constraining quotas? Recruiting diverse entry-level employees from colleges and universities represents one option. Major networks, like NBC, use such efforts but the high demand for acceptance to programs limits their impact. Not to mention, some of these entry-level positions have minimal, if any, role in news production (and, thus, on racial portrayals). The NFL offers a position-focused approach that ensures that minority candidates get to interview for high-level positions. The

“Rooney Rule”, named after Dan Rooney, the owner of the Pittsburgh Steelers, requires organizations to interview at least one minority candidate for head coaching and senior football operation positions. Enforcement of such a rule is easier for a highly-visible, national position, like an NFL head coach than it is for a position on a sports news

121 program for networks that do not necessarily make such openings public knowledge. If adopted however it would guarantee minority candidates, who might have been overlooked in the screening process, the opportunity to interview. The significant increase in minority head coaches since the Rooney Rule was implemented suggests that simply giving minority candidates the opportunity to interview positively affects their chances at employment as well as organizational success76.

Once hired it is also necessary that networks and organizations continue to promote diversity and to support minority employees. ESPN is a leader in the media in this regard because of its use of Employee Research Groups (ERGs), which are

“grassroots driven, voluntary, and run by employees formed around a shared interest or similar dimension of diversity . . . [that promote] cultural diversity, networking and learning from others, developing professional skill sets, adding value to the business, expanding the recruitment base, enhancing retention initiatives and tapping into underutilized resources (Diversity: ESPN Employee Research Groups, 2011).” As a result of ESPN’s efforts the Asian-American Journalists Association, DiversityInc,

Women in Cable Telecommunications (WICT), and the Gay & Lesbian Alliance Against

Defamation (GLAAD) have all awarded ESPN for promoting diversity through ERGs

(Diversity at ESPN, 2012). According to a Forbes study (2011) such programs are common among many major corporations. In a survey of 300 senior executives from

76 Within three years of implementation in 2003, the rule contributed to a jump in the percentage of minority head NFL coaches from 6% to 22%. With respect to efficacy in 2006 the Steelers hired Mike Tomlin—then a 34 year old black defensive coordinator. Tomlin would lead the team to a Superbowl win, a Superbowl loss, and four playoff appearances in his first five seasons. In evidence of just how quickly circumstances can change, during the 2013 offseason all 8 open head coaching positions were filled with white candidates (not to mention the additional seven general manager vacancies were filled with only whites). Now there are only three black coaches in the NFL—equating to 9% of head coaching positions— a miniscule proportion in comparison to the NFL’s playing corps, which is two-thirds black. As a result, many fans have rightly called for revisions to the Rooney rule or new rules altogether to ensure better minority representation in head coaching and other executive positions.

122 private firms around the world with revenues ranging from $500 million up to $20 billion, 53% of executives reported that their companies had ‘official’ programs dedicated to developing a diverse and inclusive workforce. Therefore, networks should complement hiring reform with initiatives that encourage networking and collaboration among minority colleagues.

Requiring that minority candidates receive interviews for all high-level broadcasting positions and adopting networking initiatives that promote diversity and opportunity may reduce racial stereotyping but, they will not solve networks’ disregard for women’s athletics. Adding more female commentators does not fix the problem as

Sportscenter relies on female anchors and analysts and the amount of commentary on women’s sports is not nearly proportional to the presence of female commentators or the number of comments that they contribute. (In fact, Hannah Storm, one of Sportscenter’s lead anchors, wrote a book that encourages women and girls to play sports; she also seems to champion more coverage of women’s athletics in the few instances that she covered them.) Requiring networks to cover women’s athletics presents a conundrum.

Why dedicate content to sports—whether they are women’s or men’s—with relatively small audiences? To support this claim networks can point to a disparity in attendance between male and female sporting events. But such an argument assumes a potentially misleading direction of causality. What if the relatively lower interest in women’s athletics reflects not the interest in those sports but the failure of the media to cover and to promote them to a wider audience?

Women’s college basketball generates substantial interest, and at the professional level women’s tennis can draw comparable ratings to that of men for the same event.

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Sportscenter largely ignored both. Creating narratives and developing identities for athletes allows programs, like Sportscenter, to spur interest in and to add dimension to the sports-watching experience. During the 2012 NBA playoffs sports networks established a narrative that the Boston Celtics were an aging dynasty with one last shot at the championship, the Oklahoma City Thunder were precocious stars arriving years before most expected, and the Miami Heat were a carefully constructed compilation of superstars that fail to reach their potential. While it’s easy to write these narratives off as media buzz, they aroused interest in the NBA and its athletes. Superficial coverage of the

Women’s National Basketball Association (WNBA), on the other hand, inhibits fans’ ability to identify with WNBA teams and athletes—many of whom were popular college stars—and diminishes the demand for more coverage. How then is it possible to get networks to dedicate more time to women’s athletics when the current male-centric model works so well?

To facilitate the transition, networks could start small, and allocate 45 seconds of content to women’s athletics for every 15 minutes (including commercials) that a program airs. Rather than covering all women’s sports, which would prove ineffective because there is too much content to capture in limited time, programs should focus on a single women’s sport and preferably a popular one, like college basketball. After spending less than 30 seconds covering major news in all women’s athletics, the programs should dedicate the remaining time (so two minutes and thirty seconds for an hour long program) to the same 10 top teams and players in women’s college basketball.

It is important that the number of teams and players receiving regular attention remain

124 limited to enable the audience to build a familiarity with them77. Coverage of athletes should include interviews and human interest segments that allow the audience to identify with them, and should focus exclusively on athletics and personality and ignore physical esthetics. Networks should closely measure audience interest and modify content based on that research. Therefore if ESPN discovers that the programming appeals most to

“hardcore” basketball fans then it could provide supplemental coverage on women’s college basketball when it airs men’s college and professional basketball games. There is the risk that some of the top 10 teams selected for continuous coverage fall out of contention or that a player receiving regular coverage gets injured. There is also the possibility that the wrong women’s sport gets selected. These are legitimate concerns but considering that the status quo consists of a virtual absence of coverage I believe that assuming such risk is warranted and that this approach is the most feasible option for increasing interest in and coverage of women’s athletics.

Limitations

Before I conclude it is necessary that I identify limitations to the study and research model. The greatest limitation is that descriptions might reflect perceptions of an individual athlete or of characteristics of athletes78 that I failed to include in the model.

While I believe the independent variables captured those characteristics of athletes that

77 This falls in line with social learning theory and the idea that behavior and environment can create a reciprocal effect on each other. So immersing audiences in coverage of women’s athletics may affect their behavior, or interest in these sports. 78 Failing to control for athlete position in team sports represents the main concern, but limitations exist when one wishes to capture this information. In particular, in every team sport there are athletes that play multiple positions or, in the case of basketball, athletes that play certain positions on offense and guard multiple different positions on defense. Thus, the coder is forced to arbitrarily assign a position or multiple positions based on the circumstances of each individual comment. This was not the case for any other independent variable that measured athletes.

125 were most important in explaining portrayals, there were individual athletes, who received disproportionate content for their subgroup. This was especially the case for

Jeremy Lin, who received all 230 comments for Asian professional basketball athletes, and Tiger Woods, who received all 121 comments for black golfers. Therefore, I cannot be confident that the emphasis on psychological attributes of Asian professional basketball athletes reflects perceptions on all Asian basketball players or just on Harvard graduate Jeremy Lin, or that the relatively high-level of criticism directed at black golfers was not actually reserved for Tiger Woods. To mitigate this concern, coders recorded the name of the athlete associated with each comment. Upon final review of the data, only

Lin and Woods dominated their athlete subcategories to the degree that I was confident that descriptions reflected subgroups and not an individual.

The GAO Training Institute’s Strategies to Help Ensure Validity and Reliability of Data presents a standardized approach that I will use to assess other potential limitations. It identifies two components of reliability. Reliable measures evaluates a model’s ability to consistently measure the same phenomenon. Reliable measurement considers the ability to consistently record data with the same decision criteria. The codebook and the use of an additional coder mitigated concerns of unreliable measurement as evidenced by the high intercoder reliability measures reported in Chapter

3. The GAO guide next identifies validity, which consists of construct validity, external validity, and internal validity79, as the other major threats to data. Construct validity assesses the accuracy of measurement. An obvious issue occurs if I assigned race to an individual and that assignment conflicts with the individual’s self-identified race.

79 The guide also lists statistical conclusion validity however these concerns were addressed in Chapter 4 alongside the explanations of the statistical techniques as I felt that was the most appropriate placement.

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(Chapter 3 includes a justification to this potential issue.) Another problem arises if the literal descriptors provided in the codebook are assigned to the wrong comment type or value. To address this concern I carefully reviewed the codebook with the coder to ensure concurrence on categories. I have also included the codebook in the Appendix to enhance transparency.

External validity is concerned with generalizing findings beyond the immediate study. One concern is that the three months I selected were unique and failed to reflect a broader sports culture. While the emergence of Tim Tebow in December 2011 and

Jeremy Lin in February 2012 garnered exceptional attention, I do not believe it or any other coverage was fundamentally different from other months. I acknowledge, however, that Sportscenter might be more progressive on social matters than local sports newscasts and in comparing the racial diversity of newscasters on the program with the RTDNA study in Chapter 1, Sportscenter was more diverse.

There is also the issue, which I can only acknowledge, that Sportscenter, a scripted news program, differs from in-game commentary, which consists of more automatic, reflexive commentary and represents the most analyzed form of content in sports media research. At the same time, time constrains the amount of live sports that audiences can watch, thus Sportscenter may serve as a substitute for sports’ information, which could grant the ESPN commentators’ greater valence for audiences than the rotating coterie of in-game commentators. The major threat to internal validity is captured in the initial comment in this section about descriptions reflecting the individual rather than the subgroup. There is also the concern that content in a highlight determines a description and not some attribute of the athlete. While this is an obvious intervening

127 factor, unlike language, which can be coded, classifying video imagery as a “good play” or “fast athlete” demands an understanding of the producer’s intent and, thus, too much subjectivity. In light of these issues I expect otherwise valid and reliable data.

Discussion

It was not my intent to expose Sportscenter or other networks or categories of commentators for stereotyping. Rather this study contributes to a catalogue of research that illustrates how ingrained social and racial beliefs emanate and persist in seemingly innocuous language. Measuring additional characteristics for athletes and commentators and analyzing data over three months showed that racial portrayals in the sports media are complex. Evidence of a black-white dichotomy in professional football supports the last 35 years of research, but in other sports and at the college level this trend appears to cease. Regardless researchers should shift from coding and counting comments to measuring how audiences internalize stereotyping in the sports media and build on two existing studies (McCarthy, Jones & Potracs, 2003; Van Sterkenburg & Knoppers, 2004).

They should include content on multiple sports, and use children, rather than undergraduates, as subjects if possible. Studies should follow-up on subjects a week and a month later to understand how perceptions hold. If stereotyping in the sports media has a negligible effect on viewers’ beliefs then this study and ones similar to it are of limited significance. If, on the other hand, it influences racial perceptions then efforts aimed at abating stereotyping deserve attention considering sports’ massive popularity.

Because the literature unequivocally illustrates vast inequality in the amount of content dedicated to women’s and men’s athletics, researchers should also redirect their

128 analysis on this disparity. Understanding media effects should become of primary concern and researchers should edit content of a varying amount of time and quality on women’s and men’s sports to understand how programming time and quality affect interest in and perceptions on women’s sports. Researchers should also use content that creates a narrative of a set number of female athletes and teams, and is complimented with in-game highlights, and compare that to basic in-game highlights. Similar to the recommendation above it is important to follow-up to understand how perceptions hold.

Ideally, this will advance the literature from acknowledging a disparity to understanding why it exists. (And end the dubious and logically fallacious argument that men’s sports are simply more interesting than women’s.)

For this project, I hope to adapt it into a larger project on the significance of race in the sports media. I’ll omit academic language, modify the structure, and move much of the technical methodological and quantitative aspects to a website for interested readers.

The project will review relevant findings from the study and literature on general and sports media, all of which will be complimented with anecdotes of racial issues in sports that I’ve collected over the years. It will aim to answer three questions. How does stereotyping emanate in the sports media? If stereotyping exists why does it matter? What does it say, if anything, about contemporary culture?

It has been little over 100 years since the US measured race in the exhaustive, mutually exclusive categories of white, Chinese, Japanese, Indian, black, and the geometric sequence of Mulatto, Quadroon, and Octoroon. Since then the emergence—or rather understanding and appreciation—of ethnicity, country of origin, religion, political ideology, wealth, tattoos, accent, family structure, attire, skin reflectance, (and you name

129 it) have come to represent some of the countless social identifiers that exponentially complicate our understanding of race, stereotyping and discrimination. Despite these additional layers of identity, significant and troubling inequities of important measures of socioeconomic progress exist among traditional Census-defined racial categories.

Although measuring racial stereotyping in the media is ancillary to public policy research aimed at establishing equal grounding for children and young people across racial categories, it presents valuable complimentary insight as to how perceptions on race may have contributed to a history of political intransigence on mitigating such inequities.

Identifying and addressing those patterns and stereotypes that qualify certain racial groups may serve to undermine broad misperceptions on ability and aptitude.

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References

2008 women and minorities survey. Retrieved July 13, 2011, from http://www.rtnda.org/pages/media_items/the-face-of-the-workforce1472.php Aicinena, S. (1999). ESPN's "sportscenter": Socialization of america's athletes, coaches and workers. Unpublished manuscript. Akalis, S. A., Banaji, M. R., & Kosslyn, S. M. (2008). CRIME ALERT!; how thinking about a single suspect automatically shifts stereotypes toward an entire group. Du Bois Review: Social Science Research on Race,5(2), 217-17. doi: 1879721441 Albert, T. C., & Jacobs, R. D. (2008). Television attitudes and TV types of african- americans, latinos, and caucasians. Journal of Advertising Research, 48(2), 235-246. Retrieved fromhttp://search.ebscohost.com.proxygw.wrlc.org/login.aspx?direct=true&db=bth& AN=32566645&site=eds-live&scope=site Aldridge, D., & Writer, W. P. S. (1987, July 3). Campanis admits error but maintains innocence. The Washington Post Allen, R. L., & Thornton, M. C. (1992). Social structural factors, black media and stereotypical self-categorizations among african americans. National Journal of Sociology, , 41-34. Amodio, D. (2010). The egalitarian brain. In J. Marsh, R. Mendoza-Denton & J. A. Smith (Eds.), Are we born racist? new insights from neuroscience and positive psychology (pp. 45-10). Boston: Beacon Press. Anderson, E. (1999). Code of the street. New York: WW Norton & Co. Baiocchi-Wagner, E., & Behm-Morawitz, E. (2010). Audience perceptions of female sports reporters: A social-identity approach. International Journal of Sport Communication, 3(3), 261-274. Retrieved fromhttp://search.ebscohost.com.proxygw.wrlc.org/login.aspx?direct=true&db=s3h &AN=54228877&site=eds-live&scope=site Bandura, A. (1971). Social learning theory Barber, R. (1982). 1947: When all hell broke loose in baseball. New York, NY: Da Capo. Billings, A. C., & Angelini, J. R. (2007). Packaging the games for viewer consumption: Gender, ethnicity, and nationality in NBC's coverage of the 2004 summer olympics. Communications Quarterly, 55(1), 95-16. doi: 10.1080/01463370600998731 Billings, A. C., & Eastman, S. T. (2007). Selective representation of gender, ethnicity, and nationality in american television coverage of the 2000 summer olympics. International Review for the Sociology of Sport, 37, 351-19. doi: 10.1177/101269020203700302 Billings, A. (2004). Depicting the quarterback in black and white: A content analysis of college and professional football broadcast commentary. Howard Journal of Communications, 15(4), 201-210. doi: 10.1080/10646170490521158 Bissell, K. L., & Zhou, P. (2004). Must-see TV or ESPN: Entertainment and sports media exposure and body-image distortion in college women. Journal of Communication, 54(1), 5-21. Retrieved fromhttp://search.ebscohost.com.proxygw.wrlc.org/login.aspx?direct=true&db=aph &AN=12438006&site=ehost-live

131

Blackshaw, T., & Crabbe, T. (2005). Leeds on trial: Soap opera, performativity and the racialization of sports-related violence. Patterns of Prejudice, 39(3), 327-342. doi: 10.1080/00313220500198441 Blinde, E., Greendorfer, S. L., & Shanker, R. J. (1991). Differential media coverage of men's and women's intercollegiate basketball: Reflection of gender ideology. Journal of Sport and Social Issues, 15, 98-114. Bonilla-Silva, E. (2003). Racism without racists: Color-blind racism and the persistence of racial inequality in the united states. Lanham, MD: Rowman and Littlefield. Brigham, J. C. (1972). Racial stereotypes: Measurement variables and the stereotype- attitude relationship. Journal of Applied Social Psychology, 2(1), 63-76. doi: 10.1111/j.1559-1816.1972.tb01264.x Brown, E., Spiro, R., & Keenan, D. (1991). Wage and nonwage discrimination in professional basketball: Do fans affect it? American Journal of Economics and Sociology, 50(3), pp. 333-345. Retrieved fromhttp://www.jstor.org.proxygw.wrlc.org/stable/3487275 Broyles, P., & Keen, B. (2010). Consumer discrimination in the NBA: An examination of the effect of race on the value of basketball trading cards. Social Science Journal, 47(1), 162-171. doi: 10.1016/j.soscij.2009.07.003 Bruce, T. (2004). Marking the boundaries of the 'normal' in televised sports: The play-by- play of race. Media Culture & Society, 26(6), 861-18. Buffington, D. (2005). Contesting race on sundays: Making meaning out of the rise in the number of black quarterbacks. Sociology of Sport Journal, 22(1), 19-18. Buffington, D., & Fraley, T. (2008). Skill in black and white: Negotiating media images of race in a sporting context. Journal of Communication Inquiry, 32(3), 292-310. Retrieved fromhttp://search.ebscohost.com.proxygw.wrlc.org/login.aspx?direct=true&db=aph &AN=32789388&site=ehost-live Burdekin, R. C. K., & Idson, T. L. (1991). Customer preferences, attendance and the racial structure of professional basketball teams. Applied Economics, 23(1), 179-186. Retrieved fromhttp://search.ebscohost.com.proxygw.wrlc.org/login.aspx?direct=true&db=buh &AN=4617742&site=ehost-live Butterworth, M. L. (2007). Race in "the race": Mark McGwire, sammy sosa, and heroic constructions of whiteness. Critical Studies in Media Communication, 24(3), 228- 244. doi: 10.1080/07393180701520926 Byrd, J., & Utsler, M. (2007). Is stereotypical coverage of african-american athletes as "dead as disco"?: An analysis of NFL quarterbacks in the pages of sports illustrated. Journal of Sports Media, 2, 1-27. Callanan, V. J., & Rosenberger, J. S. (2011). Media and public perceptions of the police: Examining the impact of race and personal experience. Policing & Society, 21(2), 167-189. doi: 10.1080/10439463.2010.540655 Cameron, A. C., & Trivedi, P. K. (2010). Microeconometrics using stata (Revised ed.). College Station, TX: Stata Press. Carter, E. M., & Carter, M. V. (2007). A social psychological analysis of anomie among national football league players. International Review for the Sociology of Sport, 42, 243-27. doi: 10.1177/1012690207088110

132

Childs, K. (1998). NABJ speakers charge bias in sports coverage. Editor & Publisher, 131(32), 10. Retrieved from http://search.ebscohost.com.proxygw.wrlc.org/login.aspx?direct=true&db=aph &AN=957442&site=ehost-live Clawson, R. A., IV, H. C. ". S., & Waltenburg, E. N. (2003). Framing supreme court decisions: The mainstream versus the black press. Journal of Black Studies, 33(6), pp. 784-800. Retrieved fromhttp://www.jstor.org.proxygw.wrlc.org/stable/3180846 Coakley, J. (2004). Sports in society: Issues and controversies (8th ed.). New York, NY: McGraw Hill. Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37-9. doi: 10.1177/001316446002000104 Cohen, S. (1981). In Young J. (Ed.), The manufacture of news: Social problems, deviance and the mass media. Thousand Oaks, CA: Sage. Cole, E. R. (2009). Intersectionality and research in psychology. American Psychologist, 64(3), 170-10. Coleman, R. (2011). Color blind: Race and the ethical reasoning of blacks on journalism dilemmas Retrieved from http://search.ebscohost.com.proxygw.wrlc.org/login.aspx?direct=true&db=a9h &AN=63238250&site=eds-live&scope=site Correll, J., Park, B., Judd, C. M., & Wittenbrink, B. (2002). The police officer's dilemma: Using ethnicity to disambiguate potentially threatening individuals. Journal of Personality and Social Psychology, 83(6), 1314-1329. doi: 10.1037/0022- 3514.83.6.1314 Coventry, B. T. (2004). On the sidelines: Sex and racial segregation in television sports broadcasting. Sociology of Sport Journal, 21(3), 322-341. Retrieved fromhttp://search.ebscohost.com.proxygw.wrlc.org/login.aspx?direct=true&db=aph &AN=14961733&site=ehost-live Crossman, J., Vincent, J., & Speed, H. (2007). 'The times they are A-changin': Gender comparison in three national newspapers of the 2004 wimbledon championships. International Review for the Sociology of Sport, 42(1), 27-41. doi: 10.1177/1012690207081828 Cunningham, G. B., Bruening, J. E., & Straub, T. (2006). The underrepresentation of african americans in NCAA division I-A head coaching positions. Journal of Sport Management, 20(3), 387-413. Retrieved fromhttp://search.ebscohost.com.proxygw.wrlc.org/login.aspx?direct=true&db=aph &AN=21260405&site=ehost-live Curry, T. J., Arriagada, P. A., & Cornwell, B. (2002). Images of sport in popular nonsport magazines: Power and performance versus pleasure and participation. Sociological Perspectives, 45(4), 397-413. Retrieved from http://search.ebscohost.com.proxygw.wrlc.org/login.aspx?direct=true&db=aph &AN=8570484&site=ehost-live Davis, L. R., & Harris, O. (1998). Race and ethnicity in US sports media. In L. A. Wenner (Ed.), Mediasport (pp. 154-15). New York: Routledge. Dekker, M. (2007). Afrikanen bij roda JC voelen zich gerespecteerd. NRC Hanelsblad, 8, 11.

133

Denham, B. E., Billings, A. C., & Halone, K. K. (2002). Differential accounts of race in broadcast commentary of the 2000 NCAA men's and women's final four basketball tournaments. Sociology of Sport Journal, 19(3), 315-332. Retrieved from http://search.ebscohost.com.proxygw.wrlc.org/login.aspx?direct=true&db=aph &AN=7409239&site=ehost-live Devine, P., Brodish, A. B., & Vance, S. L. (2005). Self-regulatory processes in interracial interactions. In J. Forgas, K. D. Williams & S. M. Laham (Eds.), Social motivation: Conscious and unconscious processes (pp. 249-24). New York: Cambridge University Press. Devine, P. G. (1989). Stereotypes and prejudice: Their automatic and controlled components. Journal of Personality and Social Psychology, 56(1), 5-18. doi: 10.1037/0022-3514.56.1.5 Diversity at ESPN. (2012). Retrieved 04/20, 2012, from http://espncareers.com/working- here/diversity.aspx Diversity: ESPN employee research groups. (2011). Retrieved 04/20, 2012, from http://jobs.espncareers.com/article/Diversity Dixon, T., & Linz, D. (2002). Television news, prejudicial pretrial publicity, and the depiction of race. Journal of Broadcasting & Electronic Media, 46(1), 112-24. Dovidio, J. F., & Gaertner, S. L. (2000). Aversive racism and selection decisions: 1989 and 1999. Psychological Science, 11(4), 315-319. Retrieved from http://www.jstor.org.proxygw.wrlc.org/stable/40063839 Dowler, K. G. (2003). Breaking news: The portrayal of crime, justice and victimization on broadcast news. Dissertation Abstracts International, A: The Humanities and Social Sciences, 64(3), 1086-A. Dubrow, J. K., & adams, j. (2010). Hoop inequalities: Race, class and family structure background and the odds of playing in the national basketball association. International Review for the Sociology of Sport, , 1-16. doi: 10.1177/1012690210384660 Dufur, M. (1998). Race logic and "being like mike": Representation of athletes in advertising, 1985-1994. In G. A. Sailes (Ed.), African americans in sport: Contemporary themes (pp. 271). New Brunswick, N.J.: Transaction Publishers. Dufur, M., & Feinberg, S. (2009). Race and the NFL draft: Views from the auction block. Qualitative Sociology, 32(1), 53-73. doi: 10.1007/s11133-008-9119-8 Duncan, M. C. (2006). Gender warriors in sport: Women and the media. In A. A. Raney, & J. Bryant (Eds.), Handbook of sports and the media (pp. 247-281) Lawrence Erlbaum. Duncan, M. C., Messner, M. A., Williams, L. & Jensen, K.Gender stereotyping in televised sports. Retrieved 02/24, 2010, from http://www.la84foundation.org/9arr/ResearchReports/ResearchReport2.htm Dyer, R. (1997). White: Essays on race and culture. London: Routledge. Eastman, S. T., & Billings, A. C. (2001). Biased voices of sports: Racial and gender stereotyping in college basketball announcing. Howard Journal of Communications, 12(4), 183-201. doi: 10.1080/106461701753287714 Edwards, H. (1980). The struggle that must be: An autobiography. New York: MacMillian Publishing Co.

134

Ehrlich, H. H. (1973). Social psychology of prejudice. New York: John Wiley & Sons Inc. Eitzen, D. S. (2006). Fair and foul: Beyond the myths and paradoxes of sport (Third ed.). Lanham, MD: Rowman and Littlefield, Inc. Eitzen, D. S., & Sanford, D. C. (1975). The segregation of blacks by playing position in football: Accident or design? Social Science Quarterly (Southwestern Social Sciences Association), 55(4), 948-959. Retrieved from http://search.ebscohost.com.proxygw.wrlc.org/login.aspx?direct=true&db=aph &AN=16642502&site=ehost-live Eitzen, S. D. (2009). Fair and foul: Beyond the myths and paradoxes of sport (Fourth ed.). Lanham, MD: Rowman & Littlefield. Eitzen, S. D., & Sage, G. H. (2003). Sociology of north american sport (7th ed.). New York: McGraw-Hill. Entman, R. M. (2006). Young men of color in the media: Images and impacts. (). Washington, DC: Joint Center for Political and Economic Studied, Health Policy Institute. Entman, R. M., & Rojecki, A. (2001). The black image in the white mind: Media and race in america. Chicago: University of Chicago Press. ESPN fact sheet. (2010). Retrieved 9/19/2011, 2011, from http://espnmediazone3.com/wpmu/ Evey, S., & Broughton, I. (2004). ESPN: The no-holds-barred story of power, ego, money, and vision that transformed a culture. Chicago: Triumph Books. Fazio, R. H., Jackson, J. R., Dunton, B. C., & Williams, C. J. (1995). Variability in automatic activation as an unobstrusive measure of racial attitudes: A bona fide pipeline? Journal of Personality and Social Psychology,69(6), 1013-1027. doi: 10.1037/0022-3514.69.6.1013 Fazio, R. H., & Olson, M. A. (2003). Implicit measures in social cognition: Their meaning and use. Annual Review of Psychology, 54, 297-30. Findlay, D. W., & Santos, J. M. (2012). Race, ethnicity, and baseball card prices: A replication, correction, and extension of hewitt, muñoz, oliver, and regoli. Econ Journal Watch, 9(2), 122-140. Fishman, M. (1980). Manufacturing the news. Austin, TX: University of Texas Press. Fisk, J. (1987). Television culture. London: Metheune & Co. Gamson, W. A., & Modigliani, A. (1987). The changing culture of affirmative action. Research in Political Sociology, 3, 137-40. Giglioli, P. P. (1990). Language and social context. Baltimore, MD: Penguin. Gilens, M. (1996). Race and poverty in america: Public misperceptions and the american news media. Public Opinion Quarterly, 60, 515-26. Gilliam, F. D., Iyengar, S., Simon, A., & Wright, O. (1996). Crime in black and white: The violent, scary world of local news. The International Journal of Press/ Politics, 1(3), 6-17. Goff, B. L., McCormick, R. E., & Tollison, R. D. (2002). Racial integration as an innovation: Empirical evidence from sports leagues. The American Economic Review, 92(1), 16-26. Retrieved fromhttp://www.jstor.org.proxygw.wrlc.org/stable/3083319

135

Goffman, E. (1974). Frame analysis: An essay on the organization of experience. Cambridge, MA: Harvard University Press. González, L., Jackson, E. N., & Regoli, R. M. (2006). The transmission of racist ideology in sport: Using photo-elicitation to gauge success in professional baseball. Journal of African American Studies, 10(3), 46-54. Retrieved from http://search.ebscohost.com.proxygw.wrlc.org/login.aspx?direct=true&db=aph &AN=24518859&site=ehost-live Gorham, B. W. (2006). News Media’s relationship with stereotyping: The linguistic intergroup bias in response to crime news. Journal of Communication, 56(2), 289- 308. doi: 10.1111/j.1460-2466.2006.00020.x Gottschalk, L. A., & Bechtel, R. (1993). Psychological and neuropsychiatric assessment survey: Computerized content analysis of natural language or verbal texts. Redwood City, CA: Mind Garden. Grano, D. A. (2007). Ritual disorder and the contractual morality of sport: A case study in race, class, and agreement. Rhetoric & Public Affairs, 10(3), 445-473. Retrieved fromhttp://search.ebscohost.com.proxygw.wrlc.org/login.aspx?direct=true&db=aph &AN=28066237&site=ehost-live Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The implicit association test. Journal of Personality and Social Psychology, 74(6), 1464-16. Grenier, G., & Lavoie, M. (1988). Francophones in the national hockey league: Test of entry and salary discrimination. Unpublished manuscript. Grose, C. R. (2006). Bridging the divide: Interethnic cooperation; minority media outlets; and the coverage of latino, african-american, and asian-american members of congress. Harvard International Journal of Press/Politics, 11(4), 115-130. doi: 10.1177/1081180X06293003 Gwartney, J., & Haworth, C. (1974). Employer costs and discrimination: The case of baseball. Journal of Political Economy, 82(4), 873-8. Hacking, I. (2000). The social construction of what?. Cambridge, MA: Harvard University Press. Hall, S. (1995). The whites of their eyes: Racist ideologies and the media. In G. Dines, & J. M. Humez (Eds.), Gender, race, and class in media (pp. 18-4). Thousand Oaks, CA: Sage. Hall, S. (Ed.). (1997). Representation: Cultural representations and signifying practices. Thousand Oaks, CA: Sage. Hamilton, B. H. (1997). Racial discrimination and professional basketball salaries in the 1990s. Applied Economics, 29(3), 287-296. doi: 10.1080/000368497327065 Hanis-Martin, J. L. (2006). Embodying contradictions: The case of women's professional basketball. Journal of Sport and Social Issues, 30(3), 265-23. doi: 10.1177/0193723506288655 Harris-Lacewell, M. V. (2004). Barbershops, bibles, and BET: Everday talk and black political thought. Princeton, NJ: Princeton University Press. Hawkins, B. (2002). Is stacking dead? A case study of the stacking hypothesis at a southeastern conference (SEC) football program. International Sports Journal, 6(2), 146. Retrieved

136

fromhttp://search.ebscohost.com.proxygw.wrlc.org/login.aspx?direct=true&db=aph &AN=6898545&site=ehost-live Henley, N. (1977). Body politics: Power, sex, and nonverbal communications. New York: Simon and Schuster. Henry, P. J., & Sears, D. O. (2002). The symbolic racism 2000 scale. Political Psychology, 23(2), 253-283. Retrieved from http://www.jstor.org.proxygw.wrlc.org/stable/3792290 Hewitt, J. D., Muñoz, J., Robert, Oliver, W. L., & Regoli, R. M. (2005). Race, performance, and baseball card values. Journal of Sport & Social Issues, 29(4), 411- 425. doi: 10.1177/0193723504273121 Hoberman, J. (1997). Darwin's athletes: How sport has damaged black america and preserved the myth of race. New York: Mariner. Jaccard, J. (2001). Interaction effects in logistic regression. Thousand Oaks, CA: Sage. Jackson, D. Z. (January 22, 1989). Calling the plays in black and white will today's superbowl be black brawn vs. white brains? The Boston Globe, pp. A25. Johnson, J. D., Adams, M. S., Hall, W., & Ashburn, L. (1997). Race, media, and violence: Differential racial effects of exposure to violent news stories. Basic & Applied Social Psychology, 19(1), 81-90. doi: 10.1207/15324839751037156 Johnson, N. R., & Marple, D. P. (1973). Racial discrimination in professional basketball: An empirical test. Sociological Focus, 6(4), 6-12. Jones, A., & Greer, J. (2011). You don't look like an athlete: The effects of feminine appearance on audience perceptions of female athletes and women's sports. Journal of Sport Behavior, 34(4), 358-377. Retrieved from http://search.ebscohost.com.proxygw.wrlc.org/login.aspx?direct=true&db=a9h &AN=67121257&site=eds-live&scope=site Jones, R., Murrell, A. J., & Jackson, J. (1999). Pretty versus powerful in the sports pages: Print media coverage of US women's olympic gold medal winning teams. Journal of Sport and Social Issues, 23(2), 183-192. doi: 10.1177/0193723599232005 Jordan, T. A., Maxwell, H., & LaVoi, N. (2009). Racial representations of women's and men's intercollegiate basketball coverage onESPN.com. 2009 North American Society for Sport Management Conference,Columbia, SC. Kahn, L. M. (1991). Discrimination in professional sports: A survey of the literature. Industrial and Labor Relations Review, 44(3), 395-418. Retrieved from http://www.jstor.org.proxygw.wrlc.org/stable/2524152 Kahn, L. M. (1992). The effects of race on professional football players' compensation. Industrial and Labor Relations Review, 45(2), 295-310. Retrieved from http://www.jstor.org.proxygw.wrlc.org/stable/2524836 Kahn, L. M., & Shah, M. (2005). Race, compensation and contract length in the NBA: 2001-2002. Industrial Relations, 44(3), 444-462. Kahn, L. M., & Sherer, P. D. (1988). Racial differences in professional basketball players' compensation. Journal of Labor Economics, 6(1), 40-61. Retrieved fromhttp://www.jstor.org.proxygw.wrlc.org/stable/2534867 Kanazawa, M. T., & Funk, J. P. (2001). Racial discrimination in professional basketball: Evidence from nielsen ratings. Economic Inquiry, 39(4), 599-608. Retrieved fromhttp://search.ebscohost.com.proxygw.wrlc.org/login.aspx?direct=true&db=aph &AN=6800572&site=ehost-live

137

Kane, M. (1971). An assessment of 'black is best'. Retrieved 2/24/2010, 2010, from http://sportsillustrated.cnn.com.proxygw.wrlc.org/vault/article/magazine/MAG 1084501/index.htm Karen, D., & Karabel, J. (1982, February 10-16, 1982). Color on the court: Race in professional basketball. In these Times, 6, 23-1. Kelly, M. (2010). Regulating the reproduction and mothering of poor women: The controlling image of the welfare mother in television news coverage of welfare reform. Journal of Poverty, 14(1), 76-96. doi: 10.1080/10875540903489447 Kennedy, R. (1998). Race, crime, and the law. New York, NY: Vintage. Kian, E.M., Vincent, J., & Mondello, M. (2008). Masculine hegemonic hoops: An analysis of media coverage of march madness. Sociology of Sport Journal, 25(2), 223-242. Retrieved fromhttp://search.ebscohost.com.proxygw.wrlc.org/login.aspx?direct=true&db=aph &AN=32534675&site=ehost-live Kian, E. M. (2007). Gender in sports writing by the print media: An exploratory examination of writers' experiences and attitudes. The SMART Journal, 4(1), 5-26. Kingsbury, J. H., & Tauer, J. M. (2009). The ESPN effect: How slam dunks affect individualism and optimism for athletic success. International Journal of Sport Communication, 2(1), 21-35. Retrieved fromhttp://search.ebscohost.com.proxygw.wrlc.org/login.aspx?direct=true&db=s3h &AN=36829433&site=eds-live&scope=site Kleinbaum, D. G., & Klein, M. (2002). Logistic regression: A self-learning text (Second ed.). New York, NY: Springer-Verlag. Klomsten, A. T., Marsh, H. W., & Skaalvik, E. M. (2005). Adolescents’ perceptions of masculine and feminine values in sport and physical education: A study of gender differences. Sex Roles, 52(9), 625-636. doi: 10.1007/s11199-005-3730-x Koch, J. V., & Vander Hill, C. W. (1988). In there discrimination in the 'black man's game?'. Social Science Quarterly, 69(1), 83-11. Koivula, N. (1999). Gender stereotyping in televised media sport coverage. Sex Roles, 41(7-8), 589-604. doi: 10.1023/A:1018899522353 Kolbe, R. H., & Burnett, M. S. (1991). Content-analysis research: An examination of applications with directives for improving research reliability and objectivity. Journal of Consumer Research, 18(2), 243-250. Retrieved from http://search.ebscohost.com.proxygw.wrlc.org/login.aspx?direct=true&db=edsj ad&AN=10.2307.2489559&site=eds- live; http://www.jstor.org.proxygw.wrlc.org/stable/2489559 Kubrin, C. E. (2005). Gangstas, thugs, and hustlas: Identity and the code of the street in rap music. Social Problems, 52(3), pp. 360-378. Retrieved fromhttp://www.jstor.org.proxygw.wrlc.org/stable/10.1525/sp.2005.52.3.360 Lapchick, R. (2003). The 2003 racial and gender report card. (). Lapchick, R., Kaiser, C., Russell, C., & Welch, N. (2010). The 2010 racial and gender report card: National basketball association. (). Lapchick, R., Kaiser, C., Caudy, D., & Wang, W. (2010). The 2010 racial and gender report card: Major league baseball. (). Lapchick, R. (1991). Five minutes to midnight: Race and sport in the 1990s. Lanham, MD: Madison Books.

138

Lapchick, R., Kitnurse, J. M., & Moss II, A. (2010). The 2010 racial and gender report card: National football league. (). Lavelle, K. L. (2010). A critical discourse analysis of black masculinity in NBA game commentary. Howard Journal of Communications, 21(3), 294-314. doi: 10.1080/10646175.2010.496675 Lavoie, M., Grenier, G., & Coulumbe, S. (1987). Discrimination and performance differentials in the national hockey league. Canadian Public Policy/ Analyse De Politiques, 13(4), 407-15. Lawrence, S. M. (2005). African american athletes' experiences of race in sport. International Review for the Sociology of Sport, 40(1), 99-110. doi: 10.1177/1012690205052171 LeDuff, K. M. (2008). Tales of two cities: How race and crime intersect on local TV news in indianapolis and new orleans.68(09), 3650. Leonard, D. J. (2006). The real color of money: Controlling black bodies in the NBA. Journal of Sport and Social Issues, 30(2), 158-179. doi: 10.1177/0193723506286725 Leonard, W. M.,II. (1997). Racial composition of NBA, NFL, and MLB teams and racial composition of franchise cities. Journal of Sport Behavior, 20(4), 424-11. Leonard, W. M., Pine, J., & Rice, C. (1988). Performance characteristics of white, black, and hispanic major league baseball players: 1955-1984. Journal of Sport and Social Issues, 12(1), 31-43. doi: 10.1177/019372358801200103 Leonard, W. M.,II. (1986). The sports experience of the black college athlete: Exploitation in the academy. International Review for the Sociology of Sport, 21(1), 35-49. doi: 10.1177/101269028602100103 List of ESPN personalities. (2011). Retrieved July, 13, 2011, from http://en.wikipedia.org/wiki/List_of_ESPN_personalities Logistic regression diagnostics. Retrieved August 10, 2012, from http://www.ats.ucla.edu.proxygw.wrlc.org/stat/stata/webbooks/logistic/chapter 3/statalog3.htm Lombard, M., Snyder-Duch, J., & Bracken, C. C. (2002). Content analysis in mass communication: Assessment and reporting of intercoder reliability. Human Communication Research, 28(4), 587-604. Retrieved fromhttp://search.ebscohost.com.proxygw.wrlc.org/login.aspx?direct=true&db=qeh &AN=BEDI09390386&site=eds-live Margolis, B., & Piliavin, J. A. (1999). "Stacking" in major league baseball: A multivariate analysis. Sociology of Sport Journal, 16(1), 16-34. Retrieved from http://search.ebscohost.com.proxygw.wrlc.org/login.aspx?direct=true&db=aph &AN=6165447&site=ehost-live Mastro, D., Lapinski, M. K., Kopacz, M. A., & Behm-Morawitz, E. (2009). The influence of exposure to depictions of race and crime in TV news on viewer's social judgments. Journal of Broadcasting & Electronic Media,53(4), 615-635. doi: 10.1080/08838150903310534 McCarthy, D., Jones, R. L., & Potrac, P. (2003). Constructing images and interpreting realities: The case of the black soccer player on television. International Review for the Sociology of Sport, 38(2), 217-238. doi: 10.1177/1012690203038002005

139

McCarthy, D., & Jones, R. L. (1997). Speed, aggression, strength, and tactical naivete: The portrayal of the black soccer player on television. Journal of Sport and Social Issues, 21(4), 348-362. doi: 10.1177/019372397021004003 McConahay, J. B. (1986). Modern racism, ambivalence, and the modern racism scale. In J. F. Dovidio, & S. L. Gaertner (Eds.), Prejudice, discrimination, and racims (pp. 99-26). Orlando: Academic Press. McConnell, A. R., & Leibold, J. M. (2001). Relations among the implicit association test, discriminatory behavior, and explicit measures of racial attitudes. Journal of Experimental Social Psychology, 37, 435-7. McKenzie-Elliott, T. M. (2004). The symbolic representation of latinos: A content analysis of prime-time television. Dissertation Abstracts International, A: The Humanities and Social Sciences, 65(3), 1133-A-1134-A. Melnick, M. J. (1988). Racial segregation by playing position in the english football league: Some preliminary observations. Journal of Sport & Social Issues, 12(2), 122-130. Retrieved fromhttp://search.ebscohost.com.proxygw.wrlc.org/login.aspx?direct=true&db=aph &AN=13544673&site=ehost-live Mendelberg, T. (2001). The race card: Campaign strategy, implicit messages, and the norm of equality. Princeton, NJ: Princeton University Press. Mercurio, E., & Filak, V. F. (2010). Roughing the passer: The framing of black and white quarterbacks prior to the NFL draft. Howard Journal of Communications, 21(1), 56- 71. doi: 10.1080/10646170903501328 Messner, M. A., & Sabo, D. F. (Eds.). (1994). Sex, violence & power in sports. Freedom, CA: The Crossing Press. Miller, J. A., & Shales, T. (2011). Those guys have all the fun: Inside the world of ESPN. New York, NY: Little Brown & Co. Mitchell, J. P., Nosek, B. A., & Banaji, M. R. (2003). Contextual variations in implicit evaluation. Journal of Experimental Psychology: General, 132(3), 455-469. doi: 10.1037/0096-3445.132.3.455 Monson, M. J. (2000). Domestic violence in the hyperreal: An examination of race and ethnicity in "real life" police drama. Dissertation Abstracts International, A: The Humanities and Social Sciences, 60(9), 3542-A. Murrell, A. J., & Curtis, E. M. (1994). Causal attributions of performance for black and white quarterbacks in the NFL: A look at the sports pages. Journal of Sport and Social Issues, 18(3), 224-9. Nardinelli, C., & Simon, C. (1990). Customer racial discrimination in the market for memorabilia: The case of baseball. The Quarterly Journal of Economics, 105(3), pp. 575-595. Retrieved fromhttp://www.jstor.org.proxygw.wrlc.org/stable/2937891 Nederveen Pieterse, J. (1995). White negroes. In G. Dines, & J. M. Humez (Eds.), Gender, race, and class in media (pp. 23-4). Thousand Oaks, CA: Sage. Neely, J. H. (1977). Semantic priming and retrieval from lexical memory: Roles of inhibitionless spreading activation and limited-capacity attention. Journal of Experimental Psychology: General, 106(3), 226-254. doi: 10.1037/0096- 3445.106.3.226

140

Netzley, S. B. (2007). Television, gays, and gay marriage: A cultivation study of television's relationship with opinions about same-sex marriage. Dissertation Abstracts International, A: The Humanities and Social Sciences, 67(12), 4377. Neuendorf, K. A. (2002). The content analysis guidebook. Thousand Oaks, CA: Sage. Newman, L. S., & Uleman, J. S. (1989). Spontaneous trait inference. In J. S. Uleman, & J. A. Bargh (Eds.), Unintended thought (pp. 155-33). New York: Guilford. Niven, D. (2004). A fair test of media bias: Party, race, and gender in coverage of the 1992 house banking scandal. Policy, 36(4), 637-12. Niven, D. (2005). Race, quarterbacks, and the media: Testing the rush limbaugh hypothesis. Journal of Black Studies, 35(5), 684-694. doi: 10.1177/0021934704266083 Oates, T. P., & Durham, M. G. (2004). The mismeasure of masculinity: The male body, 'race' and power in the enumerative discourses of the NFL draft. Patterns of Prejudice, 38(3), 301-320. Retrieved fromhttp://search.ebscohost.com.proxygw.wrlc.org/login.aspx?direct=true&db=aph &AN=14167462&site=ehost-live Olberman, K., & Patrick, D. (1997). The big show: Inside ESPN's sportscenter. New York, NY: Atria. Oliver, M. B. (1994). Portrayals of crime, race, and aggression in `reality-based' police shows: A content analysis. Journal of Broadcasting & Electronic Media, 38(2), 179. Retrieved fromhttp://search.ebscohost.com.proxygw.wrlc.org/login.aspx?direct=true&db=aph &AN=9406240973&site=ehost-live Owens, L. C. (2008). Network news: The role of race in source selection and story topic. Howard Journal of Communications, 19(4), 355-370. doi: 10.1080/10646170802418269 Pan, Z., & McCleod, J. M. (1991). Multilevel analysis in mass communication research. Communications Research, 18, 140-33. doi: 10.1177/009365091018002002 Papper, B. (2011). RTDNA / hofstra survey finds mixed news for women & minorities in TV, radio news. (). Pederson, P. M. (2002). Examining equity in newspaper photographs: A content analysis of the print media photographic coverage of interscholastic athletics. International Review for the Sociology of Sport, 37(3), 303-318. doi: 10.1177/1012690202037004895 Popping, R. (1988). On agreement indices for nominal data. In W. E. Saris, & I. N. Gallhofer (Eds.), Sociometric research: Volume 1, data collection and scaling (pp. 90-15). New York: St. Martin's Press. Potter, W. J., & Levine-Donnerstein, D. (1999). Rethinking validity and reliability in content analysis. Journal of Applied Communication Research, 27, 258-26. Price, J., & Wolfers, J. (2007). Racial discrimination among NBA referees. Quarterly Journal of Economics, , 34. Price, V., & Tewksbury, D. (1997). News values and public opinion: A theoretical account of media priming and framing. In G. A. Barnett, & F. J. Boster (Eds.), Progress in communication sciences: Advances in persuasion (pp. 173-39). Greenwich, CT: Ablex Publishing Corporation.

141

Primm, E., DuBois, S., & Regoli, R. (2007). An exercise in subtleties and the transmission of racism: An analysis of sports illustrated covers. Journal of African American Studies, 11(3), 239-250. doi: 10.1007/s12111-007-9025-2 Primm, E., Piquero, N. L., Regoli, R. M., & Piquero, A. R. (2010). The role of race in football card prices. Social Science Quarterly (Blackwell Publishing Limited), 91(1), 129-142. doi: 10.1111/j.1540-6237.2010.00685.x Prometheus radio project v. federal communications commission, (US Court of Appeals for the Third Circuit 2011). Proshanksy, H. M. (1966). The development of intergroup attitude. In L. W. Hoffman, & M. L. Hoffman (Eds.), Review of child development research (pp. 311-60). New York: Russell Sage Foundation. Rada, J. A. (1996). Color blind-sided: Racial bias in network television's coverage of professional football games. Howard Journal of Communications, 7(3), 231-239. Retrieved fromhttp://search.ebscohost.com.proxygw.wrlc.org/login.aspx?direct=true&db=ufh &AN=16280497&site=ehost-live Rada, J. A., & Wulfemeyer, K. T. (2005). Color coded: Racial descriptors in television coverage of intercollegiate sports. Journal of Broadcasting & Electronic Media, 49(1), 65-85. doi: 10.1207/s15506878jobem4901_5 Rainville, R. E., & McCormick, E. (1977). Extent of covert racial prejudice in pro football announcers’ speech. Journalism Quarterly, 54(1), 10-16. Ravens/Browns, sportscenter, WWE RAW, project runway and sons of anarchy lead cable viewing with adults 18-49 - TV ratings, nielsen ratings, television show ratings | TVbytheNumbers.com Retrieved 2/24/2010, 2010, from http://tvbythenumbers.com/2009/11/24/ravensbrowns-sportscenter-wwe-raw- project-runway-and-sons-of-anarchy-lead-cable-viewing-with-adults-18-49/34502 Regalado, S. O.Viva baseball: Latin major-leaguers and their special hunger. Urbana, IL: University of Illinois Press. Regoli, R. M., Primm, E., & Hewitt, J. D. (2007). Men and boys and the price of their toys: Race and the value football cards. Social Science Journal, 44(3), 563-571. doi: 10.1016/j.soscij.2007.07.014 Reisler, J. (Ed.). (1994). Black writers/ black baseball: An anthology of articles from black sportswriters who covered the negro leagues. Jefferson, NC: McFarland & Co. Remnick, D. (1998). King of the world. New York: Vintage. Research: Sports lanscape. Retrieved 12/19, 2011, from http://espn.go.com/mediakit/research/sports_landscapes/college_football.html Rich, M., Woods, E. R., Goodman, E., Emans, S. J., & DuRant, R. H. (1998). Aggressors or victims: Gender and race in music video violence. Pediatrics, 101(4), 669. Retrieved fromhttp://search.ebscohost.com.proxygw.wrlc.org/login.aspx?direct=true&db=aph &AN=449546&site=ehost-live Romer, D., Jamieson, K. H., & De Coteau, N. J. (1998). The treatment of persons of color in local television news: Ethnic blame discourse or realistic group conflict? Communications Research, 25(3), 286-19.

142

Rothbart, M., & John, O. P. (1993). Intergroup relations and stereotype change: A social- cognitive analysis and some longitudinal findings. In P. M. Sniderman, P. E. Tetlock & E. G. Carmines (Eds.), Prejudice, politics, and the american dilemma () Sack, A. L., Singh, P., & Thiel, R. (2005). Occupational segregation on the playing field: The case of major league baseball. Journal of Sport Management, 19(3), 300. Retrieved fromhttp://search.ebscohost.com.proxygw.wrlc.org/login.aspx?direct=true&db=aph &AN=17461363&site=ehost-live Sailes, G. A. (1985). Sport socialization comparisons among black and white adult male athletes and nonathletes. Ann Arbor, Mich.; United States: University Microfilms International. Retrieved fromhttp://search.ebscohost.com.proxygw.wrlc.org/login.aspx?direct=true&db=s3h &AN=SPH339109&site=eds-live&scope=site Sailes, G. A. (1991). The myth of black sports supremacy. Journal of Black Studies, 21(4), pp. 480-487. Retrieved from http://www.jstor.org.proxygw.wrlc.org/stable/2784690 Sandomir, R. (2003, October 2). FOOTBALL; limbaugh resigns from ESPN's N.F.L. show. The New York Times Schaffner, B. F., & Gadson, M. (2004). Reinforcing stereotypes? race and local television news coverage of congress. Social Science Quarterly (Blackwell Publishing Limited), 85(3), 604-623. doi: 10.1111/j.0038-4941.2004.00235.x Scheler, M. (1980). Problems of a sociology of knowledge. New York, NY: Routledge. Scheufele, D. (1999). Framing as a theory of media effects. Journal of Communication, 49(1), 103-122. doi: 10.1111/j.1460-2466.1999.tb02784.x Scheufele, D. A., & Tewksbury, D. (2007). Framing, agenda setting, and priming: The evolution of three media effects models. Journal of Communication, 57(1), 9-11. doi: 10.1111/j.1460-2466.2006.00337.x Schollaert, P. T., & Smith, D. H. (1987). Team racial composition and sports attendance. The Sociological Quarterly, 28(1), 71-87. Retrieved from http://www.jstor.org.proxygw.wrlc.org/stable/4121421 Schultz, J. (2005). Reading the catsuit: Serena williams and the production of blackness at the 2002 U.S. open. Journal of Sport and Social Issues, 29(3), 338-357. doi: 10.1177/0193723505276230 Schuman, H., Steeh, C., Bobo, L., & Krysan, M. (1997). Racial attitudes in america (Revised Edition ed.). Cambridge, MA: Harvard University Press. Scott, F. A.,Jr., Long, J. E., & Somppi, K. (1985). Salary vs. marginal revenue product under monopsony and competition: The case of professional basketball. Atlantic Economic Journal, 13(3), 50-9. Seate, A. A., Harwood, J., & Blecha, E. (2010). 'He was framed!' framing criminal behavior in sports news. Communication Research Reports, 27(4), 343-354. doi: 10.1080/08824096.2010.518917 Shields, D. (1999). Black planet: Facing race during an NBA season. New York: Crown. Shifflett, B., & Revelle, R. (1994). Gender equity in sports media coverage: A review of the NCAA news. Journal of Sport & Social Issues, 18, 144-150. doi: 10.1177/019372394018002004

143

Sidanius, J., & Pratto, F. (1999). Social dominance. New York, NY: Cambridge University Press. Simons, H. D. (2003). Race and penalized sports behaviors. International Review for the Sociology of Sport, 38(1), 5-22. doi: 10.1177/10126902030381001 Smith, A. F., & Hollihan, K. (2009). ESPN the company: The story and lessons behind the most fanatical brand in sports. Hoboken, NJ: John Wiley & Sons, Inc. Smith, E., & Seff, M. A. (1989). Race, position segregation and salary equity in professional baseball. Journal of Sport and Social Issues, 13(2), 92-110. doi: 10.1177/019372358901300203 Sniderman, P. M., Crosby, G. C., & Howell, W. G. (2000). The politics of race. In D. O. Sears, J. Sidanius & L. Bobo (Eds.), Racialized politics: The debate about racism in america (pp. 236-33). Chicago: University of Chicago Press. Solomon, G., & Writer, W. P. S. (1988, January 17). ' Jimmy the greek' fired by CBS for his remarks. The Washington Post Sommers, P. M., & Quinton, N. (1982). Pay and performance in major league baseball: The case of the first family of free agents. Journal of Human Resources, 17(3), 426- 10. Soulliere, D. M. (2001). Prime-time criminal justice: If all we know is what we saw on television. Dissertation Abstracts International, A: The Humanities and Social Sciences, 62(3), 1222-A-1223-A. Spencer, N. E. (2004). Sister act VI: Venus and serena williams at indian wells: "sincere fictions" and white racism. Journal of Sport and Social Issues, 28(2), 115-135. doi: 10.1177/0193723504264411 Sportscenter: Cast and details. Retrieved July 13, 2011, from http://www.tvguide.com/tvshows/sportscenter/cast/204559 Squires, C. R., & Jackson, S. J. (2010). Reducing race: News themes in the 2008 primaries. International Journal of Press/Politics, 15(4), 375-400. doi: 10.1177/1940161210372962 Stake, R. E. (1995). The art of case study research. Thousand Oaks, CA: Sage. Staples, R., & Jones, T. (1985). Culture, ideology and african american television images. The Black Scholar, 16(3), 10-10. Sully, G. W. (1974). Discrimination: The case of baseball. In R. Noll (Ed.), Government and the sports business (pp. 221-52). Washington, DC: Brookings. Szymanski, S. (2000). A market test for discrimination in the english professional soccer leagues. The Journal of Political Economy, 108(3), pp. 590-603. Retrieved fromhttp://www.jstor.org.proxygw.wrlc.org/stable/3038271 Tainsky, S., & Winfree, J. A. (2010). Discrimination and demand: The effect of international players on attendance in major league baseball. Social Science Quarterly (Blackwell Publishing Limited), 91(1), 117-128. doi: 10.1111/j.1540- 6237.2010.00684.x Tamburro, R. F., Gordon, P. L., D'Apolito, J. P., & Howard, S. C. (2004). Unsafe and violent behavior in commercials aired during televised major sporting events. Pediatrics, 114(6), e694-e698. doi: 10.1542/peds.2004-0105 ter Wal, J., D’Haenens, L., & Koeman, J. (2005). (Re)presentation of ethnicity in EU and dutch domestic news: A quantitative analysis Media, Culture & Society, 27(6), 937- 13.

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Tonry, M. (1996). Malign neglect: Race, crime, and punishment in america. New York, NY: Oxford University. Toohey, K., & Veal, A. J. (2007). The olympic games: A social science experiment (2nd Edition ed.). Oxfordshire, England: CAB International. Tuchman, G. (1979). Making news: A study in the construction of reality. New York, NY: Free Press. Van Sterkenburg, J., & Knoppers, A. (2004). Dominant discourses about Race/ethnicity and gender in sport practice and performance. International Review for the Sociology of Sport, 39(3), 301-322. Retrieved fromhttp://search.ebscohost.com.proxygw.wrlc.org/login.aspx?direct=true&db=aph &AN=14421202&site=ehost-live Van Sterkenburg, J., Knoppers, A., & De Leeuw, S. (September 2010). Race, ethnicity, and content analysis of the sports media: A critical reflection Media, Culture & Society, 32, 819-20. doi: 10.1177/0163443710373955 Vercellotti, T., & Brewer, P. R. (2006). "TO PLEAD OUR OWN CAUSE" public opinion toward black and mainstream news media among african americans. Journal of Black Studies, 37(2), 231-250. doi: 10.1177/0021934705277945 Walker, J. S. (2011). ACC basketball: The story of the rivalries, traditions, and scandals of the first two decades of the atlantic coast conference. Chapel Hill, NC: University of North Carolina. Weiss, D. (1996, March 03). Packer apologizes for slur. Daily News (New York) Weiss, J., & Sommers, P. (2009). Does team racial composition affect team performance in the NBA? Atlantic Economic Journal, 37(1), 119-120. doi: 10.1007/s11293-008- 9156-4 Wilson, B. (1997). `Good blacks' and `Bad blacks': Media constructions of african- american athletes in canadian basketball. International Review for the Sociology of Sport, 32(2), 177-189. doi: 10.1177/101269097032002005 Wilson, B., & Sparks, R. (1996). "It's gotta be the shoes": Youth, race, and sneaker commercials. Sociology of Sport Journal, 13(4), 398-29. Wimmer, R. D., & Dominick, J. R.Mass media research: An introduction (5th ed.). Belmont, CA: Wadsworth. Winters, N. J. G. (2008). Dangerous frames: How ideas about race and gender shape public opinion. Chicago: University of Chicago Press. Wolseley, R. E. (1990). Tha black press, USA (2nd Edition ed.). Ames: Iowa University Press.

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Appendices

Appendix A: Code Book Unit of Data Collection: The unit of data collection is each commentator’s description of a player.

What is an episode? In most cases an episode starts with a brief introduction of the top story and a Sportscenter graphic segment followed by a montage of the main stories and an anchor introducing the two anchors. An episode typically ends with the two anchors announcing the following ESPN programming and identifying the anchors (if new) for that following program.

Episodes may deviate from this structure in one of two ways. The recording process may have failed to capture the entire episode. In instances where this was the case the episode was captured from its beginning but a portion of the ending was omitted. There may also have been irregular programming. For example, on December 25 ESPN ran a Sportscenter that aired until 1:15am and then was followed by another ESPN program, NFL Primetime, despite the fact that on every other Sunday ESPN ran a full hour-long Sportscenter from 1am to 2am. Occasionally, anomalous news, such as the live telecast of a press conference, interrupted the regular schedule of an episode. When this occurred coding began once the program commenced.

What is a comment? A comment occurs every time a member of ESPN personnel mentions an active athlete’s name. A comment is ended the moment a different commentator mentions that athlete’s or another athlete’s name. Commercials or the language of other commentators that does not include the name of an athlete does not end the comment. Descriptions associated with the comment can come before or after the commentator mentions the athlete’s name as long as there is no interruption of another commentator mentioning any qualifying athlete’s name. A commentator may comment on athlete A then comment on athlete B and, as long as the commentator has not been interrupted by another commentator mentioning an athlete, he may provide more descriptions of athlete A and athlete A will still be coded as having one comment for that commentator. A commentator can have multiple descriptions but not multiple comments of the same player. It is only once that first commentator is interrupted by another commentator mentioning a qualifying athlete’s name that the initial commentator can have a second comment of athlete A.

A commentator may continuously refer to an athlete with a pronoun and as long as the comment is not interrupted by another commentator’s qualifying comment all descriptions associated with the pronoun are included once the commentator mentions the athlete’s name. Similarly, in cases where the commentator identifies a player by name and in the same comment later by “she” or “he” the comment and its descriptions will qualify. In cases where more than one player is mentioned in a comment all players are included with the description as long as they are the subject of the statement. For example, “LeBron James and Dwight Howard are two of the most powerful players in the

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NBA” is coded as positive physical ability for each player. Similarly, “New York’s spectacular duo of Carmelo Anthony and Amare Stoudemire” is coded as a positive on- the-field comment for each player.

To qualify as a commentator one must be an employee of ESPN. Comments from post- game recordings or live news conferences of athletes, coaches, executives or any other non-ESPN personnel are excluded. To qualify as an athlete the individual must actively be participating in one of the seven designated sports and be playing at the collegiate (any division) or professional level (including less prestigious leagues, such as the NBDL, ECHL, AHL, or minor league baseball). Commentary on top high school athletes declaring their college is not coded as they were not college athletes at that time. The athlete must be identified by her or his first, last or both names. Referring to the athlete by number, nickname or a pronoun does not qualify for coding with the exception of nicknames that include the first or last name (i.e., “Big Ben” for Ben Roethlisberger”, or “Tebow-time” and “Tebowmania” for Tim Tebow. “KG” for Kevin Garnett or “The Chosen One” for LeBron James” would not suffice80.)

The comment must be of the player or her or his play. A statement like “player A recognizes that great plays win championships” is not coded as positive. A positive comment would be “player A makes great plays, which will lead to championships”. Finally, coders should give preference to the present state of the athlete. In cases where a commentator is contrasting the past and current state of the player the description of the current state is coded. So, “player A was fast in his youth, but now he’s lost a step and is slow” is coded as negative physical ability.

To recap a qualifying comment for a player: 1. lasts from the point another commentator mentions any qualifying athlete until another commentator mentions any qualifying athlete; 2. is made by a member of ESPN personnel; 3. is of a current professional or collegiate athlete of the seven qualifying sports; 4. mentions the athlete’s first, last, or full name; 5. addresses an athlete or the play an athlete is involved in; 6. and, gives preference to present conditions.

What is a description? Descriptions can be adjectival, like “strong” or “smart”, or more extensive (i.e., “his power allows him to break through the line” or “the hours she spends each day reviewing game tape appear to really have helped her this season”), but the majority of comments qualify as neutral (i.e., “play A shot the ball”, or “player B was traded to a new team”). Neutral represents the default coding for comments with no value and no description.

80 The reason for this is there can be uncertainty over who possesses the ‘rights’ to a nickname. For example, Shaquille O’Neal has openly challenged Dwight Howard for adopting ‘Superman’ as his nickname a decade after O’Neal appropriated it. (It should be noted that Washington QB Robert Griffin III shares the nickname as well.)

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For a comment to qualify as positive or negative the comment must subjectively value an athlete. For example, the comment “player A is a record-breaker” while presumably positive is an objective statement assuming player A broke some record. But “player A is a great record-breaker” qualifies as a positive description because adding “great” indicates the commentator’s subjective assessment of player A. Related to this point, in less obvious cases commentators must be explicit in their descriptions to qualify. For example, stating “player A averages 3 points per game, a career low” is probably intended as a negative comment but because it is an objective statement it qualifies as neutral. But the comment “player A averages 3 points per game, which has been costing her team” qualifies as negative because the commentator is making the subjective assessment that not only is player A scoring less than normal but somehow player A’s low-scoring is responsible for her team’s failures.

The same comment can have multiple descriptions. If a commentator states that “player A’s speed and leadership in the locker room make player A an all-star” then the comment would be coded as positive for physical ability and positive for psychological leadership. Another example is “player A showed such great speed in getting to the basket, but that pass was foolish.” In this case the comment would be coded for two descriptions— positive physical ability (i.e., “great speed”) and “negative psychological intellect” (i.e., “that pass was foolish”).

Coding Coder Name: The name of the coder.

Episode ID: The episode ID—the first letter of the month followed by the two digit number of the day of the month (i.e., December 1, 2011 = “D01”, May 17, 2012 = “M17”).

Time of Comment: The minutes and seconds that the commentator first mentioned the name of the athlete in the comment. While it’s not expected that coders will identify the exact time of every comment, this will enable the researcher to approximate the start of the coded comment when measuring for consistency.

Commentator’s Name: The commentator’s name as given on the episode. Only ESPN personnel are included. Montages where there are comments on athletes but the speaker is indeterminable are excluded. Also, in the case of direct quotes cite only the qualifying individual being quoted. Do not cite any names included in the direct quote. Note: Coders are only to enter the commentator’s name. The researcher will enter the commentator’s race, sex, and role. 1. Speaker Race: 1= Black 2 = White 3 = Latino/a 4 = Asian 5 = Other 2. Speaker’s Sex:

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Male = 1; Female = 0 3. Speaker Role: Host = 1; Analyst = 0

Athlete Name: The athlete’s name associated with the specific comment. A commentator may comment on multiple players in the same comment. When that occurs each specific player is coded separately with the commentator being the same for all. However, broad comments about entire teams or leagues (i.e., “All University of Maryland basketball players are hard-working”) do not qualify. Only active players are eligible. So, retired players, coaches, managers, executives, team owners and the like should be excluded. The player’s name is included to assess anomalous events and for coders to compare in instances where data do not align. Note: Coders are only to enter the athlete’s name. The researcher will enter the athlete’s race, sex, sport, and level of competition. 1. Athlete’s Race: Black = 1 White = 2 Latino/a = 3 Asian = 4 Other = 5 2. Athlete’s Sex: Male = 1; Female = 0 3. Athlete’s Sport (if the athlete participates in another sport then she/ he is disregarded): Football = 1 Soccer = 2 Basketball = 3 Baseball = 4 Hockey = 5 Golf = 6 Tennis = 7 4. Level of Competition (amateur athletes, like high school or Little League World Series are excluded): Professional = 1; Collegiate = 0

There are nine types of comments: 1. Physical – Ability: These comments reflect on the performance of some physical act, and rely on descriptors, like strength, speed, power, or quickness. An example is: “Player A’s power enabled him to split the defense.” The comment may be: positive (1) or negative (0).

2. Physical – Appearance: These comments reflect on the appearance of an athlete, and emphasize characteristics, like height, weight, or mass. An example is: “Player A is too short to see over his linemen, which limits his passing window.” The comment may be: positive (1) or negative (0).

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3. Psychological – Leadership: These comments reflect an athlete’s ability to lead or set an example on the playing field. An example is: “Player A’s failure to attend team meetings set a poor example for the rookies.” The comment may be: positive (1) or negative (0).

4. Psychological – Focus/ Demeanor: These comments reflect an athlete’s ability to maintain her or his disposition and to manage stress. An example is: “Player A’s focus at the free throw line down the stretch secured the win.” Comments can also be related to non-playing in game behavior. An example is: “When Player A lost his composure and began fighting with the other team he really cost his team the win.” The comment may be: positive (1) or negative (0).

5. Psychological – Effort: These comments reflect an athlete’s effort and dedication to success in athletics. An example is: “Player A battled for that last extra yard to the goal line.” The comment may be: positive (1) or negative (0).

6. Psychological – Intelligence: These comments reflect a player’s cognitive or mental capacity. An example is: “Player A reads the secondary and identifies immediately that he’ll have man-to-man coverage on the outside.” The comment may be: positive (1) or negative (0).

7. On-the-field General: This category includes comments about an athlete or the athlete’s play and does not qualify for any of the above categories. Comments in this category tend to be broad, such as great, terrible, good, bad, helped or hurt. This category includes comments where the coder cannot discern if the positive comment is related to physical or psychological attributes. An example is: “Player A made a great play to win the game,” or “What a terrible shot.” In both cases the audience does not know if the commentator believes that the play (shot) was great (terrible) because of the athlete’s ability or because of his focus (or some other characteristic). The comment may be: positive (1) or negative (0).

8. Off-the-field General: This category includes comments about an athlete’s behavior or actions outside the field or arena. Comments qualifying for this category tend be entire segments or stories as opposed to adjectival descriptors or brief descriptions. Examples include a segment on a player’s volunteer work in the community, a story on an athlete’s arrest for driving while under the influence of alcohol, or media hysteria associated with an athlete’s emerging popularity. The comment may be: positive (1) or negative (0).

9. Neutral: This category includes neutral comments that possess no direction or value. It represents the default category and encompasses most coded comments. Examples include objective statements, like “Lebron passes to Wade” or “The Wizards traded McGee.” The comment may be: neutral (1) or non-neutral/ valenced (leave blank).

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Key Descriptors Below I provide words, phrases, and scenarios by comment type and value to assist coders with properly categorizing comments. Any negation (i.e., “player A has no power”) qualifies the comment as the opposite value. As Sportscenter is primarily dedicated to highlighting achievement it is no coincidence that most examples (and descriptions) tend to be positive. This list is not exhaustive and even if a commentator uses a word or phrase from below some discretion is necessary to ensure that the appropriate type and value is coded. Finally, focus on the literal words not the expression of the commentator. Many comments may seem positive or negative but lack clear value. (i.e., “Get player A the ball in the paint and he can score.” Is not this the case with all collegiate and professional basketball players?).

Physical Ability – Positive Accuracy, agility, athlete (such an athlete), athleticism, beast (in terms of playing), beat down, beautiful (pass/ play/ shot), blazing (speed), blows past (in terms of speed), body control, coordination (like hand-eye), crushes (homerun/ opponent), dexterity, drive (in terms of speed), dynamic (in terms of athleticism), electric, explosive, fast, finesse, flies (speed), fly (in terms of jump), grace (in terms of athleticism), hands (in terms of catching ability, not appearance), handling (basketball/ hockey possession ability), legs (in a speed sense), mobility, muscled, out-jump, physical, plays above the rim, power, powerful, pure shooter, quickness, quick release, rabbit (or any animal with speed), range (on a shot, throwing distance), reflexes, rock (with a hit), rough, rugged, skies (in terms of jumping), smashes, smooth, speed, strength/ strong, talented, touch (in terms of soft touch in basketball), tough (to tackle, guard), trucks (through), ups (in terms of jumping ability), what a hose (arm), wheels

Physical Ability – Negative Breaking-down, injury-plagued, lethargic, slow, sluggish, weak

Physical Appearance – Positive A real man, bear (any anthropomorphic description suggesting prodigiousness), beast, big, built, dominating (in terms of physical presence), looking good (in terms of physical appearance), man-child, shape (in terms of great physical shape), stud, tall

Physical Appearance – Negative Awkward (in terms of physical dimensions), fat, little, mouse (any anthropomorphic description suggesting diminutive), overweight, short, slight (in size), small, stringy, undersized

Psychological Leadership – Positive Brings the team back, builds (trust or confidence), carries (the team), character, command (of the team or play), controls (the game), demands (excellence or achievement of others), dictates, example (in terms of sets an example), guidance, guides, handle (in terms of the team or situation; not

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pressure/ focus related), influential, initiative, inspires (teammates not in an off-the-field inspires others sense), leader, locker room (in terms of sets examples and is a leader), manages the game/ team, mentor, orchestrate, rallies (to victory), rescued (the team), runs the team, sportsmanship (in terms of exhibits it), steps up (in a leadership sense), takes charge (of a team or play; not a charging call in basketball), takes over, unity (in terms of builds unity), unselfish

Psychological Leadership – Negative Attacks own team, example (in terms of sets a poor example for the team), lacks trust (of others), insubordinate

Psychological Focus/ Demeanor – Positive Aggressive, approach (in terms of disposition), calm, clutch, competitive, composure, concentration, confident, cool (as the other side of the pillow), courageous, dedicated, demeanor, determined, devotion (to game), fire (to achieve), focus, gritty, handles (in terms of the athletes handles himself or pressure well), heart, hungry, intensity, intimidates (opponents), laid back (under pressure), looked comfortable, loose (mentally), mellow (under pressure), mentality (in a positive sense), mindset (in a positive sense), on the same page, passion (for the game), patience, perseverance, poised, prepared (mentally), steadiness (in terms of demeanor), swag, unflappable, un-wavered, when it matters most (is successful),

Psychological Focus/ Demeanor – Negative Anxiety, blew up (mentally), choke, crazy (in terms of mentally), discombobulated, distracted, emotional (in a negative sense), fear, frazzled, frustrated, hesitate, rushed (mentally), skittish, temperamental, thrown (off game), unraveled, upset, wavered

Psychological Effort – Positive Battles, commands (authority), effort, energy (in an effort sense), fights, first in (last out), gamer, goes all-out, gutsy, hustle, injury (in terms of fights or battles back from), motor, persistent, plays hard, plays for 60 minutes (or the whole game), puts in time (at the gym, studying), runs with authority, selling out (in positive effort sense), stays with it, sticks with it, tough (mentally, usually in terms of dealing with injuries), wants (it), willed, works (for it; on game for improvement), work ethic; work horse

Psychological Effort – Negative Attended to the play (in terms of failed to), checked out, failed to play through the whistle, gave up, lackadaisical, lazy, quit

Psychological Intelligence – Positive Alert, anticipate (a play), aware, brilliant, calculated/ ing, crafty, creative (in playing style), decision (great), decision-making, decisive, eyes (in terms of

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uses eyes to confuse defenders—usually QBs in football), foresight, gets it, head’s up, keen, knows what to do, instincts, intelligent, out-witted, presence of mind, read (in terms of the defense or opposition), realizes, sees (what will happen or what the opposition is doing), sense, smart, student of the game, understands, veteran (play)

Psychological Intelligence – Negative Bone-headed, brainless, decision (bad), dumb, foolish, idiotic, loses track of time (in the game), out of position, pick-up (in terms of couldn’t pick-up the play or what the opposition was doing), rookie (play), stupid, understand (in terms of doesn’t)

On-the-field – Positive (Many forms of ‘sportspeak’ qualify here. Coders must use discretion in determining which are positive) Ace (in terms of pitcher), acrobatic, advantage (in the sense that the athlete grants her/ his team an advantage through play), all-star caliber, amazing, angry, answer (opponents don’t have an answer for), baller/ balling, big (not physically speaking), blanketed (in terms of defense), boost (in terms of the player adds a boost to the team), bounce back (in the sense of better performance), can fill it, challenge ( to play against), champion (but not literally, as an expression), chipping in (in terms of helping team win; should be neutral for golf),comes alive, comes through, coming on (in terms of a player is improving), complete (player), contributes (to a win), convincing (in play), counts (in terms of team counts on a player), closer, crazy play (in a positive sense), creates (plays/ baskets), credit, damage (in terms of doing damage to opponents in a positive sense), dangerous, dealing (as in pitching well), delivered (in a positive sense), difference-maker, does it all, dominates (in terms of play), durable, dynamic (duo/ trio/ etc), ease (in playing/ positive sense), effective, emergence, excellent, exciting, explodes (for points), facilitator (for the rest of the team), factor/ x-factor, fancy, fantastic, feeling it, fierce, filthy (in a positive sense), finds a way to win, force ( in a positive sense, as in a player is a force), franchise (but not in the sense of ‘franchise tag’ which is neutral), fun to watch, finisher, game-breaking, game-changer, going off, gonna be a top play, good, great, groove, had a game (in a positive sense), handles (the opposition), having his way, helps (in terms of helping a team win or succeed), heroics, historic, hot, hurts (the other team), I love [the player] (commentator saying that), important, impressive, keeps (team in game), key (to the team’s success), legacy, legend, lights it up, like (the player or his ability), looking good (in terms of general performance), impact (player), improved (in playing), in the zone, makes (the opposition pay/ teammates better), making plays, making things happen, marquee, mechanics (in a positive sense), memorable, MVP (if they player isn’t objectively an MVP), monster (slam), nasty (play), nice, notable, on the money, outperform, outplay, owns (the other team/ player), pays off (in terms of the player’s play), pedigree, phenomenal/ phenom, pick apart (opposition), playmaker, progress, prolific, quite (the player/ passer), real deal, rejuvenated, reliable, rhythm,

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robs (opposition of homerun), schooling, set the tone, show (in terms of put on a show), shut down (as in shut down pitcher), silly, sharp, skills, solid (in terms of play), something (as in that play was something), spark (in terms of provides on), special, stamina, star, stand/ stood out, stopper, success, superior, superstar, support, threat, total package, tough (play in a positive sense), transformation (of a player in a positive sense), versatility, vision (to find open teammates), watch out (for that player), weapons (describing players), what a play/ pass/ etc

On-the-field – Negative Awful, backbreaker (play), bad, black hole (for not passing), careless (play), cheap shot, coming apart, costly, has to make that play, dirty, don’t trust (the player), error, having difficulty, hurts (in the sense of the play hurt the team’s chances at success), inconsistent, liability, kryptonite, misplays (the ball/ play), nonfactor, nonthreat, not a good day, nothing (in terms of accomplished or did nothing), off-night (in terms of playing), old (in a negative sense), past his prime, pathetic, penalty (of a severe nature like personal foul, technical or intentional foul81, a yellow or red card, ejected in baseball, excessive fighting, or arguing with the referees), poor, posterized, problem (player), soft (played), struggled, subpar, terrible, tough (in terms of “tough going” with respect to playing and achievement), trouble (in terms of play), uncharacteristic (in a negative sense), way off (on shot)

Off-the-field – Positive Ability to overcome adversity, biggest star/ name in a sport, buzz (positive about the athlete), captures the spotlight (in a positive sense), celebrity, character, commodity, cornerstone of the franchise, demographics (in terms of a player being good for marketing or demographic purposes), face of the organization, fan favorite, fantasy sports (positive), foundation (the player’s or one she/ he contributes to), hype, mass excitement about a player, person (praise for the player as a human being), philanthropy, receives lots of press (positive), social media sensation, success in a business venture, (good) teammate, volunteer work, work in the community, wow factor

Off-the-field – Negative Criminal arrest, fantasy sports (negative), financial troubles, injuring self in anger after/ outside of a game, legal troubles, offensive comments (to media or a social networking site), performance enhancing substance use, suspension/ fine (for playing and non-playing reasons), tampering (a player illegally meeting with another team)

Neutral Any objective statement such as describing a player’s action or a listing of height and weight. Any discussion related to a player being injured except in

81 If the flagrant or technical foul leads to a suspension and the suspension is mentioned then only code the comment as off-the-field negative.

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the case where there is a segment discussing the medicine and science behind the injury. Other comments include active, accomplished, all-day, are you kidding me, argument (w/ coach or teammates), beat (in terms of “player A beat player B to the basket”, or “beat out a throw (in baseball)”), attack (the basket), better job (in terms of the player has to do a better job), blast/ bomb (as a hit in baseball), can play (as in the player can play), chemistry (between athletes), coming through, cut/ released (from team), doing his/ her thing, difficult (shot/ pass/ etc.), discussions on trades and other free agent transactions, dropped (the ball/ pass), dunks (basketball), easy (as in lay-up or pass)/ not easy, emphatic, enforcer (in hockey), error (in baseball in terms of a technical play), experience (in terms of playing for an extended amount of time), expressions (like “wow” that include no direction), fastball (in baseball), fatigued, feathers (golf), feed (in terms of pass), fighting (in hockey), fills up the stat sheet, finds (a player in terms of passes to another player), finish, fired up (in a neutral manner), focal point, getting it done/ going, going big (in terms of home run), good (as in the shot going in is “good”), got the job done/ it, flies (as in “flies a double” in baseball), fourth quarter (or any final period of a game . . . makes plays in), happy/ not happy, handle (in terms of the catcher couldn’t handle the wild pitch), hard (hit), hot hand, how about (name of a player), intended, issues (related to playing performance), jam (in terms of a player is in a jam), journeyman, leading (if in reference to objective statistics), laser (throw), long (hit in baseball or any discussion of distance of a hit), looking to make history, lost/ gained weight, mature, milestone, mobile, oh my goodness, potential, presence (in terms of a neutral sense that the athlete provides the team with some presence), puts a move on (another player), questionable, quiet, raking (as in hitting), ready to play, record-setting/ breaking, right place/ time, rip (in terms of hit in baseball), show-boating, science and medicine (in a technical sense) related to injuries, rob (an opponent of a play), slams (basketball), slugger (in baseball), smokes (a hit), spike, steadiness (in terms of performance), takes advantage/ off, throwing it down (in terms of a dunk), trash-talking, trick (shot), under- rated, unleashed/ leashed, upside, veteran (as a noun), vicious (hit), warning track power, wild (pitch), winner

More on Neutral comments Many comments might appear positive or negative but are actually neutral. Below is a list of common instances that qualify as neutral: 1. The program regularly closes with a Top Ten plays segment (and on Friday a Not Top Ten Plays). Qualifying for a Top Ten play does not make any commentary related to the play as positive. All Top Ten plays follow the same rules for comments and descriptions cited above. So highlight reels of dunks with expressions, like “ooohhhh”, “uhh-ohhhh”, or “showtime”, do not qualify as positive. There must be some subjective assessment of the dunk or the player. 2. Questions. For example, “is player A the greatest quarterback of all-time?” is neutral. The commentator must provide some personal direction (i.e., “player A is the greatest QB of all-time”) to qualify as positive.

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3. Related to the previous point on questions: Any comment exclaiming “did you see that?” or “watch this play” is neutral. By itself the language fails to indicate value despite the fact that when taking the commentators expression into account value might be implied. 4. Statements like, “not his best day” or “not a great shot” are neutral because they do not imply negativity. Rather they simply suggest some non-positive state. 5. In some cases there will be conflicting positive and negative language in the same comment. If the commentator uses one positive comment and one negative comment for the same category then qualify it as neutral. If there are more positive than negative comments then code the comment as positive (or vice versa).

Other Special Qualifications Related to the earlier discussion on comments and descriptions there are some commonly occurring events that should be acknowledged so as to limit confusion and maintain consistency. 1. The 6pm episode often includes a brief segment in which the hosts from Pardon the Interruption (PTI) are interviewed on a top story. Because PTI anchors are ESPN employees their comments are coded. 2. Do not code the language of ESPN personnel citing direct quotes. If a commentator is quoting an athlete from an interview or discussion then code that athlete being quoted (and any description the ESPN personnel provides about that athlete) but nothing else in the quote. Related to this point do not code replays of ESPN personnel interviewing athletes after the game. 3. Any negative commentary about an athlete associated with an injury (i.e., “player A’s injured knee really slowed her down”) is neutral. 4. There are many instances in which two athletes are compared. Unless the commentator is explicit about the lesser athletes play or ability, only code the positive comments for the preferred athlete (i.e., “player A is faster than player B” should be coded as positive physical ability for player A and neutral for player B). Related to this point, saying that an athlete is not “as strong” or “as smart” as another athlete is not coded as negative for the lesser athlete (but again if the commentator goes so far as to say that the lesser athlete is “weak” or “stupid” then the comment should be coded as negative). 5. The same language can be coded in different categories and requires the coder to use discretion in determining connotation. For example, an athlete can be physically tough (ability), mentally tough (effort), or be having a tough time (on-the-field negative). 6. Popular nicknames that include a description (e.g., “Big Ben” for Ben Roethlisberger) are not coded for the nickname description. (Similarly, “Tebowmania” and “Linsanity” are not coded as positive off-the-field.) 7. Saying that a subgroup of players has strengths/ weaknesses then naming players with that subgroup does not mean the comment should be coded positively or negatively. For example, a comment like “young players struggle in their first year in the NFL” in a discussion about a rookie QB is not negative unless something negative is said about that specific player. Of course, saying something like, “Newton and Dalton are young QBs, and young QBs struggle in the league” would be negative.

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8. There are instances when a corporation will do a cross promotional event with Sportscenter where there is a marketing slogan that is attributed to a player. Literal marketing slogans associated with cross promotional campaigns are neutral. (An example is Good Year Tires sponsors a “Superior Performance” segment where one player is attributed “superior performance”. The comment is not coded as positive on- the-field unless the commentator elaborates and offers additional praise outside the slogan.)

Comments/ Issues / Questions: This field allows the coder to record any uncertainties about the classification of a comment type or value. The researcher and coders will review questionable comments to establish consistency.

Appendix B: Sportscenter Air times from Tuesday, September 27 to Monday, October 10 2011 ESPN Sportscenter 'New' Airings from Tuesday, September 27 to Monday, October 10

September 27 September 28 September 29 September 30 October 1 October 2 October 3 October 4 October 5 October 6 October 7 October 8 October 9 October 10 Tuesday Wednesday Thursday Friday Saturday Sunday Monday Tuesday Wednesday Thursday Friday Saturday Sunday Monday 12am 12am 1am 1am 1am 1am 12:30am 12am 12am 1am 12am 1am 1am 9am 2am 1am 2am 2am 2am 2am 9am 2am 1am 2am 1am 2am 2am 10am 9am 2am 9am 9am 11pm 8am 10am 9am 2am 9am 2am 9am 8am 11am 10am 9am 10am 10am 9am 11am 10am 9am 10am 9am 10:45am 5:30pm 12pm 11am 10am 11am 11am 6pm 12pm 11am 10am 11am 10am 6pm 1pm 12pm 11am 12pm 12pm 7pm 1pm 12pm 11am 12pm 11am 11pm 2pm 1pm 12pm 1pm 1pm 11pm 2pm 1pm 12pm 1pm 12pm 6pm 6pm 1pm 2pm 2pm 6pm 6pm 1pm 2pm 1pm 11:30pm 2pm 6pm 6pm 11:30pm 11:30pm 2pm 6pm 2pm 6pm 11pm 11pm 6pm 6pm 11pm

Appendix C: Randomly Sampled Sportscenter Dates & Times

December 2011 Sun Mon Tue Wed Thu Fri Sat

1 2 3 9am 2am 1am

4 5 6 7 8 9 10 11pm 12pm 1pm 2pm 10am 1pm 1am

11 12 13 14 15 16 17 2am 1pm 9am 12pm 1pm 1am 1am

18 19 20 21 22 23 24 2am 12pm 9am 11am 2pm 6pm 2am

25 26 27 28 29 30 31 1am 6pm 1pm 12am 1am 12pm 1am

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February 2012 Sun Mon Tue Wed Thu Fri Sat

1 2 3 4 6pm 11am 2am 2am

5 6 7 8 9 10 11 11pm 10am 9am 12am 10am 11am 1am

12 13 14 15 16 17 18 11pm 10am 6pm 9am 12pm 12pm 1am

19 20 21 22 23 24 25 11pm 1pm 9am 11am 1pm 1am 1am

26 27 28 29 1am 2pm 12pm 9am

May 2012 Sun Mon Tue Wed Thu Fri Sat

1 2 3 4 5 2am 6pm 2pm 6pm 2am

6 7 8 9 10 11 12 2am 12pm 11am 12am 10am 11am 1am

13 14 15 16 17 18 19 2am 11am 10am 9am 9am 9am 2am

20 21 22 23 24 25 26 6pm 2pm 12am 11am 12pm 2am 1am

27 28 29 30 31 8am 1pm 2am 10am 1pm

Appendix D: Additional Coding Information

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Intercoder reliability is the “amount of agreement or correspondence among two or more coders (p. 141, Neuendorf, 2002).” Neuendorf (2002) explains that using multiple coders for content analysis provides a basic validation of a coding scheme and ensures that the idiosyncrasies of one coder do not result in subjective coding (p. 142). In a review of 200 studies using content analysis between 1994 and 1998, Lombard, Snyder-

Duch, and Bracken (2002) demonstrate that having two or more coders reflects standard practices (p. 598). In this study, I selected a second coder based on knowledge of sports and familiarity with media research. The coder was randomly assigned 13 episodes and paid for his services. I required that the coder attend a three-hour training that I conducted where at the end he had to code one previously coded episode from outside the sample. In line with accepted requirements of intercoder reliability, he had to score a Cohen’s kappa that exceeded 0.80 in comparison to my coding on the training episode (Lombard,

Snyder-Duch, & Bracken, 2002).

Although there is not a standard percentage of content that other coders should code to appropriately asses intercoder reliability (Kolbe & Burnett, 1991), Lombard,

Snyder-Duch, and Bracken (2002) recommend at least 50 units or 10% of the full sample

(p. 601). Wimmer and Dominick (1997) and Potter and Levine-Donnerstein (1999) also recommend at least 10%. (In her content analysis of rap music, Kubrin (2005) had a colleague code 10% of the sample.) In this study, the researcher coded every episode and the coder coded 13 episodes or 15% of the sample. (The 13 randomly selected episodes accounted for 1,412 comments or 12.5% of the 11,311 total comments.)

With respect to episodes per day, on Mondays there were 7 episodes (9am, 10am,

11am, 12pm, 1pm, 2pm, & 6pm), on Tuesdays 8 episodes (12am, 2am, 9am, 10am, 11am,

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12pm, 1pm, & 6pm),on Wednesdays 10 episodes (12am, 1am, 2am, 9am, 10am, 11am,

12pm, 1pm, 2pm, & 6pm), on Thursdays 9 episodes (1am, 2am, 9am, 10am, 11am, 12pm,

1pm, 2pm, & 6pm),on Fridays 9 episodes (1am, 2am, 9am, 10am, 11am, 12pm, 1pm,

2pm, & 6pm),on Saturdays 2 episodes (1am, & 2am), and on Sundays 5 episodes (1am,

2am, 8am, 6pm, & 11pm).

Finally, data consist of episodes of ESPN’s Sportscenter program, which were recorded on the School of Media and Public Affairs’ Snapstream system, which records video and closed captioning. In addition, I used a digital video recorder to back-up

Snapstream. Episodes were recorded in hour-long segments with a five minute buffer on each side to account for episodes that started early or finished late. Using two devices protected against recording issues, but unforeseen programming changes for sporting events resulted in some episodes going unaired or only partially aired. Unrecorded episodes occurred on December 30, May 28, and May 31. On December 30 ESPN ran the

Bell Helicopter Bowl, on May 28 it ran the NCAA Men’s Lacrosse championship, and on

May 31 it ran the NCAA Women’s Softball Championship. There were also two episodes

(December 30 and February 12) that were only partially aired and recorded because prior programming ran into the scheduled air time.

Appendix E: Description of Variable Name, Label and Value Variable Label & Value Dependent Variables appear Physical Appearance (Yes=1, No=0) appearvalue Physical Appearance Value (Positive=1, Negative=0) abil Physical Ability (Yes=1, No=0) abilvalue Physical Ability Value (Positive=1, Negative=0) lead Psychological Leadership (Yes=1, No=0) leadvalue Psychological Leadership Value (Positive=1, Negative=0) focus Psychological Focu (Yes=1, No=0) focusvalue Psychological Focus Value (Positive=1, Negative=0)

160 effort Psychological Effort (Yes=1, No=0) effortvalue Psychological Effort Value (Positive=1, Negative=0) Intel Psychological Intelligence (Yes=1, No=0) intelvalue Psychological Intelligence Value (Positive=1, Negative=0) on On-the-Field General (Yes=1, No=0) onvalue On-the-Field General Value (Positive=1, Negative=0) off Off-the-Field General (Yes=1, No=0) offvalue Off-the-Field General Value (Positive=1, Negative=0) neutral Neutral (Yes=1, No=0) Independent Variables commrace Commentator's Race (1=Black, 2=White, 3=Latino/a, 4=Asian, 5=Other) commsex Commentator's Sex (1=Male, 0=Female) commrole Commentator's Role (1=Anchor, 0=Analyst) athleterace Athlete's Race (1=Black, 2=White, 3=Latino/a, 4=Asian, 5=Other) athletesex Athlete's Sex (1=Male, 0=Female) athletesport Athlete's Sport (1=Football, 2=Soccer, 3=Basketball, 4=Baseball, 5=Hockey, 6=Golf, 7=Tennis) levelofcomp Level of Competition (1=Professional, 0=Collegiate) raceXsport Athlete Race X Athlete Sport Categorical Variable raceXcomp Athlete Race X Level of Competition Categorical Variable AraceXCrace Athlete Race X Commentator Race Categorical Variable Generated Variables positive Positive Comment (Yes=1, No=0) negative Negative Comment (Yes=1, No=0) physical Physical Comment (Yes=1, No=0) psychological Psychological Comment (Yes=1, No=0) general General Comment (Yes=1, No=0) Positive/Negative Comments (Positive=1, Negative=0, Positive & Negative = posneg 2) Positive/Negative Comments (Positive=1, Negative=0, Positive & Negative = posneg2 2, Neutral=3) Positive/NegativePlus Comments (Positive=1, Negative=0, Positive & posnegplus Negative = 2) physpsych Physical/Psychological Comments (Physical=1, Psychological=0, Both=2) Physical/Psychological Comments (Physical=1, Psychological=0, Both=2, physpsych2 Neither=3) physpsychplus Physical/PsychologicalPlus Comments (Physical=1, Psychological=0, Both=2) physicallog Physical Comment (Yes=1, No=0) psychologicalog Psychological Comment (Yes=1, No=0) positivelog Positive Comment (Yes=1, No=0) negativelog Negative Comment (Yes=1, No=0) athblack Athlete Black (Yes=1, No=0) athwhite Athlete White (Yes=1, No=0) athlatino Athlete Latino/a (Yes=1, No=0) athasian Athlete Asian (Yes=1, No=0) football Football (Yes=1, No=0)

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soccer Soccer (Yes=1, No=0) basketball Basketball (Yes=1, No=0) baseball Baseball (Yes=1, No=0) hockey Hockey (Yes=1, No=0) golf Golf (Yes=1, No=0) tennis Tennis (Yes=1, No=0) commblack Commentator Black (Yes=1, No=0) commwhite Commentator White (Yes=1, No=0) commlatino Commentator Latino/a (Yes=1, No=0) commasian Commentator Asian (Yes=1, No=0) posphysical Positive Physical Comment (Yes=1, No=0) negphysical Negative Physical Comment (Yes=1, No=0) pospsychological Positive Psychological Comment (Yes=1, No=0) negpsychological Negative Psychological Comment (Yes=1, No=0) offvalueplus Off-the-Field Comment (Positive=1, Negative=0)

Descriptive Information comment Comment Number coder Coder episodeid Episode ID time Time of Comment commname Commentator’s Name commcode Commentator’s Code athletename Athlete’s Name athletecode Athlete’s Code

Appendix F: Contingency Tables & Logistic Regressions from Chapter 4 Table A1: Comments on Athlete by Gender and Race Black White Latino/a Asian Total Female 17 (60.71%) 11 (39.29%) 0 (0.00%) 0 (0.00%) 28 (100.00%) (0.29%) (0.25%) (0.00%) (0.00%) (0.25%)

Male 5,829 (51.66%) 4,346 814 (7.21%) 294 (2.61%) 11,283 (100.00%) (99.71%) (38.52%) (100.00%) (100.00%) (99.75%) (99.75%) Total 5,851 (51.68%) 4,361 815 (7.20%) 298 (2.60%) 11,311 (100.00%) (100.00%) (38.52%) (100.00%) (100.00%) (100.00%) (100.00%)

Table A2: Comments on Athlete by Race and Sport Football Soccer Basketball Baseball Hockey Golf Tennis Total 1,447 (24.75%) 16 (0.27%) 4,001 (68.44%) 247 (4.23%) 11 (0.19%) 121 (2.07%) 3 (0.05%) 5,846 (100.00%) (42.91%) (51.68%) Black (42.71%) (15.69%) (83.93%) (12.19%) (1.54%) (10.00%)

1,844 (42.32%) 56 (1.29%) 470 (10.79%) 1,108 (25.43%) 705 (16.18%) 147 (3.37%) 27 (0.62%) 4,357 (100.00%) (52.13%) (38.52%) White (54.43%) (54.90%) (9.86%) (54.69%) (98.46%) (90.00%)

85 (10.44%) 28 (3.44%) 70 (8.60%) 630 (77.40%) 0 (0.00%) 1 (0.12%) 0 (0.00%) 814 (100.00%) (0.35%) (7.20%) Latino/a (2.51%) (27.45%) (1.47%) (31.10%) (0.00%) (0.00%)

12 (4.08%) 2 (0.68%) 226 (76.87%) 41 (13.95%) 0 (0.00%) 13 (4.42%) 0 (0.00%) 294 (100.00%) (4.61%) (2.60%) Asian (0.35%) (1.96%) (4.74%) (2.02%) (0.00%) (0.00%)

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3,388 (29.95%) 102 (0.90%) 4,767 (42.14%) 2,026 (17.91%) 716 (6.33%) 282 30 (0.27%) 11,311 Total (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (2.49%) (100.00%) (100.00%) (100.00%) (100.00%)

Table A3: Comments on Athlete by Level of Competition and Race Black White Latino/a Asian Total Collegiate 948 (69.00%) 405 (29.48%) 18 (1.31%) 3 (0.22%) 1,374 (16.22%) (9.30%) (2.21%) (1.02%) (100.00%) (12.15%) Professional 4,898 3,952 796 (8.01%) 291 (2.93%) 9,937 (49.29%) (39.77%) (97.79%) (98.98%) (100.00%) (83.78%) (90.70%) (87.85%)

Table A4: Comments on Athlete by Level of Competition and Sport Football Soccer Basketball Baseball Hockey Golf Tennis Total 534 (38.86%) 7 (0.51%) 800 (58.22%) 28 (2.04%) 1 (0.07%) 2 (0.15%) 2 (0.15%) 1,374 Collegiate (15.76%) (6.86%) (16.78%) (1.38%) (0.14%) (0.71%) (6.67%) (100.00%) (12.15%) 2,854 (28.72%) 95 (0.96%) 3,967 (39.92%) 1,998 (20.11%) 715 (7.20%) 280 (2.82%) 28 (0.28%) 9,937 Professional (84.24%) (93.14%) (83.22%) (98.62%) (99.86%) (99.29%) (93.33%) (100.00%) (87.85%) 3,388 (29.95%) 102 (0.90%) 4,767 (42.14%) 2,026 (17.91%) 716 (6.33%) 282 (2.49%) 30 (0.27%) 11,311 Total (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%)

Table A5: Comments on Athlete by Level of Competition and Gender Female Male Total Collegiate 17 (1.24%) 1,357 (98.76%) 1,374 (100.00%) (60.71%) (12.03%) (12.15%)

Professional 11 (0.11%) 9,926 (99.89%) 9,937 (100.00%) (39.29%) (87.97%) (87.85%)

Total 28 (0.25%) 11,283 (99.75%) 11,311 (100.00%) (100.00%) (100.00%) (100.00%)

Table A6: Comments on Athlete by Level of Competition and Gender Football Soccer Basketball Baseball Hockey Golf Tennis Total 0.00 (0.00%) 2 (7.14%) 16 (57.14%) 0.00 (0.00%) 0.00 (0.00%) 2 (7.14%) 8 (28.57%) 28 Female (0.00%) (1.96%) (0.34%) (0.00%) (0.00%) (0.71%) (26.67%) (100.00%) (0.25%) 3,388 (30.03%) 100 (0.89%) 4,751 (42.11%) 2,026 (17.96 716 (6.35%) 280 (2.48%) 22 (0.19%) 11,283 Male (100.00%) (98.04%) (99.66%) %) (100.00%) (99.29%) (73.33%) (100.00%) (100.00%) (95.75%) 3,388 (29.95%) 102 (0.90%) 4,767 (42.14%) 2,026 (17.91%) 716 (6.33%) 282 (2.49%) 30 (0.27%) 11,311 Total (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%)

Table A7: Comments by Commentator Race and Sex Female Male Total Black 245 (8.68%) 2,577 (91.32%) 2,822 (100.00%) (13.04%) (27.32%) (24.95%)

White 1,602 (21.86%) 5,726 (78.14%) 7,328 (100.00%) (85.26%) (60.71%) (64.79%)

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Latino/a 32 (10.13%) 284 (89.87%) 316 (100.00%) (1.70%) (3.01%) (2.79%)

Asian 0 (0.00%) 845 (100.00%) 845 (100.00%) (0.00%) (8.96%) (7.47%)

Total 1,879 (16.61%) 9,432 (83.39%) 11,311 (100.00%) (100.00%) (100.00%) (100.00%)

Table A8: Comments by Commentator Role and Sex Female Male Total Analyst 178 (5.35%) 3,150 (94.65%) 3,328 (100.00%) (9.47%) (33.40%) (29.42%)

Anchor 1,701 (21.31%) 6,282 (78.69%) 7,983 (100.00%) (90.53%) (66.60%) (70.58%)

Total 1,879 (16.61%) 9,432 (83.39%) 11,311 (100.00%) (100.00%) (100.00%) (100.00%)

Table A9: Comments by Commentator Race and Role Analyst Anchor Total Black 1,001 (35.47%) 1,821 (64.53%) 2,822 (100.00%) (30.08%) (22.81%) (24.95%)

White 2,280 (31.11%) 5,048 (68.89%) 7,328 (100.00%) (68.51%) (63.23%) (64.79%)

Latino/a 47 (14.87%) 269 (85.13%) 316 (100.00%) (1.41%) (3.37%) (2.79%)

Asian 0 (0.00%) 845 (100.00%) 845 (100.00%) (0.00%) (10.58%) (7.47%)

Total 3,328 (100.00%) 7,983 (100.00%) 11,311 (100.00%) (29.42%) (70.58%) (100.00%)

Table A10: Comments by Comment Type and Value Positive Negative Neutral Total Physical: 145 (87.88%) 20 (12.22%) 0 (0.0%) 165 (100.00%) Appearance (4.60%) (2.82%) (0.0%) (1.38%)

Physical: 304 (93.54%) 21 (6.46%) 0 (0.0%) 325 (100.00%) Ability (9.65%) (2.96%) (0.0%) (2.71%)

Psychological: 172 (97.18%) 5 (2.82%) 0 (0.0%) 177 (100.00%) Leadership (5.46%) (0.70%) (0.0%) (1.48%)

Psychological: 161 (73.18%) 59 (26.82%) 0 (0.0%) 220 (100.00%) Focus (5.11%) (8.31%) (0.0%) (1.83%)

164

Psychological: 102 (91.07%) 10 (8.93%) 0 (0.0%) 112 (100.00%) Effort (3.24%) (1.41%) (0.0%) (0.93%)

Psychological: 77 (79.38%) 20 (20.62%) 0 (0.0%) 97 (100.00%) Intelligence (2.45%) (2.82%) (0.0%) (0.81%)

General: 1,956 (86.09%) 316 (13.91%) 0 (0.0%) 2,272 (100.00%) On-the-Field (62.11%) (44.51%) (0.0%) (18.94%)

General: 232 (47.25%) 259 (52.75%) 0 (0.0%) 491 (100.00%) Off-the-Field (7.37%) (36.48%) (0.0%) (4.09%)

Neutral 0 (0.0%) 0 (0.0%) 8,137 8,151 (100.00%) (0.0%) (0.0%) (100.00%) (67.83%) (100.00%) Total 3,149 (26.22%) 710 (5.91%) 8,151 (67.87%) 11,99682 (100.00%) (100.00%) (100.00%) (100.00%) (100.00%)

Table A11: Physical Comment by Athlete Race and Comment Type and Value Appearance Ability Positive Negative Positive Negative Total Black 104 (34.32%) 14 (4.62%) 177 (58.42%) 8 (2.64%) 303 (100.00%) (71.27%) (70.00%) (58.22%) (38.10%) (61.84%)

White 30 (20.69%) 6 (4.14%) 97 (66.90%) 12 (8.28%) 145 (100.00%) (20.69%) (30.00%) (31.91%) (57.14%) (29.59%)

Latino/a 7 (25.93%) 0 (0.00%) 19 (70.37%) 1 (3.70%) 27 (100.00%) (4.83%) (0.00%) (6.25%) (4.76%) (5.51%)

Asian 4 (26.67%) 0 (0.00%) 11 (73.33%) 0 (0.00%) 15 (100.00%) (2.76%) (0.00%) (3.62%) (0.00%) (3.06%)

Total 145 (29.59%) 20 (4.08%) 304 (62.04%) 21 (4.29%) 490 (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%)

Table A12: Psychological Comment by Athlete Race and Comment Type and Value Leadership Focus Effort Intelligence Positive Negative Positive Negative Positive Negative Positive Negative Total 81 (26.21%) 3 (0.97%) 82 (26.54%) 34 (11.00%) 61 (19.74%) 5 (1.62%) 31 (10.03%) 12 (3.88%) 309 Black (100.00%) (47.09%) (60.00%) (50.93%) (57.63%) (59.80%) (50.00%) (40.26%) (60.00%) (50.99%)

71 (30.60%) 1 (0.43%) 62 (26.72%) 22 (9.48%) 33 (14.22%) 3 (1.29%) 33 (14.22%) 7 (3.02%) 232 White (100.00%) (41.28%) (20.00%) (38.51%) (37.29%) (32.35%) (30.00%) (42.86%) (35.00%) (38.28%)

5 (19.23%) 1 (3.85%) 3 (11.54%) 3 (11.54%) 7 (26.92%) 2 (7.69%) 4 (15.38%) 1 (3.85%) 26 Latino/ (100.00%) (2.91%) (20.00%) (1.86%) (5.08%) (6.86%) (20.00%) (5.19%) (5.00%) (4.29%) a 15 (38.46%) 0 (0.00%) 14 (35.90%) 0 (0.00%) 1 (2.56%) 0 (0.00%) 9 (23.08%) 0 (0.00%) 39 Asian (100.00%) (8.72%) (0.00%) (8.70%) (0.00%) (0.98%) (0.00%) (11.69%) (0.00%) (6.44%)

172 (28.38%) 5 (0.83%) 161 (26.57%) 59 (9.74%) 102 (16.83%) 10 (1.65%) 77 (12.71%) 20 (3.30%) 606 Total (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%)

Table A13: General Comment by Athlete Race and Comment Type and Value On-the-Field Off-the-Field

82 This number is higher than the total for comments in other tables of 11,311 because the 11,996 total includes 685 comments that were positive and negative and/ or that were more than one type of comment.

165

Positive Negative Positive Negative Neutral Total Black 1,071 (19.14%) 167 (2.98%) 75 (1.34%) 174 (3.11%) 4,108 5,595 (54.75%) (52.85%) (32.33%) (67.18%) (73.42%) (100.00%) (50.49%) (51.33%)

White 711 (16.84%) 121 (2.87%) 89 (2.11%) 81 (1.92%) 3,219 4,221 (36.35%) (38.29%) (38.36%) (31.27%) (76.26%) (100.00%) (39.56%) (38.72%)

Latino/a 93 (11.92%) 16 (2.05%) 10 (1.28%) 4 (0.51%) 657 (84.23%) 780 (4.75%) (5.06%) (4.31%) (1.54%) (8.07%) (100.00%) (7.16%)

Asian 81 (26.64%) 12 (3.95%) 58 (19.08%) 0 (0.00%) 153 (50.33%) 304 (4.14%) (3.80%) (25.00%) (0.00%) (1.88%) (100.00%) (2.79%)

Total 1,956 (17.94%) 316 (2.90%) 232 (2.13%) 259 (2.38%) 8,137 10,900 (100.00%) (100.00%) (100.00%) (100.00%) (74.65%) (100.00%) (100.00%) (100.00%)

Table A14: Physical Comment by Sport and Comment Type and Value Appearance Ability Positive Negative Positive Negative Total Football 39 (20.74%) 12 (6.38%) 127 (67.55%) 10 (5.32%) 188(100.00%) (26.90%) (60.00%) (41.78%) (47.62%) (38.37%)

Soccer 0 (0.00%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 0 (0.00%) (0.00%) (0.00%) (0.00%) (0.00%) (0.00%)

Basketball 88 (40.55%) 5 (2.30%) 117 (53.92%) 7 (3.23%) 217 (100.00%) (60.69%) (25.00%) (38.49%) (33.33%) (44.29%)

Baseball 12 (21.82%) 2 (3.64%) 37 (67.27%) 4 (7.27%) 55 (100.00%) (8.28%) (10.00%) (12.17%) (19.05%) (11.22%)

Hockey 6 (23.08%) 0 (0.00%) 20 (76.92%) 0 (0.00%) 26 (100.00%) (4.14%) (0.00%) (6.58%) (0.00%) (5.31%)

Golf 0 (0.00%) 1 (25.00%) 3 (75.00%) 0 (0.00%) 4 (100.00%) (0.00%) (5.00%) (0.99%) (0.00%) (0.82%)

Tennis 0 (0.00%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 0 (0.00%) (0.00%) (0.00%) (0.00%) (0.00%) (0.00%)

Total 145(29.59%) 20 (4.08%) 304 (62.04%) 21 (4.29%) 490 (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%)

Table A15: Psychological Comment by Sport and Comment Type and Value Leadership Focus Effort Intelligence Positive Negative Positive Negative Positive Negative Positive Negative Total

66 (27.97%) 1 (0.42%) 62 (26.27%) 16 (6.78%) 35 (14.83%) 4 (1.69%) 44 (18.64%) 8 (3.39%) 236 Football (100.00%) (38.37%) (20.00%) (38.51%) (27.12%) (34.31%) (40.00%) (57.14%) (40.00%) (38.94%)

0 (0.00%) 0 (0.00%) 0 (0.00%) 1 (33.33%) 2 (66.67%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 3 (100.00%) Soccer (0.50%) (0.00%) (0.00%) (0.00%) (1.69%) (1.96%) (0.00%) (0.00%) (0.00%)

90 (31.80%) 4 (1.41%) 74 (26.15%) 28 (9.89%) 49 (17.31%) 3 (1.06%) 29 (10.25%) 6 (2.12%) 283 Basketball (100.00%) (52.33%) (80.00%) (45.96%) (47.46%) (48.04%) (30.00%) (37.66%) (30.00%) (46.70%)

166

8 (20.51%) 0 (0.00%) 8 (20.51%) 6 (15.38%) 9 (23.08%) 2 (5.13%) 3 (7.69%) 3 (7.69%) 39 Baseball (100.00%) (4.65%) (0.00%) (4.97%) (10.17%) (8.82%) (20.00%) (3.90%) (15.00%) (6.44%)

7 (25.00%) 0 (0.00%) 9 (32.14%) 2 (7.14%) 6 (21.43%) 1 (3.57%) 1 (3.57%) 2 (7.14%) 28 Hockey (100.00%) (4.07%) (0.00%) (5.59%) (3.39%) (5.88%) (10.00%) (1.30%) (10.00%) (4.62%)

1 (6.67%) 0 (0.00%) 8 (53.33%) 5 (33.33%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 1 (6.67%) 15 Golf (100.00%) (0.58%) (0.00%) (4.97%) (8.47%) (0.00%) (0.00%) (0.00%) (5.00%) (2.48%)

0 (0.00%) 0 (0.00%) 0 (0.00%) 1 (50.00%) 1 (50.00%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 2 (100.00%) Tennis (0.33%) (0.00%) (0.00%) (0.00%) (1.69%) (0.98%) (0.00%) (0.00%) (0.00%)

Table A16: General Comment by Sport and Comment Type and Value On-the-Field Off-the-Field Positive Negative Positive Negative Neutral Total Football 569 (17.60%) 63 (1.95%) 80 (2.47%) 121 (3.74%) 2,399 3,232 (100.00%) (29.09%) (19.94%) (34.48%) (46.72%) (74.20%) (29.65%) (29.48%) Soccer 36 (36.36%) 2 (2.02%) 0 (0.00%) 3 (3.03%) 58 (58.59%) 99 (100.00%) (1.84%) (0.63%) (0.00%) (1.16%) (0.71%) (0.91%)

Basketball 868 (18.97%) 150 (3.28%) 115 (2.51%) 77 (1.68%) 3,365 4,575 (100.00%) (44.38%) (47.47%) (49.57%) (29.73%) (73.55%) (41.97%) (41.35%) Baseball 281 (14.13%) 48 (2.41%) 20 (1.01%) 52 (2.61%) 1,588 1,989 (100.00%) (14.37%) (15.19%) (8.62%) (20.08%) (79.84%) (18.25%) (19.52%) Hockey 156 (22.45%) 31 (4.46%) 7 (1.01%) 4 (0.58%) 497 (71.51%) 695 (100.00%) (7.98%) (9.81%) (3.02%) (1.54%) (6.11%) (6.38%)

Golf 38 (13.57%) 21 (7.50%) 10 (3.57%) 2 (0.71%) 209 (74.64%) 280 (100.00%) (1.94%) (6.65%) (4.31%) (0.77%) (2.57%) (2.57%)

Tennis 8 (26.67%) 1 (3.33%) 0 (0.00%) 0 (0.00%) 21 (70.00%) 30 (100.00%) (0.41%) (0.32%) (0.00%) (0.00%) (0.26%) (0.28%)

Total 1,956 (17.94%) 316 (2.90%) 232 (2.13%) 259 (2.38%) 8,137 10,900 (100.00%) (100.00%) (100.00%) (100.00%) (74.65%) (100.00%) (100.00%) (100.00%)

Table A17: Physical Comment by Level of Competition and Comment Type and Value Appearance Ability Positive Negative Positive Negative Total Collegiate 34 (28.33%) 7 (5.83%) 76 (63.33%) 3 (2.50%) 120 (100.00%) (23.45%) (35.00%) (25.00%) (14.29%) (24.49%)

Professional 111 (30.00%) 13 (3.51%) 228 (61.62%) 18 370 (100.00%) (76.55%) (65.00%) (75.00%) (4.86%) (75.51%) (85.71%) Total 145 (29.59%) 20 (4.08%) 304 (62.04%) 21 490 (100.00%) (100.00%) (100.00%) (100.00%) (4.29%) (100.00%) (100.00%)

Table A18: Psychological Comment by Level of Competition and Comment Type and Value Leadership Focus Effort Intelligence Positive Negative Positive Negative Positive Negative Positive Negative Total

Collegiate 21 (24.71%) 2 (2.35%) 20 (23.53%) 2 (2.35%) 20 (23.53%) 0 (0.00%) 17 (20.00%) 3 (3.53%) 85 (12.21%) (40.00%) (12.42%) (3.39%) (19.61%) (0.00%) (22.08%) (15.00%) (100.00%)

167

(14.03%)

Professional 151 (28.98%) 3 (0.58%) 141 (27.06%) 57 (10.94%) 82 (15.74%) 10 (1.92%) 60 (11.52%) 17 (3.26%) 521 (87.79%) (60.00%) (87.58%) (96.61%) (80.39%) (100.00%) (77.92%) (85.00%) (100.00%) (85.97%) Total 172 (28.38%) 5 (0.83%) 161 (26.57%) 59 (9.74%) 102 (16.83%) 10 (1.65%) 77 (12.71%) 20 (3.30%) 606 (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%)

Table A19: General Comment by Level of Competition and Comment Type and Value On-the-Field Off-the-Field Positive Negative Positive Negative Neutral Total Collegiate 283 (21.72%) 31 (2.38%) 14 (1.07%) 19 (1.46%) 956 (73.37%) 1,303 (14.47%) (9.81%) (6.03%) (7.34%) (11.75%) (100.00%) (11.95%) Professional 1,673 (17.43%) 285 (2.97%) 218 (2.27%) 240 (2.50%) 7,181 (74.83%) 9,597 (85.53%) (90.19%) (93.97%) (92.66%) (88.25%) (100.00%) (88.05%) Total 1,956 (17.94%) 316 (2.90%) 232 (2.13%) 259 (2.38%) 8,137 (74.65%) 10,900 (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%)

Table A20: Physical Comment by Commentator Race and Comment Type and Value Appearance Ability Positive Negative Positive Negative Total Black 29 (26.36%) 3 (2.73%) 71 (64.55%) 7 (6.36%) 110 (100.00%) (20.00%) (15.00%) (23.36%) (33.33%) (22.45%)

White 108 (31.21%) 15 (4.34%) 211 (60.98%) 12 (3.47%) 346 (100.00%) (74.48%) (75.00%) (69.41%) (57.14%) (70.61%)

Latino/a 2 (28.57%) 1 (14.29%) 3 (42.86%) 1 (14.29%) 7 (100.00%) (1.38%) (5.00%) (0.99%) (4.76%) (1.43%)

Asian 6 (22.22%) 1 (3.70%) 19 (70.37%) 1 (3.70%) 27 (100.00%) (4.14%) (5.00%) (6.25%) (4.76%) (5.51%)

Total 145 (29.59%) 20 (4.08%) 304 (62.04%) 21 (4.29%) 490 (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%)

Table A21: Psychological Comment by Commentator Race and Comment Type and Value Leadership Focus Effort Intelligence Positive Negative Positive Negative Positive Negative Positive Negative Total Black 53 (29.44%) 2 (1.11%) 45 (25.00%) 22 (12.22%) 30 (16.67%) 4 (2.22%) 19 (10.56%) 5 (2.78%) 180 (30.81%) (40.00%) (27.95%) (37.29%) (29.41%) (40.00%) (24.68%) (25.00%) (100.00%) (29.70%) White 107 (27.02%) 3 (0.76%) 109 (27.53%) 34 (8.59%) 64 (16.16%) 6 (1.52%) 58 (14.65%) 15 (3.79%) 396 (62.21%) (60.00%) (67.70%) (57.63%) (62.75%) (60.00%) (75.32%) (75.00%) (100.00%) (65.35%) Latino/a 4 (50.00%) 0 (0.00%) 2 (25.00%) 1 (12.50%) 1 (12.50%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 8 (100.00%) (2.33%) (0.00%) (1.24%) (1.69%) (0.98%) (0.00%) (0.00%) (0.00%) (1.32%)

Asian 8 (36.36%) 0 (0.00%) 5 (22.73%) 2 (9.09%) 7 (31.82%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 22 (100.00%) (4.65%) (0.00%) (3.11%) (3.39%) (6.86%) (0.00%) (0.00%) (0.00%) (3.63%)

Total 172 (28.38%) 5 (0.83%) 161 (26.57%) 59 (9.74%) 102 (16.83%) 10 (1.65%) 77 (12.71%) 20 (3.30%) 606 (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%)

168

Table A22: General Comment by Commentator Race and Comment Type and Value On-the-Field Off-the-Field Positive Negative Positive Negative Neutral Total Black 497 (18.48%) 72 (2.68%) 49 (1.82%) 48 (1.78%) 2,024 (75.24%) 2,690 (25.41%) (22.78%) (21.12%) (18.53%) (24.87%) (100.00%) (24.68%) White 1,287 (18.17%) 217 (3.06%) 155 (2.19%) 193 (2.72%) 5,231 (73.85%) 7,083 (65.80%) (68.67%) (66.81%) (74.52%) (64.29%) (100.00%) (64.98%) Latino/a 36 (11.73%) 7 (2.28%) 8 (2.61%) 8 (2.61%) 248 (80.78%) 307 (1.84%) (2.22%) (3.45%) (3.09%) (3.05%) (100.00%) (2.82%) Asian 136 (16.59%) 20 (2.44%) 20 (2.44%) 10 (1.22%) 634 (77.32%) 820 (6.95%) (6.33%) (8.62%) (3.86%) (7.79%) (100.00%) (7.52%) Total 1,956 (17.94%) 316 (2.90%) 232 (2.13%) 259 (2.38%) 8,137 (74.65%) 10,900 (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%)

Table A23: Physical Comment by Commentator Sex and Comment Type and Value Appearance Ability Positive Negative Positive Negative Total Female 4 (15.38%) 1 (3.85%) 20 (76.92%) 1 (3.85%) 26 (100.00%) (2.76%) (5.00%) (6.58%) (4.76%) (5.31%)

Male 141 (30.39%) 19 (4.09%) 284 (61.21%) 20 (4.31%) 464 (97.24%) (95.00%) (93.42%) (95.24%) (100.00%) (94.69%) Total 145 (29.59%) 20 (4.08%) 304 (62.04%) 21 (4.29%) 490 (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%)

Table A24: Psychological Comment by Commentator Sex and Comment Type and Value Leadership Focus Effort Intelligence

Positive Negative Positive Negative Positive Negative Positive Negative Total

Female 29 (35.80%) 0 (0.00%) 25 (30.86%) 8 (9.88%) 9 (11.11%) 3 (3.70%) 6 (7.41%) 1 (1.23%) 81 (16.86%) (0.00%) (15.53%) (13.56%) (8.82%) (30.00%) (7.79%) (5.00%) (100.00%) (13.37%) Male 143 (27.24%) 5 (0.95%) 136 (25.90%) 51 (9.71%) 93 (17.71%) 7 (1.33%) 71 19 (3.62%) 525 (83.14%) (100.00%) (84.47%) (86.44%) (91.18%) (70.00%) (13.25%) (95.00%) (100.00%) (92.21%) (86.63%) Total 172 (28.38%) 5 (0.83%) 161 (26.57%) 59 (9.74%) 102 (16.83%) 10 (1.65%) 77 20 (3.30%) 606 (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (12.71%) (100.00%) (100.00%) (100.00%) (100.00%)

Table A25: General Comment by Commentator Sex and Comment Type and Value On-the-Field Off-the-Field Positive Negative Positive Negative Neutral Total Female 255 (13.92%) 43 (2.35%) 44 (2.40%) 41 (2.24%) 1,449 (79.09%) 1,832 (13.04%) (13.61%) (18.97%) (15.83%) (17.81%) (100.00%) (16.81%) Male 1,701 (18.76%) 273 (3.01%) 188 (2.07%) 218 (2.40%) 6,688 (73.75%) 9,068 (86.96%) (86.39%) (81.03%) (84.17%) (82.19%) (100.00%) (83.19%) Total 1,956 (17.94%) 316 (2.90%) 232 (2.13%) 259 (2.38%) 8,137 (74.65%) 10,900 (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%)

169

(100.00%)

Table A26: Physical Comment by Commentator Role and Comment Type and Value Appearance Ability Positive Negative Positive Negative Total Analyst 80 (26.76%) 16 (5.35%) 187 (62.54%) 16 (5.35%) 299 (55.17%) (80.00%) (61.51%) (76.19%) (100.00%) (61.02%) Anchor 65 (34.03%) 4 (2.09%) 117 (61.26%) 5 (2.62%) 191 (44.83%) (20.00%) (38.49%) (23.81%) (100.00%) (38.98%) Total 145 (29.59%) 20 (4.08%) 304 (62.04%) 21 (4.29%) 490 (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%)

Table A27: Psychological Comment by Commentator Role and Comment Type and Value Leadership Focus Effort Intelligence

Positive Negative Positive Negative Positive Negative Positive Negative Total

Analyst 81 (24.18%) 4 (1.19%) 96 (28.66%) 37 (11.04%) 45 (13.43%) 3 (0.90%) 59 (17.61%) 10 (2.99%) 335 (47.09%) (80.00%) (59.63%) (62.71%) (44.12%) (30.00%) (76.62%) (50.00%) (100.00%) (55.28%) Anchor 91 (33.58%) 1 (0.37%) 65 (23.99%) 22 (8.12%) 57 (21.03%) 7 (2.58%) 18 (6.64%) 10 (3.69%) 271 (52.91%) (20.00%) (40.37%) (37.29%) (55.88%) (70.00%) (23.38%) (50.00%) (100.00%) (44.72%) Total 172 (28.38%) 5 (0.83%) 161 (26.57%) 59 (9.74%) 102 (16.83%) 10 (1.65%) 77 (12.71%) 20 (3.30%) 606 (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%)

Table A28: General Comment by Commentator Role and Comment Type and Value On-the-Field Off-the-Field Positive Negative Positive Negative Neutral Total Analyst 846 (26.83%) 118 (3.74%) 84 (2.66%) 83 (2.63%) 2,022 (64.13%) 3,153 (43.25%) (37.34%) (36.21%) (32.05%) (24.85%) (100.00%) (28.93%) Anchor 1,110 (14.33%) 198 (2.56%) 148 (1.91%) 176 (2.27%) 6,115 (78.93%) 7,747 (56.75%) (62.66%) (63.79%) (67.95%) (75.15%) (100.00%) (71.07%) Total 1,956 (17.94%) 316 (2.90%) 232 (2.13%) 259 (2.38%) 8,137 (74.65%) 10,900 (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%)

Table A29: Physical and Psychological Comments by Commentator Race Black White Latino/a Asian Total Psychological 152 (31.47%) 303 (62.73%) 8 (1.66%) 20 (4.14%) 483 (60.32%) (49.84%) (53.33%) (43.48%) (100.00%) (52.44%) Physical 89 (23.86%) 252 (67.56%) 7 (1.88%) 25 (6.70%) 373 (35.32%) (41.45%) (46.67%) (54.35%) (100.00%) (40.50%) Both 11 (16.92%) 53 (81.54%) 0 (0.00%) 1 (1.54%) 65 (4.37%) (8.72%) (0.00%) (2.17%) (100.00%)

170

(7.06%) Total 252 (27.36%) 608 (66.02%) 15 (1.63%) 46 (4.99%) 921 (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) X2=16.2, p=.01

Table A30: White Commentators’ Physical and Psychological Comments by Athlete Race Black White Latino/a Asian Total Psychological 162 (53.80%) 109 (36.63%) 12 (3.96%) 17 (5.61%) 303 (47.11%) (54.15%) (42.86%) (58.62%) (100.00%) (49.84%) Physical 161 (63.89%) 69 (27.38%) 15 (5.95%) 7 (2.78%) 252 (46.53%) (33.66%) (53.57%) (24.14%) (100.00%) (41.45%) Both 22 (41.51%) 25 (47.17%) 1 (1.89%) 5 (9.43%) 53 (6.36%) (12.20%) (3.57%) (17.24%) (100.00%) (8.72%) Total 345 (56.91%) 203 (33.72%) 28 (4.61%) 29 (4.77%) 608 (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) X2=18.57, p=.01

Table A31: Nonwhite Commentators’ Physical and Psychological Comments by Athlete Race Black White Latino/a Asian Total Psychological 92 (51.67%) 69 (38.33%) 10 (5.56%) 8 (4.44%) 180 (51.98%) (63.89%) (52.63%) (100.00%) (100.00%) (57.51%) Physical 80 (66.12%) 33 (27.27%) 8 (6.61%) 0 (0.00%) 121 (44.94%) (30.56%) (42.11%) (0.00%) (100.00%) (38.66%) Both 5 (41.67%) 6 (50.00%) 1 (8.33%) 0 (0.00%) 12 (2.81%) (5.56%) (5.26%) (0.00%) (100.00%) (3.83%) Total 177 (56.87%) 108 (34.50%) 19 (6.07%) 8 (2.56%) 312 (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) X2=12.75, p=.05

Table A32: Positive and Negative Comments by Commentator Race Black White Latino/a Asian Total Negative 125 (22.05%) 394 (69.66%) 16 (2.82%) 31 (5.47%) 566 (15.66%) (18.84%) (23.53%) (14.69%) (100.00%) (17.86%) Positive 639 (25.72%) 1,632 (65.15%) 50 (1.99%) 179 (7.14%) 2,500 (80.83%) (77.92%) (73.53%) (84.83%) (100.00%)

171

(79.02%) Both 28 (28.28%) 68 (68.69%) 2 (2.02%) 1 (1.01%) 99 (3.51%) (3.24%) (2.94%) (0.47%) (100.00%) (3.12%) Total 792 (25.14%) 2,094 (66.07%) 68 (2.14%) 211 (6.65%) 3,165 (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) X2=12.41, p=.10

Table A33: White Commentators’ Positive and Negative Comments by Athlete Race Black White Latino/a Asian Total Negative 233 (58.99%) 139 (35.44%) 16 (4.05%) 6 (1.52%) 394 (20.92%) (17.61%) (16.00%) (6.82%) (100.00%) (18.84%) Positive 841 (51.53%) 628 (38.49%) 82 (5.02%) 81 (4.96%) 1,632 (75.58%) (79.12%) (82.00%) (92.05%) (100.00%) (77.92%) Both 39 (57.35%) 26 (38.24%) 2 (2.94%) 1 (1.47%) 68 (3.50%) (3.27%) (2.00%) (1.14%) (100.00%) (3.24%) Total 1,113 (53.12%) 793 (37.91%) 100 (4.77%) 88 (4.20%) 2,094 (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) X2=15.8, p=.05

Table A34: Nonwhite Commentators’ Positive and Negative Comments by Athlete Race Black White Latino/a Asian Total Negative 98 (56.98%) 61 (35.47%) 8 (4.65%) 5 (2.91%) 172 (15.71%) (17.78%) (14.04%) (9.43%) (100.00%) (15.97%) Positive 501 (58.01%) 271 (31.01%) 48 (5.49%) 48 (5.49%) 868 (81.25%) (79.01%) (84.21%) (90.57%) (100.00%) (81.15%) Both 19 (61.29%) 11 (35.48%) 1 (3.23%) 0 (0.00%) 31 (3.04%) (3.21%) (1.75%) (0.00%) (100.00%) (2.88%) Total 618 (57.94%) 343 (31.85%) 57 (5.29%) 53 (4.92%) 1,071 (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) X2=5.06, p=.53

Table A35: Off-the-Field Comments by Athlete Race Black White Latino/a Asian Total Negative 174 (67.18%) 81 (31.27%) 4 (1.54%) 0 (0.00%) 259 (69.88%) (47.65%) (28.57%) (0.00%) (100.00%) (52.75%) Positive 75 (32.33%) 89 (38.36%) 10 (4.31%) 58 (25.00%) 232

172

(30.12%) (52.35%) (71.43%) (100.00%) (100.00%) (47.25%) Total 249 (50.71%) 170 (34.62%) 14 (2.85%) 58 (11.81%) 491 (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) X2=99.12, p=.00

Table A36: Comparison of Positive and Negative Attributions of Tim Tebow in December 2011 between White and Nonwhite Commentators Negative Positive Both Neutral Total Nonwhite 4 (8.89%) 12 2 (4.44%) 27 (60.00%) 45 (100.00%) (44.44%) (26.67%) (100.00%) (23.48%) (23.56%) (19.05%) White 5 (3.47%) 51 0 (0.00%) 88 (61.11%) 144 (55.56%) (35.42%) (0.00%) (76.52%) (100.00%) (80.95%) (76.44%) Total 9 (4.76%) 63 2 (0.01%) 115 (60.85%) 189 (100.00%) (33.33%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%)

Table A37: Comparison of Physical and Psychological Attributions of Tim Tebow in December 2011 between White and Nonwhite Commentators Psychological Physical Both Neither Total Nonwhite 7 (15.56%) 3 (6.67%) 0 (0.00%) 35 (77.78%) 45 (24.14%) (37.50%) (0.00%) (23.18%) (100.00%) (23.81%) White 22 (15.28%) 5 (3.47%) 1 (0.69%) 116 (80.56%) 144 (75.86%) (62.50%) (100.00%) (76.82%) (100.00%) (76.19%) Total 29 (15.34%) 8 (4.23%) 1 (0.01%) 151 (79.89%) 189 (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%)

Table A38: Comparison of Positive and Negative Attributions of Jeremy Lin in February 2012 between White and Nonwhite Commentators Negative Positive Both Neutral Total Nonwhite 5 (6.58%) 38 (50.00%) 0 (0.00%) 33 (43.42%) 76 (100.00%) (50.00%) (34.86%) (0.00%) (33.00%) (34.55%)

White 5 (3.47%) 71 (49.31%) 1 (0.69%) 67 (46.53%) 144 (50.00%) (65.14%) (100.00%) (67.00%) (100.00%) (65.45%) Total 10 (4.55%) 109 1 (0.45%) 100 (45.45%) 220 (100.00%) (49.55%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%)

173

Table A39: Comparison of Physical and Psychological Attributions of Jeremy Lin in December 2012 between White and Nonwhite Commentators Psychological Physical Both Neither Total Nonwhite 8 (10.53%) 0 (0.00%) 0 (0.00%) 68 (89.47%) 76 (33.34%) (0.00%) (0.00%) (36.56%) (100.00%) (34.55%) White 16 (11.11%) 5 (3.47%) 5 (3.47%) 118 (81.94%) 144 (66.66%) (100.00%) (100.00%) (63.44%) (100.00%) (65.45%) Total 24 (10.91%) 5 (2.27%) 5 (2.27%) 186 (84.55%) 220 (100.00%) (100.00%) (100.00%) (100.00%) (100.00%) (100.00%)

Table B1: Physical – Model 1 (All) Logistic regression Number of obs = 11161 LR chi2(13) = 251.90 Prob > chi2 = 0.0000 Log likelihood = -1721.5667 Pseudo R2 = 0.0682

------physicallog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | .7066077 .0929837 -2.64 0.008 .5459681 .9145121 3 | .856574 .2180403 -0.61 0.543 .5201064 1.41071 4 | 1.167202 .3589604 0.50 0.615 .6388048 2.132671 | athletesport | 2 | 1 (empty) 3 | .9517289 .1171809 -0.40 0.688 .7476696 1.211481 4 | .9984653 .1936028 -0.01 0.994 .6827861 1.460096 5 | 1.106239 .2804277 0.40 0.690 .6730889 1.818133 6 | .4182038 .2472519 -1.47 0.140 .1312616 1.332411 7 | 1 (empty) | levelofcomp | .3889414 .0491394 -7.47 0.000 .3036282 .4982258 | commrace | 2 | 1.194013 .1451075 1.46 0.145 .9409426 1.515147 3 | .7884138 .3158721 -0.59 0.553 .359523 1.728948 4 | 1.176447 .2797279 0.68 0.494 .738209 1.874846 | commsex | 2.253034 .4758328 3.85 0.000 1.489351 3.408306 commrole | .3112322 .0347958 -10.44 0.000 .2499885 .3874797 _cons | .0866123 .0244602 -8.66 0.000 .0497956 .1506496 ------

Table B2: Physical – Model 2 (> 10) Logistic regression Number of obs = 10579 LR chi2(11) = 243.78 Prob > chi2 = 0.0000 Log likelihood = -1670.1419 Pseudo R2 = 0.0680

------physicallog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------

174

athleterace | 2 | .7168352 .0955114 -2.50 0.012 .5520836 .9307516 3 | .9046577 .2346371 -0.39 0.699 .5441415 1.504031 4 | 1.215118 .3745374 0.63 0.527 .6641277 2.223235 | athletesport | 3 | .941734 .1167461 -0.48 0.628 .7385933 1.200746 4 | .9166809 .1832292 -0.44 0.663 .6195512 1.356311 5 | 1.080495 .2742102 0.31 0.760 .6570574 1.776816 | levelofcomp | .385941 .0490599 -7.49 0.000 .3008279 .4951351 | commrace | 2 | 1.218104 .1492676 1.61 0.107 .958026 1.548786 4 | 1.195588 .2856113 0.75 0.455 .7485833 1.909516 | commsex | 2.37993 .5231156 3.94 0.000 1.546919 3.661514 commrole | .3144759 .0356667 -10.20 0.000 .2517951 .3927601 _cons | .0817432 .0236973 -8.64 0.000 .0463115 .1442828 ------

Table B3: Physical – Model 3 (Pro + > 10) Logistic regression Number of obs = 9292 LR chi2(10) = 136.01 Prob > chi2 = 0.0000 Log likelihood = -1334.3523 Pseudo R2 = 0.0485

------physicallog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | .6290875 .1032135 -2.82 0.005 .4560953 .8676938 3 | .7842748 .2170712 -0.88 0.380 .4559037 1.34916 4 | 1.152053 .3558049 0.46 0.647 .6289036 2.110382 | athletesport | 3 | 1.057691 .1583869 0.37 0.708 .7886661 1.418484 4 | 1.046452 .2175616 0.22 0.827 .6962277 1.572851 5 | 1.248775 .3285067 0.84 0.398 .7457006 2.091241 | commrace | 2 | 1.098054 .1421806 0.72 0.470 .8519353 1.415275 4 | 1.271254 .3504224 0.87 0.384 .7406244 2.182061 | commsex | 2.869424 .7419817 4.08 0.000 1.728579 4.763215 commrole | .3615896 .0458538 -8.02 0.000 .2820159 .4636158 _cons | .0258882 .0079126 -11.95 0.000 .0142211 .0471269 ------

Table B4: Physical – Model 4 (College All) Logistic regression Number of obs = 1246 LR chi2(6) = 73.82 Prob > chi2 = 0.0000 Log likelihood = -318.48579 Pseudo R2 = 0.1039

------physicallog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------2.athleter~e | 1.046537 .2451408 0.19 0.846 .6612556 1.656304 3.athletes~t | .7206467 .1828817 -1.29 0.197 .4382372 1.185047 | commrace | 2 | 2.540243 1.221467 1.94 0.053 .9898697 6.518872

175

4 | 2.153661 1.270403 1.30 0.193 .6777508 6.843602 | commsex | 1.100213 .4777613 0.22 0.826 .4697288 2.576951 commrole | .2364464 .0630103 -5.41 0.000 .1402482 .3986286 _cons | .1010958 .0692682 -3.34 0.001 .0263944 .3872168 ------

Table B5: Physical – Model 5 (Professional Football) Logistic regression Number of obs = 2774 LR chi2(6) = 48.03 Prob > chi2 = 0.0000 Log likelihood = -403.15544 Pseudo R2 = 0.0562

------physicallog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | .5948816 .1239188 -2.49 0.013 .3954742 .8948349 3 | .2403488 .2454062 -1.40 0.163 .0324885 1.778092 4 | 1 (empty) | commrace | 2 | .62591 .1363833 -2.15 0.032 .4083555 .9593681 3 | 1 (empty) 4 | 1.386014 .6791988 0.67 0.505 .5304528 3.621498 | commsex | 3.208005 1.681414 2.22 0.026 1.148403 8.961403 commrole | .3800361 .0934958 -3.93 0.000 .2346464 .6155111 _cons | .0339129 .0188212 -6.10 0.000 .0114278 .1006397 ------

Table B6: Physical – Model 6 (Professional Football & White Commentators) Logistic regression Number of obs = 1888 LR chi2(4) = 28.26 Prob > chi2 = 0.0000 Log likelihood = -231.02133 Pseudo R2 = 0.0576

------physicallog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | .6841859 .1924716 -1.35 0.177 .3942018 1.187489 3 | .7528304 .7806866 -0.27 0.784 .0986278 5.746386 4 | 1 (empty) | commsex | 2.800914 1.735035 1.66 0.096 .8318024 9.431469 commrole | .3119261 .1016731 -3.57 0.000 .1646647 .5908851 _cons | .0236196 .0151246 -5.85 0.000 .0067331 .0828576 ------

Table B7: Physical – Model 7 (Professional Football & Nonwhite Commentators) Logistic regression Number of obs = 920 LR chi2(3) = 13.98 Prob > chi2 = 0.0029 Log likelihood = -172.6893 Pseudo R2 = 0.0389

176

| commsex | 3.984808 4.068394 1.35 0.176 .538705 29.47568 commrole | .4460637 .1428172 -2.52 0.012 .2381584 .8354643 _cons | .0283276 .0290055 -3.48 0.001 .0038075 .2107573 ------

Table B8: Physical – Model 8 (Professional Basketball) Logistic regression Number of obs = 3908 LR chi2(7) = 60.07 Prob > chi2 = 0.0000 Log likelihood = -628.45237 Pseudo R2 = 0.0456

------physicallog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | 1.186518 .3344635 0.61 0.544 .6828603 2.061658 3 | 1 (empty) 4 | 1.197111 .4064156 0.53 0.596 .6153948 2.328707 | commrace | 2 | 1.708138 .3327541 2.75 0.006 1.166016 2.502312 3 | 1.022463 .7556077 0.03 0.976 .240217 4.352028 4 | 1.577618 .6856386 1.05 0.294 .6730801 3.697747 | commsex | 2.912129 .9498184 3.28 0.001 1.536679 5.518716 commrole | .394208 .0690287 -5.32 0.000 .279688 .5556189 _cons | .0185367 .0070255 -10.52 0.000 .0088191 .0389619 ------

Table B9: Physical – Model 9 (Professional Basketball & White Commentators) Logistic regression Number of obs = 2468 LR chi2(4) = 52.04 Prob > chi2 = 0.0000 Log likelihood = -423.66527 Pseudo R2 = 0.0579

------physicallog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | 1.314044 .4203989 0.85 0.393 .7019188 2.459986 3 | 1 (empty) 4 | 1.94409 .685247 1.89 0.059 .9742882 3.879228 | commsex | 2.977392 1.030307 3.15 0.002 1.511066 5.866631 commrole | .3743127 .0768856 -4.78 0.000 .2502613 .5598548 _cons | .0302934 .0109578 -9.67 0.000 .0149091 .0615526 ------

Table B10: Physical – Model 10 (Professional Basketball & Nonwhite Commentators) Logistic regression Number of obs = 1363 LR chi2(3) = 7.51 Prob > chi2 = 0.0574 Log likelihood = -200.68939 Pseudo R2 = 0.0184

177

| commsex | 2.808708 2.862259 1.01 0.311 .3811323 20.69842 commrole | .4929143 .1470746 -2.37 0.018 .2746578 .8846081 _cons | .0201358 .0206666 -3.80 0.000 .0026936 .1505245 ------

Table B11: Physical – Model 11 (Professional Baseball) Logistic regression Number of obs = 1998 LR chi2(8) = 30.24 Prob > chi2 = 0.0002 Log likelihood = -225.92842 Pseudo R2 = 0.0627

------physicallog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | .3066588 .1202376 -3.01 0.003 .1422028 .661306 3 | .6329806 .2357673 -1.23 0.220 .3050296 1.313526 4 | 1.013253 .801773 0.02 0.987 .2148689 4.778174 | commrace | 2 | .6786905 .2201606 -1.19 0.232 .359377 1.28172 3 | 1.368878 .8024158 0.54 0.592 .4339135 4.318435 4 | .9892659 .5729103 -0.02 0.985 .3179505 3.077986 | commsex | 1.701766 .9165481 0.99 0.324 .5921807 4.890412 commrole | .3119614 .0977624 -3.72 0.000 .1687919 .5765677 _cons | .0972485 .0658881 -3.44 0.001 .0257735 .3669371 ------

Table B12: Physical – Model 12 (Professional Baseball & White Commentators) Logistic regression Number of obs = 1232 LR chi2(5) = 18.89 Prob > chi2 = 0.0020 Log likelihood = -124.19007 Pseudo R2 = 0.0707

------physicallog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | .2407754 .1364913 -2.51 0.012 .0792649 .73138 3 | .7041821 .3582843 -0.69 0.491 .2597753 1.908851 4 | 1.741771 1.48801 0.65 0.516 .3264391 9.293515 | commsex | 2.048505 1.556959 0.94 0.345 .4618322 9.086355 commrole | .3987313 .1634875 -2.24 0.025 .1785136 .8906136 _cons | .0477322 .0425106 -3.42 0.001 .0083315 .2734625 ------

Table B13: Physical – Model 13 (Professional Baseball & Nonwhite Commentators) Logistic regression Number of obs = 752 LR chi2(4) = 12.88 Prob > chi2 = 0.0119 Log likelihood = -99.844779 Pseudo R2 = 0.0606

------physicallog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | .3894948 .2128517 -1.73 0.084 .1334553 1.136757 3 | .5414953 .2990576 -1.11 0.267 .1834392 1.598443 4 | 1 (empty) |

178

commsex | 1.346592 1.020484 0.39 0.695 .3049106 5.947022 commrole | .2190755 .1011416 -3.29 0.001 .0886371 .5414668 _cons | .1641604 .1423719 -2.08 0.037 .0299947 .898447 ------

Table C1: Psychological – Model 1 (All) Logistic regression Number of obs = 11283 LR chi2(15) = 209.82 Prob > chi2 = 0.0000 Log likelihood = -2075.2243 Pseudo R2 = 0.0481

------psychologicallog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | 1.344244 .1539931 2.58 0.010 1.073907 1.682634 3 | 1.383563 .3397059 1.32 0.186 .8550736 2.238691 4 | 2.576821 .5358394 4.55 0.000 1.714274 3.873362 | athletesport | 2 | .6465939 .3868201 -0.73 0.466 .2001734 2.088607 3 | 1.066626 .1214177 0.57 0.571 .8533295 1.333237 4 | .3634047 .0730223 -5.04 0.000 .2451042 .5388035 5 | .687364 .1550663 -1.66 0.097 .4417326 1.069582 6 | 1.268359 .3570554 0.84 0.398 .7305006 2.202235 7 | 1.058139 1.08953 0.05 0.956 .1406335 7.961529 | levelofcomp | .7457465 .0987354 -2.22 0.027 .5752992 .9666931 | commrace | 2 | .8459453 .0858022 -1.65 0.099 .6934369 1.031995 3 | .5438665 .2019782 -1.64 0.101 .2626502 1.126178 4 | .6321313 .1551768 -1.87 0.062 .3907084 1.022732 | commsex | .904403 .1216429 -0.75 0.455 .6948238 1.177197 commrole | .3868487 .0383561 -9.58 0.000 .3185256 .4698269 _cons | .1370399 .0310286 -8.78 0.000 .0879261 .2135877 ------

Table C2: Psychological – Model 2 (> 10) Logistic regression Number of obs = 10846 LR chi2(12) = 200.34 Prob > chi2 = 0.0000

179

Log likelihood = -2022.497 Pseudo R2 = 0.0472

------psychologicallog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | 1.339494 .1546873 2.53 0.011 1.068175 1.679729 3 | 1.372431 .3488454 1.25 0.213 .8339342 2.258653 4 | 2.651652 .5524897 4.68 0.000 1.762637 3.989056 | athletesport | 3 | 1.0425 .1194461 0.36 0.716 .8328141 1.304979 4 | .3479847 .0716741 -5.13 0.000 .2324015 .5210524 5 | .6773253 .1529754 -1.73 0.085 .4350625 1.054491 6 | 1.294641 .3653532 0.92 0.360 .7446235 2.25093 | levelofcomp | .7378264 .098427 -2.28 0.023 .5680712 .958309 | commrace | 2 | .8549544 .0872937 -1.53 0.125 .6998942 1.044368 4 | .6295595 .154908 -1.88 0.060 .3886798 1.019721 | commsex | .9401667 .1300737 -0.45 0.656 .7168688 1.23302 commrole | .3982827 .0401018 -9.14 0.000 .326954 .4851725 _cons | .1328618 .0305404 -8.78 0.000 .0846717 .208479 ------

Table C3: Psychological – Model 3 (Pro + > 10) Logistic regression Number of obs = 9557 LR chi2(11) = 178.71 Prob > chi2 = 0.0000 Log likelihood = -1739.9925 Pseudo R2 = 0.0488

------psychologicallog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | 1.352845 .1754912 2.33 0.020 1.049132 1.74448 3 | 1.251976 .3424407 0.82 0.411 .7324455 2.140015 4 | 2.66457 .5586503 4.67 0.000 1.76671 4.018731

180

| athletesport | 3 | 1.065073 .1370129 0.49 0.624 .827712 1.370501 4 | .3407789 .0722627 -5.08 0.000 .224892 .5163824 5 | .6874788 .1566497 -1.64 0.100 .439849 1.074521 6 | 1.294424 .3674616 0.91 0.363 .7420567 2.257959 | commrace | 2 | .8041629 .0857139 -2.04 0.041 .652554 .9909954 4 | .5246611 .1584769 -2.14 0.033 .2902476 .9483947 | commsex | .9307423 .1360497 -0.49 0.623 .6988862 1.239517 commrole | .4190807 .0451255 -8.08 0.000 .3393459 .5175505 _cons | .100082 .0200379 -11.50 0.000 .0675979 .1481764 ------

Table C4: Psychological – Model 4 (College All) Logistic regression Number of obs = 1246 LR chi2(6) = 25.70 Prob > chi2 = 0.0003 Log likelihood = -262.30231 Pseudo R2 = 0.0467

------psychologicallog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------2.athleterace | 1.20751 .3254719 0.70 0.484 .7119638 2.047968 3.athletesport | .9493781 .2774318 -0.18 0.859 .535423 1.683377 | commrace | 2 | 1.470617 .6667813 0.85 0.395 .6047313 3.57632 4 | 1.621894 .9089146 0.86 0.388 .5407624 4.864504 | commsex | .8572188 .3809871 -0.35 0.729 .3587393 2.048351 commrole | .305058 .0957487 -3.78 0.000 .164898 .5643511 _cons | .1029597 .0710977 -3.29 0.001 .0265999 .3985246 ------

Table C5: Psychological – Model 5 (Professional Football) Logistic regression Number of obs = 2774 LR chi2(6) = 66.19 Prob > chi2 = 0.0000 Log likelihood = -606.26609 Pseudo R2 = 0.0518

181

------psychologicallog | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | .7032462 .1828063 3.85 0.000 .3449524 1.06154 3 | 1.102709 .3936684 2.80 0.005 .3311329 1.874285 4 | 0 (empty) | commrace | 2 | -.5779551 .1684585 -3.43 0.001 -.9081277 - .2477825 4 | -.4764403 .4962335 -0.96 0.337 -1.44904 .4961596 | commsex | -.2030272 .2442894 -0.83 0.406 -.6818256 .2757712 commrole | -.9077156 .1856471 -4.89 0.000 -1.271577 - .5438539 _cons | -2.25303 .3003034 -7.50 0.000 -2.841614 - 1.664446 ------

Table C6: Psychological – Model 6 (Professional Football & White Commentators) Logistic regression Number of obs = 1888 LR chi2(4) = 25.42 Prob > chi2 = 0.0000 Log likelihood = -357.95177 Pseudo R2 = 0.0343

------psychologicallog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | 2.438567 .6131332 3.55 0.000 1.489757 3.991662 3 | 3.454352 1.969527 2.17 0.030 1.129918 10.56054 4 | 1 (empty) | commsex | .8392344 .2500442 -0.59 0.556 .4680298 1.504849 commrole | .4602767 .1098383 -3.25 0.001 .2883316 .7347604 _cons | .0476029 .01731 -8.37 0.000 .0233404 .0970865 ------

Table C7: Psychological – Model 7 (Professional Football & Nonwhite Commentators) Logistic regression Number of obs = 956 LR chi2(4) = 35.47 Prob > chi2 = 0.0000 Log likelihood = -252.4643 Pseudo R2 = 0.0656

182

------psychologicallog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | 1.575152 .4224774 1.69 0.090 .9311472 2.664565 3 | 2.107958 1.14028 1.38 0.168 .7301506 6.085714 4 | 1 (empty) | commsex | .794586 .3393821 -0.54 0.590 .344015 1.835289 commrole | .2451569 .0681675 -5.06 0.000 .1421549 .4227916 _cons | .1374922 .0620347 -4.40 0.000 .0567841 .3329119 ------

Table C8: Psychological – Model 8 (Professional Basketball) Logistic regression Number of obs = 3966 LR chi2(8) = 75.97 Prob > chi2 = 0.0000 Log likelihood = -800.6363 Pseudo R2 = 0.0453

------psychologicallog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | .6393957 .2031751 -1.41 0.159 .3429975 1.191924 3 | .3134945 .3179256 -1.14 0.253 .0429533 2.288036 4 | 2.875104 .6267412 4.84 0.000 1.87543 4.407641 | commrace | 2 | 1.148469 .1831607 0.87 0.385 .8401712 1.569897 3 | .9906953 .5275998 -0.02 0.986 .3488418 2.813531 4 | .1054179 .1071019 -2.21 0.027 .0143918 .7721711 | commsex | 1.136731 .2296446 0.63 0.526 .7650611 1.688959 commrole | .4534263 .0672736 -5.33 0.000 .339013 .6064529 _cons | .0745287 .0190662 -10.15 0.000 .0451405 .1230499 ------

Table C9: Psychological – Model 9 (Professional Basketball & White Commentators) Logistic regression Number of obs = 2468

183

LR chi2(4) = 48.86 Prob > chi2 = 0.0000 Log likelihood = -529.98944 Pseudo R2 = 0.0441

------psychologicallog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | .6433321 .2407873 -1.18 0.239 .3089201 1.339751 3 | 1 (empty) 4 | 3.015888 .782582 4.25 0.000 1.813593 5.015225 | commsex | 1.145154 .255454 0.61 0.543 .7395762 1.773147 commrole | .3861565 .0706757 -5.20 0.000 .2697574 .5527812 _cons | .0921833 .0222508 -9.88 0.000 .0574372 .1479487 ------

Table C10: Psychological – Model 10 (Professional Basketball & Nonwhite Commentators) Logistic regression Number of obs = 1454 LR chi2(5) = 14.61 Prob > chi2 = 0.0121 Log likelihood = -273.33107 Pseudo R2 = 0.0260

------psychologicallog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | .6358303 .3833072 -0.75 0.453 .1950736 2.072449 3 | 1.897701 1.99678 0.61 0.543 .2413154 14.9235 4 | 2.337167 .9350508 2.12 0.034 1.066945 5.119616 | commsex | .7045877 .3402672 -0.73 0.468 .2734409 1.815543 commrole | .4715175 .1169794 -3.03 0.002 .2899493 .7667849 _cons | .1033085 .0508827 -4.61 0.000 .0393453 .2712562 ------

Table C11: Psychological – Model 11 (Professional Baseball) Logistic regression Number of obs = 1998 LR chi2(8) = 7.05 Prob > chi2 = 0.5310 Log likelihood = -172.72466 Pseudo R2 = 0.0200

184

------psychologicallog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | .9467603 .4803433 -0.11 0.914 .3502507 2.559182 3 | .7085596 .400623 -0.61 0.542 .2339392 2.146099 4 | 1.380779 1.539555 0.29 0.772 .1552539 12.28021 | commrace | 2 | .4072993 .1517353 -2.41 0.016 .1962489 .8453183 3 | .8886512 .6871151 -0.15 0.879 .1952418 4.044734 4 | .7169296 .4652171 -0.51 0.608 .2009695 2.557543 | commsex | .7601943 .3541587 -0.59 0.556 .3050475 1.894444 commrole | .6903348 .3271051 -0.78 0.434 .2727283 1.747387 _cons | .0573214 .0444813 -3.68 0.000 .0125253 .2623293 ------

Table C12: Psychological – Model 12 (Professional Baseball & White Commentators) Logistic regression Number of obs = 1089 LR chi2(4) = 1.01 Prob > chi2 = 0.9090 Log likelihood = -78.667928 Pseudo R2 = 0.0064

185

Table C13: Psychological – Model 13 (Professional Baseball & Nonwhite Commentators) Logistic regression Number of obs = 752 LR chi2(4) = 3.63 Prob > chi2 = 0.4589 Log likelihood = -90.458569 Pseudo R2 = 0.0197

------psychologicallog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | .5541036 .3067232 -1.07 0.286 .1872448 1.63973 3 | .3195029 .218534 -1.67 0.095 .083612 1.220902 4 | 1 (empty) | commsex | .6796956 .4351066 -0.60 0.546 .1938289 2.383474 commrole | .5744722 .3725396 -0.85 0.393 .1611659 2.047693 _cons | .1159944 .1034089 -2.42 0.016 .0202112 .6657056 ------

Table D1: Positive – Model 1 (All) Logistic regression Number of obs = 11283 LR chi2(15) = 434.64 Prob > chi2 = 0.0000 Log likelihood = -5872.0545 Pseudo R2 = 0.0357

------positivelog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | .9038297 .0555059 -1.65 0.100 .8013323 1.019437 3 | .7265543 .0848727 -2.73 0.006 .577876 .9134851 4 | 2.800669 .3496891 8.25 0.000 2.192711 3.57719 | athletesport | 2 | 2.965801 .6440933 5.01 0.000 1.937687 4.539421 3 | 1.110897 .0677283 1.72 0.085 .9857769 1.251899 4 | .9625865 .0791832 -0.46 0.643 .8192551 1.130994 5 | 1.499758 .1535142 3.96 0.000 1.227137 1.832946 6 | .9119959 .1518097 -0.55 0.580 .6581168 1.263813 7 | 2.898884 1.297645 2.38 0.017 1.205598 6.970424 | levelofcomp | .7044589 .0490518 -5.03 0.000 .6145909 .8074678 | commrace | 2 | .9984839 .0545341 -0.03 0.978 .8971211 1.111299 3 | .7683465 .1234842 -1.64 0.101 .5607341 1.052828 4 | 1.06587 .1068487 0.64 0.525 .8757395 1.297279 | commsex | 1.139918 .0783913 1.90 0.057 .996178 1.304398 commrole | .453439 .0235825 -15.21 0.000 .4094957 .5020978 _cons | .5840784 .0699891 -4.49 0.000 .4618205 .7387017 ------

186

Table D2: Positive – Model 2 (> 10) Logistic regression Number of obs = 11261 LR chi2(14) = 433.66 Prob > chi2 = 0.0000 Log likelihood = -5857.1344 Pseudo R2 = 0.0357

------positivelog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | .9025442 .0554539 -1.67 0.095 .8001459 1.018047 3 | .725918 .0848065 -2.74 0.006 .5773573 .912705 4 | 2.801998 .3498678 8.25 0.000 2.193732 3.57892 | athletesport | 2 | 2.970484 .6451261 5.01 0.000 1.940726 4.546635 3 | 1.110673 .0677188 1.72 0.085 .9855701 1.251655 4 | .9632826 .0792525 -0.45 0.649 .8198275 1.13184 5 | 1.499785 .1535394 3.96 0.000 1.227123 1.833033 6 | .9123902 .1518834 -0.55 0.582 .6583898 1.264381 | levelofcomp | .7022659 .0489296 -5.07 0.000 .6126256 .8050226 | commrace | 2 | 1.004161 .0549842 0.08 0.940 .9019756 1.117924 3 | .7709874 .1239257 -1.62 0.106 .5626369 1.056492 4 | 1.068269 .1071414 0.66 0.510 .8776269 1.300323 | commsex | 1.147981 .0791207 2.00 0.045 1.002925 1.314017 commrole | .4538211 .0236028 -15.19 0.000 .40984 .5025218 _cons | .5798337 .0695928 -4.54 0.000 .4582901 .733612 ------

Table D3: Positive – Model 3 (Pro + > 10) Logistic regression Number of obs = 9906 LR chi2(13) = 360.86 Prob > chi2 = 0.0000 Log likelihood = -5091.3654 Pseudo R2 = 0.0342

------positivelog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | .9517576 .0648893 -0.73 0.468 .8327081 1.087827 3 | .7224893 .0878436 -2.67 0.008 .5692964 .9169052 4 | 2.823663 .3551974 8.25 0.000 2.206671 3.613168 | athletesport | 2 | 3.096795 .6886664 5.08 0.000 2.002728 4.788538 3 | 1.166162 .0797194 2.25 0.025 1.01993 1.33336 4 | .9481867 .0799622 -0.63 0.528 .8037308 1.118606 5 | 1.471081 .1525332 3.72 0.000 1.200542 1.802586 6 | .9178615 .1535311 -0.51 0.608 .6612967 1.273966 | commrace | 2 | .9679311 .055444 -0.57 0.569 .8651408 1.082934 3 | .7382003 .1334646 -1.68 0.093 .5179399 1.052129 4 | 1.043953 .1173945 0.38 0.702 .8374554 1.301368 | commsex | 1.207481 .0886907 2.57 0.010 1.045583 1.394446 commrole | .4858989 .0267859 -13.09 0.000 .4361362 .5413395 _cons | .3698465 .0390597 -9.42 0.000 .3006947 .4549015 ------

187

Table D4: Positive – Model 4 (College All) Logistic regression Number of obs = 1304 LR chi2(7) = 84.44 Prob > chi2 = 0.0000 Log likelihood = -719.26154 Pseudo R2 = 0.0554

------positivelog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------2.athleter~e | .6831801 .1050596 -2.48 0.013 .5054023 .9234922 3.athletes~t | 1.026443 .1575559 0.17 0.865 .7597627 1.386729 | commrace | 2 | 1.290874 .267464 1.23 0.218 .8600388 1.937534 3 | 1.091622 .4339967 0.22 0.825 .5007949 2.379496 4 | 1.454011 .3747444 1.45 0.146 .8773774 2.409624 | commsex | .7274734 .1520945 -1.52 0.128 .482897 1.095922 commrole | .2781515 .0480575 -7.41 0.000 .1982511 .3902539 _cons | 1.077236 .3599967 0.22 0.824 .5595662 2.073815 ------

Table D5: Positive – Model 5 (Professional Football) Logistic regression Number of obs = 2854 LR chi2(8) = 102.19 Prob > chi2 = 0.0000 Log likelihood = -1456.6377 Pseudo R2 = 0.0339

------positivelog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | 1.054811 .0996326 0.56 0.572 .8765451 1.269333 3 | .6694755 .2120003 -1.27 0.205 .359905 1.245322 4 | .9023945 .7278605 -0.13 0.899 .1857067 4.384957 | commrace | 2 | .8052226 .0825499 -2.11 0.035 .6586465 .984418 3 | .114841 .0832468 -2.99 0.003 .0277378 .4754687 4 | .9831874 .2306825 -0.07 0.942 .6207565 1.557225 | commsex | 1.288715 .1851039 1.77 0.077 .9725123 1.707728 commrole | .4992218 .0500894 -6.92 0.000 .4100985 .6077134 _cons | .3773366 .0666166 -5.52 0.000 .2669649 .5333395 ------

Table D6: Positive – Model 6 (Professional Football & White Commentators) Logistic regression Number of obs = 1895 LR chi2(5) = 32.53 Prob > chi2 = 0.0000 Log likelihood = -956.42387 Pseudo R2 = 0.0167

------positivelog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | 1.213435 .1428443 1.64 0.100 .9634163 1.528337 3 | .913332 .3663578 -0.23 0.821 .4160953 2.00477 4 | .7622205 .8312078 -0.25 0.803 .0899163 6.461346 | commsex | 1.15165 .1825766 0.89 0.373 .8440642 1.571323 commrole | .5636102 .0688629 -4.69 0.000 .4435856 .7161107

188

_cons | .2887033 .0530712 -6.76 0.000 .2013625 .4139279 ------

Table D7: Positive – Model 7 (Professional Football & Nonwhite Commentators) Logistic regression Number of obs = 959 LR chi2(5) = 71.35 Prob > chi2 = 0.0000 Log likelihood = -497.19542 Pseudo R2 = 0.0669

------positivelog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | .7755565 .1245838 -1.58 0.114 .5660805 1.062548 3 | .3127899 .158004 -2.30 0.021 .116218 .841845 4 | .9868328 1.248957 -0.01 0.992 .0825933 11.79077 | commsex | 3.528658 1.453615 3.06 0.002 1.573841 7.911489 commrole | .3113788 .0500875 -7.25 0.000 .2271785 .4267866 _cons | .2002316 .0833561 -3.86 0.000 .088548 .4527791 ------

Table D8: Positive – Model 8 (Professional Soccer) Logistic regression Number of obs = 94 LR chi2(7) = 4.05 Prob > chi2 = 0.7736 Log likelihood = -60.530402 Pseudo R2 = 0.0324

------positivelog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | .6933028 .4328804 -0.59 0.557 .2039176 2.357172 3 | 1.450805 .9708876 0.56 0.578 .3908226 5.385653 4 | 1.770224 2.691431 0.38 0.707 .0899228 34.8487 | commrace | 2 | 1.054907 .5398792 0.10 0.917 .3868896 2.876347 3 | 1 (empty) 4 | 1.813967 1.391193 0.78 0.437 .4034726 8.155391 | commsex | .6483958 .3733309 -0.75 0.452 .2097677 2.004203 commrole | .9937121 .9760108 -0.01 0.995 .1449512 6.812388 _cons | .8536187 1.023606 -0.13 0.895 .0813873 8.953059 ------

Table D9: Positive – Model 9 (Professional Soccer & White Commentators) Logistic regression Number of obs = 48 LR chi2(3) = 1.36 Prob > chi2 = 0.7149 Log likelihood = -31.541546 Pseudo R2 = 0.0211

------positivelog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | .4868817 .45278 -0.77 0.439 .0786758 3.013047 3 | .874634 .8334382 -0.14 0.888 .1351192 5.661555 4 | 1 (empty) | commsex | .8169856 .5609817 -0.29 0.768 .2126857 3.138271 commrole | 1 (omitted) _cons | 1.144316 1.072368 0.14 0.886 .182332 7.181728

189

------

Table D10: Positive – Model 10 (Professional Soccer & Nonwhite Commentators) Logistic regression Number of obs = 45 LR chi2(4) = 3.18 Prob > chi2 = 0.5275 Log likelihood = -27.69466 Pseudo R2 = 0.0544

------positivelog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | .7761005 .6680177 -0.29 0.768 .1436343 4.193511 3 | 2.763267 2.65969 1.06 0.291 .4189162 18.22714 4 | 1 (empty) | commsex | .3655247 .3990858 -0.92 0.357 .0430092 3.106507 commrole | 1.103124 1.097698 0.10 0.921 .1568951 7.756021 _cons | 1.096459 1.780391 0.06 0.955 .0454823 26.43279 ------

Table D11: Positive – Model 11 (Professional Basketball) Logistic regression Number of obs = 3966 LR chi2(8) = 151.55 Prob > chi2 = 0.0000 Log likelihood = -2148.3833 Pseudo R2 = 0.0341

------positivelog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | .7107865 .106298 -2.28 0.022 .5302029 .9528757 3 | .2928023 .1381443 -2.60 0.009 .1161387 .7381963 4 | 3.077732 .4359437 7.94 0.000 2.331647 4.06255 | commrace | 2 | 1.031377 .0886006 0.36 0.719 .871555 1.220506 3 | .7333093 .2095834 -1.09 0.278 .4188028 1.283999 4 | 1.157486 .2082735 0.81 0.416 .813493 1.646939 | commsex | 1.259575 .1341525 2.17 0.030 1.02227 1.551966 commrole | .5545842 .0450466 -7.26 0.000 .4729637 .6502901 _cons | .3761981 .0518203 -7.10 0.000 .2871876 .4927962 ------

Table D12: Positive – Model 12 (Professional Basketball & White Commentators) Logistic regression Number of obs = 2512 LR chi2(5) = 94.58 Prob > chi2 = 0.0000 Log likelihood = -1354.2029 Pseudo R2 = 0.0337

------positivelog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | .9163388 .1568071 -0.51 0.610 .6552326 1.281494 3 | .3080632 .1630784 -2.22 0.026 .1091546 .8694357 4 | 3.304618 .5789593 6.82 0.000 2.344192 4.658534 | commsex | 1.282707 .1489788 2.14 0.032 1.021564 1.610607 commrole | .5676438 .0578238 -5.56 0.000 .464908 .6930822 _cons | .3674821 .0488271 -7.53 0.000 .283229 .4767984 ------

190

Table D13: Positive – Model 13 (Professional Basketball & Nonwhite Commentators) Logistic regression Number of obs = 1454 LR chi2(5) = 62.13 Prob > chi2 = 0.0000 Log likelihood = -791.47612 Pseudo R2 = 0.0378

------positivelog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | .3575095 .1167338 -3.15 0.002 .1885184 .6779871 3 | .2564402 .2673215 -1.31 0.192 .0332404 1.978363 4 | 2.818794 .675462 4.32 0.000 1.762348 4.508531 | commsex | 1.253971 .3538154 0.80 0.422 .7213004 2.180013 commrole | .5508183 .0692511 -4.74 0.000 .4305186 .7047335 _cons | .3960532 .1146042 -3.20 0.001 .2246183 .6983321

Table D14: Positive – Model 14 (Professional Baseball) Logistic regression Number of obs = 1998 LR chi2(8) = 49.27 Prob > chi2 = 0.0000 Log likelihood = -878.7979 Pseudo R2 = 0.0273

------positivelog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | .9529907 .1777296 -0.26 0.796 .6612145 1.37352 3 | .7255671 .1473712 -1.58 0.114 .4872911 1.080355 4 | 2.208252 .8466646 2.07 0.039 1.041574 4.681741 | commrace | 2 | .8302697 .1163352 -1.33 0.184 .6308862 1.092666 3 | 1.568556 .4525069 1.56 0.119 .8911275 2.760959 4 | .7401458 .1989697 -1.12 0.263 .4370117 1.25355 | commsex | .9921981 .1778354 -0.04 0.965 .6982877 1.409816 commrole | .4087893 .0627869 -5.82 0.000 .3025251 .5523796 _cons | .5221339 .1512959 -2.24 0.025 .2958924 .9213611 ------

Table D15: Positive – Model 15 (Professional Baseball & White Commentators) Logistic regression Number of obs = 1232 LR chi2(5) = 24.25 Prob > chi2 = 0.0002 Log likelihood = -534.29974 Pseudo R2 = 0.0222

------positivelog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | 1.291958 .3417937 0.97 0.333 .7692345 2.169891 3 | 1.055017 .298484 0.19 0.850 .6059525 1.836878 4 | 2.330676 1.191402 1.66 0.098 .8557799 6.347484 | commsex | .7826332 .1649064 -1.16 0.245 .5178501 1.182803 commrole | .410018 .0768807 -4.75 0.000 .2839212 .5921178 _cons | .3970031 .1360619 -2.70 0.007 .2028 .7771769 ------

191

Table D16: Positive – Model 16 (Professional Baseball & Nonwhite Commentators) Logistic regression Number of obs = 766 LR chi2(5) = 29.34 Prob > chi2 = 0.0000 Log likelihood = -342.01525 Pseudo R2 = 0.0411

------positivelog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | .6601035 .1780114 -1.54 0.124 .3891049 1.119843 3 | .4554562 .1380412 -2.59 0.009 .2514558 .8249576 4 | 2.433 1.437851 1.50 0.132 .7640119 7.747902 | commsex | 1.655813 .5985887 1.39 0.163 .8152587 3.363001 commrole | .3105218 .0836106 -4.34 0.000 .1831888 .5263629 _cons | .5713032 .2621026 -1.22 0.222 .2324622 1.404045 ------

Table D17: Positive – Model 17 (Professional Hockey) Logistic regression Number of obs = 712 LR chi2(5) = 55.34 Prob > chi2 = 0.0000 Log likelihood = -379.16942 Pseudo R2 = 0.0680

------positivelog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------2.athleter~e | .5641634 .4100288 -0.79 0.431 .1357561 2.344501 | commrace | 2 | 2.931914 1.327624 2.38 0.018 1.207012 7.121816 3 | 1 (empty) 4 | 2.921878 1.439447 2.18 0.030 1.112557 7.673646 | commsex | 1.941473 .7421465 1.74 0.083 .9178072 4.106873 commrole | .307678 .0652392 -5.56 0.000 .2030533 .4662115 _cons | .2936556 .267097 -1.35 0.178 .0493879 1.746047 ------

Table D18: Positive – Model 18 (Professional Hockey & White Commentators) Logistic regression Number of obs = 541 LR chi2(3) = 41.19 Prob > chi2 = 0.0000 Log likelihood = -302.54132 Pseudo R2 = 0.0637

Table D19: Positive – Model 19 (Professional Hockey & Nonwhite Commentators) Logistic regression Number of obs = 174 LR chi2(1) = 0.16 Prob > chi2 = 0.6924 Log likelihood = -79.908351 Pseudo R2 = 0.0010

------positivelog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]

192

------+------2.athleter~e | .617021 .7234971 -0.41 0.680 .0619742 6.143118 commsex | 1 (omitted) commrole | 1 (omitted) _cons | .3333335 .3849003 -0.95 0.341 .0346734 3.204511 ------

Table D20: Positive – Model 20 (Professional Golf) Logistic regression Number of obs = 277 LR chi2(7) = 8.65 Prob > chi2 = 0.2784 Log likelihood = -124.93538 Pseudo R2 = 0.0335

------positivelog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | 1.502396 .5209171 1.17 0.240 .7614687 2.964262 3 | 1 (empty) 4 | 2.165219 1.602834 1.04 0.297 .5074434 9.238808 | commrace | 2 | 2.324294 .9922519 1.98 0.048 1.006721 5.366275 3 | .8151753 .9107588 -0.18 0.855 .0912519 7.282161 4 | 1.509393 1.11541 0.56 0.577 .3546353 6.424251 | commsex | 2.224911 1.165149 1.53 0.127 .7971735 6.209729 commrole | 1.282286 .6966864 0.46 0.647 .4420927 3.719258 _cons | .0368695 .0326171 -3.73 0.000 .0065109 .2087823 ------

Table D21: Positive – Model 21 (Professional Golf & White Commentators) Logistic regression Number of obs = 175 LR chi2(4) = 6.18 Prob > chi2 = 0.1863 Log likelihood = -85.850533 Pseudo R2 = 0.0347

------positivelog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | 2.086041 .8534394 1.80 0.072 .935578 4.651208 3 | 1 (empty) 4 | 1.70088 2.06125 0.44 0.661 .1581707 18.29033 | commsex | 2.012505 1.080981 1.30 0.193 .7023111 5.766927 commrole | 1.170111 .6527531 0.28 0.778 .3920841 3.492004 _cons | .0832226 .062442 -3.31 0.001 .0191242 .3621587 ------

Table D22: Positive – Model 22 (Professional Golf & Nonwhite Commentators) Logistic regression Number of obs = 97 LR chi2(2) = 1.57 Prob > chi2 = 0.4571 Log likelihood = -37.431263 Pseudo R2 = 0.0205

------positivelog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | .5797101 .3755411 -0.84 0.400 .1628528 2.063604 4 | 1.777778 1.653031 0.62 0.536 .2873459 10.99892

193

| commsex | 1 (omitted) commrole | 1 (omitted) _cons | .1875 .0834147 -3.76 0.000 .0784007 .4484173 ------

Table D23: Positive – Model 23 (College Football) Logistic regression Number of obs = 531 LR chi2(6) = 71.27 Prob > chi2 = 0.0000 Log likelihood = -292.61968 Pseudo R2 = 0.1086

Table D24: Positive – Model 24 (College Football & White Commentators) Logistic regression Number of obs = 402 LR chi2(3) = 65.40 Prob > chi2 = 0.0000 Log likelihood = -224.54331 Pseudo R2 = 0.1271

Table D25: Positive – Model 25 (College Football & Nonwhite Commentators) Logistic regression Number of obs = 129 LR chi2(5) = 3.01 Prob > chi2 = 0.6980 Log likelihood = -65.980621 Pseudo R2 = 0.0223

194

_cons | 1.059956 1.290967 0.05 0.962 .0974028 11.53463 ------

Table D26: Positive – Model 26 (College Basketball) Logistic regression Number of obs = 773 LR chi2(6) = 15.63 Prob > chi2 = 0.0159 Log likelihood = -422.13674 Pseudo R2 = 0.0182

Table D27: Positive – Model 27 (College Basketball & White Commentators) Logistic regression Number of obs = 462 LR chi2(3) = 6.22 Prob > chi2 = 0.1012 Log likelihood = -268.57375 Pseudo R2 = 0.0115

Table D28: Positive – Model 28 (College Basketball & Nonwhite Commentators) Logistic regression Number of obs = 309 LR chi2(4) = 1.94 Prob > chi2 = 0.7477 Log likelihood = -151.12846 Pseudo R2 = 0.0064

------positivelog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------2.athleter~e | .8540018 .3187727 -0.42 0.672 .4108954 1.77495 | commrace | 3 | 1.479163 .7869151 0.74 0.462 .5214069 4.196193 4 | 1.526722 .5034374 1.28 0.199 .7999737 2.913695 | commsex | .9053156 .6241387 -0.14 0.885 .2344058 3.496485 commrole | 1 (omitted) _cons | .2104164 .1353382 -2.42 0.015 .0596476 .7422769 ------

Table E1: Negative – Model 1 (All) Logistic regression Number of obs = 11261

195

LR chi2(14) = 86.41 Prob > chi2 = 0.0000 Log likelihood = -2483.2571 Pseudo R2 = 0.0171

------negativelog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | .6550608 .0690492 -4.01 0.000 .5327917 .8053892 3 | .3574613 .0809509 -4.54 0.000 .2293325 .5571762 4 | .583387 .1758113 -1.79 0.074 .3231737 1.053119 | athletesport | 2 | 1.381845 .598372 0.75 0.455 .5913825 3.228866 3 | .743972 .0778922 -2.82 0.005 .6059512 .9134305 4 | 1.169574 .1578723 1.16 0.246 .8976979 1.523791 5 | .9756078 .187079 -0.13 0.898 .6699647 1.420688 6 | 1.718054 .3704325 2.51 0.012 1.125921 2.621597 7 | 1 (empty) | levelofcomp | 1.418347 .202815 2.44 0.015 1.071682 1.87715 | commrace | 2 | 1.243765 .1220636 2.22 0.026 1.026127 1.507563 3 | 1.178794 .3054174 0.63 0.526 .7094107 1.958747 4 | .8291654 .169427 -0.92 0.359 .555535 1.237573 | commsex | 1.247946 .1516436 1.82 0.068 .9834731 1.58354 commrole | .6922548 .0624613 -4.08 0.000 .5800468 .8261689 _cons | .0570982 .01263 -12.94 0.000 .0370117 .0880858 ------

Table E2: Negative – Model 2 (> 10) Logistic regression Number of obs = 11161 LR chi2(13) = 84.17 Prob > chi2 = 0.0000 Log likelihood = -2461.6783 Pseudo R2 = 0.0168

------negativelog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | .6492385 .0688721 -4.07 0.000 .5273605 .7992835 3 | .3732148 .0848358 -4.34 0.000 .2390405 .5827018 4 | .5896988 .1777567 -1.75 0.080 .3266233 1.064666 | athletesport | 3 | .74096 .0776224 -2.86 0.004 .6034253 .909842 4 | 1.159346 .1570701 1.09 0.275 .8889782 1.511943 5 | .9783955 .1877661 -0.11 0.909 .6716738 1.425183 6 | 1.715374 .3698866 2.50 0.012 1.124123 2.617602 | levelofcomp | 1.4148 .2024807 2.42 0.015 1.068745 1.872905 | commrace | 2 | 1.249453 .1235413 2.25 0.024 1.029334 1.516644 3 | 1.186167 .3076193 0.66 0.510 .7135035 1.971948 4 | .8438527 .1728456 -0.83 0.407 .5648275 1.260717 | commsex | 1.251289 .1528852 1.83 0.067 .9848182 1.589862 commrole | .6950914 .0628416 -4.02 0.000 .5822195 .8298452 _cons | .0569505 .0126473 -12.90 0.000 .0368525 .0880093 ------

196

Table E3: Negative – Model 3 (Pro + > 10) Logistic regression Number of obs = 9811 LR chi2(12) = 68.10 Prob > chi2 = 0.0000 Log likelihood = -2221.0829 Pseudo R2 = 0.0151

------negativelog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | .6217347 .0697811 -4.23 0.000 .4989643 .7747128 3 | .3691082 .0845822 -4.35 0.000 .2355583 .5783743 4 | .5418964 .1702479 -1.95 0.051 .2927479 1.003087 | athletesport | 3 | .6918448 .077543 -3.29 0.001 .5553985 .8618123 4 | 1.125553 .1544014 0.86 0.389 .8602008 1.472761 5 | .9605857 .1856706 -0.21 0.835 .6576703 1.40302 6 | 1.65501 .3580861 2.33 0.020 1.083005 2.529128 | commrace | 2 | 1.250892 .1281547 2.19 0.029 1.023325 1.529065 3 | 1.14502 .3230479 0.48 0.631 .6586594 1.990513 4 | .9638995 .2046084 -0.17 0.862 .6358372 1.461227 | commsex | 1.188134 .1481659 1.38 0.167 .9305001 1.5171 commrole | .718682 .0681896 -3.48 0.000 .596724 .8655656 _cons | .0863977 .0153571 -13.78 0.000 .0609821 .122406 ------

Table E4: Negative – Model 4 (College All) Logistic regression Number of obs = 1304 LR chi2(7) = 27.23 Prob > chi2 = 0.0003 Log likelihood = -223.61664 Pseudo R2 = 0.0574

------negativelog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------2.athleter~e | .897153 .2897891 -0.34 0.737 .4763434 1.689713 3.athletes~t | 1.816814 .5816363 1.87 0.062 .9700764 3.40263 | commrace | 2 | .8536921 .3402421 -0.40 0.691 .3908875 1.86445 3 | 1.251069 .8644093 0.32 0.746 .3229647 4.846269 4 | .2015918 .159733 -2.02 0.043 .0426595 .9526417 | commsex | 3.656519 2.712401 1.75 0.080 .8543732 15.64905 commrole | .3368284 .1131286 -3.24 0.001 .1743896 .6505744 _cons | .0264942 .0238267 -4.04 0.000 .0045462 .1544015 ------

Table E5: Negative – Model 5 (Professional Football) Logistic regression Number of obs = 2844 LR chi2(7) = 47.40 Prob > chi2 = 0.0000 Log likelihood = -697.44055 Pseudo R2 = 0.0329

------negativelog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------

197

athleterace | 2 | .3906887 .0605133 -6.07 0.000 .2883961 .529264 3 | .6619175 .2892663 -0.94 0.345 .2810723 1.558798 4 | 1 (empty) | commrace | 2 | 1.14842 .2017895 0.79 0.431 .8138339 1.620563 3 | 1.307241 .6086032 0.58 0.565 .5248891 3.255695 4 | .5586081 .2541963 -1.28 0.201 .2289631 1.362853 | commsex | 1.714247 .4083012 2.26 0.024 1.074814 2.734095 commrole | 1.039477 .1646453 0.24 0.807 .7620633 1.417878 _cons | .0675459 .0199566 -9.12 0.000 .0378537 .1205283 ------

Table E6: Negative – Model 6 (Professional Football & White Commentators) Logistic regression Number of obs = 1888 LR chi2(4) = 44.41 Prob > chi2 = 0.0000 Log likelihood = -471.63691 Pseudo R2 = 0.0450

------negativelog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | .3261135 .0621888 -5.88 0.000 .2244126 .4739039 3 | .5321127 .3241128 -1.04 0.300 .1612619 1.755801 | commsex | 1.896743 .4933144 2.46 0.014 1.139262 3.157863 commrole | 1.378834 .2614994 1.69 0.090 .9507782 1.999606 _cons | .0648679 .0191027 -9.29 0.000 .0364219 .1155306 ------

Table E7: Negative – Model 7 (Professional Football & Nonwhite Commentators) Logistic regression Number of obs = 956 LR chi2(4) = 10.36 Prob > chi2 = 0.0347 Log likelihood = -221.69362 Pseudo R2 = 0.0228

------negativelog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | .5514736 .1507621 -2.18 0.029 .3227178 .942381 3 | .8601129 .5470705 -0.24 0.813 .2472595 2.991975 | commsex | 1.61788 .9842715 0.79 0.429 .4910205 5.330805 commrole | .5461474 .1486441 -2.22 0.026 .320361 .931065 _cons | .0785878 .0483176 -4.14 0.000 .0235514 .2622366 ------

Table E8: Negative – Model 8 (Professional Basketball) Logistic regression Number of obs = 3966 LR chi2(8) = 13.11 Prob > chi2 = 0.1081 Log likelihood = -857.55671 Pseudo R2 = 0.0076

------negativelog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | 1.458533 .3188343 1.73 0.084 .9502642 2.238661 3 | .9076553 .5436269 -0.16 0.871 .2806119 2.935862

198

4 | .8954646 .2860283 -0.35 0.730 .4788049 1.674705 | commrace | 2 | 1.153075 .1819115 0.90 0.367 .8463898 1.570887 3 | .6237668 .3758487 -0.78 0.433 .1914844 2.031941 4 | .6638896 .2738883 -0.99 0.321 .2957548 1.490253 | commsex | 1.204684 .2341071 0.96 0.338 .823111 1.763143 commrole | .7524956 .1111835 -1.92 0.054 .5632955 1.005244 _cons | .0562627 .0142889 -11.33 0.000 .0342013 .0925546 ------

Table E9: Negative – Model 9 (Professional Basketball & White Commentators) Logistic regression Number of obs = 2512 LR chi2(5) = 5.80 Prob > chi2 = 0.3265 Log likelihood = -568.01414 Pseudo R2 = 0.0051

------negativelog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | 1.3784 .3624293 1.22 0.222 .8233122 2.307734 3 | .7359929 .5374537 -0.42 0.675 .1759096 3.07934 4 | .6794058 .2906362 -0.90 0.366 .2937666 1.571289 | commsex | 1.135437 .2341744 0.62 0.538 .7578961 1.701047 commrole | .7784715 .1416433 -1.38 0.169 .5449619 1.112037 _cons | .0679629 .0161118 -11.34 0.000 .0427051 .1081593 ------

Table E10: Negative – Model 10 (Professional Basketball & Nonwhite Commentators) Logistic regression Number of obs = 1454 LR chi2(5) = 7.38 Prob > chi2 = 0.1937 Log likelihood = -288.76534 Pseudo R2 = 0.0126

------negativelog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | 1.727667 .6785229 1.39 0.164 .8001284 3.730443 3 | 1.715939 1.801882 0.51 0.607 .219113 13.43803 4 | 1.326026 .6394122 0.59 0.558 .5153485 3.411953 | commsex | 2.275189 1.655104 1.13 0.258 .5467696 9.4674 commrole | .6278589 .1520893 -1.92 0.055 .3905453 1.009375 _cons | .0301799 .0222708 -4.74 0.000 .0071054 .1281883 ------

Table E11: Negative – Model 11 (Professional Baseball) Logistic regression Number of obs = 1958 LR chi2(7) = 32.11 Prob > chi2 = 0.0000 Log likelihood = -404.67942 Pseudo R2 = 0.0382

------negativelog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | 1.458414 .4534515 1.21 0.225 .7929111 2.682485 3 | .5176111 .1939789 -1.76 0.079 .2483184 1.078942

199

4 | 1 (empty) | commrace | 2 | 1.222715 .3050657 0.81 0.420 .7498104 1.993879 3 | 1.546015 .8105406 0.83 0.406 .5532839 4.319956 4 | 2.119927 .8231895 1.94 0.053 .9903546 4.537862 | commsex | .4933719 .1244199 -2.80 0.005 .300965 .8087846 commrole | .4303129 .1119827 -3.24 0.001 .2583875 .7166337 _cons | .1559039 .0732626 -3.95 0.000 .0620668 .3916109 ------

Table E12: Negative – Model 12 (Professional Baseball & White Commentators) Logistic regression Number of obs = 1206 LR chi2(4) = 19.11 Prob > chi2 = 0.0007 Log likelihood = -257.63486 Pseudo R2 = 0.0358

------negativelog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | 1.959513 .8695276 1.52 0.130 .8211648 4.67591 3 | .8032525 .4068951 -0.43 0.665 .297624 2.167885 4 | 1 (empty) | commsex | .447405 .1324304 -2.72 0.007 .2504655 .7991967 commrole | .4556389 .1426066 -2.51 0.012 .2467234 .8414557 _cons | .1464596 .0806241 -3.49 0.000 .04979 .4308175 ------

Table E13: Negative – Model 13 (Professional Baseball & Nonwhite Commentators) Logistic regression Number of obs = 752 LR chi2(4) = 12.06 Prob > chi2 = 0.0169 Log likelihood = -147.35041 Pseudo R2 = 0.0393

------negativelog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | 1.016083 .4502815 0.04 0.971 .4263003 2.421825 3 | .2696996 .162391 -2.18 0.030 .0828622 .8778172 4 | 1 (empty) | commsex | .7981389 .3983646 -0.45 0.651 .3000748 2.12289 commrole | .3865203 .1734254 -2.12 0.034 .1604176 .9313064 _cons | .2000928 .1369172 -2.35 0.019 .0523337 .765036 ------

Table E14: Negative – Model 14 (Professional Hockey) Logistic regression Number of obs = 701 LR chi2(4) = 15.38 Prob > chi2 = 0.0040 Log likelihood = -140.02811 Pseudo R2 = 0.0521

200

commrace | 2 | 3.074917 3.199548 1.08 0.280 .4000657 23.6339 3 | 1 (empty) 4 | 1.701922 1.98469 0.46 0.648 .1731068 16.73267 | commsex | 3.387613 3.522368 1.17 0.241 .4414004 25.9989 commrole | .3924123 .1427828 -2.57 0.010 .1923204 .8006818 _cons | .01275 .0195953 -2.84 0.005 .0006271 .2592419 ------

Table E15: Negative – Model 15 (Professional Hockey & White Commentators) Logistic regression Number of obs = 534 LR chi2(2) = 10.54 Prob > chi2 = 0.0051 Log likelihood = -121.26195 Pseudo R2 = 0.0416

Table E16: Negative – Model 16 (Professional Hockey & Nonwhite Commentators) Logistic regression Number of obs = 170 LR chi2(0) = 0.00 Prob > chi2 = . Log likelihood = -18.950584 Pseudo R2 = 0.0000

Table E17: Negative – Model 17 (Professional Golf) Logistic regression Number of obs = 264 LR chi2(6) = 40.18 Prob > chi2 = 0.0000 Log likelihood = -69.192092 Pseudo R2 = 0.2250

------negativelog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------2.athleter~e | .0750892 .0478934 -4.06 0.000 .0215112 .2621143 | commrace | 2 | 1.563664 .9880115 0.71 0.479 .4532168 5.394868 3 | .9158757 1.12034 -0.07 0.943 .0832931 10.0708 4 | 1.414054 1.20387 0.41 0.684 .2665546 7.501457 |

201

commsex | 9.277868 9.872313 2.09 0.036 1.152681 74.67708 commrole | .3490983 .1970886 -1.86 0.062 .1154488 1.055616 _cons | .0625454 .0846093 -2.05 0.040 .0044129 .886486 ------

Table E18: Negative – Model 18 (Professional Golf & White Commentators) Logistic regression Number of obs = 171 LR chi2(3) = 37.58 Prob > chi2 = 0.0000 Log likelihood = -42.909327 Pseudo R2 = 0.3046

Table E19: Negative – Model 19 (Professional Golf & Nonwhite Commentators) Logistic regression Number of obs = 89 LR chi2(1) = 3.78 Prob > chi2 = 0.0518 Log likelihood = -25.011787 Pseudo R2 = 0.0703

Table E20: Negative – Model 20 (College Football) Logistic regression Number of obs = 372 LR chi2(2) = 1.77 Prob > chi2 = 0.4134 Log likelihood = -88.105369 Pseudo R2 = 0.0099

Table E21: Negative – Model 21 (College Football & White Commentators) Logistic regression Number of obs = 372 LR chi2(2) = 1.77 Prob > chi2 = 0.4134 Log likelihood = -88.105369 Pseudo R2 = 0.0099

202

Table E22: Negative – Model 22 (College Football & Nonwhite Commentators) N/A

Table E23: Negative – Model 23 (College Basketball) Logistic regression Number of obs = 773 LR chi2(6) = 21.58 Prob > chi2 = 0.0014 Log likelihood = -128.66581 Pseudo R2 = 0.0774

------negativelog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------2.athleter~e | .6314481 .3465255 -0.84 0.402 .2153853 1.851225 | commrace | 2 | .4573762 .2064031 -1.73 0.083 .1888627 1.107646 3 | 1.555266 1.099333 0.62 0.532 .3891678 6.215449 4 | .1697603 .1349511 -2.23 0.026 .0357415 .8063052 | commsex | 2.55201 1.935409 1.24 0.217 .5772222 11.28293 commrole | .2494042 .1163047 -2.98 0.003 .0999915 .6220776 _cons | .1320111 .1297538 -2.06 0.039 .0192293 .9062685 ------

Table E24: Negative – Model 24 (College Basketball & White Commentators) Logistic regression Number of obs = 462 LR chi2(3) = 7.28 Prob > chi2 = 0.0635 Log likelihood = -78.716728 Pseudo R2 = 0.0442

Table E25: Negative – Model 25 (College Basketball & Nonwhite Commentators) Logistic regression Number of obs = 291 LR chi2(1) = 0.04 Prob > chi2 = 0.8376 Log likelihood = -49.989105 Pseudo R2 = 0.0004

203

_cons | .0442478 .0142986 -9.65 0.000 .023487 .0833596 ------

Table F1: Off-the-Field – Model 1 (All Comments) Logistic regression Number of obs = 433 LR chi2(2) = 26.71 Prob > chi2 = 0.0000 Log likelihood = -278.38103 Pseudo R2 = 0.0458

------offvalueplus | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | 2.549136 .526519 4.53 0.000 1.700509 3.821263 3 | 5.8 3.523616 2.89 0.004 1.763218 19.07875 4 | 1 (empty) | _cons | .4310345 .0595397 -6.09 0.000 .3288015 .5650543 ------

Table F2: Off-the-Field – Model 2 (Pro Football) Logistic regression Number of obs = 198 LR chi2(1) = 62.10 Prob > chi2 = 0.0000 Log likelihood = -101.26221 Pseudo R2 = 0.2347

------offvalueplus | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | 12.625 4.43742 7.21 0.000 6.339457 25.14263 3 | 1 (empty) | _cons | .2178218 .0512486 -6.48 0.000 .1373513 .3454379 ------

Table F3: Off-the-Field – Model 3 (College Football Only) Logistic regression Number of obs = 17 LR chi2(1) = 0.03 Prob > chi2 = 0.8576 Log likelihood = -11.737985 Pseudo R2 = 0.0014

Table F4: Off-the-Field – Model 4 (Pro Football & White Commentators) Logistic regression Number of obs = 139 LR chi2(1) = 56.13 Prob > chi2 = 0.0000 Log likelihood = -60.175654 Pseudo R2 = 0.3181

------offvalueplus | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | 21.37778 9.972804 6.56 0.000 8.567897 53.33974 3 | 1 (empty) | _cons | .1153846 .0406199 -6.13 0.000 .0578751 .2300405

204

------

Table F5: Off-the-Field – Model 5 (Pro Football & Nonwhite Commentators) Logistic regression Number of obs = 42 LR chi2(1) = 16.17 Prob > chi2 = 0.0001 Log likelihood = -20.981412 Pseudo R2 = 0.2781

Table F6: Off-the-Field – Model 6 (Pro Basketball Only) Logistic regression Number of obs = 135 LR chi2(1) = 1.99 Prob > chi2 = 0.1583 Log likelihood = -91.238136 Pseudo R2 = 0.0108

------offvalueplus | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | 2.018617 1.012691 1.40 0.161 .7551389 5.396113 4 | 1 (empty) | _cons | .6811594 .1288262 -2.03 0.042 .4701786 .9868126 ------

Table F7: Off-the-Field – Model 7 (Pro Basketball & White Commentators) Logistic regression Number of obs = 75 LR chi2(1) = 0.32 Prob > chi2 = 0.5692 Log likelihood = -51.657264 Pseudo R2 = 0.0031

------offvalueplus | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | 1.5 1.072381 0.57 0.571 .3694451 6.090214 4 | 1 (empty) | _cons | .8333333 .2060055 -0.74 0.461 .5133294 1.352824 ------

Table F8: Off-the-Field – Model 8 (Pro Basketball & Nonwhite Commentators) Logistic regression Number of obs = 44 LR chi2(1) = 0.00 Prob > chi2 = 0.9649 Log likelihood = -29.766176 Pseudo R2 = 0.0000

------offvalueplus | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | .9583333 .9287984 -0.04 0.965 .1433997 6.404495 4 | 1 (empty) | _cons | .6956522 .2264647 -1.11 0.265 .3675271 1.316724 ------

205

Table F9: Off-the-Field – Model 9 (Pro Baseball Only)

Logistic regression Number of obs = 71 LR chi2(2) = 9.96 Prob > chi2 = 0.0069 Log likelihood = -36.259228 Pseudo R2 = 0.1208

------offvalueplus | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ------+------athleterace | 2 | .2173913 .2302439 -1.44 0.150 .0272719 1.732879 3 | 1.75 2.065339 0.47 0.635 .1731631 17.68564 4 | 1 (empty) | _cons | 1 1 0.00 1.000 .1408635 7.099071 ------

Table F10: Off-the-Field – Model 10 (Pro Baseball & White Commentators)

Logistic regression Number of obs = 40 LR chi2(1) = 4.15 Prob > chi2 = 0.0416 Log likelihood = -14.833348 Pseudo R2 = 0.1227

Table F11: Off-the-Field – Model 11 (Pro Baseball & Nonwhite Commentators) Logistic regression Number of obs = 23 LR chi2(0) = 0.00 Prob > chi2 = . Log likelihood = -14.133576 Pseudo R2 = 0.0000

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