How Selection Conditions the Effect of Social Networks on Political Attitudes

Choosing to be Changed: How Selection Conditions the Effect of Social Networks on Political Attitudes

Dissertation

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University

Lauren Ratliff Santoro, M.A.

Graduate Program in Political Science

The Ohio State University

2017

Dissertation Committee:

Paul A. Beck, Advisor Janet M. Box-Steffensmeier, Co-Advisor Kathleen M. McGraw Michael A. Neblo ⃝c Copyright by

Lauren Ratliff Santoro

2017 Abstract

Do the social environments in which individuals live and work influence their attitudes and beliefs about politics? Isolating the effect of the social environment on individual political beliefs is constrained by the reality that individuals construct their own social worlds. This dissertation conceptualizes this reality, not as a problem, but as a fundamental theoretical postulate that drives if and how individuals are influenced by their social environments. Specifically, individuals choose to be changed, and this choice conditions where, if, and how they are influenced by their social environments.

Utilizing two novel empirical studies, it demonstrates that individuals’ previously established beliefs about politics both directly and indirectly inform their selection into social networks and contexts where individuals with strong political beliefs and interest in politics are more likely to select into environments that support those beliefs than individuals with weak beliefs and low interest. Furthermore, once the reasons for environment selection are held constant, influence within those settings is illuminated. Individuals who select into politically homogenous groups are more likely to be politically influenced by that group than individuals who select into politically heterogeneous groups, though the campaign can activate even politically heterogeneous groups to act on individuals’ attitudes about politics. The political composition of a social group also alters how influence works in those settings where aspects of the network itself are more likely to impact individuals’ political beliefs in

ii politically homogenous groups; where as, information sharing, cognitive dissonance, and normative pressure operates on attitudes in politically heterogeneous groups.

Taken together, this dissertation demonstrates that individuals deliberately control where, if, and how they are influenced by their social environments. Specifically, individuals are most likely to be influenced by individuals like themselves.

iii For Phillip

Russell

iv Acknowledgments

I am deeply indebted to so many at the Ohio State University, most especially my committee members. Paul Beck was the first person I spoke with at Ohio State shortly after gaining admittance into the Political Science program. We talked about the Democratic de-alignment in the South, but more broadly we wondered about why individuals change their identification with a political party. This shared interest continues to motivate our conversations to this day.

Much of the approach to the study of social influence on individual beliefs is his, including the idea of studying campus student organizations like the College

Republicans and Democrats. A good portion of the literature review in the first chapter came from our co-authored chapter in the inaugural Oxford Handbook of

Political Networks. He provided invaluable feed back at every stage of this process, and this project would not exist without it. It is an honor and privilege to be his student - his last student - and I aspire to model the work ethic, curiosity, and generous spirit with which he approaches academic work.

Janet Box-Steffensmeier was the second person I spoke to on the phone upon admittance into Ohio State, and she has been an ally ever since. She introduced me to the Political Networks community, a group in which I have always felt at home, even as a first-year graduate student. She continually fostered opportunities where I could develop and advance my career. Jan’s support was instrumental in my transition to

v California. Teaching the on-line course with her allowed me to be with my spouse, which enabled me to not only finish the dissertation but also to enjoy doing so. More than anything, she modeled and communicated that the work of a scientist, no matter how important, should always come second to family. I am grateful for this example and, considering how rare it is across academia, feel especially lucky to be her student.

I met Kathleen McGraw shortly after coming to Ohio State in her Political Psy- chology class. The psychological process measures were included at her suggestion, and the experimental design, though not discussed in the dissertation project, was a product of our conversations. She provided crucial feed back at several stages of this project even during her tenure in the administration. Her two-year facilitation of the American Politics Dissertation Workshop was vital to the advancement of many different components of this dissertation. Her commitment to rigorous and thoughtful work inspires my own, and I am thankful for the continual kindness and support that she has shown me throughout my time at Ohio State.

Michael Neblo was a late addition to the committee, but no less valuable. Michael urged me to add a fourth, non-political student group to the study and, in so doing, broadened the scope of the project in necessary ways. The title of the dissertation is his, and I am appreciative of the many ways he helped me clarify the argument of the project as well as it’s implications. He spent many hours over Skype helping me prepare for my job talk, and I would not have been as prepared without his guidance. His work with the Evans Scholars continues to motivate my own work and communicates the value of network panel data. I am thankful for the many ways in which he provided safe environments to voice doubt, frustrations, and fears.

vi There are many others at the Ohio State University that I owe a great deal of intellectual and personal gratitude. Even though Skyler Cranmer came to Ohio State as I made the transition to California, he spent many hours with me over Skype walking me through the Temporal Network Autocorrelation Model (TNAM), and I am grateful for his investment in me despite the difference in time zones. I have learned much from him in the little time that we have worked together, and I look forward to continuing to do so.

Once I decided to study influence in campus student groups, I sought out William

“Chip” Eveland to discuss his own study of student organizations. The idea to follow these student groups over time came from one of our discussions. In addition, the

Wave I survey instrument and first round of IRB applications borrowed heavily from his own study. His work continues to be a model for my own, and I am appreciative of his time and commitment to me as a scholar.

Vladmir Kogan provided comments on this project in the early stages. He en- couraged me to add Michael Neblo to my committee, even though I did not know him, and I am grateful for his insistence that I do so. His concern for the potential power problems and his worries that an individual’s year in college might drive effects provided critical push back in the beginning of this project, and it is stronger for it.

Despite not being on my committee formally, William Minozzi provided invaluable assistance along each stage of my graduate career from math camp and American politics seminars to conferences and the job market. He always took the time to help me learn a new tool or method even though it meant more work and time for himself. His approach to problems, eye for detail, and commitment to excellence have

vii informed my own research in many, unobservable ways. He was never obligated to invest in me, but I am indebted to him for doing so.

I could not have made it through graduate school without my graduate school cohort. Game nights with Jakob Miller and Sergio Diaz provided a nice reprieve from academic work, and I am appreciative of the laughs and fun we shared together. I am consistently impressed with the quality of individuals in the Comparative Politics subfield. Margaret Hanson, Gabriella Lloyd, Carolyn Morgan, and Wei-Ting Yenare particularly first class scholars and women who I am privileged to know. Two members of my cohort, Kristine Kay and Wei-Ting Yen, have been especially important. The friendship that was forged between us over humid summer nights in Ann Arbor carried me through every stage of graduate school. I am continually thankful for their support and unwavering belief in me as a scholar and person.

I always tried to emulate the work and successes of Ohio State alums Dino Chris- tenson, Matthew Hitt, and Anand Sokhey. They are not only incredible scholars, but also great human beings, and I am appreciative of their time and advice throughout many critical junctures of graduate school. There are many other current and former Ohio State students whom I have had the privilege of getting to know and learn from including Elias Assaf, Aisha Bradshaw, Benjamin Campbell, Alex Castillo, Ali- son Craig, Raphael Cunha, Jessy Defenderfer, Marina Duque, Lauren Elliott, Nicky

Mack, William Massengill, Eleonora Mattiaci, Joshua Wu, and Nicole Yadon.

I would be deeply remiss if I did not acknowledge individuals who have been so central to the operations of my research and teaching at Ohio State. I would not have been able to field so many studies over the time period without Melodie McGrothers.

Melodie helped me distribute payments to student groups and individuals in my study

viii and navigate funding regulations from the Office of Sponsored Programs. She always made time to meet, call, or e-mail me despite her busy schedule. I am appreciative of all the hours that she’s worked on my behalf and am grateful for her friendship as well.

From my very first day on Ohio State campus, Courtney Sanders has fielded every inquiry, in person or via e-mail, without complaint or even a hint of impatience. She has helped me navigate through all sorts of policies and procedures from class registration and department deadlines to graduation and funding requirements. Charles

Smith was also crucial in my transition to California, as he enabled me to teach the on-line course remotely. He handled class registration, fielded questions from students and advisors, and represented me to the university when I could not be there in person.

I would also like to thank the Democracy Studies Program and Decision Sciences

Collaborative at the Ohio State University for providing crucial funding for the early stages of this project. In addition, the later waves of the group study and the entire selection study would not have been possible without funding from the National

Science Foundation.

The Ohio State University took a big chance on me, and I am infinitely grateful that they did. I would not have arrived here, though, without my time and training at the University of Texas at Austin. I am thankful for the mentorship of many at the

University of Texas at Austin, including Terri Givens, Gretchen Ritter, and Daron

Shaw. I feel extremely fortunate to be able to continue my career in the University of Texas System - to be going to a place that already feels like home. I will forever be grateful to the University of Texas at Dallas for taking a chance on me. I want

ix to specifically thank members of the search committee, Thomas Brunell, Jennifer

Holmes, Bob Lowry, and Banks Miller. I am looking forward to embarking on the next stage of my career there.

Finally, on a personal note, I need to recognize all of my family and friends who have stood by my side during these past six years. While there are too many to name,

I want to specifically acknowledge the two to whom this dissertation is dedicated. My dad, Russell, continues to be an example of resilience in the face of adversity, and, through great personal sacrifice, imparted the value of education to me from anearly age. My husband, Phil, never wavered in his belief in me and always reminded me that my true identity and worth is found in Christ, no matter how lost or worthless I felt. I am infinitely grateful to be loved and supported by such strong men and forthe strength that they have instilled in me. This dissertation represents a culmination of human effort for an eternal purpose, and I am thankful for God’s faithfulness inthe midst of it all.

x Vita

1987 ...... Born – Dallas, Texas

2010 ...... B.A. in Government and English at The University of Texas at Austin, Austin, TX 2013 ...... M.A. in Political Science, The Ohio State University, Columbus, Ohio 2011 to present ...... Graduate Student, The Ohio State University, Columbus, Ohio

Publications

Santoro, Lauren Ratliff and Paul A. Beck “Social Networks and Vote Choice”. Oxford Handbook of Political Networks, 2017.

Fields of Study

Major Field: Political Science

Studies in: American Politics Political Methodology

xi Table of Contents

Page

Abstract ...... ii

Dedication ...... iv

Acknowledgments ...... v

Vita...... xi

List of Tables ...... xvi

List of Figures ...... xxvi

1. Choosing to Be Changed ...... 1

1.1 Literature ...... 4 1.1.1 Social Networks and Vote Choice ...... 5 1.1.2 Structural Influence Mechanisms ...... 14 1.1.3 Psychological Influence Mechanisms ...... 17 1.1.4 Problems to Inference ...... 19 1.2 Theoretical Argument ...... 23

2. Research Note on Study 1, Selection Study ...... 30

2.1 Sample ...... 32 2.2 Measurement and Instrumentation ...... 34 2.3 Conclusion ...... 37

3. Choosing Where to Change: The Role of Political Predispositions in Environment Selection ...... 39

3.1 The Development of Political Beliefs ...... 40

xii 3.2 Political Predispositions and Selection in the College Environment . 42 3.3 Hypotheses ...... 43 3.4 Data and Measurement ...... 45 3.4.1 Dependent Variables ...... 45 3.4.2 Independent Variables ...... 48 3.4.3 Controls ...... 49 3.5 Parental Influence on Political Attitudes and Beliefs . . . 51 3.6 The Likelihood of Joining Politically Homogenous Contexts . . . . 53 3.7 The Likelihood of Joining Politically Homogenous Networks . . . . 57 3.8 Over Time Analysis ...... 60 3.9 Discussion ...... 64

4. Research Note on Study 2, Group Study ...... 67

4.1 Sample ...... 68 4.1.1 Group Profiles ...... 69 4.2 Detailed Study Procedures ...... 74 4.3 Measurement and Instrumentation ...... 77 4.3.1 Dependent Variables ...... 77 4.3.2 Network Measures ...... 79 4.3.3 Independent Variables of Interest ...... 80 4.3.4 Controls ...... 81 4.4 Conclusion ...... 82

5. Choosing Whether to Change: How Group Political Composition Conditions the Likelihood of Influence in Social Settings ...... 83

5.1 Hypotheses ...... 84 5.2 Data & Measurement ...... 86 5.2.1 Dependent Variables ...... 87 5.2.2 Independent and Control Variables ...... 88 5.3 Cross-sectional Analysis of the First Wave of Data ...... 89 5.4 Cross-sectional Analysis of the Second Wave of Data ...... 96 5.5 Cross-sectional Analysis of the Third Wave of Data ...... 102 5.6 Spatial Dependence and Vote Choice ...... 109 5.7 The Effect of Group Composition on Individual Political Attitudes over Time ...... 114 5.8 Dropout Analysis ...... 123 5.9 Discussion ...... 127

xiii 6. Choosing How to Change: How Group Political Composition Conditions Mechanisms of Influence in Social Networks ...... 130

6.1 Influence Mechanisms in the Literature ...... 130 6.2 Hypotheses ...... 132 6.3 Data & Measurement ...... 132 6.3.1 Psychological Explanations ...... 133 6.3.2 Structural Explanations ...... 134 6.4 Cross-Sectional Evidence ...... 135 6.5 Longitudinal Evidence ...... 145 6.6 Discussion ...... 153

7. Conclusion ...... 155

7.1 Practical Implications ...... 159 7.2 Limitations & Future Work ...... 160 7.3 Looking Ahead ...... 166 7.4 Conclusion ...... 167

References ...... 168

Appendices ...... 177

A. Selection Study Survey Instrument, Wave I ...... 178

B. Selection Study Survey Instrument, Wave II ...... 205

C. Supplementary Material for Chapter 3 ...... 225

D. Constitution of the College Democrats ...... 232

E. Constitution of the College Republicans ...... 238

F. Group Study Survey Instrument, Wave I ...... 251

xiv G. Group Study Survey Instrument, Wave II ...... 273

H. Group Study Survey Instrument, Wave III ...... 298

I. Group Study Survey Instrument, Wave IV ...... 328

J. Supplementary Material for Chapter 5, Part 1 ...... 357

K. Supplementary Material for Chapter 5, Part 2 ...... 385

L. Supplementary Material for Chapter 6 ...... 391

xv List of Tables

Table Page

2.1 Selection Study: Wave I & II Survey Demographics ...... 33

2.2 Selection Study: Wave I & II Survey Response ...... 34

3.1 Summary Statistics of Dependent Variables in the Selection Study . . 47

3.2 Parental Influence on Political Attitudes & Beliefs ...... 52

3.3 The Selection of Homogeneous Political Groups ...... 55

3.4 The Selection of Homogenous Political Networks ...... 59

3.5 Selection Study: Over Time Analysis ...... 61

4.1 Party Identification of Individuals in Scholars Groups . . . . 73

4.2 Group Study Response Rates: Waves I-IV ...... 77

5.1 Spatial Dependence in the College Democrats, Wave I ...... 91

5.2 Spatial Dependence in the College Republicans, Wave I ...... 92

5.3 Spatial Dependence in the Politics, Society, & Law Scholars, Wave I . 93

5.4 Spatial Dependence in the STEM Exploration & Engagement Scholars, WaveI...... 94

5.5 Spatial Dependence in the College Democrats, Wave II ...... 97

5.6 Spatial Dependence in the College Republicans, Wave II ...... 98

xvi 5.7 Spatial Dependence in the Politics, Society, & Law Scholars, Wave II 99

5.8 Spatial Dependence in the STEM Exploration & Engagement Scholars, WaveII ...... 100

5.9 Spatial Dependence in the College Democrats, Wave III ...... 103

5.10 Spatial Dependence in the College Republicans, Wave III ...... 104

5.11 Spatial Dependence in the Politics, Society, & Law Scholars, Wave III 105

5.12 Spatial Dependence in the STEM Exploration & Engagement Scholars, WaveIII...... 106

5.13 Primary Vote Choice for College Democrats & College Republicans . 110

5.14 General Election Vote Choice for PSL & STEM-EE Scholars . . . . . 113

5.15 College Democrats, Modeling Change w/ Network Only ...... 116

5.16 College Republicans, Modeling Change w/ Network Only ...... 117

5.17 Politics, Society, & Law Scholars, Modeling Change w/ Network Only 117

5.18 STEM Exploration & Engagement Scholars, Modeling Change w/ Net- work Only ...... 118

5.19 College Democrats, Modeling Change w/ Network and FoF ...... 119

5.20 College Republicans, Modeling Change w/ Network and FoF . . . . . 120

5.21 Politics, Society, & Law Scholars, Modeling Change w/ Network and FoF ...... 120

5.22 STEM Exploration & Engagement Scholars, Modeling Change w/ Net- work and FoF ...... 121

5.23 Dropouts & Additions between Survey Waves in the Group Study . . 124

xvii 5.24 Mean Trump Support Amongst Dropouts & Additions in the Group Study ...... 125

6.1 Mechanisms in College Democrats, Wave I ...... 136

6.2 Mechanisms in College Republicans, Wave I ...... 136

6.3 Mechanisms in the Politics, Society, & Law Scholars, Wave I . . . . . 137

6.4 Mechanisms in the STEM Exploration & Engagement Scholars, Wave I 137

6.5 Mechanisms in College Democrats, Wave II ...... 138

6.6 Mechanisms in College Republicans, Wave II ...... 138

6.7 Mechanisms in the Politics, Society, & Law Scholars, Wave II . . . . . 139

6.8 Mechanisms in the STEM Exploration & Engagement Scholars, Wave II139

6.9 Mechanisms in College Democrats, Wave III ...... 140

6.10 Mechanisms in College Republicans, Wave III ...... 140

6.11 Mechanisms in the Politics, Society, & Law Scholars, Wave III . . . . 141

6.12 Mechanisms in the STEM Exploration & Engagement Scholars, Wave III...... 141

6.13 Mechanisms in the College Democrats, Modeling Change ...... 146

6.14 Mechanisms in the College Republicans, Modeling Change ...... 147

6.15 Mechanisms in the Politics, Society, & Law Scholars, Modeling Change 147

6.16 Mechanisms in the STEM Exploration & Engagement Scholars, Mod- eling Change ...... 148

6.17 Mechanisms in College Democrats, Modeling T2 ...... 150

6.18 Mechanisms in the College Republicans, Modeling T2 ...... 151

xviii 6.19 Mechanisms in Politics, Society, & Law Scholars, Modeling T2 . . . . 151

6.20 Mechanisms in the STEM Exploration & Engagement Scholars, Mod- eling T2 ...... 152

C.1 Parental Influence on Political Attitudes & Beliefs: No Controls . 226

C.2 Parental Influence on Political Attitudes & Beliefs: Ideology Removed 226

C.3 The Selection of Homogeneous Political Groups: No Controls . . . . . 227

C.4 The Selection of Homogeneous Political Networks: No Controls . . . . 227

C.5 Selection Study: Over Time Analysis without Controls ...... 228

C.6 Selection Study: Over Time Analysis without Controls or Lagged DV 229

C.7 Selection Study: Modeling T2 Attitudes ...... 230

C.8 Selection Study: Modeling T2 Attitudes with No Controls ...... 231

J.1 Spatial Dependence in the College Democrats (w/ Number of Ties and No Controls), Wave I ...... 358

J.2 Spatial Dependence in the College Republicans (w/ Number of Ties and No Controls), Wave I ...... 358

J.3 Spatial Dependence in the Politics, Society & Law Scholars (w/ Num- ber of Ties and No Controls), Wave I ...... 359

J.4 Spatial Dependence in the STEM Exploration & Engagement Scholars (w/ Number of Ties and No Controls), Wave I ...... 359

J.5 Spatial Dependence in the College Democrats (w/ Number of Ties and No Controls), Wave II ...... 360

J.6 Spatial Dependence in the College Republicans (w/ Number of Ties and No Controls), Wave II ...... 360

J.7 Spatial Dependence in the Politics, Society & Law Scholars (w/ Num- ber of Ties and No Controls), Wave II ...... 361

xix J.8 Spatial Dependence in the STEM Exploration & Engagement Scholars (w/ Number of Ties and No Controls), Wave II ...... 361

J.9 Spatial Dependence in the College Democrats (w/ Number of Ties and No Controls), Wave III ...... 362

J.10 Spatial Dependence in the College Republicans (w/ Number of Ties and No Controls), Wave III ...... 362

J.11 Spatial Dependence in the Politics, Society & Law Scholars (w/ Num- ber of Ties and No Controls), Wave III ...... 363

J.12 Spatial Dependence in the STEM Exploration & Engagement Scholars (w/ Number of Ties and No Controls), Wave III ...... 363

J.13 Spatial Dependence in the College Democrats (No Ties and No Con- trols), Wave I ...... 364

J.14 Spatial Dependence in the College Republicans (No Ties and No Con- trols), Wave I ...... 364

J.15 Spatial Dependence in the Politics, Society & Law Scholars (No Ties and No Controls), Wave I ...... 364

J.16 Spatial Dependence in the STEM Exploration & Engagement Scholars (No Ties and No Controls), Wave I ...... 365

J.17 Spatial Dependence in the College Democrats (No Ties and No Con- trols), Wave II ...... 365

J.18 Spatial Dependence in the College Republicans (No Ties and No Con- trols), Wave II ...... 365

J.19 Spatial Dependence in the Politics, Society & Law Scholars (No Ties and No Controls), Wave II ...... 366

J.20 Spatial Dependence in the STEM Exploration & Engagement Scholars (No Ties and No Controls), Wave II ...... 366

xx J.21 Spatial Dependence in the College Democrats (No Ties and No Con- trols), Wave III ...... 366

J.22 Spatial Dependence in the College Republicans (No Ties and No Con- trols), Wave III ...... 367

J.23 Spatial Dependence in the Politics, Society & Law Scholars (No Ties and No Controls), Wave III ...... 367

J.24 Spatial Dependence in the STEM Exploration & Engagement Scholars (No Ties and No Controls), Wave III ...... 367

J.25 Spatial Dependence in the College Democrats (No Ties w/ Controls), WaveI...... 368

J.26 Spatial Dependence in the College Republicans (No Ties w/ Controls), WaveI...... 369

J.27 Spatial Dependence in the Politics, Society & Law Scholars (No Ties w/ Controls), Wave I ...... 370

J.28 Spatial Dependence in the STEM Exploration & Engagement Scholars (No Ties w/ Controls), Wave I ...... 370

J.29 Spatial Dependence in the College Democrats (No Ties w/ Controls), WaveII ...... 371

J.30 Spatial Dependence in the College Republicans (No Ties w/ Controls), WaveII ...... 372

J.31 Spatial Dependence in the Politics, Society & Law Scholars (No Ties w/ Controls), Wave II ...... 373

J.32 Spatial Dependence in the STEM Exploration & Engagement Scholars (No Ties w/ Controls), Wave II ...... 373

J.33 Spatial Dependence in the College Democrats (No Ties w/ Controls), WaveIII...... 374

J.34 Spatial Dependence in the College Republicans (No Ties w/ Controls), WaveIII...... 374

xxi J.35 Spatial Dependence in the Politics, Society & Law Scholars (No Ties w/ Controls), Wave III ...... 375

J.36 Spatial Dependence in the STEM Exploration & Engagement Scholars (No Ties w/ Controls), Wave III ...... 375

J.37 College Democrats, Modeling Change w/ Everything ...... 376

J.38 College Republicans, Modeling Change w/ Everything ...... 377

J.39 Politics, Society, & Law Scholars, Modeling Change w/ Everything . 378

J.40 STEM Exploration & Engagement Scholars, Modeling Change w/ Ev- erything ...... 379

J.41 College Democrats, Modeling T2 w/ Network Only ...... 380

J.42 College Republicans, Modeling T2 w/ Network Only ...... 380

J.43 Politics, Society, & Law Scholars, Modeling T2 w/ Network Only . . 381

J.44 STEM Exploration & Engagement Scholars, Modeling T2 w/ Network Only ...... 381

J.45 College Democrats, Modeling T2 w/ Network and FoF ...... 382

J.46 College Republicans, Modeling T2 w/ Network and FoF ...... 382

J.47 Politics, Society, & Law Scholars, Modeling T2 w/ Network and FoF 383

J.48 STEM Exploration & Engagement Scholars, Modeling T2 w/ Network and FoF ...... 384

K.1 College Democrats: Modeling T2 w/ Everything ...... 386

K.2 College Republicans: Modeling T2 w/ Everything ...... 387

K.3 Politics, Society, & Law Scholars: Modeling T2 w/ Everything . . . . 388

K.4 STEM Exploration & Engagement Scholars: Modeling T2 w/ Everything389

xxii K.5 Support for Presidential Candidates Amongst the College Democrats 389

K.6 Support for Presidential Candidates Amongst the College Republicans 390

K.7 Support for Presidential Candidates Amongst the PSL Scholars . . . 390

K.8 Support for Presidential Candidates Amongst the STEM-EE Scholars 390

L.1 Mechanisms in College Democrats (w/ Controls), Wave I ...... 392

L.2 Mechanisms in College Republicans (w/ Controls), Wave I ...... 393

L.3 Mechanisms in the Politics, Society, & Law Scholars (w/ Controls), WaveI...... 394

L.4 Mechanisms in the STEM Exploration & Engagement Scholars (w/ Controls), Wave I ...... 395

L.5 Mechanisms in College Democrats (w/ Controls), Wave II ...... 396

L.6 Mechanisms in College Republicans (w/ Controls), Wave II ...... 397

L.7 Mechanisms in the Politics, Society, & Law Scholars (w/ Controls), WaveII ...... 398

L.8 Mechanisms in the STEM Exploration & Engagement Scholars (w/ Controls), Wave II ...... 399

L.9 Mechanisms in College Democrats (w/ Controls), Wave III ...... 400

L.10 Mechanisms in College Republicans (w/ Controls), Wave III . . . . . 401

L.11 Mechanisms in the Politics, Society, & Law Scholars (w/ Controls), WaveIII...... 402

L.12 Mechanisms in the STEM Exploration & Engagement Scholars (w/ Controls), Wave III ...... 403

L.13 Mechanisms in the College Democrats (No Ties), Wave I ...... 403

xxiii L.14 Mechanisms in the College Republicans (No Ties), Wave I ...... 404

L.15 Mechanisms in the Politics, Society, & Law Scholars (No Ties), Wave I 404

L.16 Mechanisms in the STEM Exploration & Engagement Scholars (No Ties), Wave I ...... 405

L.17 Mechanisms in the College Democrats (No Ties), Wave II ...... 405

L.18 Mechanisms in the College Republicans (No Ties), Wave II ...... 406

L.19 Mechanisms in the Politics, Society, & Law Scholars (No Ties), Wave II 406

L.20 Mechanisms in the STEM Exploration & Engagement Scholars (No Ties), Wave II ...... 407

L.21 Mechanisms in the College Democrats (No Ties), Wave III ...... 407

L.22 Mechanisms in the College Republicans (No Ties), Wave III . . . . . 408

L.23 Mechanisms in the Politics, Society, & Law Scholars (No Ties), Wave III408

L.24 Mechanisms in the STEM Exploration & Engagement Scholars (No Ties), Wave III ...... 409

L.25 Primary Vote Choice for College Democrats (Wave III) ...... 409

L.26 Primary Vote Choice for College Republicans (Wave III) ...... 410

L.27 General Election Vote Choice for PSL Scholars (Wave III) ...... 410

L.28 General Election Vote Choice for STEM-EE Scholars (Wave III) . . . 411

L.29 Mechanisms in the College Democrats, Modeling Change w/ Network &FoF ...... 411

L.30 Mechanisms in the College Republicans, Modeling Change w/ Network &FoF ...... 412

L.31 Mechanisms in the Politics, Society, & Law Scholars, Modeling Change w/ Network & FoF ...... 413

xxiv L.32 Mechanisms in the STEM Exploration & Engagement Scholars, Mod- eling Change w/ Network & FoF ...... 414

L.33 Mechanisms in the College Democrats, Modeling T2 w/ Network & FoF415

L.34 Mechanisms in the College Republicans, Modeling T2 w/ Network & FoF ...... 416

L.35 Mechanisms in the Politics, Society, & Law Scholars, Modeling T2 w/ Network & FoF ...... 417

L.36 Mechanisms in the STEM Exploration & Engagement Scholars, Mod- eling T2 w/ Network & FoF ...... 418

xxv List of Figures

Figure Page

1.1 Dissertation Argument ...... 28

xxvi Chapter 1: Choosing to Be Changed

Individuals are changed by their social environments precisely because they choose them. Individuals choose to eat at restaurants based on encouragement from friends and even based on Yelp reviews accessible on a smart phone. Individuals buy prod- ucts recommended by those in their social circles and by distant “celebrities” followed on social media sites. They make parenting decisions based on how their own parents raised them and what is the norm in their own adult social environment.

Decision-making is increasingly social in nature. Political beliefs and behaviors are no exception, and perhaps are even more dependent upon others, as much political information that individuals receive originates from the social networks that individuals actively construct. An individual’s decision on which candidate to vote for in a presidential election, for example, may depend on their socialization into politics, voting history, and the political information that’s been transmitted by their peers, family, coworkers, and the media.

Dominant theories of political behavior, however, assume that individuals make political decisions independently. Individuals are thought to rationally consider the costs and benefits of their participation in politics, choosing the course of action that best maximizes their utility (Downs 1957; Riker & Ordeshook 1968; Fiorina 1981).

1 Individual-oriented factors, such as education level, political knowledge, religious participation, and a sense of civic duty, are established influencers of individual political behavior (Campbell, Converse, Miller & Stokes 1960; Verba & Nie 1972; Wolfinger

& Rosenstone 1980; Verba, Schlozman & Brady 1995; Green, Palmquist & Schickler

2004). These theories are not misguided or wrong - they provide crucial insight into individual decision-making - but they conceptualize that individuals make political decisions in isolation and do not account for the reality that humans are social beings.

Social networks are an important, and often missing, component of understanding what shapes individual voting choices and opinions about politics.

Returning to the results of research first conducted in the 1940s (Lazarsfeld, Berel- son & Gaudet 1948; Berelson, Lazarsfeld & McPhee 1954), recent scholarship has considered the role the social environment plays in the individual vote decision and substantial headway has been made. Scholars have demonstrated that people do influence each other in politically consequential ways (Huckfeldt & Sprague 1995;

Huckfeldt, Johnson & Sprague 2004). Specifically, the social environment can influence whether or not individuals participate in politics (McAdam & Paulsen 1993;

Nickerson 2008; Sinclair 2012; Rolfe 2012) and the direction, or outcome, of that participation (Beck, Dalton, Green & Huckfeldt 2002; Sinclair 2012; Klar 2014; Santoro

& Beck 2017).

Social embeddedness is important in understanding how individuals make political decisions, yet individuals select where they are embedded and this choice is often characterized by homophily - the tendency of individuals to associate with similar others. While individuals most often choose to associate with similar others, they are also influenced by others in those environments. Separating selection from true

2 influence remains a challenge - bringing the causality of social networks’ effectson individual political beliefs into question (Shalizi & Thomas 2011).

This dissertation overcomes this inferential problem by bringing the entire influence process to bear on our understanding of if and how social ties impact individuals’ beliefs about politics. Specifically, it analyzes how political predispositions influence selection into social networks and contexts and how that selection, in turn, conditions opportunities for influence in the social setting. To do so, it presents two complementary studies, which address the dominant critique and limitation of social network studies - the selection problem. The first study follows individuals as they select into social environments over time. Utilizing a longitudinal network study of four social groups, the second study holds individual selection into groups constant so that inferences can be made about how social groups operate to affect individual political decisions once selection into those groups has occurred. This network panel study will incorporate experiments to strengthen causal claims and provides insight into the potential mechanisms of the social influence process.

Both scholars and critics of the impact of the social environment on individual political beliefs are missing part of the story. Social contexts and networks are consequential in the formation, persistence, and change of individuals’ attitudes and beliefs about politics. Furthermore, the interdependence of individuals’ beliefs about politics is important in understanding how political decisions are made. The American voter is not an isolated decision-maker, but an interdependent one. The reality that individuals choose with whom to discuss politics is an equally important part of this story, however. Individuals actively construct the environments in which they are influenced and, in so doing, control whether and how they are influenced inthose

3 settings. While most characterize selection as a nuisance, I argue that selection is the key toward understanding both if and how social networks impact individual political beliefs. In some cases, shared interests (a la selection) result in similar political beliefs. In other cases, influence between nodes (via information, social pressure, etc.) result in similar political beliefs. The conditions under which selection and influence are at play need to be understood. The rest of this chapter reviews the literature from which the dissertation is drawn and presents the theoretical expectations that later chapters will test.

1.1 Literature

Since Huckfeldt and Sprague (1987; 1991; 1995) reinvigorated foundational work from

Lazarsfeld and his colleagues on the importance of face-to-face contacts (Lazarsfeld,

Berelson & Gaudet 1948; Bereleson, Lazarsfeld & McPhee 1954), a new generation of scholars has turned toward uncovering the social determinants of political behavior.

Among other things, this body of work asks if and how individual political behavior is influenced by social ties. This research, however, is plagued by many inferential problems - chief among them is the reality that individuals construct their own social worlds.1

1Significant portions of the literature review have been taken from “Social Networks and Vote Choice” by Santoro and Beck (2017), used with permission from both authors, and revised to fit the purposes of this paper.

4 1.1.1 Social Networks and Vote Choice

Central to the study of individual political behavior is the understanding of who participates in politics and towards what ends. While much work has addressed how social networks aid our understanding of who participates (Rolfe 2012; Sinclair

2012; Nickerson 2008), much less work has looked at how social networks influence the political direction of that participation (see Santoro and Beck 2017 for a review), despite its primary focus in the foundational works on this topic (Lazarsfeld, Berelson

& Gaudet 1948; Bereleson, Lazarsfeld & McPhee 1954).

Perhaps the first evidence of the social environment’s influence on political attitudes and beliefs stems from Theodore Newcomb’s 1930s study of young women from well-to-do and mostly conservative households who entered the notoriously liberal setting of Bennington College. If they remained in attendance there, the women became more liberal during their years at Bennington, and these effects persisted across twenty-five years, influencing even their choice of spouses and careers (Newcomb 1943;

Newcomb et al. 1967). Interactions in the social environment were found to have a profound influence on the political views of these young women, moving them away from the views and voting preferences most had held when they entered Bennington.

Not long afterward Paul Lazarsfeld and his colleagues at Columbia University took the study of opinion formation further through their panel studies of voters during the 1940 and 1948 election campaigns for president. Their work in first Erie

County, Ohio (Lazarsfeld, Berelson & Gaudet 1948) and then Elmira, New York

(Berelson, Lazarsfeld & McPhee 1954) pioneered the study of the effects of social processes on voting behavior. Although it emphasized the effects of social cross- pressures that figured prominently in studies of turnout, its primary focus was onvote

5 choice. Their results became the “conventional wisdom” on social network influences

on the vote, even as studies of voting behavior left their study behind, turning in a

more autonomous-individual-decision-maker direction for the next several decades.

Perhaps most important, these foundational works established face-to-face con-

tacts as the most important influences on the stimulation of opinion change (Eulau

1980). Lazarsfeld and his colleagues argued that voting is a “group experience” in

which individuals are influenced by the groups with which they live from day to

day. In their words, “people vote not only with their social group, but also for it”

(Lazarsfeld, Berelson & Gaudet 1948, 148). Lazarsfeld and his colleagues went on to

elaborate a “two-step flow of communications” in which ideas from media and political

elites flow from elite opinion leaders to the mass public, sometimes through non-elite

opinion leaders in the individual’s discussion network. Campaigns activate preexisting

political beliefs, and the increased media attention and informal discussion heighten

interest in the campaign. This interest was seen to lead to the crystallization of po-

litical opinions through selective exposure and, in some cases, persuasion (Lazarsfeld,

Berelson & Gaudet 1948).

For individuals whose minds were made up before the campaign began, the cam-

paign was shown to reinforce those decisions, with stronger partisans insulating themselves from contrary points of view. Accordingly, only a few people were converted or convinced by the 1940 presidential campaign, which resulted in the re-election of the familiar Franklin D. Roosevelt to a third term in office. Those who were converted were exposed to an increased amount of cross-pressures: “the conflicts and inconsistencies among the factors which influence the vote decision . . . drive him[the voter] in opposite directions” (Lazarsfeld, Berelson & Gaudet 1948, 53). The more

6 cross-pressures an individual was exposed to, the later the vote decision crystalized.

However, the vote intentions of 88 percent of voters in the Erie County, Ohio study remained steadfast with one party while the remaining 12 percent of voters who switched parties were less interested in the campaign and were exposed to more conflicting pressures. Accordingly, the 12 percent were more open to persuasion froma personal contact or, ultimately, to not voting.

In their study of residents in Elmira, New York, during the 1948 presidential campaign, Berelson, Lazarsfeld, and McPhee (1954, 19) concurred “votes do not change easily, at least during a campaign” and found that cross-pressured individuals were the ones most likely to change. However, most of this instability occurred within political parties and not across them. These “molecular” changes polarized the electorate at the expense of moderation: the “ordinary campaign is characterized by numerous shifts over short distances of the political spectrum” (Berelson, Lazarsfeld & McPhee

1954, 22).2

Drawing on his involvement in the Columbia studies and expanding the “two- step communication model,” McPhee (1963) conceptualized individual vote choice as resulting from three processes: stimulation, discussion, and learning. Individuals bring their individual (psychological) dispositions and their external (political) stimuli into the stimulation process. Here, individuals sample the distributions of the external stimuli; the weaker (stronger) the internal disposition toward a party, the stronger

(weaker) the stimulus required to elicit a “yes” choice for that party. The output of the stimulation process is an initial preference for or against an issue or candidate. In the discussion process, individuals put their initial preferences through “social reality

2This work also demonstrated that in some circumstances, personal contacts were more influential on the vote choice than the media (see also Katz & Lazarsfeld 1955).

7 testing” (Lewin 1947). In this process, individuals crosscheck their preferences with the preferences of others in their networks. When the individual’s initial preference agrees with that of his or her social intimate, the initial reaction is confirmed and doubts are put to rest. However, when the individual’s initial preference disagrees with that of his or her social intimates, the individual seeks renewed exposure to the external stimuli, or objective reality. This recheck results in either a confirmation or rejection of the advice from the social environment. In the learning process, the surviving convictions from the stimulation and discussion processes inform future political choices. McPhee’s (1963) model remains one of the most comprehensive.

Though it has been tested in part, a complete test of the model remains to be done

(Sokhey & Djupe 2011).

Despite limited opportunities for social influence on vote choice within the bounds of a political campaign, social influence on partisan views may best be illuminated through one’s standing, primary relationships with other people, such as family, spouse, and friends (Jennings & Niemi 1974, 1978, 1981; Jennings, Stoker & Bowers

2009). When discord is present in these primary groups, the impact of the broader social context can be more clearly seen. However, disagreement in these groups is not widespread; “most of the political talk that went on in the living rooms of Elmira, over the back fences, at the bars, on the job, and in similar places - the everyday, informal, grassroots discussion on public affairs that serves as a base of democratic judgment - involved the exchange of mutually agreeable points of view” (Berelson,

Lazarsfeld & McPhee 1954, 108). Thus, initial evidence suggested that the decision of whom to vote for results from the absorption of cues from individuals’ networks

8 and contexts over time, where close and strong ties are of more importance than weak

ones (Granovetter 1973; see also Sinclair 2012).

From these early and foundational works, principally those of Lazarsfeld and his

colleagues, a few key themes emerge. First, voters do not make political decisions

independently, or in isolation; their choices are constrained by the social environment

in which they live. Second, outright conversion of individual political attitudes is

rare; social networks most often reinforce individual partisan views during the course

of a campaign. Third, preference change most often results from the presence of

disagreement, or cross-pressures, in immediate, or primary, groups. This discordant

information leads individuals to check and recheck their initial preferences against

other preferences and against the external context, resulting in a continual process

of preference updating in the presence of new information filtered by the social en-

vironment. And finally, the influence of the social network on individual political

attitudes is more heavily influenced by our relationships with close social ties than

by our discussions with weak ties. McPhee sums these observations up nicely:

(A) fundamental reorientation, a reversal of a previous disposition, is rare in real life . . . It occurs only in the extreme example, where not only a sustained social influence is consistently contrary over a substantial period, but the influence is also always supported by the objective reality of external events . . . [A]fter socialization or other causes have started dispositions in one direction . . . social influence alone cannot reverse that direction. (McPhee 1963, 94)

These pioneering works established the primary importance of face-to-face contacts on the stimulation of opinion change, especially among those individuals who were exposed to competing information or cross-pressures. Several decades after this

9 work, Huckfeldt and Sprague (1987, 1991, 1995) were leaders in jump-starting the political networks field after a long hiatus dominated by the conceptualization ofvoters as autonomous and rational decision-makers (Campbell, Converse, Miller & Stokes

1960; Downs 1957; Fiorina 1981). In their study of the 1984 presidential election in South Bend, Indiana they demonstrated that disagreement, once again, is a key ingredient in social influence: “[W]hen citizens encounter political information that disagrees with their own viewpoints, they may rationally reassess their positions, and herein lies the potential for influence” (Huckfeldt & Sprague 1995, 20). However, social influence on individual political behavior may not result from traditional learning after all (McPhee 1963); rather, information from the social environment may provide efficient shortcuts to the high cost of gathering political information (Downs 1957;

Huckfeldt & Sprague 1995). The social environment is influential primarily because of shared intimacy (social cohesion) and/or shared social position (structural equivalence) between respondent and discussant (Burt 1987; Huckfeldt & Sprague 1991;

Kenny 1998). Spousal and other family discussants primarily influence vote choice because of shared intimacy, respect, and experience, whereas shared social positions may account for influence among nonrelatives (but see Kenny 1998). Furthermore, nonrelative discussants are most influential when perceptions of their beliefs areac- curate and agreement is perceived (Huckfeldt & Sprague 1991; Kenny 1998).

Following this return to its foundations, many studies established correlation, and even possibly causation, between individual candidate choice and the candidate choice of their discussants. Beck et al. (2002) analyzed the effects of partisan cues from the social network within a comprehensive model of the vote decision in the 1992 presidential election. They found that information from individual social networks that

10 is biased toward one candidate or another was significantly related to individual vote choice. Individuals whose personal discussants supported the Democratic (Republi- can) candidate were more likely to vote for the Democrat (Republican). While these effects persisted across high and low levels of interest in politics, personal discussants were more important for less politicized individuals. Moreover, the effect of perceived and actual discussant bias on vote choice survived in the presence of controls for other influences on the vote choice, including party identification, ideology, and organizational and media bias. Drawing on the same national survey, Levine (2005) echoed these results and went on to suggest that both intimate (family and friends) and non-intimate discussants impact people’s political views, again after controls.

Similarly, Huckfeldt, Johnson, and Sprague (2004) demonstrated that vote choice correspondence between respondent and discussant is conditional on the distribution of candidate preferences in the rest of the network, especially among weaker partisans.3 This occurs, they contend, according to a process of autoregressive influence, in which individuals weigh every incoming message against other messages. Messages that agree with each other are accepted while discordant messages are discarded. This autoregressive process can explain opinion change resulting from the social environment.

Evidence of this distributional network effect has been replicated in other recent work. Sinclair (2012) demonstrated that as the percentage of Republicans

(Democrats) in a discussion network increases, the likelihood of a respondent voting for the Republican (Democratic) candidate increases, even when variables influencing the choice of those discussants are controlled for. Using panel data, she showed that

3Huckfeldt et al. (2004, 63) do demonstrate, however, that “strong partisans are not immune to the political messages that are filtered through networks of political communication”.

11 agreement among discussants increases over the course of the campaign. Using the

2000 American National Election Study survey, Ryan (2010) demonstrated that individuals support the candidate who is supported by the majority of their discussion partners. Dominant signals from the social network win out, even when these signals come from less politically knowledgeable discussants. Social networks provide crucial information shortcuts for citizens, and these shortcuts sometimes lead individuals to make “correct” (in the sense of voting for the candidate who best aligns with their interests) voting decisions (Ryan 2010, 2011). Where disagreement in these networks is present, social networks provide ambiguous political signals, resulting in less “correct” voting decisions (Sokhey & McClurg 2012). Taken together, the more discussants in an individual’s network who support the same presidential candidate, the more likely the voter is to vote for that candidate.

The Social Citizen provides perhaps the most direct evidence that social networks influence vote choice and even partisan identification (Sinclair 2012). Sinclair argued that an individual’s closest ties, more likely to be stable over the course of time, drove party identification. Using longitudinal analysis and propensity score matching, she demonstrated that the “partisanship of an individual’s discussion network appears to affect not only the probability that the individual will change party identification but also the party to which the individual will change” (Sinclair 2012, 138). Importantly, these ties go above and beyond ties to parents and family members to include close friends and more expansive social influence circles. Whereas weak ties are important for the introduction of new information, the decision to turn out, and the exposure to diverse political views (Granovetter 1973; Mutz 2006; Rolfe 2012), strong ties are more important for influencing long-term political beliefs, especially with more

12 frequent interaction amongst individuals who are geographically proximate (Sinclair

2012). Similarly, Ryan and Milazzo (2015) demonstrated that Catholics identified as

Republicans in greater numbers over time as a result of their movement into social contexts with pro-Republican and anti-Democratic messages.4

Other work with implications for voting choice has emphasized the dynamic nature of network influence on attitudes about politics. Lazer (2001) conducted a two-wave study with whole-network data of individuals who worked in the Office of Information and Regulatory Affairs (OIRA). He found that while individuals’ policy attitudes conformed to the attitudes of other members of their organization at any single point in time, these policy attitudes appeared to stabilize across time. Individuals who changed their attitudes changed in a direction that was consistent with the attitudes of individuals in their network. Building on this work in the political science context,

Lazer et al. (2010) demonstrated that individuals’ political attitudes become more similar to the attitudes of those they are connected to over time, and this influence is especially powerful among friends in contrast to coworkers.

In summary, recent work in the American context echoes the foundational research of Lazarsfeld and his colleagues in pointing to the consistent influence of social networks on candidate choice in an election (for a review of similar work in the comparative context see Santoro & Beck 2017). While research on the impact of social networks on individuals’ beliefs and attitudes about politics is more limited, there is

4Occasionally, research results show little support for a linkage between political discussions in social networks and partisan views. Klofstad (2011), for example, studied students who lived on campus at the University of Wisconsin, Madison, throughout their first year. His findings demonstrated that civic talk, or discussion about politics and current events, affects individual’s participation in politics. However, he found no support that civic talk affects individual partisan strength or direction. This research result, though, is a lone outlier and may be restricted to partisanship without generalizing to vote choice.

13 initial evidence that networks impact political beliefs.

1.1.2 Structural Influence Mechanisms

Much theoretical and empirical work is devoted to explaining how individuals influence each other’s decision making. I divide these explanations into two categories

- structural and psychological. Structural explanations refer to the specific aspects of the network that affect whether individuals are influenced by their social environments.

The primary structural explanation that the dissertation project addresses is the political composition of the social network and, specifically, how networks of individuals with differing political viewpoints operate on individuals’ own political beliefs and attitudes. It asks: what are the consequences of network homogeneity for individual political behavior? Many scholars have shed light on the effects of network homogeneity on political outcomes, where homogeneity occurs within networks of individuals with similar political attitudes, though a consensus of the role of homogeneity on political outcomes is still elusive.

On the one hand, Mutz (2002a) demonstrates that “high levels of participation

(in politics) go hand-in-hand with homogenous networks” (849-850) while individuals

“entrenched in politically heterogeneous groups retreat from political activity” (851).

Individuals who reside in politically heterogeneous groups are also more likely to make decisions closer to the election (Mutz 2002a) and have less interest in politics overall (Huckfeldt, Mendez & Osborn 2004). This may be especially true for women more than men (Djupe, McClurg & Sokhey 2016; Santoro 2017). However, Mutz

14 (2002b) and Gibson (1992, 2001) also demonstrate that individuals who reside in heterogeneous networks are more likely to be tolerant of diverse political viewpoints than those in politically homogeneous groups. Thus, we face a “trade-off” between a deliberating and participatory public.

To sum results of this research, individuals who reside in homogenous political networks participate at greater rates, hold higher levels of partisanship, have more intimacy and trust in their networks (strong, close relationships) but are less tolerant of diverse political viewpoints. Individuals who reside in heterogeneous networks withdraw from politics to a greater extent and experience more ambivalence and general indecision regarding their political choices; they are more likely to reside in networks with weaker ties but are more tolerant of diversity. Furthermore, individuals’ presence in politically homogenous social networks should lead to vote choices that agree with the network, but their presence in more politically heterogeneous networks fosters indecision and disagreement with at least some members of the network. Of course, not all of these statements are true at all times and are often conditional on the frequency of interaction and the amount of political expertise in the network, among others.

Research is also clear that “the vast majority of citizens live their lives in settings where most of the people they know hold political preferences and beliefs that reinforce their own inclinations” (Ahn, Huckfeldt & Ryan 2014, 11; Mutz 2006; McPher- son, Smith-Lovin & Cook 2001). However, evidence suggests that residual levels of disagreement persist, even though it might not be widespread, within even the most intimate social groups (Huckfeldt & Sprague 1995; Huckfeldt, Johnson & Sprague

15 2004). Additionally, the amount of homogeneity and heterogeneity in personal communication networks is context dependent - constrained by the context in which individuals live and work - where disagreement is more commonly found in the work environment (Mutz & Mondak 2006).

Much of this work, however, is focused on understanding how individuals’ presence in homogenous or heterogeneous groups effects their participation in politics - largely ignoring how network composition affects individuals’ beliefs about politics in the first place. Do homogenous networks result in homogenous political beliefs

- providing opportunities for contagion, where contagion represents influence transferred from one individual to another? Or, do homogenous beliefs result in selection into homogenous groups - providing evidences for homophily? Does the composition of an individual’s social network have meaningful consequences for the formation, persistence, and change of their political beliefs?

In addition to political composition, other aspects of the network itself may also impact individuals’ beliefs about politics. The number and strength of connections between individuals in the network may result in similar behavior. This may result as a consequence of social cohesion, or the shared intimacy of discussants often operationalized through the frequency of conversation (Friedkin 1998; Kenny 1998). Or, it may result as a consequence of structural equivalence, or individuals’ imitation of other similarly positioned individuals in the network (Burt 1987; Huckfeldt & Sprauge

1991; Lazer 2001). Stated differently, while individuals’ political attitudes may notbe determined by the attitudes of the group as a whole (via group political composition, for example), their attitudes may be influenced by the attitudes of individuals that they interact in the group with the most via social cohesion or structural equivalence.

16 Taken together, the structure of the network - in whole or in part - may explain if and how individuals are influenced by their social networks.

1.1.3 Psychological Influence Mechanisms

Psychological explanations, on the other hand, refer to the internal processes by which individuals come to make political decisions. Social learning theory, for example, argues that information transferred between ties leads to change in attitudes or behaviors (Hovland, Janis & Kelley 1953). Information, then, is changing or initializing behavior. Network research has shown that information from the social environment may provide efficient shortcuts to the high costs of gathering political information

(Downs 1957; Huckfeldt & Sprauge 1995; Ryan 2011). Campaigns, political elites, and opinion leaders, often distribute information that is diffused through networks of friends, family, neighbors, and co-workers (Rolfe 2012).

The group dynamics approach of social psychology asserts that group norms and pressure to comply with them leads to attitude change. In this theory, humans are viewed as social beings, needing other individuals as a basis for self-knowledge, determining appropriate responses to environmental demands, and for channeling and regulating their current behavior through the operation of group norms. The instrument of change is the group norm, formally communicated or informally perceived, that is discrepant with an individual’s own attitude or behavior. The agent of change is the pressure toward uniformity with a group coupled with the need to be accepted in or a fear of being rejected by the group (Lewin 1947). Accordingly, individuals

17 may be influenced to change their political behaviors and attitudes when theyfeel pressure to do so by the individuals that they interact with (Sinclair 2012).

Theodore Newcomb found evidence of normative pressure in his influential study of women at the then all-female Bennington College (Newcomb 1943). Young women from conservative households entered the dominantly liberal university setting. Over time, these women adopted more liberal political positions because of the clarity in which liberal norms were conveyed on campus and because of the immediate social pressure to comply with them. Women whose beliefs did not change were found to be more socially isolated. Similarly, Betsy Sinclair (2012) demonstrates that individuals confirm to particular opinions in their social networks, especially when

(non)conformity is highly visible.

An individual may also be motivated to resolve inconsistency in their social milieu.

Cognitive dissonance theory assumes that individuals cannot tolerate inconsistency in their social environment (Festinger 1962). Dissonance, or psychological inconsistency, occurs when one piece of information, or cognitive element, conflicts with another.

Depending on the magnitude and severity of the inconsistency, individuals are motivated to resolve the dissonant elements by updating or changing their own attitudes toward the object. This need for cognitive balance is echoed in other psychological literatures as well (Heider 1958) and in work on motivated reasoning (Kunda 1990).

Research in political science and social psychology has demonstrated that individuals’ motivations to participate in politics are also conditional on the composition of the network. In the presence of conflicting information - as when their parties’ position on a political issue is misaligned with an individual’s own - partisans may be motivated to hold their parties accountable (good citizen) or motivated to maintain

18 allegiances to their side (the fan) (Groenendyk 2013). These internal motivations may be shaped by network composition, where motivations to be a good team player may be more prominent than motivations to be good citizens in politically homogenous groups. Klar (2014) demonstrated that partisans engaged in more partisan motivated reasoning when in homogeneous ideological groups while partisans pursued accuracy driven motivations in more politically diverse settings. Information sharing, social pressure, cognitive dissonance, and internal motivations may be doing the work of network influence on individual political beliefs.

1.1.4 Problems to Inference

Researchers’ ability to establish a causal link between social networks and political behavior is frustrated by the reality that individuals construct their own social worlds.

Because individuals choose with whom to discuss politics, it is difficult to establish whether social networks act on political behaviors independently or if they do so by (other) shared, and perhaps unobservable, characteristics that predict the formation of a tie. Furthermore, this choice is often characterized by homophily, or likes associating with likes (see McPherson, Smith-Lovin & Cook 2001 for a summary).

The question of causality may be an even greater challenge for vote choice than for turnout, and the smaller number of studies in this area might be indicative of this problem. Thus, despite evidence that individual political attitudes are influenced by the attitudes of others, evidence also suggests that individuals unconsciously or, even, consciously associate with others who share similar political attitudes.

19 It follows that work on social networks and vote choice requires assuming that individuals do not form relationships with others for explicitly political reasons. And, in fact, most of the literature suggests that this is the case. Individuals primarily establish ties for non-political reasons, though political attitudes are often shared among ties (Huckfeldt & Sprague 1995; Walsh 2004; Sinclair 2012), and both general and political networks have similar levels of political homophily (Eveland & Kleinman

2013). However, some recent work suggests that politics may directly be involved in the selection of social alters (Bello & Rolfe 2014). In their analysis of an online dating community, Huber and Malhotra (2017) demonstrate that individuals use information about a potential match’s political affiliation to select mates. The potential mate’s political affiliation rivals other consequential predictors of selection, such as education levels (but see Klofstad, McDermott & Hatemi 2012). Political preferences also affect whom individuals rate as attractive (Nicholson, Coe, Emory

& Song 2016). If individuals do select discussants for primarily political reasons, then “shared political preferences precede relationship formation rather than following from it“, further confounding efforts to separate homophily and contagion (Huber &

Malhotra 2012, 26).

The direction of causality between networks and vote choice also presents an issue. Networks can influence individual attitudes while, at the same time, individual attitudes can influence the social network. This co-evolution of the individual and the network is presumed, though rarely addressed empirically (but see Lazer 2001;

Lazer, Rubineau, Chetkovich, Katz & Neblo 2010). Analysis of purely cross-sectional data prevents ruling out reverse causation because the researcher cannot differenti- ate between subjects who are more likely to demonstrate certain political attitudes

20 despite a change in their social ties. Additionally, because individuals share common social settings, these environments could account for similar attitudes among individuals. Shared environmental influences could act upon individuals leading to similar behavior instead of the ties between individuals in that environment.

Like the dominant psychological and economic explanations of voting behavior, the majority of work on social influence in politics analyzes influence at a single point in time. Name generators, a common tool for studying social influence, ask survey participants for political and background information of a few people that they discuss politics or important matters with. These “single shots” are isolated in time and cannot account for the entire influence network; specifically, name generators often do not penetrate into networks deeply enough to find heterogeneity - a necessary but insufficient condition for change to occur (Eveland, Appiah & Beck 2016). Fur- thermore, they cannot address the broader social setting in which an individual is embedded.

Responses to selection and other inferential problems are often shortsighted and incomplete. Most work aims to statistically control for the factors that might influence discussant selection, such as race, gender, social class, and even party identification. In fact, many of the findings in this literature can only hold under the explicit assumption that controlling for characteristics about the respondent and her discussants effectively eliminates the selection problem (Sinclair 2012). While controlling for shared characteristics is important, the possibility that other, unobserved factors are causing the increased similarity in political attitudes cannot be definitively ruled out. Simulations, propensity score, and other matching techniques have been utilized

21 to overcome these limitations in observational settings (Sinclair 2012); however, there are unique limitations and problems with employing these techniques as well.

While there remains no perfect or complete solution to the inferential problems that plague social network research, researchers have begun to take selection more seriously by designing research in light of this reality. This includes designing research that occurs across time (longitudinally) and space (comparatively) (Hopmann,

Matthes & Nir 2015). Returning to the foundational work of Lazarsfeld and his colleagues at Columbia University, panel studies can be utilized to study voters before, during, and after political campaigns. Panel studies contain advantages that single- shot election surveys, especially post-election surveys, do not. Small group studies also provide a path forward, where depths of understanding of the influence process are gained with a sacrifice of external validity (Mendelberg 2005; Sokhey &Djupe

2011; Eveland & Kleinman 2013).

Despite the difficulties of experimental work in research on social influence, creative work, such as that by Klar (2014), provides a path forward. Klar (2014) embedded experimental subjects into groups with contrasting homogenous or heterogeneous political preferences. In discussions of two political issues, partisans were found to engage in more partisan motivated reasoning in ideologically homogeneous groups, but to pursue more accuracy-driven information when their settings were diverse - providing experimental evidence that the partisan make-up of a social network is consequential for the vote decision (see also Krupnikov, Ryan & Milita 2015). Utilizing novel software that links individuals together in real time, Assaf, Bond, Cranmer,

Kaizer, Santoro and Sivakoff (2017) randomly assign information about space policy in the United States to study how information diffuses across individuals in campus

22 student organizations (see also Assaf, Bond, Kaizar, Sivakoff, Cranmer & Shikano

2016). Experimental work, however, can be difficult given the realities of the social world; researchers cannot randomly assign individuals to interact with each other in meaningful ways in the real world, for example.

Certainly, the tension between homophily (selection) on the one hand and contagion (influence) on the other is well noted. Recent experimental work has begunto establish robustness in the causality between networks and political attitudes; however, there is no perfect or complete solution to the selection problems that plague social network research. The most innovative work seeks to draw inferences from a variety of data sources and clever research designs, using triangulation to provide leverage on causality. In summary, social explanations of political decision-making face both similar and unique limitations from their individual-oriented predecessors.

1.2 Theoretical Argument

The systematic study of social influence on politics has exploded into the discipline of political science in recent years. While the influence of the social context was not entirely ignored in the latter half of the twentieth century, the individual oriented approaches to studying voting behavior, grounded in both psychological and economic traditions, proved remarkably dominant. Two primary and competing views about the origins and persistence of political attitudes and beliefs, namely involving party identification, arise from these traditions.5

5While political attitudes and beliefs represent different concepts - the former including positive or negative feelings about politics that are more likely to fluctuate and change and the latter involving more long-standing evaluations that persist over time - I use both terms interchangeably.

23 Under one view, party identification is the primary driver of the vote, representing a long-standing identity based on membership in social groups. This view predicts that party identification is relatively stable throughout an individual’s lifetime as it is developed in childhood and is largely exogenous to short-term political factors

(Campbell, Converse, Miller & Stokes 1960; Green, Palmquist & Schikler 2002). Only significant periods of political re(de)-alignment can move party identification onan aggregate level (Beck 1977). Explanations for voter turnout based on characteristics unique to the individual such as education levels, income, and a sense of civic duty, among others follow from this perspective. This view, and revisions to it, is championed by a long line of scholars interested in individual and psychological un- derstandings of voting behavior.

Out of the economic tradition arose an alternate explanation of the role of party identification in the development and persistence of political beliefs. The vote represents a cognitive updating of “tallies” - the information that voters use to make political decisions. If there are a greater number of positive assessments of one party, a rational individual would be well served to vote for that party in a particular contest and, if that tally continues over time, to associate as a member of that party over the others. Political events, candidates, retrospections about the economy, and more information in general allow individuals to continually update the tallies that inform their political decisions. Party identification, under this view, is more flexible atleast in the short-term (Fiorina 1981; Downs 1957). Accordingly, party identification is an affective identity with a political party in the first view but a rational association in the second view.

24 While persuasive in their own right, both views remain somewhat agnostic to the role of the social environment, including social relationships and contexts, in the development and persistence of individual political beliefs.6 Despite mounting empirical work demonstrating the realities of an interdependent electorate, a unifying theoretical framework placed within mainstream ideas of how political beliefs form and persist is lacking. The role of the social environment in influencing individual political beliefs is particularly unclear. I argue that individuals integrate their social identities into the political spectrum vis-á-vis their social networks in rational ways.

This dissertation is premised on the view that the development and persistence of individual political beliefs have important social roots. Parents, among many other things, impart values, political predispositions, and interest in politics. Politi- cal beliefs, adopted by children from their parents, are further shaped by the social and political environments of their childhood. Children neither select their parents nor their siblings, family, or the environments in which their childhood takes place and yet, they are substantially influenced by them both biologically, through shared genetic make-up, and environmentally, through shared environments. Pre-adult political socialization then can be conceptualized as a social process by which political beliefs and behaviors are transferred through the strong, dyadic tie between parent and child. Parental influence on the political beliefs of their child is consequential primarily because of the nature of this tie. The extent of the parental influence, however, is conditional on the strength and clarity of that tie as well as on the larger social structure in which the dyad is embedded.

6Though, Campbell et al. (1960) do articulate that a change in partisanship can be attributable to changes in the social context such as marriage or a change in neighborhood.

25 The formation of political beliefs is consequential not only toward understanding the social forces that shape individual political beliefs, but also toward understanding the reasons why individuals select into social environments. As individuals age and mature, their ability to make independent selections into social environments increases. Their previously established beliefs about politics help predict which social environments they select into in the first place, and this selection can be indirect, when tie formation occurs for non-political reasons, or direct, when tie formation occurs for explicitly political reasons. Individuals from politically homogenous backgrounds, for example, may prefer to associate with like partisans in the future. Individuals who associate with similar individuals on other, non-political dimensions, such as religion, might, at the same time, also be exposed to co-partisans as some personality and demographic traits are shared between co-partisans (Graham, Haidt & Nosek 2009;

Gerber, Huber, Doherty & Dowling 2011). Additionally, the social environments in which individuals are embedded affect and constrain the beliefs and behaviors that occur within them. Yes, individuals create their own social worlds, but the materials with which they do so are context dependent - constrained by the environments in which they live and work. However, the opportunity structure of who individuals become friends with or the environments that they select into is not well understood.

Individuals choose the environments in which they are changed and, in so doing, choose both if and how they will be influenced in those settings. First, by selecting into politically homogenous groups, individuals choose to be influenced by individuals like themselves both inside and outside of a political campaign and especially when individuals in the group are interested in politics. By selecting into politically heterogeneous settings, individuals both consciously and unconsciously choose settings

26 where their attitudes and beliefs about politics are not likely to be influenced by the

attitudes of the group as a whole - even though they may be influenced by the ties

they have to other individuals in the group. When politics is more salient, however,

individuals have less ability to control messages from their social environments, and

even politically heterogeneous settings can act on their beliefs.

Second, individuals choose how they will be influenced in social environments.

The influence process should work differently amongst individuals in different types of groups. In politically homogeneous groups, aspects of the network itself, such as the number of ties to the group, may be more important predictors of individual political attitudes than in politically heterogeneous groups where internal process mechanisms may be at work. Selection, then, is no nuisance; it is an important - if not the most important - determinant of where, if, and how individuals are influenced by their social environments.

Knowledge of how individuals select into their social environments and how these environments shape behavior is necessary to understand how political influence occurs in social groups. This work characterizes the process of social influence as a feedback loop in which political predispositions drive selection into contexts and those contexts affect individual political beliefs. This relationship is displayed graphically in

Figure 1.1. The hypothesized reciprocal relationship acknowledges both independent and simultaneous effects of context selection and the social structure and provides the opportunity to test three questions. First, to what extent do individuals chose to be with people like themselves (homophily question)? Second, to what extent does the environment channel individuals towards people like themselves (common environment question)? Finally, and in light of what we will discover about the first

27 two, to what extent are individuals influenced by those in their networks (contagion question)? Influence Selection Social networks & contexts Political Predispositions (T1) Political beliefs (T2)

Figure 1.1: Dissertation Argument

This dissertation will address these questions utilizing evidence from two novel empirical studies. First, it examines selection into social networks and contexts by following young people as they embark upon their college career. Chapter Two de- scribes the first empirical study, which sheds light on the “black box” of selection.

Chapter Three uses this study to understand if and how individuals’ political predispositions factor into their selection of social environments. Second, the dissertation examines how partisan beliefs develop within different kinds of groups - partisan, nonpartisan but political, and apolitical. Following Newcomb’s creative lead, these examinations draw upon focused panel surveys of college students at a large Mid- western university at a time when their social networks are being established and their political beliefs may be most open to change, details of which are described in

Chapter Four. Chapter Five moves away from the selection of groups to understand how much individuals’ political attitudes are connected to the political attitudes of

28 other individuals in social groups that they have consciously selected for political versus non-political reasons. In Chapter Six, the mechanisms through which individuals influence other individuals in politically consequential ways are uncovered, reveal- ing that, in selection, individuals also choose how they are influenced in their social groups. The dissertation applies two separate yet complementary empirical studies to uncover both the process of selection into and influence within social environments.

Finally, this project concludes with a discussion of future work and the implications of results for the American public.

29 Chapter 2: Research Note on Study 1, Selection Study

Understanding how individuals’ political predispositions affect their selection into social contexts and, in turn, how that selection affects their influence in those contexts requires uncovering two black boxes. First, we must understand the link between individual’s political predispositions and their selection into networks and contexts.

Second, we must understand the link between selection and the political influence that occurs in those networks and contexts. This dissertation takes on these tasks by designing, fielding, and analyzing two separate empirical studies.

These studies build upon previous research on both selection and social influence yet go beyond them to overcome their key limitations - mainly their inattention to studying both topics simultaneously. Both dissertation studies draw upon undergraduate students at a large Midwestern university - the Ohio State University in Columbus, Ohio. As of Fall 2016, there were 45,831 undergraduate students at

Ohio State, a majority of whom were Ohio residents (69.6% of all students). Most full-time undergraduate Ohio State students are required to live on campus during their first two years unless space constraints prohibit them from doing so. Beforethe studies discussed here began, individuals already selected into and were selected by

30 the institution of Ohio State. Presumably, this initial selection could limit the rep- resentativeness of the study by social class, personality, and political beliefs, among others.

While the undergraduate population is certainly a convenient one, the university provides an advantageous environment for studying how social networks influence political decision-making during a particularly important time in individuals’ lives.

Most college students have left their homes for the first time, especially at a residential university like Ohio State. They are forming new social networks, both in terms of the individuals with whom they interact in general and through their group memberships.

As they interact with one another in these networks, especially during a presidential campaign cycle, the opportunities for network influence seem particularly strong.

Moreover, the focus on a single campus allows me to uncover the processes of selection and influence by digging deeply into their social networks more than most studies are able to do while concentrating on social environments, including their choices of which informal and formal groups to join. Although I am cognizant that results for college students may not generalize well to the broader adult population in an actuarial sense, they should lay bare the mechanisms of influence, which is a primary interest ofthe dissertation.

Additionally, the studies occur in a unique and tumultuous time in American politics. The 2016 election campaign season was bitter and divisive in both parties’ primary battles and, perhaps, especially so in the state of Ohio. The Republican

Party’s unconventional nominee, Donald Trump, further polarized an already highly politicized electorate and brought campaigning to an all-time low. If anything, Amer- icans today divide themselves and remain divided more often for explicitly political

31 purposes. Thus, despite the uniqueness of the 2016 presidential campaign, I expect

selection to be an even more important factor to address in studies of social influence

on politics than at any time before.

How do political predispositions inform individuals’ selection into social networks

and contexts? In order to study how predispositions influence selection into social

environments, it is necessary to focus on a time of transition into new social environ-

ments. While periods of life-cycle transition can occur at several points throughout

individuals’ lives, it uniformly occurs during their first year of college, as Theodore

Newcomb appreciated almost eighty years ago (Newcomb 1943; Newcomb et al. 1967).

By measuring both political predispositions at the beginning of this transition period and the networks and contexts into which individuals select, I anticipate being able to advance our understanding of how political predispositions influence selection into social environments.

2.1 Sample

To uncover the link between political predispositions and selection, I study adult college students who are incoming first-years at the Ohio State University. Incoming first-year students have yet to enter the university setting, and their selection into campus groups is most likely based on pre-college factors. All incoming first-year students at Ohio State were eligible to participate in this study. From the universe of incoming first-year students to the Ohio State University in the Fall of 2016, 1,000 were randomly selected and invited to participate in the research study via e-mail.

Individuals were eligible to receive a $10 gift-card to Amazon or Target, conditional

32 only on providing their survey consent and an e-mail address for gift-card distribu-

tion. From this sample, 387 individuals participated in the first survey wave, which

ran from August 22, 2016 to October 9, 2016.7 The second survey wave, which launched on January 30, 2017 and concluded on March 5, 2017, followed up with the 387 students who completed the Wave I survey as well as the 613 who did not respond in order to check for potential bias in the Wave I sample.8 Individuals who participated in Wave I of the survey were eligible to receive a $15 incentive for their

Wave II participation. Demographics for each survey wave are included in Table 2.1 and survey response rates are included in Table 2.2.

Mean Std. Deviation Min Max N White 0.72 0.45 0 1 385 Female 0.57 0.50 0 1 385 Age 18.20 0.74 18 28 382 US Citizen 0.94 0.23 0 1 385 White 0.73 0.45 0 1 302 Female 0.57 0.50 0 1 302 Age 18.18 0.74 18 28 301 US Citizen 0.96 0.20 0 1 302

Table 2.1: Selection Study: Wave I & II Survey Demographics

Upon acceptance into the university, students apply for on-campus housing, as

most are required to live on Ohio State campus during their freshmen and sophomore

7The Fall 2016 semester officially began on August 23, 2016, so I am contacting first-year students at a time when their selections are first being established. 8A third survey wave is planned for the Summer of 2017.

33 years. In this application, they can indicate their roommate, visitation, and learning community requests. While they may have a direct role in selecting their roommates, they have less (if any) ability to select their other floor- and dorm-mates. First-years at Ohio State learn about student organizational groups at different activities fairs and programming at the beginning of the school semester and via welcome communications. In addition to the individuals they already knew going into Ohio State,

I expect their broader peer groups to be constructed by their encounters in class, residence halls, and in the student groups that they join.

Contacted Responded Percentage Wave I 1,000 387 38.7% Wave II 387 301 77.8%

Table 2.2: Selection Study: Wave I & II Survey Response

2.2 Measurement and Instrumentation

Because I expect participants’ political backgrounds to be consequential correlates and predictors of their involvement in campus student groups and in their selection of peers, the Wave I survey included a thorough question battery of individuals’ pre-collegiate political background and demographic profile. Among others, these questions included measures of their interest in the 2016 presidential campaign and the interest of their closest friends in politics. The survey asked respondents how frequently they discussed public affairs and politics with family members during their

34 time at home as well as about the number of different people they talked with about the election campaign. Information on the partisanship of their father and mother was also obtained.

A variety of measures of respondent’s political beliefs and attitudes were included in the instrument. The standard seven-point measure of ideology utilized in the

American National Election Study (ANES), which allows for cross-measure compar- ison, was included. In the same vein, the standard measure of partisanship was also included. Because prior research demonstrates that political attitudes are more “movable” over time than political beliefs, the survey incorporated measures of opinions on political issues, perceptions of political leaders from both sides of the political aisle, and feelings toward political and non-political groups, among others. The survey also contained items specific to the 2016 presidential election, as the Wave I battery occurred during the campaign season.

Importantly, I distinguish between individuals’ selection into social contexts and their selection into social networks. Social contexts refer to the physical locations or places where social influence occurs, and social networks refer to the interpersonal relationships of peers, friends, and family in which social influence occurs. The Wave

I survey battery included many measures of the contexts of their current and future involvement on Ohio State campus. Because individuals in this study already selected into the Ohio State University, the survey asked about their reasons for deciding to come to the university in the first place. Individuals were also asked about their college major and their plans to join any academic, social, or athletic groups on campus in Fall 2016. If they mentioned that they planned on joining one of these campus groups, individuals were then asked to list those groups as well as to indicate

35 if they had already been contacted by or participated in any group activities thus far.

If they mentioned that they did not plan on joining a campus group, they were asked to list any groups that they think they wanted to join in Fall 2016. Respondents were explicitly asked if they had any interest in joining political groups at Ohio State and could select response options which included the College Democrats, College

Republicans, Multi-Partisan Coalition, Young Americans for Liberty, Other, and to indicate that they had no interest in joining a political group.

The Wave I survey battery included many measures of respondents’ social networks in and outside of campus life. Respondents were asked to provide the party- identification of their closest friends as well the interest of those friends in politics.

Respondents were also asked about how important politics when it comes to choosing new friends on campus. All respondents were asked if they planned to live on campus, where they would be living on campus (or where they planned to live instead), the first and last name of their assigned roommate, as well as how well theyknew their roommate at this point in time. Finally, respondents were asked if they knew other first-year students at Ohio State as well as any other current students atthe university.

Several important controls were also included in the Wave I questionnaire. Because it is likely that selection into social groups could be a product of personality, where more extraverted individuals join more groups, I included the Ten-Item Personality

Inventory (TIPI). This inventory includes measures of agreeableness, extraversion, openness to experiences, conscientiousness, and emotional stability (Gosling, Rent- frow & Swann 2003). Relatedly, I included a few questions that seek to understand a participant’s level of comfort with conflict. It is also likely that selection into campus

36 groups, especially political groups, and an individual’s beliefs about politics may be correlated with their political knowledge. The survey included a four- and five-point political knowledge battery in Waves I and II, respectively. Standard demographic measures, such as race, social class, religion, and gender, were included as well. Fi- nally, the instrument collected information about the city and state where they were born, where they lived before coming to Ohio State, and where their parents live now.

The Wave II survey included many of the same measures as Wave I. Respondents were again asked about their Fall 2016 involvement in campus organizations in order to verify if they actually joined the groups they said they were going to join in Wave I.

Unsurprisingly, individuals reported that they were going to join many more campus groups than they actually did join. The Wave II survey also included measures of their Spring 2017 campus involvement and again measured the political interest and beliefs of their family and informal friendship groups. The entire survey batteries for

Waves I and II of the selection study are included in Appendix A and B.

2.3 Conclusion

This chapter provides an in-depth preview of the research study that will be analyzed in the next chapter. While previous research assumes that political beliefs do not impact individuals’ selection into social environments, the study described above puts this assumption to the test. It explicitly tests what role, if any, individuals’ previously established beliefs about politics play in their selection of social contexts and networks using a two-wave panel study. If political predispositions are consequential predicators of individuals’ selection into social environments, then researchers must directly take

37 this into account in studies of if and how individual political attitudes are influenced by their social environments.

38 Chapter 3: Choosing Where to Change: The Role of Political Predispositions in Environment Selection

When studying if and how individuals influence one another researchers often lament the “problem” of selection. This problem is typically addressed, often indirectly and unsatisfactorily, using a variety of statistical and design solutions including regression, matching, and experimental design. I argue, however, that selection presents a yet unrealized opportunity that informs how individuals are influenced by their social environments. When selecting into social contexts and networks, individuals choose not only if but also how they will be influenced. The selection decision is not independent from the political influence decision. Often times the selection decision is characterized by homophily, or the tendency of individuals to associate with similar others, in social networks along both demographic and personality characteristics

(see McPherson, Smith-Lovin & Cook 2001 for an extensive review). While these similarities are not explicitly political in nature, they are often correlated with political beliefs and sometimes politics directly factors into how individuals select into contexts and relationships.

This chapter addresses how individuals select into social contexts and networks; specifically, the role political predispositions - previously established political beliefs

- play in that decision utilizing the two-wave selection study described in Chapter

39 Two. Do individuals consider political factors when selecting into social environments? What factors lead individuals to select into politically homogenous and heterogeneous environments? This chapter demonstrates that individuals choose, both directly and indirectly, the environments in which they are changed. Greater initial political homogeneity in individuals’ social environments feed backs into preferences for even greater sorting along those lines over time. Furthermore, the political attitudes of individuals who select into homogenous political environments do change, but this change is most often the result of the selection process itself rather than true influence. Because individuals now have an even greater ability to select both their networks and the information they receive from them today than ever before, it is imperative to understand how individuals choose their contexts and how that selection, in turn, conditions opportunities for influence in those environments.

3.1 The Development of Political Beliefs

The very formation of political beliefs is “social” in nature as beliefs are transferred between the strong tie of parent and child. In fact, the resemblance in political beliefs and behaviors between parent and child is one of the most empirically robust findings in the discipline. Seminal work on political socialization established that parents are consequential influencers of the partisanship of their children (Jennings &Niemi

1974), and that the intergenerational transfer of political attitudes can persist as the children age and become adults themselves (Jennings & Niemi 1981; Jennings,

Stoker & Bowers 2009). The exact impact of the parents on a child’s political beliefs is dependent on the political interest and partisan attachment of the parent, the

40 strength of tie between parent and child, and the outside political events and life forces that operate on the social structure in which the dyad is embedded (Jennings

& Niemi 1968).9

It should then follow that individuals who are socialized in partisan, high-interest environments more firmly adopt the political views of this environment and, thus, seek out similar (partisan, high-interest) environments in the future. Children whose parents’ political views are ambiguous and weak are more likely to diverge from the political beliefs of their parents than children whose parents’ political views are clear and strong. Individuals socialized in partisan, high-interest environments may be less likely to change their political views from that of their parents because of the potential risk of cognitive inconsistency.10

9Limitations to this work exist. While correlations between the political beliefs of the parent and child have been demonstrated, establishing causality, or the direct link between the parent’s political influence and the beliefs and behaviors of the child, has proved especially difficult to demonstrate (but see Healy & Malhotra 2013; Cesarini, Johannesson & Oskarsson 2014). Additionally, pre-birth, or genetic, factors are argued to account for some of the political association between parent and child. When these factors are not accounted for, socialization influences can be overestimated and biased (Alford, Funk & Hibbing 2005; Fowler, Baker & Dawes 2008). Genetics are argued to affect partisanship through personality traits, which are somewhat aligned with parties and partisans today. However, this work cannot account for the fact that parties and the individuals that make them up change over time. The Democratic Party of the 1950s is remarkably different than the Democratic Party of the twenty-first century. Thus, genetic explanations provide an unstable ground on which to build a theory of how the social environment influences individual political beliefs. 10On the other hand, individuals might feel more comfortable disagreeing with their family members than with peers or co-workers. The need for cognitive consistency among family members may, in fact, be lower.

41 3.2 Political Predispositions and Selection in the College En- vironment

While parental influence is consequential, political beliefs and attitudes can change

throughout an individual’s lifetime, open to other forces of influence, such as the col-

lege experience. Theodore Newcomb’s study of the then all-female Bennington College

in the 1930s remains perhaps the quintessential example of this. Young women from

affluent, conservative families entered into the liberal setting of Bennington College.

By following these women over time, Newcomb uncovered that most of their social and

political attitudes became more liberal because liberal attitudes were perceived as the

social norm. Women whose attitudes did not change were not as actively involved on

campus and were more likely to be socially isolated than individuals whose attitudes

did change. These attitude changes were quite stable, persisting over twenty-five and

fifty-year periods (Newcomb 1943; Newcomb et al. 1967; Alwin, Cohen, & Newcomb

1991). Accordingly, late adolescence provides a unique opportunity in which to study

selection, influence, and change in individual political attitudes over time, asthese

changes are consequential throughout the course of their life.

The social environment in which an individual’s beliefs are developed should be

consequential for understanding how an individual selects into social relationships

and contexts in the college environment. If political beliefs influence social sorting,

then the development of these beliefs must be understood ex ante environment selection. Greater political homogeneity in primary groups and environments may lead to greater preference for homogenous political environments in the future. Understand- ing the selection process requires answering, if and how political predispositions affect

42 the social contexts and relationships individuals select into.

3.3 Hypotheses

Most research on social influence on politics assumes that individuals do not form relationships for political reasons in order to make causal claims. This assumption is problematic for at least two reasons. First, even though a majority of the time individuals select into contexts for reasons other than politics (Walsh 2004; Sinclair

2012; Lazer, Rubineau, Chetkovich, Katz & Neblo 2010), those reasons are often correlated with political ones. In fact, research has established that there are political dimensions to personality traits (Gerber, Huber, Doherty & Dowling 2011), religious beliefs (Putnam & Campbell 2010), education levels (Miller & Shanks 1996), race, ethnicity, gender, and sexual orientation (Box-Steffensmeier, De Boef & Lin 2004), and region of country (Gelman 2009), among others. Additionally, a high degree of political congruence has been demonstrated in social relationships, especially spousal ones, and this congruence can increase over time (Jennings & Niemi 1968; Stoker

& Jennings 2008). Intimate discussion partners are likely to share the same party identification, and the polarization in these networks is growing (Beck &Sokhey

2012). Second, this assumption does not account for the fact that, in some cases, an individual’s beliefs about politics directly factor into context selection. Recent work, for example, demonstrates that politics can directly factor into mate (Huber &

Malhotra 2017) and residential selection (Glaser 1996; Bishop 2008).

Distinctions should be made between environments that are selected into indirectly, for non-political reasons, and those selected into directly, for explicitly political

43 reasons, because direct and indirect selection lead to diverse political outcomes. In some cases, political predispositions do directly inform selection, where individuals who more strongly identify as Democrats (Republicans) are more likely to select social contexts that are generally supportive of their same party. Specifically, I expect individuals with strong political beliefs and high interest in politics to be more likely to select into politically homogeneous social contexts than individuals with weak political beliefs and low interest in politics (Hypothesis 1). Relatedly, I expect these same individuals (strong beliefs, high interest) to be more likely to select into homogeneous social networks (select friends) than individuals with weak beliefs and low interest (Hypothesis 2).

Whether and how individuals are influenced by their social environments is conditional on the amount to which politics plays into the selection decision. Direct selection into contexts (Hypothesis 1) and networks (Hypothesis 2) should condition opportunities for influence in these environments. Over time, I expect that movement into social groups that reinforce an individual’s political beliefs will lead to a stronger identification with their original political party. However, the stronger identification is most likely to result from shared similarities (homophily) than from true influence

(Hypothesis 3). Movement into social environments that contradict an individual’s political beliefs will lead to weaker identification with their original political party over time. On the other hand, individuals who select into contexts and networks for non-political reasons may also experience changes in their attitudes toward politics, and this may be more likely to occur due to direct influence from others in those environments (Hypothesis 4). Untangling selection effects from contagion effects is challenging, however, if not impossible.

44 In the context of analyzing the selection decision, I control for other explanations for why individuals select into social environments such as personality, gender, and race, among others. Certainly, these alternative explanations constitute indirect ways political beliefs could factor into social environment selection. An individual may select into a social network based on other interests, but because this interest may be correlated with party identification, they may also be selecting into a group oflike partisans. Taken together, these hypotheses articulate how previously established political beliefs drive the selection of contexts and networks.

3.4 Data and Measurement

To determine the role of political beliefs in context selection, I look specifically at a two-wave panel study of incoming first-year students at the Ohio State University described in detail in Chapter Two. Entrance into the college setting provides a unique opportunity to study how individuals select into social environments because decisions made in the college environment represent some of the first selection decisions independent of parental influence and can persist throughout individuals’ adult lives.

3.4.1 Dependent Variables

In selecting into social environments, individuals select both into social contexts - the places social influence occurs - and social networks - the interpersonal relationships in which social influence occurs. I address each of these in turn. First, I assess therole of previously established political beliefs in the selection of social contexts. In Wave

I, participants were asked, “Do you have any interest in joining any of the following

45 political groups at Ohio State?” Response options were the College Democrats, Col- lege Republicans, Multi-Partisan Coalition, Young Americans for Liberty, Other, and no interest in joining a political group. The College Democrats and College Repub- licans are explicitly partisan political groups, while the Multi-Partisan Coalition and the Young Americans for Liberty (YAL) are explicitly non-partisan political groups according to their mission statements. Though, there is obvious overlap between the

College Republicans and the Young Americans for Liberty; in fact, 56% of those who indicated that they planned on joining YAL also indicated that they wanted to join the College Republicans as well.

From this question, four dichotomous variables were constructed. First, the join partisan groups measure takes the value of ‘1’ if the respondent indicated that they wanted to join one of the two explicitly partisan groups (the College Democrats and

Republicans), and a ‘0’ otherwise. Seventy respondents indicated that they planned to join one of these groups. Because reasons for selection into politically partisan groups could vary between Democrats and Republicans, I model the decision to join the College Democrats and the College Republicans separately. The measure join

Democrats (join Republicans) takes the value of ‘1’ if the respondent indicated that they planned on joining the College Democrats (Republicans) and a value of ‘0’ otherwise. Forty and thirty individuals indicated that they planned on joining the College

Democrats and Republicans, respectively. Finally, I model the decision to not join any political student groups. The measure no political groups takes the value of ‘1’ if a respondent indicated that they did not plan on joining any political group (partisan and non-partisan) and the value of ‘0’ otherwise. Table 3.1 displays summary

46 statistics for these four context measures.

Mean Standard Deviation Min Max Number Join Partisan Group 0.18 0.39 0 1 385 Join Democratic Group 0.10 0.30 0 1 390 Join Republican Group 0.08 0.27 0 1 387 No Political Group 0.78 0.42 0 1 392 Close Friends Dem 0.34 0.48 0 1 409 Close Friends Rep 0.20 0.40 0 1 409 Choose Friends 0.09 0.28 0 1 409

Table 3.1: Summary Statistics of Dependent Variables in the Selection Study

I then turn to an analysis of whether political predispositions factor into the selection of social networks, or in their relationships with other individuals. In both survey waves, respondents were asked, “Are your closest friends Democrats, Republi- cans, or both?” Respondents had the option to select “Almost all Republicans”, “More

Republicans than Democrats”, “About equally Republicans and Democrats”, “More

Democrats than Republicans”, “Almost all Democrats”, “Most do not Identify with a

Particular Party”, and “Unsure of what they are”. From this question, I create two dichotomous measures. First, I create the variable close friends Republicans, which takes the value of ‘1’ if almost all of their friends are Republicans or if they report having more Republican friends than Democratic friends and a value of ‘0’ otherwise.

Second, I create a similar variable, close friends Democrats, for those respondents with all or most Democratic friends. I also asked participants, “How important is politics when choosing new friends on campus?” on a five-point Likert scale from Not

47 at all Important (1) to Very Important (5). From this question another dichotomous variable was constructed where the value of ‘1’ indicates that politics is ‘Very Impor- tant’ and ‘Important’ when choosing new friends on campus.11 Summary statistics for these variables are provided in Table 3.1.

3.4.2 Independent Variables

I expect that previously established political beliefs affect which environments individuals select into, where individuals from political and strongly partisan backgrounds are more likely to select into homogenous political environments than individuals from less interested and political backgrounds. Accordingly, I expect both the direction and strength of political beliefs to be important toward understanding the reasons for which individuals join politically partisan groups. I include a seven-point measure of party identification as well as, and in separate models, two categorical indicators for the strength of identification with the Democratic (Republican) Party, where a value of ‘2’ indicates strong identification with the Democratic (Republican) Party, ‘1’ indicates not very strong identification, and ‘0’ otherwise (Mutz 2002a). I also expect that the party identification of individuals’ parent will be important determinants not only of their own political beliefs but also of the social environments that they select into in the college setting. Parent party identification was also measured on the seven-point scale, where higher values indicate that the parent is more strongly

11Respondents were also asked to list two individuals with whom they most frequently talk about matters that are important to them. However, I do not include this social network measure in the analysis for this chapter.

48 Republican and lower values indicate that the parent identifies more strongly asa

Democrat.12

Feelings about the Democratic and Republican Parties should also predict selection into partisan groups where individuals who feel more favorably toward their own party (closer to ‘10’ on the feeling thermometer scale) and more unfavorably toward the out-party (closer to ‘0’ on the feeling thermometer scale) are more likely to join partisan groups than individuals who feel neutrally toward one or both parties. Be- cause the first survey wave took place during a presidential election year, itmay also be possible that an individual’s feelings towards the two major party candidates,

Hillary Clinton and Donald Trump, might predict whether or not individuals select into partisan groups.

3.4.3 Controls

I include other variables that could impact both the likelihood of joining political groups as well as an individual’s beliefs about politics in the first place. Interest in the 2016 presidential campaign should be an important predictor of joining partisan groups. The variable interest is included in the model as a predictor variable where higher values indicate more interest in the 2016 election and lower values indicate less interest (on a scale from 1-5). Because all individuals in the sample are first- year students at Ohio State, I do not need to control for year in college. Similarly, there is little variation in respondent age (mean=18.2; median=18; standard deviation=0.7), and it is not included in the model. I do include dichotomous measures of a respondent’s gender, where a value of ‘1’ indicates that the respondent is female,

12The mean of the party identification measure for fathers and mothers is 4.54 and 4.04, respectively.

49 and for a respondent’s race, where the value of ‘1’ indicates that the respondent is white; however, neither of these variables have consistent effects on the decision to join partisan groups in the models displayed below. I also include five continuous variables controlling for the Big-Five personality traits, including extraversion, agreeableness, conscientiousness, emotional stability, and openness to experience (Gosling,

Rentfrow & Swann 2003). Personality traits could help explain why individuals select into social environments as well as if and how those environments influence them.

The desire to avoid conflict, especially in the current political environment, could lead individuals to avoid or withdraw from partisan environments as well as to feel more neutrally toward mainstream political parties and candidates. The measure conflict avoidance is an additive measure created from individuals’ responses to three questions regarding their reluctance to talk about politics a) because they don’t like arguments, b) because it creates enemies, and c) because they worry about what people would think. Higher values of this measure indicate less tolerance of conflict

(i.e., more conflict avoidance).13

I also include controls for the total number of groups an individual plans on joining, created from summing up the number of groups respondents indicated they wanted to join, and a measure of whether or not the respondent lived on Ohio State campus to control for an individual’s access to those student groups in the first place. Measures of the frequency of political discussion in their home environment and their friends’ interest in politics were also included.

13The Cronbach’s alpha for the conflict avoidance scale is 0.60.

50 3.5 Parental Influence on Political Attitudes and Beliefs

Before uncovering how individuals’ previously established political beliefs inform con-

text and network selection, I first take a quick look at the resemblance in politi-

cal beliefs and behaviors between parent and child. Work on political socialization

demonstrates that the direction and intensity of parents’ political attitudes are con-

sequential predictors of their child’s political attitudes. The following set of analyses

seeks to replicate those results. The correlation between respondents’ party identifi-

cation and the party identification of their father and mother is quite high (0.59and

0.64, respectively) in the first survey wave.

Turning to more robust tests, I look at the effect of the father and mother’s

party identification on an individual’s own party identification as well as ontheir

feelings toward the Democratic Party, the Republican Party, Hillary Clinton, and

Donald Trump in Table 3.2. For the party identification measures for respondents and

parents, higher values indicate stronger identification as a Republican and lower values

indicate stronger identification as a Democrat.14 In most every case, parents’ party identification is a consequential predictor of respondents’ attitudes about politics.

The more strongly an individual’s mother and father identify with the Republican

Party, the more strongly they identify with the Republican Party. The more strongly

Democratic (Republican) their parents are, the more positive the respondent reports feeling about the Democratic (Republican) Party.

14Individuals who only reported party identification information for one parent are not included in these analyses, including 19 individuals who did not provide their father’s party identification and 9 individuals who did not provide their mother’s party identification. “Don’t know” responses were mean imputed.

51 Party-ID DemFeel RepFeel ClintonFeel TrumpFeel Dad PID 0.07∗∗ -.14∗ -.08 -.21∗∗∗ -.03 (0.03) (0.07) (0.07) (0.08) (0.08) Mom PID 0.2∗∗∗ -.17∗∗ 0.23∗∗∗ -.18∗∗ 0.17∗∗ (0.03) (0.08) (0.07) (0.08) (0.09) Ideology 0.74∗∗∗ -1.00∗∗∗ 1.08∗∗∗ -.87∗∗∗ 1.11∗∗∗ (0.04) (0.08) (0.08) (0.09) (0.09) Interest -.02 0.09 -.07 0.03 -.10 (0.05) (0.12) (0.12) (0.13) (0.14) Talk Politics at Home 0.02 -.16∗∗ -.08 0.01 -.02 (0.03) (0.07) (0.07) (0.07) (0.08) White 0.27∗∗ -.53∗∗ 0.59∗∗ -.42 0.13 (0.11) (0.25) (0.25) (0.26) (0.28) Female -.13 0.76∗∗∗ 0.46∗∗ 0.68∗∗∗ -.20 (0.09) (0.22) (0.21) (0.23) (0.25) Constant -.40∗ 10.46∗∗∗ -.41 8.29∗∗∗ -1.79∗∗∗ (0.22) (0.53) (0.52) (0.56) (0.6) Num. Obs. 362 357 357 354 356 R2 0.75 0.50 0.48 0.45 0.41 F 149.39 50.72 45.42 39.85 33.96 Log-Likelihood -459.45 -753.76 -746.57 -764.58 -794.19

∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.10

Table 3.2: Parental Influence on Political Attitudes & Beliefs

52 This same pattern persists for respondents’ feelings about the two major party

candidates. The more strongly Republican their parents are, the more negative indi-

viduals report feeling about Hillary Clinton and the more positive individuals report

feeling about Donald Trump.15 Interestingly, and in almost every case, the frequency of political discussion in the home environment does not have a significant effect on individuals’ attitudes about politics. Thus, the strength and direction of parent’s political attitudes and not necessarily the frequency of the family’s political discussion drives similarities in the political beliefs of parent and child. Finally, and consistent with conclusions of other works, the mother’s party identification is a more consequential predictor of the child’s political attitudes than the father’s party identification.

3.6 The Likelihood of Joining Politically Homogenous Con- texts

I now turn to testing the central argument of this chapter - that political predispositions both directly and indirectly influence the social environments individuals select into. Do individuals’ previously established political beliefs affect which types of contexts they select into? An exploration into the reasons individuals select into social contexts, especially politically homogenous ones, is important primarily because if previously established political beliefs predict context selection, then shared similarities - and not social influence - may be at work. Researchers cannot reasonably point to influence between dyads unless there is understanding of how the tie formedbe- tween them in the first place. I utilize logistic regression models in order to predict

15The predictive power of parent’s party identification on respondents’ political attitudes persists with and without controls and even when ideology is removed from the model (see Appendix C).

53 the likelihood of joining partisan groups given certain values of individuals’ political predispositions. Coefficient estimates are displayed in Table 3.3.

Table 3.3 provides obvious yet interesting insights into why individuals join certain political environments. The four dichotomous dependent variables are arrayed as headings across the first row, and each column represents results from separate models. Because I am interested in understanding the factors that influence why individuals join partisan groups and which factors are more important in the selection decision than others, I present results of the models that best explain the variance in context selection.

The model of the decision to join partisan groups includes the strength of a respondent’s identification with the Democratic and Republican Parties, their interest in politics, how frequently they discussed politics at home, whether respondents choose friends for political reasons, and the interest of their friends in politics, among other variables. The odds ratios of 2.33 and 3.44 for Democratic and Republican identifiers, respectively, indicate that the odds of joining a partisan group are two and three times larger for strong identifiers than weak identifiers. Similarly, the odds of joining apar- tisan group are higher for those who express more interest in politics and for those who talked more frequently about politics at home.16 The variable choose friends is an ordinal measure indicating how important politics is when choosing new friends on campus, where the value of ‘1’ indicates that it is “Not at all important” and the value of ‘5’ indicates that it is “Very important”. When politics is more important of a factor in choosing new friends on campus, which corresponds to higher values, the odds of joining a partisan group are almost two times the odds of those who report

16This scale ranged from 1-7 where ‘7’ indicates that they spoke about politics with members of their family daily and ‘1’ indicates that they never discussed politics at home.

54 Join Partisan Join Dems Join Reps No Political Group Party-ID - -.98∗∗∗ 1.92∗∗∗ -.07 (0.22) (0.47) (0.09) Strength of Dem-ID 0.85∗∗∗ --- (0.25) Strength of Rep-ID 1.24∗∗∗ --- (0.28) Interest 0.72∗∗∗ 0.40 1.65∗∗∗ -.59∗∗∗ (0.24) (0.31) (0.54) (0.19) Talk Politics at Home 0.33∗∗ 0.51∗∗ 0.16 -.40∗∗∗ (0.14) (0.21) (0.29) (0.12) Choose Friends 0.51∗∗ 0.68∗∗ -0.17 -0.46∗∗∗ (0.22) (0.31) (0.46) (0.18) Interest of Friends 0.19 0.27 -.37 0.16 (0.26) (0.36) (0.55) (0.2) Number of Groups 0.07 -.11 0.51∗∗ -.19∗∗ (0.12) (0.16) (0.25) (0.10) White 0.27 -.17 1.92 0.11 (0.46) (0.54) (1.77) (0.36) Female -.77∗ 0.05 -3.13∗∗∗ 0.47 (0.41) (0.57) (1.02) (0.33) Conflict Avoidance 0.03 -.14 0.63 0.05 (0.18) (0.24) (0.39) (0.15) Extraversion 0.09 0.07 0.29∗ -.07 (0.06) (0.08) (0.15) (0.05) Agreeableness 0.06 0.06 -.10 -.03 (0.08) (0.12) (0.18) (0.07) Conscientiousness 0.10 0.13 -.00 -.01 (0.10) (0.13) (0.21) (0.08) Emotional Stability -.04 -.18∗ 0.26 0.07 (0.08) (0.10) (0.19) (0.06) Openness to Experience 0.01 0.03 -.09 0.03 (0.09) (0.13) (0.19) (0.08) Constant -7.78∗∗∗ -3.13 -25.47∗∗∗ 3.60∗∗ (2.22) (2.79) (6.72) (1.64) Num. Obs. 347 355 355 355 Pseudo R2 0.35 0.44 0.70 0.21 Log-Likelihood -101.42 -60.19 -27.85 -142.98 χ2 110.39 94.65 130.28 77.49 AIC 234.84 150.38 85.69 315.96

∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.10

Table 3.3: The Selection of Homogeneous Political Groups 55 that politics is not an important factor when selecting new friends. Females are less

likely to report wanting to join a political group.

Differences emerge when breaking the analysis up by the decision to joinDemo-

cratic and Republican groups. The odds of joining the Democratic group decrease

by 63% the more strongly Republican an individual reports being; whereas, the odds

of joining the Republican group are almost seven times larger for individuals who

identify more strongly as a Republican.17 Three particularly interesting results merit discussion when modeling the decision to join the College Republicans. First, female respondents are less likely to want to join the Republican group than males are. Sec- ond, the variance in the decision to join the College Republicans explained by this model is 0.70 and the model fit (as measured by the AIC) improves. Finally, more extraverted individuals are more likely to indicate that they want to join a Republican group.

In modeling the decision to not join a political group, many of the variables work in the opposite direction than in the first three models, which suggests that the lack of strong beliefs about and interest in politics may be just as important for context selection as the presence of them. For example, the odds of not joining

a political group increase when interest in politics is low, when political discussion

at home occurred less frequently, and when choosing friends for political reasons is

less important.18 This model also does not have much predictive power and further

exploration into why individuals choose to not engage in political groups is necessary.

17The party identification scale ranges from 1-7, where ‘1’ indicates Strong Democrat and ‘7’ indicates Strong Republican. 18These results persist with and without controls. See Appendix C.

56 Of course, indicating that you will join a partisan group and actually joining a partisan group are two different things. The longitudinal nature of the study allows me to verify if and which partisan groups were actually joined. However, the question “Do you have any interest in joining any of the following political groups at Ohio

State?” was not asked in the second survey wave. Wave II of the survey did ask respondents to report which groups they joined in the previous Fall 2016 semester.

The number of groups an individual planned on joining in Wave I (before the Fall

2016 semester began) is substantially larger than the number of group actually joined in the Fall of 2016. The mean of the number of groups joined in Wave I (2.39) is significantly different (t=11.21, p=0.00) than the mean number of groups thatthey reported joining in Wave II (1.26). Specifically, while 51 individuals who participated in Waves I and II of the survey indicated that they planned to join an explicitly partisan group in Wave I, only one reported actually joining these groups in Wave

II. Comparing respondents’ written responses between Waves I and II, this difference is 14 to 1. Accordingly, there should be further differences between individuals who report that they want to join political groups and those who actually do. However, I do not have the data at this time to determine whether or not this is the case.

3.7 The Likelihood of Joining Politically Homogenous Net- works

Individuals’ beliefs about politics predict their selection into politically homogenous contexts, but do they also predict the friends and networks they choose in college?

Here again, I utilize logistic regression to understand the likelihood of choosing friends based on previously established political beliefs. In the first two columns of Table

57 3.4, I model the likelihood of having all or mostly Democratic and Republican friends.

Individuals who report identifying more strongly with the Democratic (Republican)

Party are more likely to have close friends that are mostly Democrats (Republicans).

Interestingly, while an individual’s own interest in politics does not impact their likelihood of choosing like-party friends, the interest of those friends in politics does.

The more interested their friends are in politics, the more likely respondents are to select friends with clear political preferences. This result is intuitive; friends who are interested in politics provide better signals of their political preferences. Individuals who describe themselves as being more open to new experiences are less likely to have close Republican friends but more likely to have close Democratic friends.

In Column 3 of Table 3.4, I model whether or not politics is very important and important in the decision to select new friends on campus using logistic regression.

Individuals who are more interested in politics are over two times more likely to indicate that politics is important when choosing friends than less politically interested individuals. Individuals who identify more strongly with the Republican Party are less likely to indicate that politics is important when choosing new friends. Finally, individuals who report being more agreeable are less likely to choose friends for political reasons, hinting at today’s contentious political environment.19

Taken together, these results demonstrate that individuals’ previously established beliefs about politics can and do play a primary role in their decisions about which groups to join and which relationships to form. Strong political beliefs and increased interest in politics does factor into the selection of certain types of social environments: mainly, politically homogenous ones. It remains to be seen, however, whether

19Once again, these results persist with and without controls. See Appendix C.

58 Close Friends Dems Close Friends Reps Choose Friends Party-ID -.70∗∗∗ 0.72∗∗∗ -.42∗∗∗ (0.10) (0.10) (0.15) Interest -.20 0.04 0.88∗∗∗ (0.14) (0.17) (0.27) Interest of Friends 0.65∗∗∗ 0.45∗∗ 0.08 (0.17) (0.20) (0.28) White -.19 0.27 -.13 (0.28) (0.40) (0.49) Female 0.31 0.12 0.78 (0.27) (0.32) (0.50) Conflict Avoidance -.01 0.03 0.01 (0.13) (0.15) (0.21) Extraversion -.08∗ -.02 -.07 (0.04) (0.05) (0.07) Agreeableness -.07 -.02 -.30∗∗∗ (0.06) (0.07) (0.09) Conscientiousness -.10 0.11 0.12 (0.07) (0.08) (0.12) Emotional Stability 0.07 0.04 -.10 (0.05) (0.06) (0.08) Openness to Experience 0.18∗∗∗ -.14∗ 0.13 (0.07) (0.08) (0.11) Constant 0.18 -5.99∗∗∗ -3.57∗ (1.10) (1.38) (2.06) Num. Obs. 370 370 370 Pseudo R2 0.23 0.24 0.24 Log-Likelihood -182.61 -140.66 -77.18 χ2 110.72 88.98 48.98 AIC 389.23 305.32 178.36

∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.10

Table 3.4: The Selection of Homogenous Political Networks

59 these selection decisions - the decisions about which social environments to select into

- feed back into the strengthening of political beliefs over time.

3.8 Over Time Analysis

I now turn to an analysis of whether or not the selection of politically homogeneous contexts and networks affects individuals’ attitudes about politics. Specifically, does an individual’s presence in a politically partisan social environment lead to attitude change, and, specifically, the strengthening of political beliefs over time? To answer this question, I model the change in political beliefs between Waves I and II as a result of an individual’s presence in homogenous contexts and networks, their parent’s party identification, their Wave I attitudes, and important controls. Specifically, Imodel the change in party identification, change in feelings toward the Democratic and

Republican Parties, and change in feelings toward Hillary Clinton and Donald Trump.

Results are displayed in Table 3.5.20

The five dependent variables are arrayed as headings across the first row, andeach column depicts results from separate models. Column 1 of Table 3.5 models the difference in party identification between survey waves utilizing a linear regression model.

Fifty-seven percent of respondents’ party identification did not change between survey waves, but 43% of respondents’ beliefs did change. Respondents who identify more strongly with the Republican Party in Wave I come to identify more with the

20Alternatively, I also model individuals’ political attitudes in Wave II as a result of their Wave I attitudes and their presence in partisan social environments. Results, displayed in Appendix C, are remarkably consistent to those discussed in text. The one exception is the change in sign on the lagged dependent variable; more positive attitudes towards the parties and candidates in Wave I result in more favorable Wave II attitudes.

60 ∆ PID ∆ DemFeel ∆ RepFeel ∆ ClintonFeel ∆ TrumpFeel Join Partisan 0.28 -.50∗ 0.24 -.17 0.56∗ (0.17) (0.28) (0.32) (0.33) (0.31) Choose Friends -0.06 0.42 0.36 0.28 -0.33 (0.22) (0.36) (0.41) (0.43) (0.40) Friends Reps 0.19 0.21 0.62∗∗ -.37 -.25 (0.16) (0.26) (0.30) (0.31) (0.29) Friends Dems -.26∗ 0.49∗∗ -.03 0.2 -.25 (0.14) (0.23) (0.26) (0.27) (0.25) ∗∗∗ ∗∗ Party-IDT 1 -.45 -.20 -.03 -.29 -.03 (0.07) (0.12) (0.13) (0.14) (0.12) ∗∗∗ Dem Pty FeelT 1 - -.51 --- (0.05) ∗∗∗ Rep Pty FeelT 1 - - -.47 -- (0.06) ∗∗∗ Clinton FeelT 1 - - - -.45 - (0.06) ∗∗∗ Trump FeelT 1 - - - - -.36 (0.05) Ideology 0.37∗∗∗ -.26∗∗ 0.37∗∗∗ -.23∗ 0.41∗∗∗ (0.06) (0.11) (0.12) (0.12) (0.12) Interest -.09 -.12 -.20 0.13 -.22∗ (0.07) (0.11) (0.13) (0.13) (0.13) Female -.14 0.55∗∗∗ 0.01 0.92∗∗∗ -.03 (0.12) (0.2) (0.22) (0.23) (0.21) Conflict Avoid -.10∗ 0.10 -.19∗ 0.10 -.01 (0.05) (0.09) (0.10) (0.10) (0.10) Extraversion -.02 0.08∗∗ 0.02 -.01 0.02 (0.02) (0.03) (0.03) (0.04) (0.03) Conscientiousness -.00 -.04 0.09 -.02 0.09∗ (0.03) (0.05) (0.06) (0.06) (0.05) EmotionalStability -.01 -.01 -.08∗ -.01 0.02 (0.02) (0.04) (0.04) (0.04) (0.04) Openness to Exp. -.04 0.11∗∗ -.01 0.09 -.09∗ (0.03) (0.05) (0.06) (0.06) (0.05) Num. Obs. 263 264 264 260 263 R2 0.25 0.35 0.29 0.27 0.24 F 4.53 6.97 5.35 4.71 3.98 Log-Likelihood -336.34 -466.26 -501.90 -500.95 -489.48

∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.10; Due to space constraints, variables not significant across models were removed.

Table 3.5: Selection Study: Over Time Analysis

61 Democratic Party in Wave II. However, more conservative individuals report being more strongly Republican in the second survey wave. This pattern suggests that while partisan affiliations did change during the course of the 2016 presidential elections, individuals became more entrenched in their ideologies over time. An individual’s decision to join a partisan group in Wave I had no effect on the change in their party identification over time. Here, we may be seeing the limitation of this measure- being an indicator of wanting to join a partisan group and not for actually joining the group. Respondents whose friends are all or mostly all Democrats identify more strongly as Democrats over time.

Columns 2-5 of Table 3.5 also utilize linear regression to model the difference in political attitudes between Waves I and II. Specifically, if the difference between attitudes in survey waves is negative, feelings about the political parties and candidates have become more negative (unfavorable) over time; a positive difference indicates that feelings have become more positive (favorable) over time. The difference in a respondent’s feelings about the Democratic and Republican Parties are modeled in

Columns 2 and 3, respectively. Here, having more positive feelings toward the Re- publican (Democratic) Party in Wave I results in more negative feelings about the

Republican (Democratic) Party over time. This pattern persists when modeling the difference in feelings about Hillary Clinton and Donald Trump (Columns 4and5).

Individuals’ attitudes about the mainstream parties and candidates, both of whom were relatively unpopular among young Americans, soured over the course of the 2016

62 presidential campaign.21 However, as with party identification, we see that while atti-

tudes do sour, ideologies remain consistent predictors of positive and negative feelings

toward the parties and candidates. Liberals (Conservatives) feel more strongly posi-

tively toward both the Democratic (Republican) Party and Hillary Clinton (Donald

Trump) over time.

While individuals’ social networks, including whether or not their close friends are

mostly Democrats or Republicans, have predictive power over their attitudes about

the political parties, they do not affect attitudes about the parties’ candidates. Indi-

viduals with almost all Republican (Democratic) friends feel more positively toward

the Republican (Democratic) Party over time. Individuals who join partisan groups

feel both more negatively toward the Democratic Party and equally more positively

toward Donald Trump over time. Both of these results can be explained specifically

by individuals who indicated that they wanted to join the College Republicans and

not by individuals who indicated that they wanted to join the College Democrats.22

The attitudes of individuals with interest in joining Republican groups - in choosing to publicly identify with an unpopular political party - do strengthen over the course of the campaign.

Unlike in Table 3.2, parents’ party identification and the frequency of political discussion at home are never consequential predictors of attitude change. Results for these variables are not presented in Table 3.5 even though they are included in the

21Comparing feeling thermometer scores between Waves I and II, mean attitudes towards the Repub- lican Party and Donald Trump decrease (become more negative), while attitudes towards Hillary Clinton and the Democratic party increase (become more positive) over time. 22That individuals who reported being interested in joining the College Republicans - and not individuals interested in joining the College Democrats - are driving the effects in Columns 2 and 5 of Table 3.5 was determined by running the models with both the join Democrats and join Republicans variables in place of the join partisan groups measure.

63 models. These results suggest that parental influence is embedded in the respondent’s own attitudes about politics and does not have an independent effect on attitude change. The college environment is truly a time in which respondents are on their own.

Other control variables behave in intuitively consistent ways given the current political context. The more conflict avoidant an individual reports being, the more unfavorably they feel about the Republican Party (and not the Democratic Party) and the more strongly they identify as a Democrat over time. Female respondents report feeling more favorably toward the Democratic Party and Hillary Clinton over time. Finally, individuals who are more open to new experiences feel more positively toward the Democratic Party but more negatively toward Donald Trump over time.23

3.9 Discussion

This analysis represents an attempt to understand the role of political predispositions in individuals’ selection into social environments. The body of evidence presented here demonstrates that political predispositions are consequential for the selection of social contexts and networks, especially for selection into politically homogenous environments. I find empirical evidence that individuals with strong beliefs about and interest in politics do select into politically homogenous social environments.

Further, an individual’s lack of strong political beliefs and interest in politics also

23In Appendix C, I model the difference in political attitudes between Waves I and II without the control variables. Results persist without controls, though the impact of social networks on attitude change is stronger.

64 predicts their likelihood of abstaining from those environments. Individuals choose the environments in which they are influenced.

Certainly, the difference between the number of individuals who reported wanting to join partisan groups on campus and those who reported actually joining is trou- bling. Unfortunately, and as reported previously, the “Do you have any interest in joining any of the following political groups at Ohio State?” question was not asked again in the second wave of the survey. Accordingly, there is a lack of comparable data from which to compare first and second survey wave responses. Despite these limitations, important differences between those who planned and those who didnot plan on joining partisan groups were still found. Individuals who identified more strongly with a political party and had more interest in politics had higher odds of intentions to select into partisan environments, even if their beliefs did not result in them actually joining a partisan group. While we should expect even starker differences between actual joiners and non-joiners, that there are differences between individuals who planned and did not plan on joining partisan groups is interesting in its own right.

The presence of individuals in certain types of environments does impact whether their political attitudes change over time as well as the direction of that change. The attitudes of individuals in politically homogenous friendship groups strengthen over time toward the direction of the beliefs of those groups. However, it remains to be seen if networks operate on individual political beliefs independent of the reasons that they select into them. While longitudinal designs improve upon inferences from cross- sectional data, they still cannot isolate causal effects. Future work will incorporate a third survey wave as well as matching techniques to bolster causal claims.

65 Future work will clarify and extend the empirical results presented in this chapter. First, it may be worthwhile to incorporate a single additive measure of parent party-identification instead of separate measures for an individual’s mother andfa- ther. Doing so would incorporate the 28 individuals who only reported their mother or their father’s party identification including individuals from non-traditional families. Second, because I expect that individuals with both strong political beliefs and high interest in politics are more likely to select into politically homogeneous environments, it is necessary to include an interaction term between these variables in the models displayed in Tables 3.3 and 3.4. Third, though political knowledge is likely correlated with individual political beliefs and selection into politically homogeneous environments, the knowledge battery is not included in the models presented in this chapter, and future work will incorporate it. Finally, a logical extension of this work would be to test whether individuals’ presence in homogenous political contexts and networks affects their choice of which candidate to vote for in addition totheir attitudes about them.

66 Chapter 4: Research Note on Study 2, Group Study

This dissertation set out to understand how individuals are influenced by their social environments. It argues that individuals’ political predispositions affect their selection into social environments and that that selection, in turn, affects both if and how those environments influence their attitudes and beliefs about politics. Chapters Twoand

Three addressed how individuals’ previously established beliefs about politics impact the social contexts and networks they select into. Political predispositions do factor into selection decisions, where individuals who are more interested in politics and who more strongly identify with a political party are more likely to select into social environments for explicitly political reasons. This direct selection is problematic for making inferences about if and how individuals are influenced by their social environments because even though individuals’ political attitudes do strengthen and change over time, it is difficult to parse out the cause of these changes. Do shared similarities lead to attitude change? What about sharing common environments?

Or, is it the influence of one individual on another? The study described belowis designed to parse out these competing explanations.

In order to study how selection affects influence in social environments, I needthe following. First, I need data on the networks individuals select into. Second, because

I expect individuals to select in to contexts for varying political and non-political

67 reasons, I need variation in the networks into which people select. Third, I need variation in network type, including multiple explicitly political and non-political groups.

Finally, because my theory is about change, I need to follow individuals’ movement in and out of different networks across time. I have designed the following research study to accomplish all four of these things.

4.1 Sample

I utilize a network panel study of four student organizational groups at the Ohio State

University including the College Democrats (CDs), the College Republicans (CRs), the Politics, Society, and Law Scholars (PSL Scholars), and the STEM Exploration and Engagement Scholars (STEM-EE). The variation in the type of group allows me to study social influence under different network conditions and in environments selected for different reasons. Accordingly, I study a group of individuals who,by joining the group, have publicly identified as Democrats (College Democrats), a group of individuals who have publicly identified as Republicans (College Republicans), a group of individuals who have expressed a general interest in politics, but have not immediately identified with a particular partisan side by joining the group (Politics,

Society, and Law Scholars), and finally, a group of individuals who have joined a non-political, academic group (STEM-EE).24

24The use and study of student organizational groups in political behavior is an established, yet resourcefully intensive, empirical method. For example, Eveland and Kleinman (2013), Eveland and Hutchens (2013), and Song and Eveland (2015) use a probability sample of intact voluntary student groups at a large Midwestern research university to study influence networks at a single point of time. Assaf, Bond, Kaizar, Sivakoff, Shikano, and Cranmer (2016) utilize study groups at a German university to understand how information about HPV spreads through real-world social networks. Assaf, Bond, Cranmer, Kaizer, Santoro and Sivakoff (2017) utilize thirty student organizations at a large Midwestern research university to understand how space policy information

68 Wave I of the panel study was conducted in September through November of 2015 before the election season officially began, and Wave II was conducted in February through March of 2016 during the primary campaign in the state of Ohio. The third wave of the panel study launched September 30, 2016 and finished on December

7, 2016 incorporating the general election period. The fourth and final survey wave launched on February 13, 2017, after the presidential election was over, and concluded on April 8, 2017.25 Following individuals in these groups over four periods of time can speak to the effect of group membership on individual political beliefs before, during the primary and general election portions of, and after a campaign. Additionally, because each survey wave followed up with all individuals who were group members in previous time periods, even if they were no longer a group member in that wave,

I can track individuals who have dropped out of the group to understand how their attitudes diverge from the attitudes of individuals who stayed in the group.

4.1.1 Group Profiles

The College Democrats and the College Republicans are recognized student organizations at the Ohio State University. The groups are led by and consist entirely of students, though each group has an affiliated faculty advisor. Individuals are re- cruited to join these groups in student involvement fairs and communications at the beginning of each school semester. Individuals do not apply for membership in these

spreads in organizational networks. Song, Minozzi, Lazer, Neblo and Ognyanova (2016) utilize whole-network panel data of individuals in a nationwide fellowship program from fourteen universities across a five-year period to assess whether individuals enter political discussion purposefully or incidentally. These and similar studies assess how the entire influence network bears on group and individual political behavior. 25Both the third and fourth waves were supported by a doctoral dissertation grant from the National Science Foundation.

69 groups; their membership in the group is determined by their attendance at meetings throughout the semester. While the College Democrats do not require monetary dues from their members, the College Republicans require a small monetary fee.26

Group members participate in weekly meetings, host watching parties around political events, and work with local political campaigns. Their campaign involvement was especially deep during the 2016 election season, beginning as early as 2015. Mem- bers in both the College Democrats and College Republicans come from a variety of years in school. The official constitutions and bylaws for these groups are included in

Appendix D and E.

In the first two survey waves, a majority of members of the College Republicans favored Ohio Governor John Kasich - the winner of the Republican primary in the state and the last remaining challenger to Trump in the nomination race - as well as former Republican presidential candidate Marco Rubio. The Republican group had significantly less favorable views towards Donald Trump especially during the Repub- lican primary in the state (Wave II). The Ohio State College Republicans were also responsible for recruiting students for the Kasich campaign, which helps explain both their pro-Kasich and anti-Trump sentiment. Kasich continued to publically denounce

Trump through the November election and even reported that he did not vote for

Trump for president. Ultimately, however, a majority of individuals in the College

Republicans came to support the Republican nominee for President, Donald Trump, in the general election (Wave III) - some being eventually swayed by the James Comey letter to support him. Though, the group turned most of their efforts during the general election campaign to mobilizing voters for Rob Portman’s reelection campaign

26For the College Republicans, this fee was $10 in Fall 2015 and Spring 2016. The fee changed to $15 in the Fall of 2016.

70 to the United State Senate. Post election (Wave IV), the group struggles to balance

competing interests in the group. Neither is the group anti-Trump enough for the for-

mer Kasich supporters, the more moderate wing of the group, nor are they pro-Trump

enough for the most ardent Trump supporters among them. The struggles within the

College Republicans mirror the dynamics and divisions within the Republican Party

nationwide, though this group is unique in their ties to John Kasich throughout the

early stages of the campaign.27

The College Democrats, on the other hand, initially favored Bernie Sanders over

Hillary Clinton but their favorability ratings equaled out during the Democratic primary in the state (Wave II). However, the bitter and divisive Democratic primary campaign left many unenthusiastic Clinton supporters even though all of them voted for her in the general election. During the general election campaign, the group focused their efforts on the same Senate race as the College Republicans. Here,the

College Democrats mobilized for former Ohio governor Ted Strickland’s campaign against Rob Portman for United State Senator.28 Now that the presidential election

is over, there is much more interest and engagement in politics than before, as its

group members are unified both in their opposition to Trump and in their support of

traditional liberal causes that have come under attack during the Trump presidency.29

The Politics, Society, and Law Scholars and the STEM Exploration and Engage-

ment Scholars are Scholars Programs at the Ohio State University and are not par-

tisan political groups. The Ohio State Scholars Program offers interested students

27This information was gleaned from the survey results as well as from informal conversations with the former and current College Republicans president. 28The Republican Rob Portman won his re-election campaign with 58% of the vote. 29This information was gleaned from the survey results as well as from informal conversations with the current, as of Spring 2017, president of the College Democrats.

71 the opportunity to interact and live with other students who share common interests.

Individuals apply to scholars programs after their admittance into the Ohio State Uni- versity and are selected based on a combination of their high-school performance and a written essay. The programs are led by an Ohio State University academic advisor who facilitates group activities and is involved in the selection, facilitation, and re- tention of students in their groups. Both scholars programs provide students with an immersive first-year experience including communal housing, clustered courses, peer mentorship, and support services such as seminars, meetings, and workshops centered on both academic and professional development. Group members live together in a university dorm for their first year at Ohio State, but they are not required toin later years.30 Some second- and third-year students in the group apply to serve as residence hall coordinators for the first-year members in the group. Waves I andII follow the first-year members of the PSLS and the STEM-EE groups throughout their first year at Ohio State. Waves III and IV follow these same individuals throughout their second year at Ohio State.

The Politics, Society, and Law Scholars Program (PSL Scholars) fosters interest and engagement in politics and society at large, attracting many pre-law students.

The PSL Scholars is an explicitly non-partisan group; students in this group come from a variety of political backgrounds, and many of them report being moderate or still politically “undecided” when they arrive in the PSL Scholars program. A majority of individuals in the PSL Scholars reported identifying with the Democratic Party

(Table 4.1). The PSL Scholars group is led by Ohio State staff member, Kevin Free- man, who oversees their activities and arranges speakers for the group. They invite

30While I have information on which individuals shared rooms together during their first year in both Scholars groups, they are not included in these analyses.

72 speakers from across campus, including the Moritz College of Law, as well as local community, business, and political leaders. One recent speaker that the PSL Scholars hosted was Professor William Eveland of the Ohio State University Communications

Department who spoke to the group about echo chambers. Some members of the PSL

Scholars were also members of the College Republicans and the College Democrats, including both groups’ presidents as of Spring 2017.31

SDem NVSDem Ind/Dem Ind Ind/Rep NVSRep SRep PSLS Wave I 28% 16% 11% 11% 12% 8% 15% PSLS Wave II 35% 10% 10% 7% 8% 18% 13% PSLS Wave III 36% 8% 3% 11% 13% 11% 16% STEM Wave I 10% 9% 15% 40% 9% 9% 9% STEM Wave II 9% 13% 22% 24% 20% 6% 7% STEM Wave III 10% 8% 38% 13% 18% 8% 8%

Table 4.1: Party Identification of Individuals in Scholars Groups

The STEM Exploration and Engagement Scholars (STEM-EE) Program introduces and engages students in STEM activities and resources around campus. The

STEM-EE Scholars are an explicitly non-political group, and none of its activities were related to politics. Individuals in this group are likely to major in one of the

STEM fields at Ohio State. It’s members also reported being more Republican and

31While the PSL Scholars cohort that this study follows - second years during the 2016-2017 academic year - remained cordial with one another during the divisive presidential election period, the cohort below them (not included in this study) was especially hostile to one another during this time. The president of the College Democrats for the 2016-2017 academic year who was also the Resident Assistant for the first-year PSL Scholars cohort (again, not the cohort in the current study) confirmed that the PSL Scholars director, Kevin Freeman, had to go to many lengths todispel tension amongst this group.

73 conservative overall than the PSL Scholars (Table 4.1). The STEM-EE Scholars

Program was led by former Ohio State staff person Sarah Eulitt in the 2015-2016 academic year. However, the program transitioned over to being led by Jorge Ed- uardo Mendoza in the 2016-2017 academic year. There was minimal overlap between individuals in the STEM-EE Scholars and the College Democrats or Republicans.

Accordingly, there are important differences between the four student groups above and beyond their varying political purposes. Both the College Democrats and

Republicans accept new members at the beginning of each semester and see some members drift away into inactivity. The Scholars Program groups only admit new students at the beginning of the academic year and while individuals in these groups can drop or be forced out of the group, no new members join the PSL or STEM-EE

Scholars once the initial first-year cohort is established. This makes the networks in the partisan groups more fluid, and their participants vary in their time spent inthe group as well as at their time at Ohio State. It will be important to account for these differences in the analyses that follow.

4.2 Detailed Study Procedures

Groups were contacted and given a $400 group incentive if they decided to participate in the study. If the groups agreed to participate, membership lists were obtained from

74 the groups’ president or leaders.32 Individuals were invited to participate in an online survey regarding their membership in the group and their political beliefs for an additional monetary incentive. The amount of the monetary incentive varied by survey wave. In Waves I and II, individuals were eligible to receive $10 for their participation. In Wave III, individuals who had participated two are three times were eligible to receive $15. In Wave IV, individuals who participated three and four times where eligible to receive $20.

I followed these groups across four points of time. The key to such an approach is to establish a baseline before voters have even begun to think about the election campaign and their vote decisions and then to follow voters across the campaign period. The problem with this approach is finding the “before” baseline. Voters develop partisan preferences early in their lives, most often during childhood or early adult- hood, and carry them from election to election, often predetermining their selection of discussion partners and, ultimately, their vote. Nonetheless, panel studies have advantages that single-shot election surveys, especially post-election surveys, do not.

Response rates from the first, second, third, and fourth survey waves are displayed in Table 4.2. Specifically, response rates displayed in Table 4.2 represent both the total and current response rates. The total response rate includes current group members and individuals who have dropped out of the group over time; thus, the number contacted grows over time in the College Democrats and Republicans because they

32How membership was defined varied by group. For the PSL and STEM-EE Scholars groups, membership was straightforward. All first-year members, as of Fall 2015, were considered apart of these groups. Accordingly, these individuals were second-years during Waves III and IV of the study. The College Democrats defined their membership according to individuals’ attendance at two of three of the first meetings of the semester, and this was consistent across survey waves. The College Republicans defined their membership to include all individuals who had paiddues by the third meeting of the semester; this was also consistent across survey waves. Accordingly, the College Democrats and Republicans membership is more fluid.

75 admit new members each school semester. The current group response rates reflect the response rates of individuals who are current group members at each survey wave; the response rates of which are higher overall. Table 4.2 does not reflect the total network response rates, except for the PSL and STEM-EE Scholars.33 There were fairly high response rates in the first wave and slightly lower response rates in the later waves. While a decline in response rates is a normal and expected part of a panel study, it does introduce unique challenges to the analyses and conclusions drawn from the study. Importantly, the networks that are measured in each survey wave do not represent the entire influence network of each student group; they constitute the network of individuals in the group who responded to the survey.

Data on how both individuals and networks change over time have several advantages for the study of dynamic social influence processes. While panel data allow me to establish what temporally happens first, the cause, and then measure the effect, the outcome at a future time point, it cannot, however, completely demonstrate causality and evidence from experimental analysis is necessary to bolster causal claims in future work.

33The network battery changed in each survey wave for individuals in the College Democrats and College Republicans because individuals can both join and dropout of these groups at any time. The network batteries in Waves I and III included the current group membership lists; however, the batteries in Waves II and IV included both dropouts from the previous wave and new members.

76 T. Contact T. Resp Total % C. Contact C. Resp Current % CDs Wave I 48 36 75% 48 36 75% CDs Wave II 60 34 57% 32 20 63% CDs Wave III 126 52 41% 81 36 44% CDs Wave IV 130 62 48% 62 35 56% CRs Wave I 37 33 89% 37 33 89% CRs Wave II 50 26 52% 44 24 55% CRs Wave III 95 47 49% 56 34 61% CRs Wave IV 99 41 41% 59 27 46% PSLS Wave I 110 93 85% 110 93 85% PSLS Wave II 110 69 63% 109 69 63% PSLS Wave III 110 67 61% 95 59 62% PSLS Wave IV 110 66 60% 95 61 64% STEM Wave I 96 66 69% 96 66 69% STEM Wave II 96 57 59% 87 53 61% STEM Wave III 96 52 54% 61 41 67% STEM Wave IV 96 50 52% 61 38 62%

Table 4.2: Group Study Response Rates: Waves I-IV

4.3 Measurement and Instrumentation

Each survey wave contained a variety of measures of dependent, independent, network, and control variables.34

4.3.1 Dependent Variables

Because I am interested in analyzing the link between social networks and individual political beliefs and attitudes, the panel study contained a multitude of these measures.35 Party identification is a long-standing belief and identity with a political

34The full survey batteries for each wave can be found in Appendix F - I. 35While political beliefs and political attitudes represent different concepts, I use the terms interchangeably.

77 party. I utilize the standard seven-point measure of party identification ranging from

‘Strong Democrat’ (1) to ‘Strong Republican’ (7). I also measure respondent ideology. On the other hand, individuals’ attitudes about politics are more movable over time. I measure an individual’s feelings about the Democratic and Republican Par- ties, Hillary Clinton, Donald Trump and many other political figures on a scale from zero to ten where the value of ‘0’ indicates ‘very unfavorable’, ‘5’ indicates feeling

‘neutral’, and the value of ‘10’ indicates ‘very favorable’ feelings.

Each survey wave also included a question battery on respondents’ opinions of current political issues ranging from abortion, gun control, and immigration to opinions on transgender bathroom rights, Syrian refugees, and domestic acts of terrorism.

I also included questions on respondents’ opinions about the direction of the country and their approval of the way Barak Obama, Congress, and, eventually, Donald

Trump are handling their jobs, though not in every survey wave. Importantly for future work in this project, each survey wave contained measures of individual candidate choice. The first and second survey waves asked whom respondents voted for in the 2014 Ohio gubernatorial race. The third survey wave asked whom respondents voted for in the 2016 primary campaigns in the state, and the third and forth survey waves asked whom they voted for in the 2016 presidential election.

In addition to measures of individual political beliefs, attitudes, and candidate choice, each survey wave contained multiple measures of political participation. In addition to asking whether or not respondents voted in the 2014 Ohio gubernatorial race (Waves I and II), Democratic, Republican, or third party primaries (Wave

III), and the 2016 presidential election (Waves III and IV), measures of their broader participation in the polity were also recorded, such as campaign involvement, sign or

78 bumper sticker display, money donation, rally attendance, and political persuasion efforts, among others. While this project is centrally concerned with understanding if and how social networks influence individual political beliefs and attitudes, participation measures are ripe for future analysis.

4.3.2 Network Measures

The survey contained a network battery in which participants were asked the following questions about every other individual in their group: if you know this person, where you met him or her (for the College Democrats and College Republicans only), how often you talk with him or her, how often you have personally talked to him or her about politics, and where you would place this person on a party-identification scale.

In the final two survey waves, this network battery also asked respondents to indicate which presidential candidate they think he or she supported in the 2016 presidential campaign. For the PSL and STEM-EE Scholars, this network battery remained the same across all four survey waves. For the College Democrats and Republicans, this battery changed in every wave because individuals were able to join the group at the beginning of each new semester.

From this battery of information, multiple measures were created. First, I created an additive measure of the total number of ties an individual has with other members of their group from the “do you know this person” question. I also use this question to create networks based on “knowing ties”, or if a respondent reports knowing the alter or not, for each group across all four survey waves. Thus, it constitutes a very basic indication of a group member’s interactions with other members of their group. The

79 networks of knowing ties created across survey waves only include the current group members who responded to the survey. Additionally, while the College Democrats and Republicans add new members each school semester and nodes change over time, the PSL and STEM-EE Scholars groups do not add new members each semester.

While only the network of “knowing” ties is used in the analyses that follow, future work will utilize other network measures. Ties based on talking and talking politics, for example, should be even more influential on attitudes and beliefs about politics than ties based simply on reports of knowing another person or not. The frequency of discussion and frequency of political discussion measures can be utilized to create networks based on general and political discussion. Future work will utilize the network battery to create measures of cross-cutting discussion for both the ego and alter

(Minozzi, Neblo, Santoro, Sokhey & Lazer 2017) as well as to calculate total network support for Hillary Clinton and Donald Trump. Additionally, because I have whole network data across survey waves, a whole slew of network measures can be brought to bear on the analyses that follow, including calculations of degree, betweenness, and eigenvector centrality, among others.

4.3.3 Independent Variables of Interest

Chapters Six will test hypotheses about the mechanisms of group influence on individual political attitudes. Specifically, it tests whether social influence materializes in different ways depending on the reasons for selection into the group. If social influence on political decision-making occurs in which individuals become more similar to other group members over time, then time spent in the group, participation in

80 the group, and the amount and strength of the network ties should be important correlates or predictors of more similar political views among group members. Psy- chological process mechanisms could also be at work. Accordingly, respondents were asked about their commitment to and participation in the group, their level of comfort with political disagreement in the group (cognitive dissonance), their habits of political information seeking from other members of the group (informational influence), and if group support of their political decisions was important or not (normative influence).

4.3.4 Controls

Several important controls were included in the survey batteries. Because it may be likely that the number of network ties established could be a product of personality, where more extraverted individuals form more network ties, I include the Ten-Item

Personality Inventory (TIPI). This inventory includes measures of agreeableness, extraversion, and openness to experiences, among others (Gosling, Rentfrow & Swann

2003). Similarly, I include a few questions that seek to understand a participant’s level of comfort with conflict. Demographic information, such as gender, race, and education levels, among others, was asked of respondents as well. Demographic information was only requested during the first survey wave that a respondent 36took.

36Measures of an individual’s level of comfort with conflict were asked in all survey waves, however.

81 4.4 Conclusion

This chapter previewed the final empirical study through which to assess the claims of the dissertation project. The group study uses longitudinal network data to establish a link between selection and if and how individuals are influenced by different types of networks. Chapter Five will assess if membership in politically homogeneous groups affects the likelihood of influence in social settings. Chapter Six will assess ifgroup membership affects the mechanisms of influence in social settings.

82 Chapter 5: Choosing Whether to Change: How Group Political Composition Conditions the Likelihood of Influence in Social Settings

Individuals’ previously established beliefs about politics do impact the social contexts and networks they select into. People with strong political beliefs and high interest in politics consider those beliefs when deciding which environments to select into; they choose the environments in which they are changed. Therefore in these, and potentially other, cases the assumption that political predispositions do not play a role in the construction of social contexts and networks is brought into question. It remains to be seen, however, if and how individuals are influenced by their social environments once the selection process is accounted for. Specifically, does the political composition of the environments that individuals select into affect the likelihood of influence in those settings? This chapter analyzes if individuals are influenced by their social environments in meaningful ways and discusses results from the network panel study of four real-world social groups described in Chapter Four. This design sheds light on if membership in social groups affects political beliefs over time while accounting for the reasons individuals select into those groups in the first place, the political composition of the group, and the complete network of connections within

83 the group.

5.1 Hypotheses

Individuals select into many different kinds of settings for varying reasons. This analysis looks at selection into one specific type of setting: voluntary student organizations. These organizations provide a valuable opportunity to study group influence on political behavior as college aged students are in the process of forming and clarifying their own political beliefs. In addition, these organizations often provide one of the first selection opportunities students make independently and without direct influence from their parents or broader socialization environment, and the reasons individuals select into these groups vary.

Chapter Three demonstrated that individuals’ previously established beliefs about politics factor into decisions about which social environments to select into directly and indirectly. Social influence on political attitudes should look different basedon the extent to which political beliefs factored into the selection decision in the first place. Individuals who select into partisan groups, for example, explicitly select into politically homogeneous social settings. While their selection into the group indicates a shared political preference, their ideas of what it means to be a Democrat or a

Republican are largely drawn from their personal socialization experience. A greater amount of social influence on individual political beliefs should occur in politically homogenous settings than in politically diverse settings. Stated formally, individuals who select into politically homogenous settings are more open to political influence in these environments (homogeneity hypothesis).

84 Individuals who join non-partisan and non-political groups select into social environments that are often, but not always, more politically diverse. The reason for selection into the group itself does not communicate shared political preferences. In- dividuals who select into politically heterogeneous networks are less open to political influence in those environmentsheterogeneity ( hypothesis). When political influence does occur among members in heterogeneous groups, it occurs as a result of homophily along some other factor. Taken together, I argue that group political composition should condition the likelihood of group influence on political attitudes, where more influence is expected to occur in politically homogenous groups.

Of course, the campaign environment can activate and politicize even non-political social settings. The increased media attention, interest, and discussion that the campaign introduces alter the context of political influence in social groups. Specifically, when politics is more salient, individuals have less ability to control the increased amount and frequency of messages coming from their social environments. Accord- ingly, I expect the campaign environment to activate politically heterogeneous settings to act on individual political attitudes. When politics is salient, politically homogenous and heterogeneous environments operate on individual political beliefs (salience hypothesis).

Over time, I expect the political attitudes of individuals in politically homogenous groups to become more similar and, specifically, strengthened in the direction of the political group (strengthening hypothesis). It could also be the case that individuals in politically homogenous groups learn what the groups’ position on certain political issues are. In this case, individual political attitudes are clarified (clarification hypothesis). While clarification would result in “stronger” political attitudes, as in the

85 strengthening hypothesis, the process is a conceptually distinct one. Clarification of political attitudes could also occur among individuals in heterogeneous groups, especially in the context of a campaign. This clarification, however, would not necessarily result from membership in the group.

Finally, I expect the political opinions of individuals who possess opposing views from that of the group’s majority to conform either through clarifying or strengthening processes; if they do not conform, I expect them to leave the group, attitudinally or physically. Individuals whose political attitudes are at odds with the majority of the attitudes of other members of their social group will either conform (strengthen or clarify) or drop out of the group (exit hypothesis).

This study accounts for how the selection process affects individual political attitudes by evaluating several types of groups, joined for political and non-political reasons. Varying the amount of group political homogeneity should have consequences on the influence that occurs in those environments. Because each voluntary student organization is described in great detail in the previous chapter, I move on to examine the relationship between respondent political beliefs and the beliefs of other members of the social group before, during, and after a particularly hard-fought presidential campaign. In the last section of this chapter, I look at individuals who have dropped out of the groups between survey waves.

5.2 Data & Measurement

To determine the impact of group political composition on opportunities for influence in social settings, I look specifically at the four-wave network panel study described

86 in Chapter Four.37 Two of the four groups are selected into for explicitly partisan reasons; individuals who join these groups select into homogenous political settings - one

Democratic and one Republican. A third group is selected into based on an individual’s general interest in politics but not based on partisanship; individuals who join this group select into politically minded yet politically diverse settings. The fourth and final group is selected into for completely non-partisan and non-political reasons; individuals who join this group select into an academic and politically diverse setting where any political influence that does occur as a result of group membership islikely to be indirect. These four groups - two partisan groups (the campus Democratic and

Republican clubs), one political and nonpartisan group (the Politics, Society, and

Law Scholars), and one non-political group (the STEM Exploration and Engagement

Scholars) - vary in their political composition and provide a unique opportunity to study social influence on individual political beliefs once selection is accounted for.

5.2.1 Dependent Variables

I am interested in understanding the impact of group membership on individuals’ beliefs about politics. Though the panel study contains many different potential measures of political beliefs and attitudes, the analyses that follow focus on five. As in Chapter Three, I utilize the traditional seven-point scale ranging from ‘Strong

Democrat’ (1) to ‘Strong Republican’ (7), and feelings about the Democratic and

Republican Parties, Hillary Clinton, and Donald Trump, which were measured on a scale from zero to ten where the value of ‘0’ indicates ‘very unfavorable’, ‘5’ indicates

37This chapter, however, utilizes data from Waves I-III.

87 feeling ‘neutral’, and the value of ‘10’ indicates ‘very favorable’ feelings.38 I also analyze respondent vote choice in the 2016 presidential election in Wave III.

Once again, I have chosen to focus on these measures here because of their importance for the 2016 presidential campaign environment and for consistency across empirical chapters. Cross-sectional analyses will utilize static measures of these five concepts while longitudinal analyses will utilize both the Wave II measure and a difference score of these measures between survey waves, controlling for the WaveI measure.39

5.2.2 Independent and Control Variables

Because individuals often choose to associate with individuals similar to themselves in some way, I control for those similarities beyond partisan loyalties in the analyses that follow. Specifically, I include control for respondent’s gender, religion, yearin college, and interest in politics. I also control for the number of ties individuals have to other members of their student group. This is an additive measure of the total number of ties an individual has with other members of their group from the “do you

38Missing values of the dependent variables were infrequent but did occur. Specifically, in Wave I there were 0.01%-0.04% missing values among the outcome variables across all groups. In Wave II, there were only 0.02% missing values among individuals in the Politics, Society, and Law Scholars Program. In Wave III, there were only 0.03% missing values in the STEM Exploration and En- gagement Scholars. In partisan groups (the campus Democratic and Republican groups), missing data for these dependent variables were mean or mode imputed. As individuals in these groups are likely to share similar political beliefs, this imputation method is sensible. In non-partisan groups, missing data were also mean or mode imputed. This is potentially problematic because individuals in politically heterogeneous groups possess attitudes across the political spectrum. Missing data in future iterations of this project will be computed using simulated datasets or last observation carry forward method, in which missing values are replaced by the last observed value of that variable. 39The cross-sectional analysis utilizes Waves I-III of the network panel study. Longitudinal analysis only incorporates the first two survey waves. Future work will incorporate the third and fourth survey waves into the longitudinal analysis.

88 know this person” question. It constitutes a very basic indication of a group member’s

interactions with other group members.

5.3 Cross-sectional Analysis of the First Wave of Data

What is the relationship between participants’ political attitudes and the attitudes of

other members of their social group? How does the political composition of the group

affect the likelihood of group influence on individual political attitudes? Analyzing

data from the first survey wave, which occurred between September and November of

2015, I put the homogeneity and heterogeneity hypotheses to the test and find initial support for each.

Because the assumption of the independence of respondents’ political attitudes cannot feasibly be supported, I utilize the spatial autoregressive model that effectively controls for the effects of individuals on each other in a social network (Anselin

1988; Cliff & Ord 1981; Lazer 2001; Nyhan & Montgomery40 2015). The autoregres-

sive model is rooted in spatial statistics and accounts for “how being embedded in

the network affects the behavior or attributes of the actor” (Leifeld & Cranmer 2015,

1-2). Specifically, I estimate the following relationship:

40Most regression models assume independence between observations; however, in this study, group members not only know each other but network diffusion is also expected. More specifically, the classic linear regression model assumes that there is no, or zero, auto-correlation between the disturbance, or error, terms. Detection of autocorrelation among the residuals can imply the presence of a non-linear relationship among dependent and independent variables, the omission of a confounding variable, or, and especially important in our case, that the model should have an auto-regressive structure. The presence of positive or negative correlations among the error terms leads to biased estimates of the residual variance and inefficient estimates of the regression coefficients. While the independence assumption is largely upheld in survey research - itcannot be made with any certainty here. Individuals in the group do know one another, and I expect diffusion of political attitudes across the network to occur.

89 Yt = ρW1Yt + Xβ + ϵ

Where Yt is the vector of political attitudes of respondents in the social group;

W1 is the matrix of social interactions within the student organizational groups; X

is a matrix of variables included to control for alternate explanations of individual

political attitudes and to control for other likely causes of communication; β is a

vector of coefficients for each of those controls; ρ is a scalar that represents the effect of a change in the average response of the people any individual speaks to on that individual’s response; and ϵ is a vector of stochastic errors. Thus, the outcome (Yt)

for each individual is determined by his or her own covariate values and the outcome

values of his or her network ties.

Here, the spatial weighting matrix represents the network of “knowing” ties among

the group where the value of ‘1’ indicates that an individual knows the other person

and a value of ‘0’ indicates the absence of a relationship. This weighting matrix could

also be specified to include whether or not individuals talk or talk politics withone

another, along with other flexible and generic applications. Including this weighting

matrix is important because it effectively controls for the interdependence - network

of ties - between individuals in the group. The coefficient on the weighting matrix, ρ,

will be utilized to determine whether interdependence among the political attitudes

of individuals in each student organization exists. I expect there to be evidence of

significant spatial dependence in the College Democrats and College Republicans, but

not necessarily in every circumstance.

Tables 5.1 - 5.4 depict results from estimating the spatial autocorrelation model

for each student group separately. I assess the amount of spatial dependence in

five measures of political beliefs and attitudes including partisanship, feelings toward

90 the Democratic Party, feelings toward the Republican Party, and feelings toward

Hillary Clinton and Donald Trump. I run models with and without controls for

each individual’s interest in politics, number of ties to other group members, sex

(1=female), religion (1=Christian), and for their year in college.41

Party-ID DemFeel RepFeel ClintonFeel TrumpFeel Interest -.22∗ -.30 -.21 0.14 0.41 (0.11) (0.2) (0.23) (0.42) (0.44) Number of Ties -.20∗∗∗ 0.21 -0.01 0.3 -.17∗∗ (0.05) (0.17) (0.07) (0.31) (0.07) Female -.19 0.63∗ -.02 -.16 -1.11 (0.20) (0.35) (0.40) (0.74) (0.76) Christian 0.24 0.05 0.85∗∗ 1.35∗ -.51 (0.19) (0.35) (0.39) (0.73) (0.74) Year 0.05 0.37∗ -.21 0.48 0.01 (0.11) (0.20) (0.22) (0.41) (0.44) Constant 2.21∗∗∗ 7.92∗∗∗ 2.37∗∗ 4.54∗∗ 0.44 (0.49) (0.89) (1.02) (1.86) (1.92) ρ 0.18∗∗∗ -0.02 0.00 -0.04 0.18∗∗∗ (0.04) (0.02) (0.05) (0.05) (0.05) Num. Obs. 37 37 37 37 37 Log-Likelihood -27.68 -50.65 -55.44 -77.94 -78.28 χ2 24.40 14.09 6.28 5.48 8.69

∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.10

Table 5.1: Spatial Dependence in the College Democrats, Wave I

Do the political attitudes of an individual’s connections in the group predict their

own political attitudes? Results convey compelling yet intuitively reasonable findings.

41Because members of the PSL and STEM-EE Scholars are all from the same year in college (first years in Waves I and II and second years in Waves III and IV), this variable is not included in these models. Analyses with no control variables are included in Appendix J.

91 Party-ID DemFeel RepFeel ClintonFeel TrumpFeel Interest -.01 -.82∗∗∗ -.17 -.57∗∗ 0.06 (0.16) (0.30) (0.46) (0.25) (0.60) Number of Ties -.15 -.38∗∗∗ -1.27∗∗∗ -.18∗∗∗ 0.27 (0.19) (0.10) (0.45) (0.06) (0.33) Female -.33 0.18 -.82 0.47 -0.68 (0.31) (0.56) (0.86) (0.47) (1.08) Christian 1.42∗∗∗ -3.95∗∗∗ 1.59 -3.58∗∗∗ 1.30 (0.41) (0.75) (1.15) (0.63) (1.45) Year 0.28∗ 0.42 -.50 0.07 -0.45 (0.15) (0.28) (0.43) (0.23) (0.55) Constant 4.70∗∗∗ 7.77∗∗∗ 7.48∗∗∗ 6.35∗∗∗ 4.80 (0.9) (1.66) (2.54) (1.39) (3.31) ρ 0.03 0.33∗∗∗ 0.18∗∗∗ 0.26∗∗∗ -0.09 (0.03) (0.10) (0.06) (0.09) (0.08) Num. Obs. 33 33 33 33 33 Log-Likelihood -38.28 -55.38 -70.84 -49.75 -80.27 χ2 13.51 59.39 13.61 51.08 2.75

∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.10

Table 5.2: Spatial Dependence in the College Republicans, Wave I

92 Party-ID DemFeel RepFeel ClintonFeel TrumpFeel Interest -.38∗ 0.50 -.61∗∗ 0.76∗∗ -.44∗ (0.2) (0.31) (0.28) (0.3) (0.26) Number of Ties -.05 -.04 0.02 -.05 -.04 (0.04) (0.06) (0.06) (0.04) (0.03) Female -.12 0.42 0.28 0.41 -1.16∗∗ (0.38) (0.61) (0.54) (0.60) (0.51) Christian 2.20∗∗∗ -2.54∗∗∗ 2.50∗∗∗ -1.87∗∗∗ 0.96∗ (0.38) (0.61) (0.54) (0.59) (0.50) Constant 4.29∗∗∗ 4.03∗∗∗ 4.09∗∗∗ 1.68 4.10∗∗∗ (0.84) (1.33) (1.20) (1.30) (1.11) ρ 0.01 0.01 -0.00 0.02 0.01 (0.01) (0.01) (0.02) (0.01) (0.01) Num. Obs. 95 95 95 95 95 Log-Likelihood -193.19 -236.56 -226.62 -234.13 -219.67 χ2 37.45 20.65 25.80 18.55 15.17

∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.10

Table 5.3: Spatial Dependence in the Politics, Society, & Law Scholars, Wave I

Rho, ρ, is the indication of spatial dependence where a significant ρ indicates mod-

erate spatial autoregressive (SAR) dependence in the dependent variable. I expect

more SAR dependence among the political attitudes of individuals in partisan and

politically homogenous groups, and this is exactly what happens. There is evidence

of SAR dependence in the outcome variables for members of the College Democrats

and Republicans, though not in every case. For the College Democrats (Table 5.1),

the positive and significant rho coefficients in the models of party identification and

feelings about Donald Trump suggest that an individuals’ own identification with the

Democratic Party and feelings about Donald Trump depend, in part, on those atti-

tudes of other members of their social group. Similarly, in the College Republicans

(Table 5.2), there is evidence of spatial dependence in feelings about the Republican

93 Party-ID DemFeel RepFeel ClintonFeel TrumpFeel Interest -.16 0.16 -.33 0.34 -.22 (0.16) (0.26) (0.27) (0.26) (0.32) Number of Ties 0.02 -.02 0.06 0.02 0.09 (0.06) (0.07) (0.09) (0.05) (0.05) Female -.62 1.02 -.31 1.36∗∗ -1.74∗∗ (0.38) (0.63) (0.63) (0.62) (0.75) Christian 1.53∗∗∗ -2.27∗∗∗ 1.70∗∗∗ -1.96∗∗∗ 0.13 (0.40) (0.65) (0.64) (0.64) (0.79) Constant 4.11∗∗∗ 4.87∗∗∗ 4.18∗∗∗ 2.16∗∗ 3.15∗∗ (0.64) (1.03) (1.05) (1.01) (1.24) ρ -0.01 0.01 -0.02 0.00 -0.04 (0.02) (0.02) (0.03) (0.02) (0.03) Num. Obs. 68 68 68 68 68 Log-Likelihood -121.53 -155.08 -153.44 -153.44 -168.68 χ2 18.21 14.85 9.56 14.81 7.90

∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.10

Table 5.4: Spatial Dependence in the STEM Exploration & Engagement Scholars, Wave I

94 Party, Democratic Party, and feelings about Hillary Clinton. More specifically, a sig-

nificant rho coefficient represents that the effect of a change in the attitudes ofthe

people that an individual reports knowing in the group significantly impacts their

own attitudes about the parties and candidates.

In partisan groups, simply knowing other group members introduces dependence

in the political attitudes amongst individuals in the group. In other words, an indi-

vidual’s own political attitudes depend on the political attitudes of other members

in their group. No evidence of SAR dependence is found in politically heterogeneous

groups, however (Tables 5.3 and 5.4). Additionally, since the spatial weights matrix

was constructed from the matrix of knowing ties in the group, it provides an espe-

cially “hard test” of the SAR dependence. More SAR dependence would be expected

if the discussion networks, and especially political discussion networks, were utilized.

Evidence of spatial dependence among the political attitudes of individuals in politi-

cally homogenous groups and the lack of dependence in the attitudes of individuals in

heterogeneous groups provides initial support for the homogeneity and heterogeneity hypotheses.

Results from Wave I demonstrate that the political composition of social groups influences whether or not individuals’ political attitudes are impacted by thegroup.

Individuals in politically homogenous groups hold more similar attitudes to other members of their group; whereas, individuals in politically heterogeneous groups are not affected by the political attitudes of other members of their social group. The impact of the control variables on individuals’ beliefs and attitudes about politics will be reviewed in the discussion of Wave III results.

95 5.4 Cross-sectional Analysis of the Second Wave of Data

Wave II of this study launched in February of 2016 and concluded on March 28th.

Thus, it incorporates a period of increased interest and engagement in politics and, specifically, the Ohio Democratic and Republican primary, which took place onMarch

15, 2016. Hillary Clinton and John Kasich, the Ohio Governor at the time, won their hotly contested respective party primaries in the state.42 Notably, the campus Re- publican group was largely pro-Kasich and played a role in recruiting volunteers for the Kasich campaign in the state. This period of increased interest and engagement with politics - not to mention the sustained campaign activities in the primary battleground state - should dramatically alter respondents’ awareness and understanding of politics in both politically homogenous and heterogeneous groups. Accordingly, I expect that the presence and uniqueness of the presidential election to increase the likelihood of political influence in politically heterogeneous groups (salience hypothesis).

Utilizing the spatial autoregressive model, I first look at the cross-sectional analysis of the second wave of survey data utilizing the same controls as in Wave I.43. There are several interesting observations to take away from the results in Tables 5.5 - 5.8.

First, results for the College Democrats and Republicans remain remarkably similar in magnitude and significance between Waves I and II. However, some of the rho coefficient estimates become negative (Tables 5.5 and 5.6). Certainly, negative spatial dependence is a “curious” but still common case. Negative spatial autocorrelation is

42John Kasich won the Republican primary with 46.8% of the vote compared to Donald Trump’s 35.6% and Ted Cruz’ 13.1%. Hillary Clinton won the Democratic Primary with 56.5% of the vote compared to Bernie Sanders’ 42.7%. 43Again, results without control variables are available in Appendix J.

96 likely to occur “when competition between individuals outweigh cooperative factors”

(Kao & Bera 2013, 1). However, it is unclear what competition vs. cooperation means

in the context of individuals in social groups. More research is necessary to better

understand negative spatial dependence.

Party-ID DemFeel RepFeel ClintonFeel TrumpFeel Interest -.14 -.50 -.38 0.05 0.07 (0.21) (0.77) (0.62) (0.86) (0.23) Number of Ties 0.22∗∗∗ 0.83 -.06 0.51 0.04∗ (0.07) (0.54) (0.07) (0.49) (0.02) Female -.20 0.97∗ -.44 0.29 -.26 (0.15) (0.55) (0.44) (0.60) (0.16) Christian 0.03 0.53 1.04∗∗ 1.93∗∗∗ -.21 (0.13) (0.50) (0.41) (0.58) (0.15) Year 0.2∗∗∗ -.22 0.00 0.47 -.03 (0.07) (0.27) (0.21) (0.30) (0.08) Constant 1.62 10.16∗∗ 3.69 6.47 -.13 (1.07) (3.96) (3.20) (4.40) (1.18) ρ -0.18∗∗∗ -0.10 -0.03 -0.08 -0.14∗∗ (0.05) (0.07) (0.06) (0.07) (0.06) Num. Obs. 27 27 27 27 27 Log-Likelihood -10.45 -44.36 -37.74 -46.89 -12.44 χ2 26.31 9.72 8.93 16.83 9.26

∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.10

Table 5.5: Spatial Dependence in the College Democrats, Wave II

Curiously, attitudes towards their own parties’ primary candidates are not spa-

tially dependent in the College Democrats and Republicans. Perhaps the realities of a

more anti-Trump Republican group, especially in the primary season, and increasing

ambivalence for Hillary Clinton among the College Democrats explain these results.

Attitudes towards the out-parties’ presidential candidates are spatially dependent,

97 Party-ID DemFeel RepFeel ClintonFeel TrumpFeel Interest 0.10 0.10 0.22 0.17 -2.40∗ (0.37) (0.56) (0.77) (0.48) (1.36) Number of Ties -8.36∗∗∗ 0.08 1.45∗∗ 0.21∗∗∗ 0.07 (1.43) (0.11) (0.61) (0.08) (0.27) Female -.83∗ -.41 -.86 0.04 -.22 (0.43) (0.65) (0.90) (0.55) (1.58) Christian 1.65∗∗∗ -2.24∗∗∗ 1.64 -3.64∗∗∗ -.66 (0.49) (0.74) (1.02) (0.62) (1.78) Year -.003 0.12 -.53 -.43∗ -.81 (0.19) (0.29) (0.41) (0.25) (0.71) Constant 3.31∗ 2.78 5.01 3.47 17.37∗∗ (1.91) (2.89) (3.97) (2.46) (6.95) ρ 1.26∗∗∗ -0.09 -0.18∗∗ -0.17∗∗∗ -0.05 (0.21) (0.09) (0.08) (0.07) (0.08) Num. Obs. 24 24 24 24 24 Log-Likelihood -16.91 -38.93 -47.73 -35.80 -60.02 χ2 49.60 14.10 12.74 42.82 4.12

∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.10

Table 5.6: Spatial Dependence in the College Republicans, Wave II

98 Party-ID-mode DemFeel RepFeel ClintonFeel TrumpFeel Interest -.01 0.21 -.68∗ 0.18 -.56∗ (0.28) (0.42) (0.39) (0.44) (0.32) Number of Ties -.41∗∗∗ -.53∗∗∗ -.02 -.36∗∗∗ -.03 (0.07) (0.10) (0.08) (0.08) (0.04) Female -.25 0.32 -.18 -.44 -.59 (0.47) (0.71) (0.64) (0.73) (0.55) Christian 2.16∗∗∗ -2.52∗∗∗ 2.09∗∗∗ -2.19∗∗∗ 0.26 (0.46) (0.70) (0.64) (0.73) (0.54) Constant 3.52∗∗ 4.52∗∗ 6.15∗∗∗ 3.51 5.26∗∗∗ (1.40) (2.13) (1.94) (2.22) (1.63) ρ 0.11∗∗∗ 0.10∗∗∗ -0.00 0.10∗∗∗ -0.01 (0.02) (0.02) (0.02) (0.02) (0.02) Num. Obs. 72 72 72 72 72 Log-Likelihood -145.16 -176.21 -172.24 -179.51 -159.99 χ2 58.29 38.92 14.64 29.62 5.62

∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.10

Table 5.7: Spatial Dependence in the Politics, Society, & Law Scholars, Wave II

however, suggesting that partisans are more likely to draw negative perceptions of

the out-party from their social groups than favorable attitudes towards their own

parties’ candidates.

The absence of significant SAR dependence in some of the political attitudes

among individuals in the College Democrats and Republicans is problematic for my

theory, especially in the primary season where we might expect individuals in ho-

mogeneously partisan groups to draw their attitudes from other members of their

group. This may be result of the low power to find effects in these groups; or, itmay

highlight the division in both partisan groups on which candidate to support in their

parties’ primary. If these groups are divided over whom to support in the primary

- and they are - their attitudes about those candidates may not be impacted by the

99 Party-ID DemFeel RepFeel ClintonFeel TrumpFeel Interest -.44∗∗∗ 0.44∗ -.66∗∗ 0.77∗∗∗ -.88∗∗ (0.17) (0.25) (0.28) (0.28) (0.36) Number of Ties 0.08 0.16 0.01 0.16 0.05 (0.11) (0.17) (0.11) (0.11) (0.09) Female 0.00 1.15∗ -.13 1.07 -.92 (0.43) (0.62) (0.72) (0.69) (0.86) Christian 1.31∗∗∗ -1.90∗∗∗ 1.63∗∗ -1.26∗ 1.17 (0.38) (0.57) (0.65) (0.66) (0.83) Constant 5.10∗∗∗ 3.80∗∗∗ 4.85∗∗∗ -.36 5.10∗∗∗ (0.74) (1.09) (1.26) (1.26) (1.57) ρ -0.03 -0.03 0.00 -0.02 -0.03 (0.03) (0.03) (0.03) (0.03) (0.04) Num. Obs. 55 55 55 55 55 Log-Likelihood -94.79 -116.51 -123.37 -124.56 -136.94 χ2 19.48 18.06 11.61 15.41 8.94

∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.10

Table 5.8: Spatial Dependence in the STEM Exploration & Engagement Scholars, Wave II

100 attitudes of others in their group because there are too many conflicting messages. It

may also be evidence that individuals draw on other psychological or economic con-

siderations when making political decisions that outweigh influence from the social

environment. Individuals in homogeneous political groups very clearly look to other

members of those groups to characterize the strength of their own party identification

as well as their opinions about out-groups, but opinions of members of their own party

could feasibly drawn from other considerations, such as their own issue positions or

personality.

The campaign environment alters how social networks act upon individual polit-

ical beliefs in politically heterogeneous groups. Specifically, SAR dependency, ρ, is

activated in the political but non-partisan group. Where there was no evidence of

SAR dependency for the Politics, Society, and Law Scholars in Wave I (Table 5.3),

Wave II results provide evidence of individuals’ increased reliance on the political

attitudes of other group members in making their own political decisions (Table 5.7)

- supporting the salience hypothesis. An individual’s party identification and feelings about both the Democratic Party and Hillary Clinton are significantly determined by the party identification and the attitudes of other members of their group towhom they are connected. Individuals in the majority Democratic PSL Scholars look to their peers for guidance as to whom to support in the Democratic primary, and, unlike the significant rho coefficients for the College Democrats and Republicans in

Wave II, these coefficients are positive in the PSL Scholars.

Wave II results support the salience hypothesis. The start of the official campaign season, the increased interest and discussion that accompany it, and the increased intimacy among group members over time activates even politically heterogeneous

101 groups to act on the political attitudes of group members. The activation effect is

limited to political groups, however, and is not present in the STEM Exploration and

Engagement Scholars during the primary campaign (Table 5.8). Politically interested

individuals seek out information from their groups in the primaries. Accordingly, we

find SAR dependence amongst the attitudes of individuals in the College Democrats,

College Republicans, and PSL Scholars. Primary campaigns do not seem to motivate

politically uninterested individuals - as those in the STEM-EE Scholars - to seek out

political information from their social groups.44

5.5 Cross-sectional Analysis of the Third Wave of Data

Does the general election alter whether individuals are influenced by the attitudes of others in their social network? Tables 5.9 - 5.12 display cross-sectional results from the third survey wave, which took place between September 30, 2016 and Decem- ber 7, 2016.45 This is a time when, once the party nominees were chosen, students were deciding whether and how strongly to support them. Here again, results remain remarkably consistent for the College Democrats and Republicans; the political attitudes of other members of their group are consequential predictors of their own attitudes about politics in some cases. For the College Democrats (Table 5.9), this is the

44The lack of SAR dependence in the STEM-EE Scholars group during the primary campaign (Wave II) may be indicative of the fact that not very many individuals in the group voted in the primary campaign; thus, they had no reason to consult others’ opinions on the primary candidates. While the third wave of the survey included a question about which candidate individuals preferred in the 2016 Democratic or Republican primary elections, it did not explicitly ask respondents if they voted in the primary election or not. 45The Wave III models are the exact same as those run in Waves I and II. However, year in college was not asked of new respondents in the College Democrats and Republicans in Wave III and is therefore not included in models for those groups.

102 case for party identification and for feelings about Donald Trump. Evaluations about

the out-party candidate, Donald Trump, were influenced by the attitudes of other

group members towards him. For the College Republicans (Table 5.10), this is the

case for feelings about the Democratic Party, Republican Party, and Hillary Clinton.

Individuals’ feelings about the two major parties and Hillary Clinton are significantly

influenced when the feelings of the individuals that they know in the College Repub-

licans change. Across three points of time, consistent evidence of spatial dependence

is found in individual political attitudes in homogeneous social groups, though not in

every case.

Party-ID DemFeel RepFeel ClintonFeel TrumpFeel Interest -.15 0.49 -.30 1.20∗ -.20 (0.21) (0.70) (0.51) (0.69) (0.34) Number of Ties 0.22∗∗∗ 0.12 0.05 0.37 -.02 (0.08) (0.46) (0.08) (0.50) (0.02) Female 0.36∗∗ -.50 0.73∗ 0.23 0.12 (0.16) (0.52) (0.39) (0.52) (0.25) Christian -.20 1.23∗∗ 0.06 0.85 -.15 (0.17) (0.56) (0.41) (0.56) (0.27) Constant 1.43 5.98∗ 1.76 1.70 1.09 (1.00) (3.28) (2.41) (3.24) (1.59) ρ -0.15∗∗ -0.02 0.01 -0.05 0.33∗∗∗ (0.06) (0.06) (0.04) (0.06) (0.09) Num. Obs. 36 36 36 36 36 Log-Likelihood -24.46 -65.48 -54.41 -65.34 -34.14 χ2 15.69 6.20 3.64 6.23 2.28

∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.10

Table 5.9: Spatial Dependence in the College Democrats, Wave III

103 Party-ID DemFeel RepFeel ClintonFeel TrumpFeel Interest 0.15∗∗ 0.23 -.28 0.01 0.38 (0.07) (0.36) (0.32) (0.29) (0.63) Number of Ties 1.00 0.22 0.91 0.35∗∗∗ 0.26 (0.72) (0.17) (0.55) (0.13) (0.46) Female 0.00 1.09 1.56∗∗ -.48 0.97 (0.15) (0.75) (0.65) (0.59) (1.27) Christian 0.11 -.73 0.98 -1.58∗∗ 1.02 (0.16) (0.79) (0.70) (0.63) (1.37) Constant 6.04∗∗∗ 0.84 8.12∗∗∗ 1.73 4.30 (0.41) (1.99) (1.74) (1.60) (3.45) ρ -0.15 -0.17∗ -0.16∗ -0.24∗∗ -0.10 (0.11) (0.10) (0.08) (0.10) (0.10) Num. Obs. 32 32 32 32 32 Log-Likelihood -9.89 -60.14 -56.46 -54.19 -77.54 χ2 6.72 5.40 12.38 14.71 1.46

∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.10

Table 5.10: Spatial Dependence in the College Republicans, Wave III

Unlike during the primary campaign (Wave II results), the attitudes of members

of the PSL Scholars were not influenced by their ties to the group (Table 5.11).

There is no evidence of significant SAR dependence in any case. Individuals inthis

group may be utilizing other considerations to form their political opinions in the

course of a very bitter and hard-fought general election campaign. In the politically

heterogeneous STEM-EE Scholars, however, individuals’ party identification, feelings

about the Republican Party, and attitudes about Donald Trump are significantly

influenced by the attitudes of others they are connected to in the group (Table 5.12).

During the general election, individuals in non-political groups start looking to others

they are connected to for guidance about which candidates and party to support.

104 Party-ID DemFeel RepFeel ClintonFeel TrumpFeel Interest -.52∗∗ 0.80∗∗ -.75∗∗ 0.75∗∗ -.40 (0.26) (0.35) (0.33) (0.36) (0.27) Number of Ties -.02 0.06 -.08 0.01 -.07∗∗ (0.10) (0.13) (0.09) (0.11) (0.03) Female -.55 0.48 0.13 0.01 -.31 (0.52) (0.70) (0.64) (0.72) (0.52) Christian 2.14∗∗∗ -2.84∗∗∗ 2.06∗∗∗ -2.71∗∗∗ 0.72 (0.51) (0.69) (0.64) (0.71) (0.52) Constant 5.47∗∗∗ 2.55 6.85∗∗∗ 2.38 3.97∗∗∗ (1.19) (1.61) (1.49) (1.66) (1.22) ρ -0.00 -0.01 0.01 0.00 0.02 (0.03) (0.02) (0.02) (0.02) (0.02) Num. Obs. 61 61 61 61 61 Log-Likelihood -127.74 -146.24 -141.72 -148.02 -129.39 χ2 25.05 24.96 18.90 20.69 11.47

∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.10

Table 5.11: Spatial Dependence in the Politics, Society, & Law Scholars, Wave III

The control variables help explain individuals’ attitudes about politics in ways we might expect. One of the most consistent predictors of individuals’ beliefs and attitudes about politics in these - admittedly limited - models is whether or not an individual is religiously Christian or not. In almost every case, individuals who identity as Christians, on average, identify more strongly with the Republican Party and feel more negatively (positively) toward the Democratic (Republican) Party and

Hillary Clinton (Donald Trump). Even individuals who identify as Christians in the

College Democrats feel more strongly positively toward the Republican Party. These same individuals also feel more strongly positively toward Hillary Clinton (Tables 5.1 and 5.5), which is the opposite trend for all other groups.

105 Party-ID DemFeel RepFeel ClintonFeel TrumpFeel Interest -.54∗∗ 1.16∗∗∗ -.56∗ 1.22∗∗∗ -.58∗ (0.22) (0.41) (0.3) (0.42) (0.32) Number of Ties 0.37∗∗ 0.30 0.53∗∗∗ 0.23 0.34∗∗∗ (0.17) (0.20) (0.19) (0.18) (0.10) Female -.25 1.27 -.45 1.99∗∗ -.81 (0.48) (0.88) (0.63) (0.89) (0.68) Christian 1.41∗∗∗ -1.78∗∗ 1.67∗∗∗ -2.12∗∗ 1.36∗∗ (0.44) (0.83) (0.58) (0.84) (0.64) Constant 4.75∗∗∗ 0.87 4.25∗∗∗ -.05 2.88∗∗ (1.00) (1.82) (1.34) (1.87) (1.43) ρ -0.10∗∗ -0.06 -0.14∗∗∗ -0.05 -0.15∗∗∗ (0.04) (0.04) (0.05) (0.05) (0.05) Num. Obs. 40 40 40 40 40 Log-Likelihood -67.84 -91.58 -79.98 -92.14 -83.93 χ2 20.58 14.08 21.69 18.08 18.35

∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.10

Table 5.12: Spatial Dependence in the STEM Exploration & Engagement Scholars, Wave III

106 The additional control variables have varied and inconsistent effects across models. An individual’s year in college, for example, rarely predicts their attitudes about politics. The amount of interest individuals have in politics is sometimes a significant predictor of their political attitudes, especially in non-partisan groups. In almost every case, increased interest in politics leads individuals to identify more strongly with the Democratic Party and feel more negatively (positively) toward the Republican

(Democratic) Party and Donald Trump (Hillary Clinton). This pattern is the exact opposite of the pattern for individuals who identify as Christian.

In almost every case, when significant spatial (SAR) dependence is detected in individuals’ attitudes about politics, the total number of ties an individual has to other members of their group is also significant (except in some cases in Wave III). However, the effect of the total number of ties on individual political attitudes is all overthe place. In Table 5.2, for example, individuals in the College Republicans with more ties to the group members report not only feeling more negatively toward the Democratic

Party and Hillary Clinton, but also more negatively toward the Republican Party.

Similarly, while more ties to group members in Wave I leads individuals in the College

Democrats to identify more strongly as Democrats (Table 5.1), this pattern reverses in Waves II (Table 5.5) and III (Table 5.9) - where more ties lead to weaker personal identification as a Democrat. Interpretation of these effects is admittedly difficult.

It could be that as the number of ties increases, individuals become more networked within the group, which increases group loyalty before a contested campaign, but then diminishes it as divisions appear in the primaries (Wave II). This does not explain

Wave III results, however.

107 It is apparent from these results that the number of ties variable may be driving

spatial dependence in these models. And, in fact, when this variable is removed

from the models, support for the hypotheses do weaken (see results in Appendix J).

Specifically, there is weaker evidence for significant spatial dependence in partisan

groups, and the magnitude of the rho coefficient decreases. There is evidence of

significant spatial dependence in both non-partisan groups but only in the primary

and general election contests. Accordingly, general support for the homogeneity,

heterogeneity, and salience hypotheses remain, though they are not as persuasive as

the results that include the number of ties measure. I believe it is important to

include this measure, however, in order to ensure that the quantity of signals - in

addition to the content of that signal - is accounted for in the model. An individual

who has a few very strong ties to the group may be more, or as equally, influenced

by those ties than an individual with multiple weak ties to the group.

Results from Waves I-III provide support for the homogeneity and heterogeneity hypotheses. No matter the political season, politically homogeneous environments operate on individuals’ attitudes about politics. Significant spatial dependence is present among the political attitudes of individuals in the College Democrats and

Republicans, though not in every case. Results from Waves II and III also support the salience hypothesis but to various effects. Individuals in politically heterogeneous networks begin to look to other group members when politics is more salient: for the

PSL Scholars in the primary campaign and for the STEM-EE Scholars in the general election.

108 5.6 Spatial Dependence and Vote Choice

The cross-sectional analyses above demonstrate that individuals’ attitudes and beliefs about politics are influenced by the attitudes of others around them, especially in politically homogeneous groups. It remains to be seen, however, if their decisions on which candidate to vote for are also influenced by the decisions of other members of their group. Analyzing vote choice, however, proves to be more challenging than analysis of their thermometer evaluations because of the lack of variation. The political groups were essentially consensual in their vote choices for the general election.

Because of this, I only examine vote choice in the March primary for the College

Democrats and Republicans (Table 5.13). For the PSL and STEM-EE Scholars, on the other hand, there was sufficient variation in their general election votes to seek account for it in the modeling. But, it is unclear how many individuals in the Scholars groups actually participated in the primary to support analysis of their votes in that contest.

In the third survey wave, respondents were asked about which candidate they preferred in the 2016 Republican and Democratic primaries and which candidate they had already (via early voting) or planned to vote for in the general election.

All thirty-six respondents in the College Democrats reported that had already or planned to vote for Hillary Clinton in the presidential election, and all but three of the thirty-two respondents of the College Republicans reported that they had already or planned to vote for Donald Trump. Accordingly, for these groups I model their decision on which candidate to vote for only in their parties’ primary campaigns in

Table 5.13.

109 BernieVote ClintonVote KasichVote RubioVote TrumpVote (CDs) (CDs) (CRs) (CRs) (CRs) Interest -.44∗∗ 0.42∗∗ 0.12 -.24∗∗∗ 0.01 (0.19) (0.18) (0.1) (0.08) (0.09) Number of Ties 0.02 0.03 -.05∗∗ 0.03 -.02 (0.03) (0.02) (0.03) (0.04) (0.02) Female -.23∗ 0.29∗∗ 0.08 -.07 0.11 (0.13) (0.13) (0.21) (0.15) (0.18) Christian -.40∗∗∗ 0.32∗∗ -.14 0.16 -.18 (0.15) (0.14) (0.22) (0.17) (0.20) Constant 3.03∗∗∗ -1.96∗∗ -.16 1.19∗∗∗ 0.39 (0.88) (0.86) (0.57) (0.42) (0.50) ρ -0.04 -0.08 0.23∗∗∗ -0.08 -0.03 (0.04) (0.05) (0.07) (0.08) (0.10) Num. Obs. 36 36 32 32 32 Log-Likelihood -17.19 -16.86 -18.34 -9.99 -15.14 χ2 17.42 19.61 8.30 18.17 2.89

∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.10

Table 5.13: Primary Vote Choice for College Democrats & College Republicans

110 The first two columns of Table 5.13 look at the decision to support Bernie Sanders or Hillary Clinton in the Democratic primary among College Democrats. Sixty-one percent of respondents preferred Sanders in the primary campaign, yet their decisions to do so do not appear to be predicted by the decisions of others in their group

(Column 1). The rho coefficient from the model of vote choice for Hillary Clinton

(Column 2) just fails to meet standard levels of statistical significance (p=0.11).

Many of the control variables work in opposite ways in both models of primary vote decisions in the College Democrats. Being female and religiously identifying as a

Christian makes respondents less likely to vote for Bernie Sanders but more likely to vote for Hillary Clinton. Respondents with less (more) interest in politics were also more likely to vote for Sanders (Clinton).

In the primary election, the College Republicans were a divided group; 28% of respondents preferred John Kasich, 22% preferred Donald Trump, 22% preferred Marco

Rubio, 16% preferred Ted Cruz, and 12% preferred other candidates.46 The final three columns of Table 5.13 model the decisions to vote for John Kasich, Marco Rubio, and

Donald Trump among College Republicans. Results from the third column depict that individuals’ decisions to vote for John Kasich in the Ohio Republican Primary were significantly influenced by the decisions of everyone in their group todoso.This result is intuitive given that many individuals in the group were tied to the Kasich campaign. Though, despite this, the candidate preference of group members varied.

46Unfortunately, Wave III respondents were not asked if or for which candidate they voted for in the 2016 primary election. Instead, they were asked which candidate they preferred in the primary. While candidate preference does not necessarily indicate which candidate they voted for in the primary election, there is variation in this measure unlike in the measure of vote choice in the presidential election for the College Democrats and Republicans.

111 However, it is not the case that decisions to support Rubio or Trump were determined

by the preferences of others in the group.

Because the PSL and STEM-EE Scholars are politically heterogeneous groups,

modeling vote choice in the general election is feasible. The first two columns in

Table 5.14 depict the models of vote choice for the two main party candidates, Hillary

Clinton and Donald Trump, in the PSL Scholars. Sixty-two percent of the PSL

Scholars reported already voting or planning to vote for Hillary Clinton, 13% reported

voting for Trump, 15% reported voting for Gary Johnson, and 10% of respondents

planned to vote for other candidates. However, only the decision to vote (or not vote)

for Trump is spatially dependent in this group. Individuals may not be voting for

Clinton because their peers are, but they may be choosing not to vote for Trump

because other individuals they are connected to in their group are not either.

The last two columns of Table 5.14 represent results from the STEM-EE Scholars.

In this group, 53% reported already voting or planning to vote for Hillary Clinton,

20% reported voting for Trump, 10% reported voting for Gary Johnson, and 17% of

respondents planned to vote for other candidates. The positive and significant rho

coefficient in the third column indicates that respondents’ decisions to vote forHillary

Clinton were dependent on the decisions of other members of their group. The rho

coefficient in the model of the decision to vote for Donald Trump (Column 4)just

reaches standard levels of significance. Results for the STEM-EE Scholars provide

perhaps the strongest evidence in support of the salience hypothesis. When the group is activated in the general election, group members do look to others in their networks for clues on who and who not to support.

112 ClintonVote TrumpVote ClintonVote TrumpVote (PSL) (PSL) (STEM) (STEM) Interest 0.14∗∗∗ -.03 0.15∗ -.01 (0.05) (0.04) (0.08) (0.06) Number of Ties 0.01 -.02∗∗∗ -.05∗∗ 0.02 (0.01) (0.00) (0.02) (0.01) Female 0.09 0.07 0.24 0.09 (0.11) (0.08) (0.17) (0.14) Christian -.37∗∗∗ 0.23∗∗∗ -.35∗∗ 0.12 (0.11) (0.08) (0.16) (0.13) Constant 0.01 0.33∗ -.09 0.01 (0.25) (0.18) (0.35) (0.29) ρ -0.00 0.13∗∗∗ 0.11∗∗∗ -0.08∗ (0.02) (0.02) (0.04) (0.05) Num. Obs. 61 61 40 40 Log-Likelihood -31.68 -10.84 -22.94 -18.18 χ2 23.08 40.63 13.90 3.88

∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.10

Table 5.14: General Election Vote Choice for PSL & STEM-EE Scholars

113 Analyzing whether individuals’ decisions on which candidate to vote for in both the 2016 presidential primary and general elections conveys similar themes as those found when modeling feeling thermometer scores, which is not surprising because they are significantly correlated with vote. Individuals’ decisions about which candidate to support in the primary election are influenced by the decisions of others around them in the two partisan groups, but not in every case; specifically, in circumstances where the vote decision is supported by the norms of and majority in the student group even though it may not be supported by the Republican or Democratic Parties overall. Individuals in politically heterogeneous groups especially rely on their social ties to decide on which candidate to support in the general election.

5.7 The Effect of Group Composition on Individual Political Attitudes over Time

To what extent do individuals’ attitudes change as a result of membership in social groups? In politically homogenous and heterogeneous settings, individual attitudes may be strengthened towards the partisan direction of the group, clarified as a result of information sharing or participation, or changed completely.47 Even if individuals’ political attitudes are not determined by the attitudes of the group as a whole - as this chapter tests - their political attitudes may be influenced by the attitudes of individuals that they interact most within the group. Individuals whose attitudes do

47There is descriptive evidence that attitudes change over time. Anywhere between 17-78% of attitudes in the College Republicans, 14-59% of attitudes in the College Democrats, 40-69% of attitudes in the PSL Scholars, and 47-65% of attitudes in the STEM-EE Scholars changed between Waves I and II.

114 not conform to the majority of attitudes in their social group may even choose to leave the group.

Longitudinal data allows me to test hypotheses about the effect of group membership on the change in individual political attitudes over time in both politically homogenous and heterogeneous settings. However, modeling temporal dynamics with spatial dependence among actors presents unique challenges, and the spatial autocorrelation model described previously cannot account for the temporal dependencies in the data. I therefore utilize the Temporal Network Autocorrelation Model (TNAM) to account for both the spatial and temporal dependencies (Leifeld & Cranmer 2015).

Specifically, because Y, the vector of political attitudes, W, the spatial weights matrix of knowing ties, and X, the matrix of exogenous covariates, can be observed longitudinally, the temporal dynamics of the model are critical to it’s fit. The TNAM represents “the most fully featured model for the behavior of actors embedded in a network yet achieved” (Leifeld & Cranmer 2015, 8).

Results from the models, run separately by social group, are displayed in Tables

5.15 - 5.18. In each model, I regress the change in political attitudes between times one and two on the network from time two, second-order effects at time two (friends of friends), the lagged dependent variable from time one, and the same controls included in the cross-sectional models.48 I model the change in behavior between survey waves because I am interested in testing whether individuals’ attitudes about politics were strengthened or clarified as a result of the network over time. However, Ialso

48However, results presented and discussed in text do not contain control variables because of the low number of respondents who participated in each survey wave. Results of the fully-specified models are available in Appendix J and K. The controls for gender and religion are from the first survey wave, while controls for interest in politics and the number of ties to the group are taken from the second survey wave.

115 model individuals’ behavior at time two, controlling for their Wave I attitude (see

Appendix J and K). The coefficient estimate of the measure of the network attime two will indicate whether an individual’s connections to others in the group act on their political attitudes over time. Only current group members who responded to surveys in both Waves I and II are included in the analysis.49

∆ PID ∆ DemFeel ∆ RepFeel ∆ ClintonFeel ∆ TrumpFeel Intercept −0.31∗ −0.19 −0.23 1.11 −1.04∗ (0.15) (0.36) (0.33) (0.81) (0.57) Network at T2 −0.16 0.08 −0.06 −0.08 −0.11 (0.09) (0.12) (0.11) (0.09) (0.09) AIC 38.89 67.98 65.83 92.19 91.65 BIC 42.17 71.25 69.10 95.47 94.93 Log-Likelihood -16.45 -30.99 -29.92 -43.10 -42.83 Deviance 5.75 21.55 19.55 64.79 63.21 Num. Obs. 22 22 22 22 22 ∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.1

Table 5.15: College Democrats, Modeling Change w/ Network Only

49The Temporal Network Autocorrelation Model (TNAM) is able to incorporate more than two time points. There are several data constraints that need to be considered, however. First of all, statistical power is a concern because of the low number of individuals in the College Democrats and Republicans who responded to both of the first two survey waves. There simply is not enough power to study these groups across all four time points using only individuals who responded to all four waves. Additionally, there is considerable turnover amongst the College Democrats and Republicans at the start of each academic year - more turnover than occurs between the Fall and Spring semesters. For the College Democrats and Republicans, it makes more sense to analyze data from Waves I and II and Waves III and IV separately. For the PSL and STEM-EE Scholars, there is considerably less turnover between semesters and academic years. Individuals in these groups can drop out of the group, but no new individuals are added. Still, the low number of observations makes finding effects difficult. There are 36 and 38 members from thePSLand STEM-EE Scholars who responded to all four survey waves, respectively. Future work will utilize the TNAM to look at change between T1 − T3, T1 − T4, and T3 − T4 in these two groups, but they are not included in this analysis.

116 ∆ PID ∆ DemFeel ∆ RepFeel ∆ ClintonFeel ∆ TrumpFeel Intercept −0.27 −0.50 −0.65 −0.35 −0.52 (0.25) (0.84) (0.44) (0.35) (0.91) Network at T2 0.24 0.14 −0.06 0.15 0.05 (0.23) (0.22) (0.11) (0.32) (0.08) AIC 38.71 82.75 68.47 64.53 89.12 BIC 41.38 85.42 71.14 67.21 91.79 Log-Likelihood -16.35 -38.37 -31.24 -29.27 -41.56 Deviance 6.49 74.91 33.89 27.23 106.71 Num. Obs. 18 18 18 18 18 ∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.1

Table 5.16: College Republicans, Modeling Change w/ Network Only

∆ PID ∆ DemFeel ∆ RepFeel ∆ ClintonFeel ∆ TrumpFeel Intercept 0.14 0.03 −0.05 −0.08 −0.21 (0.23) (0.29) (0.28) (0.34) (0.39) Network at T2 0.01 0.01 −0.06∗ 0.02 −0.00 (0.03) (0.04) (0.03) (0.02) (0.03) AIC 227.12 286.92 307.98 299.41 313.39 BIC 233.78 293.58 314.64 306.07 320.05 Log-Likelihood -110.56 -140.46 -150.99 -146.71 -153.70 Deviance 102.86 247.86 337.84 297.82 365.82 Num. Obs. 68 68 68 68 68 ∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.1

Table 5.17: Politics, Society, & Law Scholars, Modeling Change w/ Network Only

117 ∆ PID ∆ DemFeel ∆ RepFeel ∆ ClintonFeel ∆ TrumpFeel Intercept −0.26 0.31 −0.15 0.07 −0.86∗ (0.18) (0.21) (0.37) (0.32) (0.48) Network at T2 −0.04 −0.06 0.02 0.03 −0.12∗∗ (0.04) (0.05) (0.04) (0.05) (0.06) AIC 139.75 156.28 188.82 222.18 234.94 BIC 145.43 161.96 194.49 227.86 240.62 Log-Likelihood -66.88 -75.14 -91.41 -108.09 -114.47 Deviance 43.97 61.62 119.69 236.48 306.82 Num. Obs. 49 49 49 49 49 ∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.1

Table 5.18: STEM Exploration & Engagement Scholars, Modeling Change w/ Net- work Only

Tables 5.15 - 5.18 depict results from the TNAM run separately by student group.

Because of the small number of observations in both the College Democrats and

Republicans, I only include the control for the network at time two in initial models.

Results from Tables 5.15 - 5.18 show that the network at time two is rarely a significant predictor of attitude change across all groups. It is never a significant predictor in the College Democrats and Republicans and only in a single case for the PSL and

STEM-EE Scholars. The more connected the individual is to the group in time two, the more negative their attitudes toward the Republican Party (PSL Scholars, Table

5.17) and Donald Trump (STEM-EE Scholars, Table 5.18) become. The insignificance of the network at time two in the partisan groups may be due to the small number of observations, or it may be because the direct impact of individuals’ ties to the group do not influence attitude change in these groups.

In Tables 5.19 - 5.22, I add a measure of individuals’ indirect ties to members of the group to the TNAM. This measure incorporates the influence of the friends

118 their friends are connected to, or their second-degree connections. The addition of this variable significantly changes results in a few key ways. First, with the addition of the friends of friends variable, the measure for the network at time two becomes a significant predictor of attitude change in most cases. In other words, oncethe influence of an individual’s indirect ties are accounted for, the influence of their direct ties becomes consequential. This is especially the case for individuals in the College

Democrats and the two Scholars groups. It is increasingly apparent that there is not enough power to find effects in the College Republicans.

∆ PID ∆ DemFeel ∆ RepFeel ∆ ClintonFeel ∆ TrumpFeel Intercept −2.01∗∗∗ 1.77∗∗∗ −0.77 7.88∗∗∗ −9.00∗∗∗ (0.18) (0.32) (0.65) (0.71) (0.73) Network at T2 −0.99∗∗∗ −0.75∗∗∗ −0.21 −0.83∗∗∗ −1.00∗∗∗ (0.09) (0.12) (0.19) (0.08) (0.09) Friends of Friends −1.91∗∗∗ −1.40∗∗∗ −0.34 −1.29∗∗∗ −1.93∗∗∗ (0.19) (0.19) (0.35) (0.12) (0.17) AIC 0.29 39.68 66.77 51.30 48.16 BIC 4.65 44.04 71.13 55.67 52.52 Log-Likelihood 3.86 -15.84 -29.38 -21.65 -20.08 Deviance 0.91 5.44 18.62 9.22 7.99 Num. Obs. 22 22 22 22 22 ∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.1

Table 5.19: College Democrats, Modeling Change w/ Network and FoF

Second, the friends of friends variable is also a highly significant predictor of attitude change in most every case (again, except in the College Republicans). Studies of social influence that only account for first-degree connections may be missing some of the influence story. Finally, the coefficient estimates from the direct and indirect

119 ∆ PID ∆ DemFeel ∆ RepFeel ∆ ClintonFeel ∆ TrumpFeel Intercept −0.57∗ −0.12 −0.72 −0.75∗ −0.04 (0.32) (1.09) (0.71) (0.38) (1.41) Network at T2 0.29 0.03 −0.06 −0.55 0.08 (0.22) (0.30) (0.11) (0.46) (0.09) Friends of Friends 0.86 −0.34 −0.02 −1.21∗ 0.10 (0.60) (0.60) (0.16) (0.62) (0.22) AIC 38.43 84.36 70.45 62.39 90.86 BIC 41.99 87.92 74.01 65.96 94.42 Log-Likelihood -15.21 -38.18 -31.23 -27.20 -41.43 Deviance 5.71 73.30 33.85 21.64 105.21 Num. Obs. 18 18 18 18 18 ∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.1

Table 5.20: College Republicans, Modeling Change w/ Network and FoF

∆ PID ∆ DemFeel ∆ RepFeel ∆ ClintonFeel ∆ TrumpFeel Intercept 7.46∗∗∗ −0.53∗∗ −4.03∗∗∗ −5.93∗∗∗ −5.88∗∗∗ (0.75) (0.24) (0.50) (1.35) (1.01) Network at T2 −0.58∗∗∗ −0.34∗∗∗ −0.60∗∗∗ −0.27∗∗∗ −0.44∗∗∗ (0.06) (0.06) (0.07) (0.07) (0.08) Friends of Friends −1.13∗∗∗ −0.66∗∗∗ −1.07∗∗∗ −0.57∗∗∗ −0.80∗∗∗ (0.11) (0.10) (0.12) (0.13) (0.14) AIC 166.47 255.91 258.17 283.29 286.19 BIC 175.35 264.79 267.05 292.17 295.07 Log-Likelihood -79.23 -123.96 -125.08 -137.64 -139.10 Deviance 40.94 152.54 157.68 228.15 238.11 Num. Obs. 68 68 68 68 68 ∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.1

Table 5.21: Politics, Society, & Law Scholars, Modeling Change w/ Network and FoF

120 ∆ PID ∆ DemFeel ∆ RepFeel ∆ ClintonFeel ∆ TrumpFeel Intercept −2.25∗∗∗ 2.78∗∗∗ −9.10∗∗∗ 1.98∗∗∗ −6.18∗∗∗ (0.56) (0.34) (1.69) (0.43) (0.64) Network at T2 −0.28∗∗∗ −0.41∗∗∗ −0.59∗∗∗ −0.40∗∗∗ −0.70∗∗∗ (0.08) (0.05) (0.12) (0.09) (0.07) Friends of Friends −0.57∗∗∗ −0.81∗∗∗ −1.29∗∗∗ −0.88∗∗∗ −1.47∗∗∗ (0.15) (0.10) (0.24) (0.16) (0.16) AIC 128.83 115.43 166.93 198.94 185.15 BIC 136.40 122.99 174.50 206.51 192.72 Log-Likelihood -60.42 -53.71 -79.47 -95.47 -88.58 Deviance 33.78 25.70 73.52 141.27 106.62 Num. Obs. 49 49 49 49 49 ∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.1

Table 5.22: STEM Exploration & Engagement Scholars, Modeling Change w/ Net- work and FoF

influence measures are always negative (when significant). Interpretation of theneg- ative coefficients does not always make sense substantively, however. The negative coefficients on both measures suggest that as direct and indirect connections increase individuals report identifying more strongly as a Democrat and feeling more negatively toward both political parties and their candidates over time. Given the context of the 2016 presidential election, it is feasible to imagine that attitudes towards the entire American political system would sour. However, that they would do so in a consistent direction across multiple groups is puzzling, and further exploration into these results is necessary. Importantly, these results remain with the addition of control variables (see Appendix J and K).50

50In Appendix J and K, I also model the attitudes of each individual at time two as a function of demographic covariates, the behavior of other individuals they are connected to at time two (t), the behavior of other individuals their connections are connected to at time two, the behavior of other individuals they are connected to at time one (t-1), and the individual’s own previous behavior at time one (t-1). These results convey a similar picture, which differs in a few ways.

121 Taken together, individuals’ attitudes do strengthen - specifically, they become more negative - over time as a result of both their direct and indirect ties to individuals in their group. However, without including a control for indirect ties, the influence of individuals’ direct ties is almost always not a consequential predictor of both attitude change and attitudes at time two. Interestingly, and in contrast to cross-sectional results, there are no differences between individuals in politically homogenous and heterogeneous groups. These results may suggest that while group political composition does condition whether an individual is influenced by their group statically, these affects may not be dynamic - or, in other works, they may notpersist across time.51

First of all, the network at time two is a significant predictor of individuals’ attitudes at time twoin the College Republicans and the PSL Scholars groups, and only in predicting party identification (College Republicans) and attitudes toward the Democratic Party (PSL Scholars) and Donald Trump (both groups). When there is ample statistical power, the lagged dependent variable - individuals’ attitude at time one - is always a significant predictor of their current attitudes. Second, while the addition of the friends of friends measure does also change the significance of the network at time two measure, it does not do so in every case. And, with the addition of the indirect ties measure in the model for the College Republicans, the network at time two actually loses significance. I also include a control for the network at time one, which is rarely a significant predictor of individuals’ beliefs and attitudes about politics across all groups. The addition of control variables does not alter the results. 51There is some initial support of the clarifying hypothesis, but only for individuals in heterogeneous groups. For all measures of political attitudes addressed in this paper, respondents were provided with a ‘don’t know’ response option. However, only respondents from the non-political group (STEM-EE) chose this answer option. Accordingly, the opportunity for clarification of political attitudes existed only in the non-political group. Individuals who join political groups, even heterogeneous political groups, are able to report their political attitudes when asked. Of the nine STEM-EE respondents who reported not knowing their party identification in Wave I and also participated in the Wave II survey, four reported still not knowing their party identification in Wave II. These four individuals were also likely to report not knowing their feelings toward the political parties or candidates in Wave II. Thus, while there is some evidence of political beliefs being clarified between Waves I and II, clarification only occurs among individuals in heterogeneous, non-political groups. Respondents from all political groups, both politically homogenous and heterogeneous, can report on their political beliefs when asked. Distinctions exist both between homogeneous and heterogeneous groups and political and non-political groups. More sophisticated tests are necessary, however.

122 5.8 Dropout Analysis

There were several individuals who dropped out of the groups between Waves I and

II and between Waves III and IV both voluntarily and forcefully. Table 5.23 displays the number of individuals who dropped out and were added to the College Democrats and College Republicans between waves. The table also displays the number of individuals who dropped out of the Scholars groups across all four survey waves. Results are separated by student vs. academic group because of the difference between membership in each group. I follow the same PSL and STEM-EE Scholars across two years. While individuals can voluntarily drop or be forcibly removed from the Schol- ars groups, no new individuals can be added to the cohorts. Students in the Scholars groups can be removed from the group if they fail to maintain a certain GPA. The

College Democrats and College Republicans receive new members each semester, and individuals drop out due to inactivity. The full academic years - Waves I and II and then Waves III and IV - provide a better picture of individuals who drop out of the partisan groups than between Waves I and IV as the dropouts across that amount of time is substantial. For example, only 19 and 14 individuals in the College Democrats and College Republicans, respectively, responded to all four survey waves, which includes dropouts compared to the 36 and 38 individuals from the PSL and STEM-EE

Scholars, respectively, who responded to each survey wave, including dropouts.

Individuals in the Scholars groups who dropped out between Waves I and II, II and III, and III and IV were still included in the network battery in each additional survey wave and were contacted to take every survey. For the College Democrats and

College Republicans, the network battery remained the same between Waves I and

II and between Waves III and IV, with the addition of newcomers in Waves II and

123 W1 − W1 + W1 W2 − W2 + W2 W3 − W3 + W3 W4 CDs 48 28 12 32 - - 81 24 5 62 CRs 37 6 13 44 - - 56 2 5 59 PSL 110 1 0 109 14 0 95 0 0 95 STEM 96 9 0 87 26 0 61 0 0 61 Where ‘-’ indicates dropouts and ‘+’ indicates additions

Table 5.23: Dropouts & Additions between Survey Waves in the Group Study

IV. However, respondents who dropped out of the group between survey waves but still responded to the survey in Waves II-IV are not included in the cross-sectional analysis of Wave II and III results (Tables 5.5 - 5.8) nor in the over time analysis

(Tables 5.15 - 5.22).52

Individuals who dropped out of the student activity groups between Waves I and II were unique in a few key ways compared to individuals who remained in the group.53 Specifically, individuals who dropped out of the Republican group between

Waves I and II had stronger positive feelings towards Donald Trump (Table 5.24).

This difference is significant (t=2.061; p=0.05). In contrast, new additions tothe

Republican group between Waves I and II had similar feelings about Trump as existing group members. Interestingly, individuals who dropped out of the non-political group, a more Republican identifying group overall, between Waves I and II have

52Specifically, 50% of individuals who dropped out of the College Democrats, 33% of thosethat dropped out of the College Republicans, and 44% of those who dropped out of the STEM Ex- ploration and Engagement Scholars responded to the Wave II survey. I expect individuals who dropped out of the group to have less ties to and less similar attitudes than other group members. 53At this time, I have not looked at individuals who have dropped out of groups between Waves II and III (for Scholars groups) and between Waves III and IV (for all groups).

124 slightly stronger positive feelings toward Trump, but this difference is not significant (t=0.919). Similarly, individuals who dropped out of the College Democrats between survey waves had more favorable views of Bernie Sanders than those who remained. As stated previously, there were no new additions to the non-partisan and non-political groups. Materials in Appendix K depict the total levels of support for each candidate across the first three survey waves.

Support (W1) Support (W2) Support (Drop) Support (Add) CDs 1.0 0.1 0.1 0.3 CRs 4.6 3.5 8.5 4.0 PSL 1.7 1.4 - - STEM 2.3 2.1 4.0 -

Table 5.24: Mean Trump Support Amongst Dropouts & Additions in the Group Study

Individuals who dropped out of the groups between survey waves not only had stronger positive feelings about Donald Trump, but they also had varying amounts of network support for him. Individuals who dropped out of the Democratic group between Waves I and II had less network support for Trump (at both time periods) than individuals who stayed in the group.54 The dropouts also had less ties to members of the group overall and talked about politics less frequently with other group members than those who stayed in the group. Individuals who dropped out of the

Republican group between Waves I and II had more network support for Trump (at both time periods) than individuals who stayed in the group. However, the dropouts

54These results are not displayed in text but are available upon request.

125 had less ties to members of the group overall and talked about politics less frequently with other group members than those who stayed in the group. Finally, individuals who dropped out of the non-political group (STEM-EE) between Waves I and II had more network support for Trump (at both time periods) than individuals who stayed in the group. And, as with the Republican group, individuals who dropped out of the STEM-EE group also had less network ties and discussed politics less frequently

(at both time periods) than those who stayed in the group. These results suggest that even when individuals’ political attitudes are supported among the people they are connected with, the group can still operate to push them out of the group when those attitudes are divergent from the group’s majority.

Across groups individuals with more favorable attitudes toward Donald Trump dropped out of the group between Waves I and II. While the individuals who dropped out of the group between survey waves had varying amounts of network support for

Donald Trump, they had a smaller number of ties to other members of the group and participated in political conversation with other members of their group less frequently. In general, initial evidence suggests that dropouts are less engaged with other group members and in group activities. Initial evidence suggests that individuals with political attitudes divergent from those attitudes shared by the group majority do, in fact, drop out of the group (exit hypothesis). Future work will assess if Trump supporters come back to the Republican group during the general election

(Wave III) and if they stay once the election is over (Wave IV).

126 5.9 Discussion

This chapter set out to determine whether individuals’ political attitudes are impacted by the social groups that they are embedded in. It hypothesized that the likelihood of social influence on individual political attitudes is conditional on thepo- litical composition of the social setting. The conditional effect of group composition on social influence was demonstrated in a few key ways.

First, results from Wave I, which occurred before the first votes were cast in the primaries and caucuses, demonstrate that individuals’ attitudes about politics are more likely to be impacted by the attitudes of individuals they are connected to in politically homogeneous groups than in heterogeneous groups (homogeneity and heterogeneity hypotheses). Social influence on individual political beliefs can occur outside of campaign environments in politically homogenous groups. That individuals are more likely to be influenced by individuals like themselves does not necessarily bode well for democratic society as influence in homogeneous groups could exacerbate polarization in the polity.

Second, the campaign activates social groups, initializing even politically heterogeneous groups to impact individual political attitudes (salience hypothesis). Accord- ingly, the campaign environment may in fact be an ideal setting in which to study social influence on politics, as the biases introduced by selection seem to be mitigated.

In other words, during political campaigns individuals can be influenced in politically homogenous and heterogeneous social environments potentially allowing for exposure to diverse viewpoints not available in non-campaign settings.

While there is evidence that the likelihood of influence in social settings does depend on the political composition of the group, this evidence is weakened when

127 the number of ties variable is removed. Future work will clarify these empirical results including the interpretation of the rho parameters and the justification for the inclusion, or removal, of the number of ties variable.

Results from the Temporal Network Autocorrelation Model (TNAM) provide pause for the conclusion that a group’s political composition conditions influence in social groups. Individuals’ connections to the group can be important determinants of attitude change, but primarily only when their indirect ties (friends of friends) are also accounted for. However, the meaning and significance of the indirect ties measure is still elusive. Future work will utilize the TNAM to model data from Waves I-III separately in order to better understand how individuals’ indirect ties affect spatial dependencies at a cross-section in time. Additionally, the TNAM enables incorpora- tion of network effects and processes yet untapped in this analysis. The spatial lag term, for example, can be operationalized not only as political discussion networks but also as structural equivalence and social cohesion networks, among others. The

TNAM allows for the inclusion of a similarity matrix, which can directly model at- tribute homophily amongst individuals. Other network effects that have not been addressed in this analysis, such as centrality and transitivity measures, can also be encompassed. Finally, future work will incorporate the third and fourth waves into the analysis of groups over time.

This analysis focused on understanding whether the composition of the group as a whole acted on individual political attitudes in and outside of a political campaign, but certainly other network effects may be at work. Even though individuals may not be influenced by the political attitudes of their group as a whole, theymay be influenced by the attitudes of the individuals that they are connected tointhe

128 group. Future work will explore the networks within the group network to uncover the processes of influence there. Community detection can also be utilized to uncover networks of Bernie and Clinton supporters in the College Democrats and networks of

Kasich and Trump supporters in the College Republicans, among others.

An initial look at individuals who dropped out of the groups between Waves I and II suggests that individuals whose attitudes do not conform with the attitudes of the majority of individuals in their social group do leave the group (exit hypothesis). Analysis of individuals who dropped out of the groups between survey waves is imperative in understanding how group membership causally acts on individuals’ beliefs about politics. By following dropouts, it is possible to compare the attitude change of individuals who left the group with the attitude change of individuals who stayed in the group. Future work will address dropouts more thoroughly and also incorporate roommate data to strengthen causal claims.

Of course, individuals who establish relationships for explicitly political reasons may be more open to (political) influence in the first place. They might be different in real and fundamental ways than individuals who do not establish relationships for political and partisan reasons. Perhaps the personalities of individuals who select into explicitly homogenous political settings differ in significant ways from the personalities of individuals who do not select into partisan settings. Future work will test this alternative explanation using the personality measures included in the survey battery.

129 Chapter 6: Choosing How to Change: How Group Political Composition Conditions Mechanisms of Influence in Social Networks

Individuals’ political attitudes and beliefs are influenced by the attitudes of others

with whom they are connected both inside and outside of a political campaign. The

group’s political composition alters whether individuals are influenced by their ties to

other individuals in the group, where social influence on individual political beliefs is

more likely to occur in homogenously partisan groups. In this chapter, I demonstrate

that group political composition also alters how individuals are influenced by their social environments utilizing the network panel study described in Chapter Four.

Scholars should expect social influence to work differently conditional on the political composition of the group.

6.1 Influence Mechanisms in the Literature

The introductory chapter discussed mechanisms through which social environments may act on individuals’ attitudes and beliefs about politics. These mechanisms were divided into two classes of explanations - psychological and structural. I define psychological explanations as the internal processes by which an individual comes to make

130 a decision and include cognitive dissonance, social pressure, and information seeking.

An individual experiences cognitive dissonance when their desire to resolve conflicting pieces of information results in updating or changing their attitudes (Heider 1958;

Festinger 1962; Kunda 1990). An individual experiences social, or normative, pressure when they feel pressure to align their (discrepant) behaviors and attitudes to those shared by the majority of the group (Lewin 1947; Newcomb 1943; Sinclair 2012). Fi- nally, the information individuals receive from their social environments may influence them to update or change their behavior (Hovland et al. 1953; Huckfeldt & Sprauge

1995; Ryan 2011). All three of these concepts - cognitive dissonance, social pressure, and information seeking - represent how individuals internally process influence from their social environments.

Structural explanations, on the other hand, refer to specific aspects of the group itself that affect whether individuals are influenced by their social environments. I have already looked at one such explanation: group political composition. In fact, this project centers on understanding if and how the political composition of social groups affect inter-personal influence in social settings. In addition to network composition, other aspects of the network itself could act on individual political behavior.

The number of ties an individual has to other members of their social group as well as their level of commitment to and participation in the group can impact the likelihood and direction of social influence on individual political behavior. For example, an individual who is more committed to, connected to, and active in their social group should be more likely to adopt the views of the group.55

55Still, more “formal” network concepts could be at work, including degree, eigenvector centrality, etc.

131 6.2 Hypotheses

The consideration of psychological and structural influence mechanisms leads me to offer the following hypotheses about how they might work in various social groups.

In politically homogenous social groups, aspects of the group itself (structural explanations) affect individuals’ attitudes and beliefs about politics. In politically heterogeneous groups, however, psychological mechanisms are more likely to influence individuals’ attitudes and beliefs about politics. Of course, I expect the context of the campaign to alter this reality. Over time, the updating of individuals’ beliefs about politics should result more from structural explanations in politically homogenous social groups and more from psychological explanations in politically heterogeneous social groups. These hypotheses mirror those of the previous chapter (Chapter Five) with a crucial addition; they argue that how individuals are changed - and not just if they are changed - should also be conditional on group political composition.

6.3 Data & Measurement

Utilizing data from the first three waves of the network panel study described in

Chapter Four, I model five dependent variables, which include party identification and feelings about the Democratic Party, Republican Party, Hillary Clinton, and

Donald Trump using the spatial autoregressive model in order to account for interdependence among observations.56

56I run these models including all the control variables included in Chapter Five. Results are provided in Appendix L. Substantive results do not change when controls are included.

132 6.3.1 Psychological Explanations

The internal process mechanisms by which individuals make decisions can affect both

their political attitudes and the effect the social group has on those attitudes. To

account for these explanations, I asked each respondent whether they feel uncom-

fortable when their political views are different from those held by other members

of their group (cognitive dissonance), if they often seek out information from other members of their group before making a political decision (information seeking), and whether it is important to them that other members of their group support their political decisions (social pressure). Each of these variables is measured on a Likert scale where 1: Strongly disagree, 2: Disagree, 3: Neither agree nor disagree, 4: Agree, and 5: Strongly agree. From these three measures, I created one additive measure, psychological explanations, which ranges from 1 to 15.57 Higher values of this measure indicate higher levels of uncomfortability with disagreement, more information seeking, and stronger feelings of importance that the group supports their political decisions. Notably, cognitive dissonance, information seeking, and normative pressure are not typically combined together in a single scale, as they represent distanced psychological processes; however, they are combined here because of power concerns.58

57The three measures that create the additive psychological explanations variable are distinctive measures of psychological concepts. Correlations between these measures vary between groups and across survey waves. The Cronbach’s alpha scale reliability coefficient for the index lies between 0.25-0.67 for groups in Wave I, 0.24-0.68 for groups in Wave II, and 0.40-0.64 for groups in Wave III. Certainly, alpha scores below 0.60 are considered low and suggest that the separate explanations do, in fact, represent different concepts. 58Accordingly, I run models with all three psychological explanations instead of the single scale. Results are available upon request.

133 6.3.2 Structural Explanations

If social influence on political decision-making occurs in which individuals become more similar to other group members over time, then we would expect time spent in the group, participation in the group, and the amount and strength of the network ties to be important correlates or predictors of similar political views among group members. More specifically, respondents were asked how committed they feel toward their social group (group commitment; 1: Not at all committed, 2: Slightly committed, 3: Moderately committed, 4: Strongly committed, 5: Very strongly committed)59 as well as to indicate how much they participated in group activities (group participation; 1: very few, 2: some, 3: most, 4: all).

I also summed up the total number of ties they reported having to other members of the group from the “do you know this person” question in the network battery

(number of ties). From this continuous measure, I created one four-point ordinal measure where the value of ‘1’ indicates that an individual’s amount of ties to other group members was in the first quarter of individuals in their group (0-25%), avalue of ‘2’ indicates that an individual’s ties were in the second quarter (26-50%), a value of

‘3’ indicates that an individual’s ties were in the third quarter (51-75%), and a value of ‘4’ indicates that an individual’s ties were in the fourth quarter of ties (76-100%).

From these three measures I created the structural explanations measure, which is an additive measure of group commitment (on a scale of 1-4), group participation (on a scale of 1-4), and the ordinal measure of the number of ties to the group (on a scale

59I re-scaled this measure with response options from 1-4 to ensure that each measure contributed equally to the structural explanations index. Specifically, I combined the first two response options (1: Not at all or slightly committed; 2: Moderately committed; 3: Strongly committed; 4: Very strongly committed).

134 of 1-4). Accordingly, the structural explanations measure ranges from 1-12, where higher values indicate more connectedness to the group.60 Because both psychological and structural explanation measures were asked toward the front of each survey instruments, there are no missing data for these measures.

6.4 Cross-Sectional Evidence

I model the effect of psychological and structural explanations on individuals’ beliefs and attitudes about politics in Tables 6.1 - 6.12. The results discussed in text do not include the control variables described in Chapter Five. However, controls are included in the models in Appendix L, and the substantive results described below do not change with their inclusion.

I hypothesized that individuals’ commitment to, participation in, and ties to the group would be consequential predictors of political attitudes in homogeneously partisan groups, and results are consistent with this trend. Structural, not psychological, explanations predict individual political attitudes in the College Democrats (Table

6.1) and College Republicans (Table 6.2), though not in every case. Increased involvement in the group leads to stronger identification with and more positive feelings for the in-party and candidate but more strongly negative feelings for the out-party and candidate in the non-campaign season. More specifically, as involvement in the group increases, individuals in the College Democrats report identifying more strongly as

Democrats and feeling more negatively about Donald Trump while individuals in the

60Because analysis in Chapter Five revealed that results are sensitive to the number of ties variable, I run all analyses presented here with a structural explanations measure that does not include the four-point network ties measure. Results are presented in Appendix L.

135 Party-ID DemFeel RepFeel ClintonFeel TrumpFeel Psychological -.04 0.01 0.03 0.09 0.13 (0.05) (0.10) (0.10) (0.19) (0.19) Structural -.12∗ 0.10 -.18 -.16 -.40∗∗ (0.07) (0.14) (0.12) (0.26) (0.18) Constant 2.27∗∗∗ 7.16∗∗∗ 2.39∗∗∗ 6.50∗∗∗ 2.25 (0.45) (0.91) (0.88) (1.72) (1.59) ρ 0.03∗ 0.00 0.02 0.02 0.16∗∗∗ (0.02) (0.01) (0.03) (0.01) (0.04) Num. Obs. 37 37 37 37 37 Log-Likelihood -31.50 -56.30 -57.18 -80.29 -79.94 χ2 5.90 0.63 2.46 0.44 4.94

∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.10

Table 6.1: Mechanisms in College Democrats, Wave I

Party-ID DemFeel RepFeel ClintonFeel TrumpFeel Psychological 0.07 0.24 0.17 0.15 0.07 (0.08) (0.17) (0.16) (0.14) (0.24) Structural 0.12 -.81∗∗∗ 0.83∗∗∗ -.46∗∗∗ -.01 (0.11) (0.20) (0.23) (0.14) (0.35) Constant 5.08∗∗∗ 4.86∗∗∗ 0.74 2.85∗∗ 4.79∗∗ (0.75) (1.57) (1.54) (1.25) (2.42) ρ -0.00 0.24∗∗∗ -0.03∗∗∗ 0.22∗∗∗ -0.03 (0.01) (0.08) (0.01) (0.08) (0.04) Num. Obs. 33 33 33 33 33 Log-Likelihood -42.64 -67.69 -66.61 -62.11 -81.53 χ2 2.67 16.59 18.75 10.67 0.08

∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.10

Table 6.2: Mechanisms in College Republicans, Wave I

136 Party-ID DemFeel RepFeel ClintonFeel TrumpFeel Psychological -.21∗ 0.40∗∗∗ -.10 0.39∗∗∗ -.14 (0.11) (0.15) (0.15) (0.15) (0.13) Structural -.02 0.10 0.02 0.12 -.24 (0.18) (0.26) (0.25) (0.24) (0.19) Constant 5.28∗∗∗ 1.64 3.86∗∗ 0.27 4.31∗∗∗ (1.27) (1.84) (1.75) (1.77) (1.50) ρ -0.00 -0.00 0.00 -0.00 0.01 (0.00) (0.01) (0.01) (0.01) (0.01) Num. Obs. 95 95 95 95 95 Log-Likelihood -206.91 -242.35 -237.79 -238.90 -225.01 χ2 3.99 7.28 0.48 7.42 3.18

∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.10

Table 6.3: Mechanisms in the Politics, Society, & Law Scholars, Wave I

Party-ID DemFeel RepFeel ClintonFeel TrumpFeel Psychological 0.07 0.06 0.03 -.09 -.24 (0.09) (0.15) (0.14) (0.14) (0.17) Structural -.16 0.26 0.01 0.09 0.15 (0.17) (0.28) (0.26) (0.27) (0.29) Constant 4.78∗∗∗ 2.63 3.88∗∗ 2.61 3.39∗ (1.03) (1.65) (1.57) (1.61) (1.88) ρ -0.00 0.00 0.00 0.01 -0.01 (0.01) (0.01) (0.01) (0.01) (0.01) Num. Obs. 68 68 68 68 68 Log-Likelihood -128.98 -161.16 -157.88 -159.93 -171.33 χ2 1.23 1.26 0.07 0.43 2.17

∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.10

Table 6.4: Mechanisms in the STEM Exploration & Engagement Scholars, Wave I

137 Party-ID DemFeel RepFeel ClintonFeel TrumpFeel Psychological 0.05 -.13 0.09 -.11 0.09∗∗∗ (0.04) (0.13) (0.11) (0.16) (0.03) Structural -.09∗ 0.24 -.12 0.25 0.06∗∗ (0.05) (0.18) (0.10) (0.22) (0.03) Constant 1.01∗∗ 8.59∗∗∗ 1.46 8.26∗∗∗ -.87∗∗∗ (0.40) (1.25) (0.99) (1.52) (0.32) ρ 0.03 -0.01 -0.03 -0.02 0.16∗∗∗ (0.02) (0.01) (0.05) (0.02) (0.05) Num. Obs. 27 27 27 27 27 Log-Likelihood -16.22 -47.04 -40.79 -52.44 -10.96 χ2 4.44 2.78 1.79 1.68 14.17

∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.10

Table 6.5: Mechanisms in College Democrats, Wave II

Party-ID DemFeel RepFeel ClintonFeel TrumpFeel Psychological 0.00 0.13 -.01 0.04 0.33 (0.07) (0.13) (0.17) (0.13) (0.23) Structural 0.27∗∗ 0.10 0.50∗ 0.39∗∗ -.58∗ (0.12) (0.20) (0.27) (0.18) (0.31) Constant 4.37∗∗∗ 0.17 3.70∗∗ -1.63 5.26∗∗ (0.78) (1.31) (1.83) (1.38) (2.63) ρ -0.00 -0.08 -0.01 -0.15∗∗ 0.02 (0.01) (0.07) (0.02) (0.06) (0.04) Num. Obs. 24 24 24 24 24 Log-Likelihood -30.16 -43.44 -50.77 -45.03 -59.80 χ2 5.77 2.05 3.60 5.73 4.70

∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.10

Table 6.6: Mechanisms in College Republicans, Wave II

138 Party-ID DemFeel RepFeel ClintonFeel TrumpFeel Psychological -.00 -.10 0.16 0.05 -.01 (0.13) (0.17) (0.16) (0.17) (0.13) Structural -.23 0.47 -.10 0.29 -.06 (0.22) (0.31) (0.29) (0.30) (0.20) Constant 5.01∗∗∗ 2.88 3.68∗∗ 1.60 2.66∗ (1.40) (1.96) (1.78) (1.92) (1.38) ρ 0.00 -0.00 -0.01 -0.00 -0.02 (0.01) (0.01) (0.01) (0.01) (0.02) Num. Obs. 72 72 72 72 72 Log-Likelihood -161.26 -184.91 -178.43 -183.72 -162.64 χ2 1.17 2.39 0.97 1.09 0.12

∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.10

Table 6.7: Mechanisms in the Politics, Society, & Law Scholars, Wave II

Party-ID DemFeel RepFeel ClintonFeel TrumpFeel Psychological -.07 0.14 -.00 0.02 0.12 (0.11) (0.15) (0.17) (0.18) (0.21) Structural -.08 0.31 0.11 0.43∗ 0.27 (0.16) (0.22) (0.25) (0.24) (0.30) Constant 5.06∗∗∗ 2.34 2.88∗ -.00 0.60 (1.06) (1.51) (1.68) (1.70) (2.10) ρ -0.00 -0.00 0.00 0.00 -0.02 (0.01) (0.01) (0.01) (0.01) (0.02) Num. Obs. 55 55 55 55 55 Log-Likelihood -102.75 -122.94 -128.54 -129.94 -140.53 χ2 0.65 2.81 0.21 3.15 1.07

∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.10

Table 6.8: Mechanisms in the STEM Exploration & Engagement Scholars, Wave II

139 Party-ID DemFeel RepFeel ClintonFeel TrumpFeel Psychological -.04 0.01 -.01 0.11 0.05 (0.05) (0.14) (0.10) (0.14) (0.06) Structural -.00 0.23 0.08 0.16 -.09 (0.07) (0.20) (0.12) (0.20) (0.05) Constant 1.46∗∗∗ 6.94∗∗∗ 0.55 5.98∗∗∗ 0.26 (0.46) (1.29) (0.89) (1.28) (0.56) ρ 0.01 -0.01 0.02 -0.00 0.34∗∗∗ (0.02) (0.01) (0.03) (0.01) (0.09) Num. Obs. 36 36 36 36 36 Log-Likelihood -30.06 -67.64 -55.95 -67.31 -33.93 χ2 0.96 1.43 0.45 1.85 2.70

∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.10

Table 6.9: Mechanisms in College Democrats, Wave III

Party-ID DemFeel RepFeel ClintonFeel TrumpFeel Psychological 0.01 -.06 0.20 -.27∗∗ 0.35 (0.03) (0.14) (0.14) (0.12) (0.24) Structural 0.11∗∗ -.07 0.24 0.07 -.18 (0.04) (0.18) (0.21) (0.15) (0.34) Constant 6.18∗∗∗ 2.52 4.95∗∗∗ 2.27∗ 5.58∗∗ (0.29) (1.54) (1.34) (1.34) (2.30) ρ -0.01∗∗ -0.03 -0.04∗∗ -0.02 -0.03 (0.00) (0.07) (0.02) (0.07) (0.04) Num. Obs. 32 32 32 32 32 Log-Likelihood -9.69 -62.38 -59.20 -57.45 -77.22 χ2 7.09 0.31 4.94 4.97 2.13

∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.10

Table 6.10: Mechanisms in College Republicans, Wave III

140 Party-ID DemFeel RepFeel ClintonFeel TrumpFeel Psychological 0.07 0.00 0.12 0.13 0.00 (0.14) (0.19) (0.17) (0.19) (0.13) Structural 0.02 0.21 -.12 0.14 -.14 (0.21) (0.28) (0.26) (0.28) (0.19) Constant 3.45∗∗∗ 3.80∗∗ 4.41∗∗∗ 2.88 2.59∗∗ (1.31) (1.76) (1.58) (1.76) (1.21) ρ -0.01 0.00 -0.01 0.00 -0.01 (0.01) (0.01) (0.01) (0.01) (0.02) Num. Obs. 61 61 61 61 61 Log-Likelihood -138.10 -156.43 -149.59 -156.48 -133.90 χ2 0.27 0.55 0.64 0.90 0.63

∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.10

Table 6.11: Mechanisms in the Politics, Society, & Law Scholars, Wave III

Party-ID DemFeel RepFeel ClintonFeel TrumpFeel Psychological -.13 0.41∗∗ 0.07 0.14 -.07 (0.12) (0.19) (0.16) (0.22) (0.17) Structural -.01 0.30 0.26 0.02 0.40∗ (0.17) (0.28) (0.23) (0.30) (0.24) Constant 4.89∗∗∗ 0.57 2.44∗ 2.53 1.03 (1.01) (1.61) (1.35) (1.81) (1.42) ρ -0.00 -0.00 -0.01 0.01 -0.03 (0.01) (0.01) (0.01) (0.01) (0.02) Num. Obs. 40 40 40 40 40 Log-Likelihood -75.12 -94.63 -87.06 -99.33 -89.13 χ2 1.21 6.39 1.50 0.43 2.98

∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.10

Table 6.12: Mechanisms in the STEM Exploration & Engagement Scholars, Wave III

141 College Republicans report feeling more negatively about the Democratic Party and

Hillary Clinton and more positively about the Republican Party.

This pattern, for the most part, persists during the primary campaign with a few exceptions. Notably, increased involvement in the College Republicans results in more strongly negative attitudes about Donald Trump but more strongly positive attitudes towards Hillary Clinton (Table 6.6). Considering the group’s disdain for

Trump and the norm of supporting John Kasich, the former result is intuitively understandable. That feelings about Hillary Clinton would become more positive as a result of increased involvement in the College Republicans is more difficult to make sense of, but perhaps it further signals the group’s frustration with Donald Trump and the messy Republican primary. It is also unclear why increased psychological attachment to and structural involvement in the group would result in more positive feelings for Donald Trump in the College Democrats during the primary (Table 6.5).

Consistent with results from Wave I, increased involvement in the group results in stronger Democratic identification in the College Democrats (Table 6.5) and stronger

Republican identification in the College Republicans (Table 6.6).

In the general election (Tables 6.9 - 6.10), however, the College Democrats and

Republicans follow their parties’ positions and structural explanations of political attitudes play less of a predictive role though coefficient estimates, for the most part, remain in the expected direction even if they do not reach statistical significance. Once again, increased involvement in the College Republicans results in stronger identification with the Republican Party (Table 6.10). Increased psychological attachment to the College Republicans also results in more negative feelings toward Hillary Clinton.

Across the election, there is consistent evidence that structural explanations are at

142 work in politically homogenous social groups. Most of the time, increased commitment and number of ties to and participation in the group results in attitudes that conform to the attitudes of other members of their group.

Psychological influence mechanisms are at work in the political, but non-partisan group. Here, increased “psychological attachment” to the group results in stronger identification as a Democrat as well as in more positive assessments of Hillary Clinton and the Democratic Party (Table 6.3). Considering that the PSL Scholars are a majority Democratic group, these results make sense. However, it could also be possible that individuals who report having more Democratic attitudes are also more connected with Democrats in the group. This analysis, however, does not address those networks within the group. Neither psychological nor structural explanations predict political attitudes among individuals in the STEM-EE Scholars before the campaign begins (Table 6.4).

As expected, and consistent with results in Chapter Five, the campaign does alter if and how social influence on political attitudes operates in politically heterogeneous groups. The political campaign, and especially the general presidential election campaign, makes politics more salient and changes dynamics within groups. Psychological explanations that once predicted political attitudes in the PSL Scholars before the campaign season are no longer significant predictors in the primary (Table 6.7) or general election (Table 6.11) campaigns. Psychological and structural explanations are activated in the STEM-EE Scholars throughout the election, where increased involvement in the group leads to more strongly positive feelings for Hillary Clinton

(Table 6.8) and Donald Trump (Table 6.12), and increased psychological attachment to the group leads to more positive feelings for the Democratic Party (Table 6.12).

143 As depicted the Appendix materials from Chapter Five (Appendix J), the STEM-EE

Scholars become more favorable to Hillary Clinton over the course of the presidential campaign, though her support in this group is still low compared to attitudes towards

Bernie Sanders and John Kasich. But, it is unclear what explains increased support for Donald Trump as involvement in the STEM-EE Scholars increases. These results merit pushing forward to discover whether and how networks within the group operate on individual political attitudes.

Importantly, the rho coefficient estimates displayed in Tables 6.1 - 6.12 follow similar patterns for politically homogeneous groups as discussed in detail in Chapter Five and are included in these tables because the spatial dependence amongst individuals in these groups must still be accounted for. However, when structural and psychological influence mechanisms are controlled for, no significant spatial dependence is found in either of the Scholars groups across all survey waves. These results for politically heterogeneous groups differ from results in Chapter Five, which found evidence of spatial dependence in individuals’ attitudes in the primary campaign (PSL Schol- ars) and in the general election (STEM-EE Scholars). It is unclear why this is the case. Perhaps, accounting for how individuals are influenced in heterogeneous groups explains the dependencies in attitudes between individuals.

Results from Chapter Five also indicated that the inclusion of the number of ties variable increased the likelihood of spatial dependence amongst the student groups.

Accordingly, I created a structural explanations measure, which includes commitment to and participation in the group, but not the four-point measure of ties to individuals in the group. In this case, results for the structural ties measure remain consistent with those discussed above - structural explanations may even be more important

144 predictors of political attitudes in homogenous groups when the number of ties are

removed. Though, the spatial dependence among those attitudes does weaken with-

out the number of ties included in the measure as in Chapter Five (see Appendix J).

Also, and as in Chapter Five, I modeled whether or not psychological or structural

explanations predict the choice of primary (College Democrats and College Republi-

cans) or general election (PSL and STEM-EE Scholars) candidates in Wave III using

both the structural explanations measure and the measure that does not include ties to the group. Neither structural or psychological explanations predict vote choice in any group (see Appendix L).

To summarize the results from the cross-sectional analyses, there is evidence that different influence mechanisms are at work in social groups with varying amounts of political homogeneity. In politically homogenous social groups, aspects of the group itself consistently influence individuals’ attitudes about politics though not inevery case. In politically heterogeneous groups, psychological explanations are more important predictors of individuals’ attitudes about politics. The campaign season, once again, alters the context of social group influence on individual attitudes by changing if and how individuals are influenced by their social environments in heterogeneous settings.

6.5 Longitudinal Evidence

Structural explanations predict political attitudes in homogeneously partisan social groups, though not in every case. However, it remains to be seen if structural (psychological) mechanisms explain the change in attitudes over time in homogeneous

145 (heterogeneous) groups. In the analyses that follow, I utilize the Temporal Network

Autocorrelation Model (TNAM) described in Chapter Five to model both the change in individual political attitudes between survey waves as well as individual political attitudes in the second wave controlling for their attitudes in the first wave. I expect structural explanations to predict attitude change for individuals in homogeneously partisan groups and for psychological explanations to be more important in heterogeneous social groups. I first present results from modeling the change in attitudes between Waves I and II in Tables 6.13 - 6.16.61

∆ PID ∆ DemFeel ∆ RepFeel ∆ ClintonFeel ∆ TrumpFeel Intercept −0.30 −0.21 0.59 −1.21 −0.54 (0.55) (1.05) (0.88) (1.59) (1.77) Network at T2 −0.18∗ 0.01 −0.33∗∗ −0.27∗∗ −0.05 (0.10) (0.14) (0.15) (0.11) (0.11) Psychological 0.02 −0.06 0.05 0.09 −0.18 (0.06) (0.11) (0.09) (0.16) (0.18) Structural −0.03 0.10 −0.24∗∗ 0.37∗∗ 0.19 (0.04) (0.09) (0.10) (0.16) (0.16) AIC 42.46 70.36 62.82 89.03 93.34 BIC 47.91 75.82 68.27 94.48 98.79 Log-Likelihood -16.23 -30.18 -26.41 -39.51 -41.67 Deviance 5.63 20.03 14.21 46.78 56.89 Num. Obs. 22 22 22 22 22 ∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.1

Table 6.13: Mechanisms in the College Democrats, Modeling Change

61As mentioned in the previous chapter, there simply is not enough power to look at individuals in the College Democrats and College Republicans across all four survey waves. Accordingly, I only model attitude change in these groups in Waves I and II. Future work will utilize the TNAM to look at change between T1 − T3, T1 − T4, and T3 − T4 in the two Scholars groups, but are not included in this analysis.

146 ∆ PID ∆ DemFeel ∆ Rep Feel ∆ ClintonFeel ∆ TrumpFeel Intercept 0.17 1.60 −0.19 −0.54 1.63 (0.60) (1.76) (1.48) (1.14) (2.49) Network at T2 0.14 0.02 −0.09 −0.09 −0.15 (0.36) (0.25) (0.16) (0.34) (0.14) Psychological −0.09∗ −0.40∗∗ −0.02 −0.15 0.19 (0.05) (0.17) (0.13) (0.10) (0.21) Structural 0.05 0.20 −0.04 0.20 −0.70∗ (0.08) (0.20) (0.18) (0.12) (0.37) AIC 38.80 80.33 72.34 63.70 88.76 BIC 43.25 84.78 76.79 68.15 93.21 Log-Likelihood -14.40 -35.17 -31.17 -26.85 -39.38 Deviance 5.22 52.44 33.63 20.82 83.78 Num. Obs. 18 18 18 18 18 ∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.1

Table 6.14: Mechanisms in the College Republicans, Modeling Change

∆ PID ∆ DemFeel ∆ Rep Feel ∆ ClintonFeel ∆ TrumpFeel Intercept −1.48∗ 2.67∗∗ −0.50 1.79 0.01 (0.78) (1.16) (1.44) (1.31) (1.48) Network at T2 −0.02 0.00 −0.06∗ 0.02 −0.02 (0.03) (0.04) (0.03) (0.03) (0.04) Psychological 0.08 −0.22∗∗ 0.02 −0.17 0.15 (0.07) (0.10) (0.13) (0.12) (0.13) Structural 0.17 −0.14 0.04 −0.08 −0.19 (0.10) (0.14) (0.17) (0.16) (0.19) AIC 226.25 284.17 311.88 300.41 315.51 BIC 237.35 295.27 322.98 311.51 326.61 Log-Likelihood -108.13 -137.09 -150.94 -145.21 -152.76 Deviance 95.76 224.43 337.32 284.98 355.83 Num. Obs. 68 68 68 68 68 ∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.1

Table 6.15: Mechanisms in the Politics, Society, & Law Scholars, Modeling Change

147 ∆ PID ∆ DemFeel ∆ Rep Feel ∆ ClintonFeel ∆ TrumpFeel Intercept −0.07 0.23 −1.20 −2.11 −1.98 (0.67) (0.78) (1.04) (1.52) (1.80) Network at T2 −0.06 −0.06 −0.01 0.02 −0.14∗∗ (0.05) (0.05) (0.04) (0.05) (0.06) Psychological 0.03 0.10 0.28∗∗ 0.28∗ 0.28 (0.07) (0.08) (0.11) (0.16) (0.19) Structural −0.06 −0.07 −0.12 0.08 −0.09 (0.08) (0.08) (0.13) (0.16) (0.18) AIC 142.82 157.78 185.96 222.95 236.36 BIC 152.28 167.24 195.42 232.41 245.82 Log-Likelihood -66.41 -73.89 -87.98 -106.47 -113.18 Deviance 43.15 58.55 104.07 221.36 291.08 Num. Obs. 49 49 49 49 49 ∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.1

Table 6.16: Mechanisms in the STEM Exploration & Engagement Scholars, Modeling Change

Tables 6.13 - 6.16 present the TNAM results utilizing the “bare bones” model, which includes the Wave II measures of psychological and structural explanations and controls for the Wave II network, which incorporates the spatial dependence amongst individuals in the group. The same trends apparent in cross-sectional analyses are also uncovered when looking across time. Increased involvement in the College Democrats in Wave II leads to more strongly negative attitudes towards the Republican Party

(individuals’ attitudes became more negative toward the Republican Party over time) and more strongly positive attitudes toward Hillary Clinton (individuals’ became more favorable toward Hillary Clinton over time) (Table 6.13). Increased psychological attachments to the group leads individuals in the College Republicans to feel more negatively toward the Democratic Party over time, and increased structural ties to

148 the group leads these same individuals to feel more negatively toward Donald Trump over time (Table 6.14).

Psychological explanations continue to influence attitudes in Scholars groups (Ta- bles 6.15 and 6.16). Interestingly, increased psychological attachment to the PSL

Scholars results in more negative attitudes toward the Democratic Party over time

(Table 6.15). Increased psychological attachment to the group in Wave II leads individuals in the STEM-EE Scholars to feel more favorably towards the Republican

Party and Hillary Clinton over time (Table 6.16). These results persist when controls for indirect ties in Wave II (friends of friends) are included (see Appendix L).

Structural explanations do not explain attitude change for individuals in politically heterogeneous groups.

For the most part, these results are consistent with results from modeling individuals’ political attitudes in the second survey wave controlling for their previous attitudes (Tables 6.17 - 6.20), though weaker support for the hypotheses exists in this case. Structural explanations do predict individuals’ attitudes about politics, but only in the College Democrats. Increased involvement in the College Democrats in Wave II leads to stronger identification as a Democrat, controlling for individuals’ party identification in Wave I (Table 6.17). However, there is no evidence that structural (or psychological) explanations predict attitudes for individuals in the College

Republicans, though there is low power to find effects in this group (Table 6.18).

Psychological explanations are at work in both politically heterogeneous groups but only in explaining individuals’ feelings towards the Democratic and Republican

Parties. Increased psychological attachment to the group leads to more negative feelings toward the Democratic Party in the PSL Scholars (Table 6.19) and to more

149 strongly positive feelings toward the Republican Party in the STEM-EE Scholars

(Table 6.20). Importantly, the above results persist when the Wave I network and indirect ties in Wave II (friends of friends) are included in the models (see Appendix

L).

Party-ID DemFeel RepFeel ClintonFeel TrumpFeel Intercept 0.77∗∗ 4.07∗∗ 0.86 3.06 −0.95∗∗ (0.30) (1.70) (0.92) (1.86) (0.36) Network at T2 0.04 0.01 −0.03 −0.01 −0.27∗∗ (0.02) (0.01) (0.07) (0.02) (0.10) Lagged DV 0.18∗ 0.55∗∗∗ 0.65∗∗∗ 0.50∗∗∗ 0.06 (0.10) (0.16) (0.16) (0.14) (0.05) Psychological 0.05 −0.13 0.03 0.03 0.08∗ (0.03) (0.09) (0.10) (0.15) (0.04) Structural −0.10∗∗ −0.00 −0.07 0.14 0.06∗ (0.04) (0.11) (0.11) (0.19) (0.04) AIC 12.26 62.11 64.57 83.98 23.38 BIC 18.81 68.66 71.12 90.52 29.92 Log-Likelihood -0.13 -25.06 -26.29 -35.99 -5.69 Deviance 1.30 12.57 14.05 33.95 2.16 Num. Obs. 22 22 22 22 22 ∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.1

Table 6.17: Mechanisms in College Democrats, Modeling T2

In summary, longitudinal evidence demonstrates that mechanisms of social influence are conditional on the political composition of the group. Structural explanations only predict attitude change in politically homogenous groups while psychological explanations predict attitude change amongst individuals in politically heterogeneous groups, though not in every case. In contrast to results found in Chapter Five, longitudinal evidence consistently supports cross-sectional evidence, despite the low

150 Party-ID DemFeel RepFeel ClintonFeel TrumpFeel Intercept −0.50 1.20 0.54 −0.82 1.30 (1.05) (1.33) (1.37) (0.59) (2.37) Network at T2 0.02 0.04 −0.02 −0.09 −0.04 (0.01) (0.15) (0.02) (0.09) (0.06) Lagged DV 1.22∗∗∗ 0.31 0.67∗∗∗ 0.32∗∗ 0.63∗∗∗ (0.22) (0.19) (0.13) (0.11) (0.20) Psychological −0.07 0.04 −0.08 0.09 0.24 (0.05) (0.18) (0.12) (0.08) (0.21) Structural −0.13 −0.11 0.36 0.06 −0.26 (0.14) (0.28) (0.24) (0.10) (0.32) AIC 37.64 69.63 66.43 40.05 87.42 BIC 42.98 74.97 71.77 45.39 92.76 Log-Likelihood -12.82 -28.81 -27.22 -14.03 -37.71 Deviance 4.38 25.90 21.68 5.01 69.57 Num. Obs. 18 18 18 18 18 ∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.1

Table 6.18: Mechanisms in the College Republicans, Modeling T2

Party-ID DemFeel RepFeel ClintonFeel TrumpFeel Intercept −0.83 1.96∗ 1.33 1.97 1.38 (0.91) (1.16) (1.41) (1.33) (1.30) Network at T2 0.00 −0.01∗ 0.00 0.00 −0.03 (0.00) (0.00) (0.01) (0.01) (0.02) Lagged DV 0.94∗∗∗ 0.86∗∗∗ 0.66∗∗∗ 0.80∗∗∗ 0.52∗∗∗ (0.07) (0.07) (0.09) (0.08) (0.10) Psychological 0.08 −0.20∗ 0.07 −0.12 0.03 (0.07) (0.10) (0.12) (0.12) (0.12) Structural 0.05 0.20 −0.12 −0.11 0.01 (0.13) (0.19) (0.23) (0.21) (0.20) AIC 227.48 277.42 302.60 296.78 295.55 BIC 240.80 290.74 315.92 310.10 308.87 Log-Likelihood -107.74 -132.71 -145.30 -142.39 -141.78 Deviance 94.68 197.32 285.76 262.32 257.62 Num. Obs. 68 68 68 68 68 ∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.1

Table 6.19: Mechanisms in Politics, Society, & Law Scholars, Modeling T2

151 Party-ID DemFeel RepFeel ClintonFeel TrumpFeel Intercept 0.37 0.88 −0.83 −1.55 −0.97 (0.84) (0.72) (1.10) (1.47) (1.77) Network at T2 0.00 0.00 0.00 −0.01 −0.02 (0.00) (0.00) (0.01) (0.01) (0.02) Lagged DV 0.93∗∗∗ 0.80∗∗∗ 0.89∗∗∗ 0.70∗∗∗ 0.67∗∗∗ (0.10) (0.06) (0.09) (0.12) (0.12) Psychological 0.02 0.09 0.25∗∗ 0.19 0.21 (0.07) (0.07) (0.11) (0.16) (0.18) Structural −0.08 −0.08 −0.06 0.30 0.20 (0.10) (0.10) (0.15) (0.20) (0.26) AIC 144.98 147.76 186.53 216.91 235.98 BIC 156.33 159.11 197.88 228.26 247.33 Log-Likelihood -66.49 -67.88 -87.26 -102.45 -111.99 Deviance 43.28 45.81 101.06 187.86 277.25 Num. Obs. 49 49 49 49 49 ∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.1

Table 6.20: Mechanisms in the STEM Exploration & Engagement Scholars, Modeling T2

152 number of respondents - especially in politically homogeneously groups - that makes

finding effects difficult.

6.6 Discussion

Evidence from cross-sectional and longitudinal analysis suggests that social influence

on individual political beliefs may occur as a result of different mechanisms in po-

litically homogenous and heterogeneous groups. In homogenous groups, individuals’

commitment to, participation in, and the number of ties to the group - all indicators

of structural mechanisms - predict their attitudes about politics in some key cases.

In heterogeneous groups, individuals’ psychological attachments to the group predict

their political attitudes, but not in every case. These results persist with and with-

out controls, with and without the number of ties variable, but not when modeling individuals’ decisions about which candidates to support in the primary and general election campaigns. In some cases, psychological explanations predict attitudes in politically homogeneous groups, but structural explanations never impact individuals’ attitudes in heterogeneous groups.

Longitudinal evidence supports these results as well. Structural explanations predict some attitudes and attitude change in politically homogeneous groups but do not predict attitudes in heterogeneous groups. These results persist when looking at attitudes at time two and the change in attitudes between times one and two, when including or not including the number of ties in the structural explanations index, and when controlling for individuals’ indirect ties (friends of friends) or not. Importantly, significant effects are found despite the low number of observations across groups.

153 All in all, results presented here provide initial, yet minimal causal evidence that structural and psychological explanations are the mechanisms by which individuals’ network ties influence attitudes about politics and attitude change. Causal mediation analysis provides a good next test of this relationship though I am unaware if the method can incorporate spatial dependencies (Imai, Keele, Tingley & Yamamoto

2011). Bringing experimental evidence to bear on the relationship between network ties, psychological and structural explanations, and attitude change would be even better. Initial experiments in pursuit of these matters were conducted with the College

Democrats and College Republicans in the Spring of 2017. Because of the small number of individuals in these student groups, however, there are only two small groups in each experimental condition, making analysis difficult. As a result, I leave these causal issues unresolved at this point.

154 Chapter 7: Conclusion

If individuals are changed by their social environments, it is because they allow themselves to be. The evidence presented here demonstrates that individuals choose not only if and how they are changed by their social environments but also the environments in which they expose themselves to influence in the first place. These decisions may not always be conscious efforts to control their influence environments. Even unconsciously, though, selection allows individuals to monitor and control what will or will not influence them.

Individuals do make decisions on which contexts and networks to select into based on their personal political beliefs and personal circumstances. Individuals with strong political beliefs and interest in politics may rely on those beliefs in decisions about which environments to select into, and these preferences feed back into preferences for similar types of environments in the future. Once individuals make decisions about which contexts to select into, both if and how they are influenced is conditional on the political composition of that environment. Accordingly, the influence of one person on another is most likely to occur among politically similar individuals - both in and outside of a political campaign. Selection, then, is not merely an inferential, empirical, and methodological problem (though it is not less); it is a fundamental

155 theoretical postulate that drives if and how individuals are influenced in their social environments.

This dissertation set out to demonstrate this in a few ways. First, selection provides individuals the opportunity to choose the contexts and networks in which they are influenced. Chapter Three utilized a novel, two-wave panel study of first-year students at the Ohio State University to understand if and how individuals’ political predispositions informed their selection into social networks and contexts. It demonstrated that individuals’ previously established beliefs about politics both directly and indirectly inform environment selection, and, in so doing, calls the standard assumption that shared political preferences do not precede relationship formation into question. Individuals with strong beliefs about and interest in politics choose social environments that align with their political beliefs, and it is in these environments that their attitudes about politics are affected. The lack of strong political beliefs also predicts environment selection where individuals with weak political preferences and low interest in politics avoid more partisan environments. Furthermore, individuals’ social networks of close friends were consequential predictors of attitude change when all or most of those friends identified with the same political party. Individuals choose the environments in which they are changed, and this choice can directly and indirectly be informed from political beliefs.

Chapter Three uncovered a noticeable difference between those who joined apolitical group and those who planned on joining. Most of the individuals who indicated that they planned to join partisan environments did not end up actually joining those

156 groups. While the individuals that planned to join homogeneous political environments did significantly differ from individuals who did not, I am not able todiffer- entiate between the attitudes of individuals who joined and the attitudes of those who did not join, weakening conclusions that can be made about if and how political beliefs inform environment selection. This disparity may also explain why indicators for joining partisan social contexts did not consistently explain attitude change.

Second, selection provides opportunities for individuals to control whether or not they are influenced in their social environments. Utilizing a novel four-wave whole network study, Chapter Five demonstrated that the political attitudes of individuals who selected into politically homogenous environments were affected by the attitudes of other individuals in the group with whom they are connected. The political composition of the social environment conditions whether individuals allow themselves to be influenced in those settings. Whether these decisions are conscious or not,influence is most likely to occur among similar individuals and, in the subject ofthis dissertation, in politically homogeneous groups.

While there is consistent evidence of spatial dependence in the attitudes of individuals in politically homogeneous groups before, during, and after a campaign, it is not the case that all of their political attitudes are influenced in those settings all of the time. Homogeneously partisan groups can send mixed and even conflicting signals to their members, as was the case for individuals in the College Democrats and College Republicans during the 2016 primaries. Divisions in these groups over which candidate to support in their parties’ primary led to differing vote choices even if individuals in the group still look to each other for signals on how to be a good party member and, especially, to construct opinions of the out-party and candidates.

157 Results suggest that the 2016 presidential election was an election in which voters, especially in partisan groups, voted against rather than for a particular candidate.

Third, selection provides opportunities for individuals to control how they are influenced in their social environments. Analysis of the same network panel study demonstrated that how individuals are influenced by their social environments is also conditional on the amount of political similarity amongst the group. Specifi- cally, Chapter Six demonstrated that individuals in politically homogenous groups are influenced by others in those groups because of their level of commitment to, participation in, and the number of ties to other individuals in the group. Structural explanations predict if the attitudes of individuals in like-minded groups are changed before, during, and after a campaign. Additionally, structural explanations predicted the attitudes of individuals in politically homogeneous groups both statically and across time and with and without the inclusion of the number of an individual?s ties to other members of the group.

Internal process mechanisms are important indicators of political attitudes in politically heterogeneous groups where increased psychological attachment to the group results in political attitudes that, for the most part, move toward the direction of the group’s majority. While internal psychological processes can also be at work in politically homogeneous groups, structural explanations are never at work in politically heterogeneous groups, providing further evidence that how individuals are influenced in social environments is conditional on the reasons they selected into them in the first place.

Importantly, and as researchers have noted since the inception of work on individual political behavior, dynamics shift during the course of a political campaign. In the

158 context of an election, individuals have less control over if and how they are influenced by the people around them. Individuals receive more messages, information, and cues about politics and have less ability to monitor them because the political campaign activates even heterogeneous and non-political environments. Chapter Five demonstrated that individuals in politically heterogeneous groups do rely on the choices of other members they are connected to when making their own choices about which candidates to vote for in the general election. Accordingly, an organization devoted to the pursuit of STEM interests and education can be activated to act on individual political attitudes when the group’s shared interest, STEM, is politicized during the general election - as it certainly did in 2016 with an uncharacteristically anti-science candidate - providing opportunities for individuals in the group to affect each other’s political attitudes.

7.1 Practical Implications

If social influence on individual political beliefs is most likely to occur in politically homogenous environments that individuals construct and monitor themselves, then homogeneity may come at the expense of difference. If individuals construct their own social environments and select into networks full of like-minded individuals, then opportunities for learning and tolerance of differing political views are foiled. Thus, the predicted “trade-off” between an active and tolerant citizenry may be less about exposure, or non-exposure, to disagreement and more about exposure to difference.

Though, certainly, these are related terms.

159 It leads practitioners to ask: what leads individuals to choose difference over sameness? If individuals characterize someone as “similar” to them in some way, influence among them may be more likely to occur - no more so than in choosing tojoin a campus party group like the College Democrats and College Republicans. However, the characterization of “different” inhibits individuals from allowing themselves to be influenced by that person. Distinctions of “different” or “other” are damagingto the ideals of democratic society and further entrench individuals in their politically polarized environments. Selection, then, is not only an inferential and empirical nuisance and a fundamental theoretical principle, as this dissertation has argued, but it is also a very real threat to democratic society. Placing selection more centrally within focus is imperative.

Thankfully, results discussed in these pages are not all bad news for the polity.

Even when consciously opting to join campus partisan groups, College Democrats and College Republicans were subjected to outside influences during the campaign.

Campaigns expose individuals to “difference” giving them less ability to monitor and control their information environments. The very times in which individuals actively participate in the polity are also the times in which they might be exposed to the most amount of difference. This “difference” may expose them to new information and ways of thinking, or it may further entrench them in their own echo chambers.

7.2 Limitations & Future Work

Admittedly, while the theoretical claims made here are strong, the empirical evidence provided to support those claims is weak. Future work will need to clarify and sharpen

160 these empirical results. Specifically, there are limitations that pertain to the analysis as a whole, to either the selection or group studies, and to analyses conducted in each empirical chapter. This section addresses each in turn.

Each study presented here relies on data from a single presidential election in an especially unique environment in the state of Ohio in which both macro and micro forces operated in the state to effect election outcomes. Ohio, formerly atrue political battleground state, seems to be moving more safely toward the Republican

Party in presidential elections. In an environment where individuals cannot solely rely on party heuristics, the parties’ primary fields, especially on the Republican side, were crowded. During the primary election on March 15th in the state there were three viable Republican candidates in the race - Ted Cruz, John Kasich, and

Donald Trump. When early primary voting opened, Marco Rubio was still in the race, and many other Republican candidates were on early voting ballots despite having officially dropped. Republican John Kasich was the sitting state Governor with fairly high approval ratings. The Democratic field, on the other hand, was divided between

Hillary Clinton and Bernie Sanders. After Clinton’s shocking loss in the Michigan primary on March 8th, she and her team were increasingly active in Ohio to ensure her victory there.

Ohio Republicans, including Republican identifiers at Ohio State, were more likely to support and vote for Kasich than Republicans elsewhere. In fact, Ohio was the only state that Kasich won in the nomination contests. While a majority of Democratic identifiers in the state eventually supported Clinton in the primary, young Democratic identifiers, and young voters in general, favored Bernie Sanders. Both hard-fought primary battles left a bitter taste in the general election in the state. The state

161 Republican Party and Republican voters were forced to come to terms with Donald

Trump even when their own Governor refused to publicly support him, and the state

Democratic Party tried to salvage down-ballot races when winning the presidential vote seemed no longer feasible.

These unique dynamics in both political parties in the state throughout the election season may have pushed and pulled on individuals’ social networks in different ways than in a “normal” presidential campaign or even a presidential campaign in a different state. These dynamics had real effects not only in the College Democrats and College Republicans, but also with Ohio State students overall, as a majority of them are state residents (70%). Additionally, inferences drawn from a single election campaign may reflect trends not replicable in other election years. Future work will seek to replicate results beyond 2016 and with data from previous elections.

Second, the two empirical studies described in this project rely solely on responses from college-aged students. While this sample is certainly a convenient one - not only in location but also in incentivizing participation - it is a demographic where change is expected to occur. In a project primarily focused on if and how individuals create their influence environments, this demographic is, perhaps, ideal. Where elsecan one observe how individual political predispositions factor into the selection of new social environments almost completely devoid of feedback from previous, independent selection decisions? The college environment also enables easier tracking of individuals across time. Outside of the college setting, it is arguably more difficult to establish boundaries on real world social networks.

Still, adults outside of the college setting may be better able to choose where, if, and how they are influenced. For example, they may have stronger preferences for

162 certain types of homophily, better ability to control information from those environments, and essentially more “feedback” in their social influence loop. Accordingly, findings of influence in heterogeneous college settings during a political campaign may be overstated just as findings of influence amongst individuals in homogeneous environments in and outside of the campaign may be understated. Future work will look to expand these analyses outside of the college environment to establish external validity and replicate them in other college and election environments to establish reliability.

Lastly, and perhaps most egregiously, while the evidence presented here is, for the most part, consistent with the story I tell about if and how individuals influence one another’s political beliefs, it is not causal. Panel data can model the dynamics of change and processes of social influence in the real world while accounting for temporal trends in the data. Additionally, the use of the spatial autoregressive and the

Temporal Network Autocorrelation (TNAM) models accounts for the spatial dependence as well as the autocorrelation of the errors though the analyses may still fall prey to omitted variable bias. While the design relies on novel, hard-to-get data and, as is the case for the TNAM, brings new methods to bear on substantive questions, it is not able to isolate causal effects.

The introductory chapter presented three questions that were to be addressed by this research. First, to what extent do individuals chose to be with people like themselves (homophily question)? Second, to what extent does the environment channel individuals towards people like themselves (common environment question)? Finally, and in light of what we will discover about the first two, to what extent are individuals influenced by their social networks (contagion question)? It could still be thecase

163 that the contagion we see among the political attitudes of individuals in politically homogeneous and heterogeneous groups in the college environment is a result instead of shared environments. The evidence presented here cannot definitively rule out this alternative explanation. Collecting data on which group members attending which group events from the four student groups may be worthwhile, though it introduces new privacy concerns as well as empirical complexities.

Furthermore, the analyses presented here do not distinguish between homophily vs. contagion effects. There is evidence that homogenous beliefs result in selection into homogenous groups (homophily) and that homogeneous groups result in homogenous beliefs (contagion). Selection and influence (homophily and contagion) do not appear to be an “either or”, but both - processes that occurs simultaneously and all at once. It is unclear, however, what the causal effect of one individual on another is in light of the reality that individuals construct their own social worlds. Experi- mental evidence is necessary to more clearly isolate contagion vs. selection effects.

And, while the proposed experiments represent a first step, additional experimental analysis is necessary.

Regarding the group study (Study 2) expounded in Chapters Four through Six, there are three additional limitations to the design and the inferences that can be drawn from it. First of all, this analysis is centered on understanding a very specific form of social influence - the impact of group political composition on individuals’ political attitudes. While individual political attitudes may not be determined by the attitudes of the group as a whole, their attitudes may instead be influenced by the attitudes of individuals that they interact with most in the group. Certainly, it could be the case that an individual initially favorable to Donald Trump in the

164 STEM-EE Scholars is influenced not to support him by the other individuals she interacts with most in the group, even though her attitudes may not be influenced by the STEM group as a whole. The analyses presented here do not explore these dynamics, but they are crucial to understanding if and how influence occurs in the social environment, and future work will work to uncover them.

Secondly, the low response rates in the group study limit the inferences that can be made. The low number of individuals who responded, especially across survey waves, limits the statistical power to find effects. This power is especially limited inthe longitudinal analysis of individuals in the College Democrats and College Republicans.

Accordingly, the analyses presented in text represent the “bare bones” models that do not include any superfluous variables while the appendix materials contain the full models, though results, for the most part, remain consistent between them. There is, perhaps, more confidence in the validity of the significant effects that arefound because of low response rates. Low response rates within groups indicate that only the interactions between individuals that responded to the survey, not the interactions between individuals in the group as a whole, are modeled, and this missing network data is problematic.

Third, and relatedly, the group study captures only one influence network that may operate on individual political behavior: one influence network of potentially hundreds of networks that operate on individual political attitudes simultaneously.

While a few influence networks might pull an individual to support a particular political candidate, a majority of their other networks may be pulling them in the opposing

165 direction. It may be that individuals are swayed by the quantity of networks in support or opposition, but it is more likely that the quality and importance of the influence network to the individual weighs more heavily on their minds. Likely, there are many more important influence networks that operate on individual political beliefs than an individual’s campus student organization. This may especially be the case for individuals in politically heterogeneous groups while the “signal” from politically homogeneous networks may be more important to individuals in those groups.

That said, individuals are a part of multiple overlapping influence networks, and this analysis has focused only on one. While external validity was consciously ex- changed for depth of understanding of influence in a particular type of setting, future work will seek to understand if and how influence operates on individual political attitudes not only in other formal associations but also in more informal influence networks of co-workers, friends, and family. One initial way in which influence in overlapping networks could be understood would be to analyze the attitudes of the handful of individuals who are members of both a Scholars and a partisan group.

7.3 Looking Ahead

In addition to the suggestions provided above, future work will translate these insights to the on-line setting. Individuals have arguably more ability to control their information environments on-line, and the realities of the information age mean that individuals may spend less time interacting face-to-face. Individuals choose who and who not to follow and their interactions with social mediums are consequential for future interactions not only on that medium but also for future in-person interactions.

166 For example, by “liking” something on the Facebook platform individuals essentially input information into the Newsfeed algorithm that communicates that they want to see more from that individual or on that topic. An individual can also choose to not see specific types of posts from specific people. Yes, some of this maybeig- noring a family member who posts too many food recipes, but some of it also may constitute deliberate attempts to block information that conflicts with individuals’ own views, political or otherwise, and can affect future face-to-face interactions with those people. Individuals using social media can isolate themselves within their own echo chambers without even leaving their house; and, while they do not escape interpersonal influence entirely, they may be better able to control what and from whom that influence comes.

7.4 Conclusion

Adults are very much in control of which environments they are influenced in; thus, individuals are not passively influenced by their social environments, as some research suggests. Individuals are deliberate, either directly or indirectly, in a) selecting environments that conform to their previously established beliefs about politics and b) choosing whether and c) how they will be influenced in those environments. This dissertation reorients understanding of if and how social environments affect individuals’ views about politics around the selection process, but there is considerably more work to be done.

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176 Appendices

177 Appendix A: Selection Study Survey Instrument, Wave I

178 Appendix A: Selection Study Survey Instrument, Wave 1

Q56 CONSENT FOR PARTICIPATION IN SOCIAL AND BEHAVIORAL RESEARCH

Protocol title: Selection into College Groups: Social Networks and Political Beliefs Protocol number: 2016B0281 Principal Investigator: Janet Box-Steffensmeier Co-Investigator: Lauren Ratliff

You are being asked to consent to participating in research entitled: Selection into College Groups: Social Networks and Political Beliefs, which consists of you filling out an online survey. The survey will take approximately 25 minutes to complete, and asks participants a few questions about your attitudes and opinions about politics.

Risks of participating in this research include the possibility that your responses could be released. However, this is highly unlikely given that the servers where your information is held is in a locked and gated area that is supervised 24 hours a day, seven days a week. Another potential risk is a breach of privacy because your survey responses are identifiable by e-mail address. However, that information is only available to the researchers on this project and they will use that information only to distribute the incentive. Additionally, each respondent’s e-mail will be substituted with a numerical code to ensure further confidentiality. The key will be kept separately from the survey data. All e-mail addresses will be permanently deleted once the $10 individual incentives are distributed at the completion of the study. We will work to make sure that no one sees your online responses without approval. But, because we are using the Internet, there is a chance that someone could access your online responses without permission. In some cases, this information could be used to identify you.

Benefits of participating in this research include a better understanding of the way that individuals select into social groups. Your participation in this project is completely voluntary and your responses will be kept confidential. Your refusal to participate will involve no penalty or loss of benefits to which you are otherwise entitled, including the $10 gift-card to either Amazon or Target. You may discontinue participation at any time without penalty or loss of benefits to which you are otherwise entitled.

For any questions or if you feel you were harmed as a result of study participation you may contact Lauren Ratliff at [email protected] or 972-839-1869. For questions about your rights as a participant in this study or to discuss other study-related concerns or complaints with someone who is not part of the research team, you may contact Ms. Sandra Meadows in the Office of Responsible Research Practices at 1-800-678-6251.

------

I consent to participating in the research entitled: Selection into College Groups: Social Networks and Political Beliefs.

Janet Box-Steffensmeier or his/her authorized representative has explained the purpose of the study, the procedures to be followed, and the expected duration of my participation. Possible benefits of the study have been described, as have alternative procedures, if such procedures are applicable and available.

I acknowledge that I have had the opportunity to obtain additional information regarding the study and that any questions I have raised have been answered to my full satisfaction. Furthermore, I understand that I am free to withdraw consent at any time and to discontinue participation in the study without prejudice to me.

By clicking on "I consent" I am indicating that I have read this form or I have had it read to me and that I am at least 18 years old. I do this freely and voluntarily. If I would like to keep a copy, I can print this from my web browser, or request a copy from Lauren Ratliff at [email protected]. m I consent (4)

Q81 Enter your e-mail address to receive the $10 gift-card. Gift-cards will be distributed to your e-mail inbox once you complete the study. At the end of the study, you will have the opportunity to indicate whether you would like a gift-card from Amazon or Target. All e- mail addresses will be deleted once gift-cards are distributed at the conclusion of the study.

Q14 Now we have a few questions about the activities you have engaged in during your life.

Q15 Please indicate if you have participated in the following: Yes (1) No (2) Have you ever worked or volunteered for a political m m party or candidate? (1) Have you ever displayed a button, yard sign, bumper sticker, or any other kind of m m sign showing a political party or candidate you supported? (2) Have you ever donated money to any candidates or m m to a political party? (3) Have you ever attended any political meetings, rallies, m m speeches, dinners, or things like that? (4) Have you ever tried to persuade anyone else to vote m m or how to vote? (5)

Display This Question: If Please indicate if you have participated in the following: Have you ever worked or volunteered for a political party or candidate? - Yes Is Selected Q107 For which candidate(s) or party did you work or volunteer for?

Display This Question: If Please indicate if you have participated in the following: Have you ever displayed a button, yard sign, bumper sticker, or any other kind of sign showing a political party or candidate you supported? - Yes Is Selected Q115 For which candidate(s) or party did you display a button, yard sign, bumper sticker, or any other kind of sign for?

Display This Question: If Please indicate if you have participated in the following: Have you ever donated money to any candidates or to a political party? - Yes Is Selected Q116 For which candidate(s) or party did you donate money to?

Q110 Which presidential candidate did you prefer in the Democratic or Republican PRIMARIES earlier in 2016? m Hillary Clinton (1) m Bernie Sanders (2) m Ted Cruz (3) m John Kasich (4) m Marco Rubio (8) m Donald Trump (5) m Other (please specify): (6) ______

Display This Question: If Which presidential candidate did you prefer in the Democratic or Republican PRIMARIES earlier in... Bernie Sanders Is Selected Q120 Beyond the 2016 presidential election, how likely are you to support Bernie Sanders' future political career? m Extremely unlikely (1) m Unlikely (5) m Neutral (6) m Likely (2) m Extremely likely (3)

Display This Question: If Which presidential candidate did you prefer in the Democratic or Republican PRIMARIES earlier in... Donald Trump Is Selected Q121 Beyond the 2016 presidential election, how likely are you to support Donald Trump's future political career? m Extremely unlikely (1) m Unlikely (5) m Neutral (6) m Likely (2) m Extremely likely (3)

Q108 Are you ELIGIBLE to vote in the 2016 presidential election in November? m Yes (1) m No (2)

Q109 Are you REGISTERED to vote in the 2016 presidential election in November? m Yes (1) m No (2) m Unsure (3)

Q111 For which candidate are you planning on voting for president? m Hillary Clinton (1) m Donald Trump (2) m Gary Johnson (3) m Jill Stein (4) m Other (please specify): (5) ______m Don't know (6)

Display This Question: If For which candidate are you planning on voting for president? Donald Trump Is Selected And Which presidential candidate did you prefer in the Democratic or Republican PRIMARIES earlier in... Ted Cruz Is Selected Or Which presidential candidate did you prefer in the Democratic or Republican PRIMARIES earlier in... John Kasich Is Selected Or Which presidential candidate did you prefer in the Democratic or Republican PRIMARIES earlier in... Marco Rubio Is Selected Or Which presidential candidate did you prefer in the Democratic or Republican PRIMARIES earlier in... Other (please specify): Is Selected Q122 Beyond the 2016 presidential election, how likely are you to support Donald Trump's future political career? m Extremely unlikely (1) m Unlikely (5) m Neutral (6) m Likely (2) m Extremely likely (3)

Display This Question: If For which candidate are you planning on voting for president? Hillary Clinton Is Selected Or For which candidate are you planning on voting for president? Donald Trump Is Selected Or For which candidate are you planning on voting for president? Gary Johnson Is Selected Or For which candidate are you planning on voting for president? Jill Stein Is Selected Or For which candidate are you planning on voting for president? Other (please specify): Is Selected Q112 When did you decide to vote for that candidate? m After the party conventions (1) m Around the time of the party conventions in late July (2) m Between April and July, before the party conventions (3) m In January through March, during the early primaries and caucuses (4) m Before the election campaign started in January (5)

Q24 How interested are you in the 2016 presidential campaign? m Not at all (1) m Very little (2) m Somewhat (3) m Quite a lot (4) m A great deal (5)

Q25 Thinking about your time at home, how often did you talk about public affairs and politics with members of your family? m Never (1) m Less than Once a Month (2) m Once a Month (3) m 2-3 Times a Month (4) m Once a Week (5) m 2-3 Times a Week (6) m Daily (7)

Q26 Are your closest friends Democrats, Republicans, or both? m Almost all Republicans (1) m More Republicans than Democrats (2) m About equally Republicans and Democrats (3) m More Democrats than Republicans (4) m Almost all Democrats (5) m Most do not identify with a particular party (6) m Unsure of what they are (7)

Q27 How interested are your closest friends in politics? m Not at all (1) m Very little (2) m Somewhat (3) m Quite a lot (4) m A great deal (5)

Q113 How important is politics when choosing new friends on campus? m Very important (1) m Important (2) m Neither important nor unimportant (3) m Not very important (4) m Not at all important (5)

Q114 How many different people have you talked with about the current election campaign? Please enter the specific number as closely as you can approximate it.

Q22 Where would you place the following on this scale? Extre Libe Sligh Moder Slightly Conserv Extreme Have Do mely ral tly ate: conserva ative (6) ly n't n't liberal (2) liber middle tive (5) conserva thou kno (1) al (3) of the tive (7) ght w road abou (9) (4) t this much (8) Yoursel m m m m m m m m m f (1) The Democr atic m m m m m m m m m Party (2) The Republi can m m m m m m m m m Party (3)

Q23 Where would you place yourself on the following scale? Strong Not Independ Independ Independ Not Strong Do Democ very ent - ent (4) ent - very Republi n't rat (1) strong Closer to Closer to strong can (7) kno Democ Democra Republic Republi w rat (2) t (3) an (5) can (6) (8) Yours m m m m m m m m elf (1)

Q84 Where would you place your parents on the following scale? Strong Not Indepen Indepen Indepen Not Strong Do N/ Demo very dent - dent (4) dent - very Republi n't A crat (1) strong Closer Closer strong can (7) kno (9) Demo to to Republi w crat (2) Democr Republic can (6) (8) at (3) an (5) Your Fath m m m m m m m m m er (2) Your Mot m m m m m m m m m her (3)

Q30 Now we have some statements that people have made about themselves. Please indicate how much you agree or disagree with each statement. Strongly Disagree (2) Neither Agree (4) Strongly disagree (1) Agree nor Agree (5) Disagree (3) I consider myself to be well qualified m m m m m to participate in politics. (1) I feel that I have a pretty good understanding of the m m m m m important political issues facing our country. (2)

Q31 Read the following statements and determine if they are true or false as they apply to you. True (1) False (2) I am sometimes reluctant to talk about politics because I m m don't like arguments. (1) I am sometimes reluctant to talk about politics because it m m creates enemies. (2) I am sometimes reluctant to talk about politics because I m m worry about what people would think of me. (3)

Q35 We would like to know your feelings towards some political figures and groups on a scale from 0-10, where '0' is Very unfavorable, '5' is Neutral, and '10' is Very favorable. 0: Very 1 2 3 4 5: 6 7 8 9 10: Very NA: Unfavorab (2 (3 (4 (5 Neutr (7 (8 (9 (10 Favorab Don't le (1) ) ) ) ) al (6) ) ) ) ) le (11) know this perso n (12) The Democrat m m m m m m m m m m m m ic Party (1) The Republica m m m m m m m m m m m m n Party (2) George W. Bush m m m m m m m m m m m m (5) Jeb Bush m m m m m m m m m m m m (22) Hillary Clinton m m m m m m m m m m m m (10) Ted Cruz m m m m m m m m m m m m (23) Gary Johnson m m m m m m m m m m m m (24) John Kasich m m m m m m m m m m m m (20) Barak Obama m m m m m m m m m m m m (29) Nancy Pelosi m m m m m m m m m m m m (30) Marco Rubio m m m m m m m m m m m m (21) Paul Ryan m m m m m m m m m m m m (25) Bernie Sanders m m m m m m m m m m m m (27) Jill Stein m m m m m m m m m m m m (28) Donald Trump m m m m m m m m m m m m (13)

Q36 To what extent do you agree or disagree with the following statements? Strongly Disagree Neither Agree (4) Strongly Don't disagree (2) Agree nor Agree (5) know (6) (1) Disagree (3) Universities should be allowed to increase the number of minority students studying at their schools m m m m m m by considering race along with other factors when choosing students. (1) Federal spending to protect the environment m m m m m m should be increased. (2) To reduce the federal deficit, the government m m m m m m should cut military spending. (3) The government should reduce taxes, even m m m m m m if it means reducing government services and social assistance. (4) Prostitution should be legalized in m m m m m m the United States. (5) The government should restrict where m m m m m m drones can fly and film to protect people’s privacy. (6)

Q37 Now we would like to ask you about your opinions on some of the issues facing the country today.

Q38 How do you feel about the health care reform law passed in 2010? This law requires that all Americans buy health insurance and requires health insurance companies to accept everyone. m Favor a great deal (1) m Moderately favor (2) m Favor a little (3) m Neither favor nor oppose (4) m Oppose a little (5) m Moderately oppose (6) m Oppose a great deal (7) m Don't know (8)

Q39 Which comes closest to your view about what government policy should be toward unauthorized immigrants now living in the United States? m Make all unauthorized immigrants felons and send them back to their home country. (1) m Have a guest worker program that allows unauthorized immigrants to remain. (2) m Allow unauthorized immigrants to remain in the United States if they meet certain requirements. (3) m Allow unauthorized immigrants to remain in the United States without penalties. (4) m Don't know (5)

Q40 Which statement comes closest to your view about what government policy should be on access to guns? m The government should make it MORE DIFFICULT for people to buy a gun than it is now. (1) m The government should make it EASIER for people to buy a gun than it is now. (2) m The government should make KEEP THE RULES ABOUT THE SAME. (3) m Don't know (4)

Q41 Which one of the opinions below best agrees with your view? m By law, abortion should never be permitted. (1) m The law should permit abortion only in case of rape, incest, or when the woman's life is in danger. (2) m The law should permit abortion for reasons other than rape, incest, or danger to the woman. (3) m By law, a woman should always be able to obtain an abortion as a matter of personal choice. (4) m Don't know (5)

Q43 Which comes closest to your views on gay marriage? m Gay and lesbian couples should be allowed to marry legally. (1) m Gay and lesbian couples should be allowed to form civil unions but not legally marry. (2) m There should be no legal recognition of a gay or lesbian couple's relationship. (3) m Don't know (4)

Q78 In general, do you favor or oppose legalizing the possession of small amounts of marijuana for personal use? m Favor (1) m Oppose (2) m Don't know (3)

Q125 Do you think people who are transgender -- that is, someone who identifies themselves as the sex or gender different from the one they were born as -- should be allowed to use the public bathrooms of the gender they identify with or should they have to use the public bathrooms of the gender they were born as? m They should be allowed to use the public bathrooms of the gender they identify with. (1) m They should be allowed to use the public bathrooms of the gender they were born as. (2) m Don't know (3)

Q123 How worried are you that you or someone in your family will become a victim of a mass shooting? m Very worried (1) m Somewhat worried (2) m Not too worried (3) m Not worried at all (4)

Q124 How worried are you that you or someone in your family will become a victim of an act of mass terrorism planned by or inspired by the Islamic State or ISIS? m Very worried (1) m Somewhat worried (2) m Not too worried (3) m Not worried at all (4)

Q86 Why did you decide to come to the Ohio State University?

Q4 What is your planned major(s) in college?

Q87 Have you joined or are you planning on joining any academic, social, or athletic groups on campus in Fall 2016? m Yes (1) m No (2) m Maybe (4)

Display This Question: If Have you joined or are you planning on joining any academic, social, or athletic groups on campus in Fall 2016? Yes Is Selected Q88 Which group(s) have you joined or are you planning on joining?

Display This Question: If Have you joined or are you planning on joining any academic, social, or athletic groups on campus in Fall 2016? Yes Is Selected Q89 Have you been contacted by or participated in any activities of the group(s) you have already joined or are planning on joining? m Yes (1) m No (2)

Display This Question: If Have you joined or are you planning on joining any academic, social, or athletic groups on campus in Fall 2016? No Is Selected Or Have you joined or are you planning on joining any academic, social, or athletic groups on campus in Fall 2016? Maybe Is Selected Q90 Which academic, social, or athletic group(s) at Ohio State campus do you think you want to join in the Fall of 2016?

Q117 Do you have any interest in joining any of the following political groups at Ohio State (select all that apply)? q College Democrats (1) q College Republicans (2) q Multi-Partisan Coalition (4) q Young Americans for Liberty (5) q Other (please specify): (6) ______q No interest in joining a political group (3)

Q91 Will you be living on Ohio State campus in the Fall of 2016? m Yes (1) m No (2) m Maybe (3)

Display This Question: If Will you be living on Ohio State campus in the Fall of 2016? Yes Is Selected Q92 Where will you be living on campus? If dorm or residence hall, please list which one.

Display This Question: If Will you be living on Ohio State campus in the Fall of 2016? Yes Is Selected Q93 Please list the first and last name of your assigned roommate(s).

Display This Question: If Will you be living on Ohio State campus in the Fall of 2016? Yes Is Selected Q94 How well do you know your roommate(s) at this point in time? m Very well (1) m Sort of well (2) m Not very well (3) m Not at all (4)

Display This Question: If Will you be living on Ohio State campus in the Fall of 2016? Yes Is Selected Q96 Besides your roommate(s), do you know anyone else who is going to be a first-year student at Ohio State in the Fall of 2016? m Yes (1) m No (3)

Display This Question: If Will you be living on Ohio State campus in the Fall of 2016? No Is Selected Or Will you be living on Ohio State campus in the Fall of 2016? Maybe Is Selected Q95 Where do you plan to live instead?

Q97 Approximately how many students at Ohio State do you currently know? Please enter the specific number as closely as you can approximate it.

Q57 Now we would like you to think of the person with whom you most frequently talk about matters that are important to you. What is this person's relation to you? m Spouse (1) m Close family member (2) m More distant family member (3) m Close friend (4) m Co-worker (5) m Neighbor (6) m Other (please specify): (7) ______

Q127 Is this person a current Ohio State student? m Yes (1) m No (3)

Display This Question: If Is this person a current Ohio State Student? Yes Is Selected Q128 What is their first and last name?

Q58 Is this person a male or female? m Male (1) m Female (2)

Q59 How often do you talk about politics with this person? m Never (1) m Sometimes (2) m Often (3)

Q62 Where would you place him or her on the following scale? m Strong Democrat (1) m Not very strong Democrat (2) m Independent - closer to Democrat (3) m Independent (4) m Independent - closer to Republican (5) m Not very strong Republican (6) m Strong Republican (7) m Don't know (8)

Q103 Which presidential candidate do you think this person is supporting in the 2016 election? m Hillary Clinton (1) m Donald Trump (2) m Gary Johnson (3) m Jill Stein (4) m Other (please specify): (5) ______m Don't know (6)

Q105 How informed would you say this person is when it comes to politics? m Not at all informed about politics (1) m Not well informed about politics (2) m Somewhat informed about politics (3) m Very well informed about politics (4)

Q64 Now we would like you to think of one more person with whom you most frequently talk about matters that are important to you. What is this person's relation to you? m Spouse (1) m Close family member (2) m More distant family member (3) m Close friend (4) m Co-worker (5) m Neighbor (6) m Other (please specify): (7) ______

Q129 Is this person a current Ohio State student? m Yes (1) m No (3)

Display This Question: If Yes Is Selected Q130 What is their first and last name?

Q65 Is this person a male or female? m Male (1) m Female (2)

Q66 How often do you talk about politics with this person? m Never (1) m Sometimes (2) m Often (3)

Q69 Where would you place him or her on the following scale? m Strong Democrat (1) m Not very strong Democrat (2) m Independent - closer to Democrat (3) m Independent (4) m Independent - closer to Republican (5) m Not very strong Republican (6) m Strong Republican (7) m Don't know (8)

Q104 Which presidential candidate do you think this person is supporting in the 2016 election? m Hillary Clinton (1) m Donald Trump (2) m Gary Johnson (3) m Jill Stein (4) m Other (please specify): (5) ______m Don't know (6)

Q106 How informed would you say this person is when it comes to politics? m Not at all informed about politics (1) m Not well informed about politics (2) m Somewhat informed about politics (3) m Very well informed about politics (4)

Q102 Now we'd like to know a little more about your current understanding of American politics.

Q98 Which political party currently holds the majority in the United States House of Representatives? m The Democratic Party (1) m The Green Party (2) m The Republican Party (3) m Other (please specify): (4) ______m Don't know (5)

Q99 Which political party currently holds the majority in the United States Senate? m The Democratic Party (1) m The Green Party (2) m The Republican Party (3) m Other (please specify): (4) ______m Don't know (5)

Q100 John Kasich is a member of which political party? m The Democratic Party (1) m The Green Party (4) m The Republican Party (2) m Other (please specify): (3) ______m Don't know (5)

Q101 The two current Senators from Ohio are in which political party? m Both are members of the Democratic Party (1) m Both are members of the Republican Party (2) m One is a member of the Democratic Party and one is a member of the Republican Party (3) m Don't know (4)

Q45 In which city and state were you born?

Q46 In which city and state did you live in immediately before you came to the Ohio State University?

Q47 Please indicate your gender. m Male (1) m Female (2)

Q48 Please indicate your age in years.

Q49 Which race do you consider yourself? m American Indian or Alaska Native (1) m Asian (2) m Black or African American (3) m Hispanic, Latino, or Spanish (4) m Native Hawaiian or Pacific Islander (5) m White (6) m Multiracial (7)

Q50 How would you best describe your religious affiliation? m Protestant Christian (1) m Catholic (2) m Christian Orthodox (3) m Other Christian religion (please specify): (4) ______m Jewish (5) m Muslim (6) m Other, Non-Christian religion (please specify): (7) ______m No religion (8)

Q51 How often have you gone to church or attended religious services during the last year, outside of weddings and funerals? m Never (1) m 1, 2, or 3 times, such as only during holidays (2) m More than 3 times a month (3) m 2 or 3 times a month (4) m At least once a week (5) m More than once a week (6)

Q52 Are you a United States citizen? m Yes (1) m No (2)

Q53 Many people say they belong either to the middle or the working class. Do you ever think of yourself as belonging to one of these classes? m Yes (1) m No (2)

Q54 If you had to make a choice, with which class would you say you or your parents belong? m Working class (1) m Lower-middle class (2) m Middle class (3) m Upper-middle class (4) m Upper class (5)

Q44 Here are a number of personality traits that may or may not apply to you. You should rate the extent to which the pair of traits applies to you, even if one characteristic applies more strongly than the other. Disagree Disagree Disagree Neither Agree Agree Agree strongly moderately a little agree a little moderately strongly (1) (2) (3) nor (5) (6) (7) disagree (4) I see myself as extroverted, m m m m m m m enthusiastic. (1) I see myself as critical, m m m m m m m quarrelsome. (2) I see myself as dependable, m m m m m m m self- disciplined. (3) I see myself as anxious, m m m m m m m easily upset. (4) I see myself as open to new m m m m m m m experiences, complex. (5) I see myself as reserved, m m m m m m m quiet. (6) I see myself as m m m m m m m sympathetic, warm. (7) I see myself as m m m m m m m disorganized, careless. (8) I see myself m m m m m m m as calm, emotionally stable. (9) I see myself as conventional, m m m m m m m uncreative. (10)

Q77 Which $10 gift-card would you like to receive? m Amazon (1) m Target (3)

Appendix B: Selection Study Survey Instrument, Wave II

205 Appendix B: Selection Study Survey Instrument, Wave 2

Q51 CONSENT FOR PARTICIPATION IN SOCIAL AND BEHAVIORAL RESEARCH

Protocol title: Selection into College Groups: Social Networks and Political Beliefs Protocol number: 2016B0281 Principal Investigator: Janet Box-Steffensmeier Co-Investigator: Lauren Ratliff

Risks of participating in this research include the possibility that your responses could be released. However, this is highly unlikely given that the servers where your information is held is in a locked and gated area that is supervised 24 hours a day, seven days a week. Another potential risk is a breach of privacy because your survey responses are identifiable by e-mail address. However, that information is only available to the researchers on this project and they will use that information only to distribute the incentive. Additionally, each respondent’s e-mail will be substituted with a numerical code to ensure further confidentiality. The key will be kept separately from the survey data. All e-mail addresses will be permanently deleted once the $15 individual incentives are distributed at the completion of the study. We will work to make sure that no one sees your online responses without approval. But, because we are using the Internet, there is a chance that someone could access your online responses without permission. In some cases, this information could be used to identify you.

Benefits of participating in this research include a better understanding of the way that individuals select into social groups. Your participation in this project is completely voluntary and your responses will be kept confidential. Your refusal to participate will involve no penalty or loss of benefits to which you are otherwise entitled, including the $15 gift-card to either Amazon or Target. You may discontinue participation at any time without penalty or loss of benefits to which you are otherwise entitled.

------

I consent to participating in the research entitled: Selection into College Groups: Social Networks and Political Beliefs

Q52 Enter your e-mail address to receive the $15 gift-card. Gift-cards will be distributed to your e-mail inbox once you complete the study. At the end of the study, you will have the opportunity to indicate whether you would like a gift-card from Amazon or Target. All e- mail addresses will be deleted once gift-cards are distributed at the conclusion of the study.

Q2 What is your current major(s) in college?

Q3 Which academic, social, or athletic groups on campus did you join last semester (in Fall 2016)?

Q171 Are you planning on joining or have you already joined any NEW academic, social, or athletic group on campus this semester (Spring 2017)? m Yes (1) m No (2) m Unsure (3)

Display This Question: If Are you planning on joining or have you already joined any NEW academic, social, or athletic grou... Yes Is Selected Or Are you planning on joining or have you already joined any NEW academic, social, or athletic grou... Unsure Is Selected Q6 Which academic, social, or athletic group(s) at Ohio State campus do you plan on joining this semester (in the Spring of 2017)?

Q8 Are you currently living on Ohio State campus? m Yes (1) m No (2)

Q15 Now we have a few questions about the activities you have engaged in during your life.

Q16 Please indicate if you have participated in the following: Yes (1) No (2) Have you ever worked or volunteered for a political m m party or candidate? (1) Have you ever displayed a button, yard sign, bumper sticker, or any other kind of m m sign showing a political party or candidate you supported? (2) Have you ever donated money to any candidates or m m to a political party? (3) Have you ever attended any political meetings, rallies, m m speeches, dinners, or things like that? (4) Have you ever tried to persuade anyone else to vote m m or how to vote? (5)

Display This Question: If Please indicate if you have participated in the following: Have you ever worked or volunteered for a political party or candidate? - Yes Is Selected Q17 For which candidate(s) or party did you work or volunteer for?

Display This Question: If Please indicate if you have participated in the following: Have you ever displayed a button, yard sign, bumper sticker, or any other kind of sign showing a political party or candidate you supported? - Yes Is Selected Q18 For which candidate(s) or party did you display a button, yard sign, bumper sticker, or any other kind of sign for?

Display This Question: If Please indicate if you have participated in the following: Have you ever donated money to any candidates or to a political party? - Yes Is Selected Q19 For which candidate(s) or party did you donate money to?

Q172 Did you vote in the 2016 presidential election? m Yes (1) m No (2)

Display This Question: If Did you vote in the 2016 presidential election? Yes Is Selected Q25 For which candidate did you vote for president? m Hillary Clinton (1) m Donald Trump (2) m Gary Johnson (3) m Jill Stein (4) m Other (please specify): (5) ______

Display This Question: If Did you vote in the 2016 presidential election? No Is Selected Q173 For which candidate would you have voted for president? m Hillary Clinton (1) m Donald Trump (2) m Gary Johnson (3) m Jill Stein (4) m Other (please specify): (5) ______

Display This Question: If Did you vote in the 2016 presidential election? Yes Is Selected Q27 When did you decide to vote for that candidate? m On election day (1) m In the last week before election day (2) m After the conventions, but before the last week, including during the debates (3) m Around the time of the party conventions in late July (4) m Between April and July, before the party conventions (5) m In January through March, during the early primaries and caucuses (6) m Before the election campaign started in January (7)

Q31 How interested were you in the 2016 presidential campaign? m Not at all (1) m Very little (2) m Somewhat (3) m Quite a lot (4) m A great deal (5)

Q32 Thinking about your time at home, how often did you talk about public affairs and politics with members of your family? m Never (1) m Less than Once a Month (2) m Once a Month (3) m 2-3 Times a Month (4) m Once a Week (5) m 2-3 Times a Week (6) m Daily (7)

Q33 Are your closest friends Democrats, Republicans, or both? m Almost all Republicans (1) m More Republicans than Democrats (2) m About equally Republicans and Democrats (3) m More Democrats than Republicans (4) m Almost all Democrats (5) m Most do not identify with a particular party (6) m Unsure of what they are (7)

Q34 How interested are your closest friends in politics? m Not at all (1) m Very little (2) m Somewhat (3) m Quite a lot (4) m A great deal (5)

Q35 How important is politics when choosing new friends on campus? m Very important (1) m Important (2) m Neither important nor unimportant (3) m Not very important (4) m Not at all important (5)

Q36 How many different people did you talk with about the 2016 presidential election? Please enter the specific number as closely as you can approximate it.

Q177 During the course of the 2016 election campaign, how often did you post your own personal thoughts or opinions about a particular political party, candidate, or list of candidates via email, texting, or social networking applications to which you below m Never (1) m Rarely (2) m Sometimes (3) m Often (4)

Q178 How afraid are you to openly share with others on social media platforms or websites what you think about political candidates or topics? m Not afraid at all (1) m A little afraid (2) m Somewhat afraid (3) m Fairly afraid (4) m Very afraid (5)

Q28 Where would you place the following on this scale? Extre Libe Sligh Moder Slightly Conserv Extreme Have Do mely ral tly ate: conserva ative (6) ly n't n't liberal (2) liber middle tive (5) conserva thou kno (1) al (3) of the tive (7) ght w road abou (9) (4) t this much (8) Yoursel m m m m m m m m m f (1) The Democr atic m m m m m m m m m Party (2) The Republi can m m m m m m m m m Party (3)

Q29 Where would you place yourself on the following scale? Strong Not Independ Independ Independ Not Strong Do Democ very ent - ent (4) ent - very Republi n't rat (1) strong Closer to Closer to strong can (7) kno Democ Democra Republic Republi w rat (2) t (3) an (5) can (6) (8) Yours m m m m m m m m elf (1)

Q37 Now we have some statements that people have made about themselves. Please indicate how much you agree or disagree with each statement. Strongly Disagree (2) Neither Agree (4) Strongly disagree (1) Agree nor Agree (5) Disagree (3) I consider myself to be well qualified m m m m m to participate in politics. (1) I feel that I have a pretty good understanding of the m m m m m important political issues facing our country. (2)

Q38 Read the following statements and determine if they are true or false as they apply to you. True (1) False (2) I am sometimes reluctant to talk about politics because I m m don't like arguments. (1) I am sometimes reluctant to talk about politics because it m m creates enemies. (2) I am sometimes reluctant to talk about politics because I m m worry about what people would think of me. (3)

Q39 We would like to know your feelings towards some political figures and groups on a scale from 0-10, where '0' is Very unfavorable, '5' is Neutral, and '10' is Very favorable. 0: Very 1 2 3 4 5: 6 7 8 9 10: Very NA: Unfavorab (2 (3 (4 (5 Neutr (7 (8 (9 (10 Favorab Don't le (1) ) ) ) ) al (6) ) ) ) ) le (11) know this perso n (12) The Democrat m m m m m m m m m m m m ic Party (1) The Republica m m m m m m m m m m m m n Party (2) George W. Bush m m m m m m m m m m m m (3) Jeb Bush m m m m m m m m m m m m (4) Hillary Clinton m m m m m m m m m m m m (5) Ted Cruz m m m m m m m m m m m m (6) Gary Johnson m m m m m m m m m m m m (7) John m m m m m m m m m m m m Kasich (8) Barak Obama m m m m m m m m m m m m (9) Nancy Pelosi m m m m m m m m m m m m (10) Mike Pence m m m m m m m m m m m m (11) Marco Rubio m m m m m m m m m m m m (12) Paul Ryan m m m m m m m m m m m m (13) Bernie Sanders m m m m m m m m m m m m (14) Jill Stein m m m m m m m m m m m m (15) Donald Trump m m m m m m m m m m m m (16)

Q175 Where do you think things in the United States are generally going? m Right direction (1) m Wrong direction (2) m Don't know (3)

Q177 Do you approve or disapprove of the way the current U.S. Congress has been handling its job? m Strongly approve (1) m Approve (2) m Somewhat approve (3) m Neither approve or disapprove (4) m Somewhat disapprove (5) m Disapprove (6) m Strongly disapprove (7) m Don't know (8)

Q179 Do you approve or disapprove of the way Donald Trump is handling his job? m Strongly approve (1) m Approve (2) m Somewhat approve (3) m Neither approve or disapprove (4) m Somewhat approve (5) m Disapprove (6) m Strongly disapprove (7) m Don't know (8)

Q40 To what extent do you agree or disagree with the following statements? Strongly Disagree Neither Agree (4) Strongly Don't disagree (2) Agree nor Agree (5) know (6) (1) Disagree (3) Universities should be allowed to increase the number of minority students studying at their schools m m m m m m by considering race along with other factors when choosing students. (1) Federal spending to protect the environment m m m m m m should be increased. (2) To reduce the federal deficit, the government m m m m m m should cut military spending. (3) The government should reduce taxes, even m m m m m m if it means reducing government services and social assistance. (4) Prostitution should be legalized in m m m m m m the United States. (5) The government should restrict where m m m m m m drones can fly and film to protect people’s privacy. (6)

Q41 Now we would like to ask you about your opinions on some of the issues facing the country today.

Q42 How do you feel about the health care reform law passed in 2010? This law requires that all Americans buy health insurance and requires health insurance companies to accept everyone. m Favor a great deal (1) m Moderately favor (2) m Favor a little (3) m Neither favor nor oppose (4) m Oppose a little (5) m Moderately oppose (6) m Oppose a great deal (7) m Don't know (8)

Q43 Which comes closest to your view about what government policy should be toward unauthorized immigrants now living in the United States? m Make all unauthorized immigrants felons and send them back to their home country. (1) m Have a guest worker program that allows unauthorized immigrants to remain. (2) m Allow unauthorized immigrants to remain in the United States if they meet certain requirements. (3) m Allow unauthorized immigrants to remain in the United States without penalties. (4) m Don't know (5)

Q44 Which statement comes closest to your view about what government policy should be on access to guns? m The government should make it MORE DIFFICULT for people to buy a gun than it is now. (1) m The government should make it EASIER for people to buy a gun than it is now. (2) m The government should make KEEP THE RULES ABOUT THE SAME. (3) m Don't know (4)

Q45 Which one of the opinions below best agrees with your view? m By law, abortion should never be permitted. (1) m The law should permit abortion only in case of rape, incest, or when the woman's life is in danger. (2) m The law should permit abortion for reasons other than rape, incest, or danger to the woman. (3) m By law, a woman should always be able to obtain an abortion as a matter of personal choice. (4) m Don't know (5)

Q46 Which comes closest to your views on gay marriage? m Gay and lesbian couples should be allowed to marry legally. (1) m Gay and lesbian couples should be allowed to form civil unions but not legally marry. (2) m There should be no legal recognition of a gay or lesbian couple's relationship. (3) m Don't know (4)

Q47 In general, do you favor or oppose legalizing the possession of small amounts of marijuana for personal use? m Favor (1) m Oppose (2) m Don't know (3)

Q48 Do you think people who are transgender -- that is, someone who identifies themselves as the sex or gender different from the one they were born as -- should be allowed to use the public bathrooms of the gender they identify with or should they have to use the public bathrooms of the gender they were born as? m They should be allowed to use the public bathrooms of the gender they identify with. (1) m They should be allowed to use the public bathrooms of the gender they were born as. (2) m Don't know (3)

Q49 How worried are you that you or someone in your family will become a victim of a mass shooting? m Very worried (1) m Somewhat worried (2) m Not too worried (3) m Not worried at all (4)

Q50 How worried are you that you or someone in your family will become a victim of an act of mass terrorism planned by or inspired by the Islamic State or ISIS? m Very worried (1) m Somewhat worried (2) m Not too worried (3) m Not worried at all (4)

Q53 Now we would like you to think of the person with whom you most frequently talk about matters that are important to you. What is this person's relation to you? m Spouse (1) m Close family member (2) m More distant family member (3) m Close friend (4) m Co-worker (5) m Neighbor (6) m Other (please specify): (7) ______

Q54 Is this person a current Ohio State student? m Yes (1) m No (3)

Display This Question: If Is this person a current Ohio State Student? Yes Is Selected Q55 What is their first and last name?

Q56 Is this person a male or female? m Male (1) m Female (2)

Q57 How often do you talk about politics with this person? m Never (1) m Sometimes (2) m Often (3)

Q58 Where would you place him or her on the following scale? m Strong Democrat (1) m Not very strong Democrat (2) m Independent - closer to Democrat (3) m Independent (4) m Independent - closer to Republican (5) m Not very strong Republican (6) m Strong Republican (7) m Don't know (8)

Q59 Which presidential candidate do you think this person supported in the 2016 election? m Hillary Clinton (1) m Donald Trump (2) m Gary Johnson (3) m Jill Stein (4) m Other (please specify): (5) ______m Don't know (6)

Q60 How informed would you say this person is when it comes to politics? m Not at all informed about politics (1) m Not well informed about politics (2) m Somewhat informed about politics (3) m Very well informed about politics (4)

Q61 Now we would like you to think of one more person with whom you most frequently talk about matters that are important to you. What is this person's relation to you? m Spouse (1) m Close family member (2) m More distant family member (3) m Close friend (4) m Co-worker (5) m Neighbor (6) m Other (please specify): (7) ______

Q62 Is this person a current Ohio State student? m Yes (1) m No (3)

Display This Question: If Yes Is Selected Q63 What is their first and last name?

Q64 Is this person a male or female? m Male (1) m Female (2)

Q65 How often do you talk about politics with this person? m Never (1) m Sometimes (2) m Often (3)

Q66 Where would you place him or her on the following scale? m Strong Democrat (1) m Not very strong Democrat (2) m Independent - closer to Democrat (3) m Independent (4) m Independent - closer to Republican (5) m Not very strong Republican (6) m Strong Republican (7) m Don't know (8)

Q67 Which presidential candidate do you think this person supported in the 2016 election? m Hillary Clinton (1) m Donald Trump (2) m Gary Johnson (3) m Jill Stein (4) m Other (please specify): (5) ______m Don't know (6)

Q68 How informed would you say this person is when it comes to politics? m Not at all informed about politics (1) m Not well informed about politics (2) m Somewhat informed about politics (3) m Very well informed about politics (4)

Q81 Now we'd like to know a little more about your current understanding of American politics.

Q82 Which political party currently holds (as of 2017) the majority in the United States House of Representatives? m The Democratic Party (1) m The Green Party (2) m The Republican Party (3) m The Libertarian Party (4) m Don't know (5)

Q83 Which political party currently holds (as of 2017) the majority in the United States Senate? m The Democratic Party (1) m The Green Party (2) m The Republican Party (3) m The Libertarian Party (4) m Don't know (5)

Q84 Nancy Pelosi is a member of which political party? m The Democratic Party (1) m The Green Party (4) m The Republican Party (2) m The Libertarian Party (3) m Don't know (5)

Q85 The two current Senators from Ohio are in which political party? m Both are members of the Democratic Party (1) m Both are members of the Republican Party (2) m One is a member of the Democratic Party and one is a member of the Republican Party (3) m Don't know (4)

Q180 Which office or political position does Clarence Thomas currently hold? m President (1) m Supreme Court Justice (2) m Speaker of the House of Representatives (3) m Senator (4) m Don't know (5)

Q69 Which $15 gift-card would you like to receive? m Amazon (1) m Target (3)

Appendix C: Supplementary Material for Chapter 3

225 Party-ID DemFeel RepFeel ClintonFeel TrumpFeel Dad PID 0.21∗∗∗ -.34∗∗∗ 0.14 -.39∗∗∗ 0.2∗∗ (0.05) (0.09) (0.09) (0.09) (0.1) Mom PID 0.39∗∗∗ -.41∗∗∗ 0.5∗∗∗ -.37∗∗∗ 0.42∗∗∗ (0.05) (0.09) (0.09) (0.09) (0.1) Constant 1.07∗∗∗ 8.44∗∗∗ 1.57∗∗∗ 7.06∗∗∗ -.14 (0.16) (0.31) (0.31) (0.31) (0.34) Num. Obs. 386 380 380 377 379 R2 0.45 0.26 0.21 0.27 0.16 F 156 66.87 51.28 70.21 36.80 Log-Likelihood -642.57 -878.66 -873.72 -868.32 -913.16

∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.10

Table C.1: Parental Influence on Political Attitudes & Beliefs: No Controls

Party-ID DemFeel RepFeel ClintonFeel TrumpFeel Dad PID 0.19∗∗∗ -.30∗∗∗ 0.09 -.36∗∗∗ 0.17∗ (0.05) (0.09) (0.09) (0.09) (0.1) Mom PID 0.38∗∗∗ -.41∗∗∗ 0.49∗∗∗ -.38∗∗∗ 0.42∗∗∗ (0.05) (0.09) (0.09) (0.09) (0.1) Interest -.12 0.23∗ -.22 0.16 -.25 (0.08) (0.14) (0.14) (0.14) (0.16) Talk Politics at Home 0.06 -.20∗∗ -.03 -.02 0.02 (0.04) (0.08) (0.08) (0.08) (0.09) White 0.29∗ -.54∗ 0.61∗∗ -.43 0.15 (0.16) (0.3) (0.3) (0.3) (0.33) Female -.38∗∗∗ 1.08∗∗∗ 0.1 0.97∗∗∗ -.58∗∗ (0.14) (0.26) (0.26) (0.26) (0.29) Constant 1.32∗∗∗ 8.08∗∗∗ 2.12∗∗∗ 6.20∗∗∗ 0.86 (0.31) (0.58) (0.59) (0.58) (0.66) Num. Obs. 365 360 360 357 359 R2 0.45 0.30 0.22 0.30 0.17 F 48.83 25.69 16.37 24.59 12.28 Log-Likelihood -606.69 -820.69 -825.05 -814.82 -860.15

∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.10

Table C.2: Parental Influence on Political Attitudes & Beliefs: Ideology Removed

226 Join Partisan Join Dems Join Reps No Political Grp Party-ID - -1.13∗∗∗ 1.38∗∗∗ -.03 (0.19) (0.23) (0.07) Strength of Dem-ID 0.94∗∗∗ --- (0.20) Strength of Rep-ID 1.21∗∗∗ --- (0.24) Interest 0.87∗∗∗ 0.63∗∗ 1.22∗∗∗ -.71∗∗∗ (0.2) (0.25) (0.34) (0.16) Talk Politics at Home 0.36∗∗∗ 0.44∗∗∗ 0.18 -.37∗∗∗ (0.12) (0.15) (0.2) (0.1) Constant -7.39∗∗∗ -3.73∗∗∗ -14.86∗∗∗ 5.60∗∗∗ (0.92) (1.18) (2.18) (0.71) Num. Obs. 385 390 387 392 Log-Likelihood -126.28 -79.26 -47.97 -175.20 χ2 112.52 99.41 115.10 67.10

∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.10

Table C.3: The Selection of Homogeneous Political Groups: No Controls

Close Friends Dems Close Friends Reps Choose Friends Party-ID -.73∗∗∗ 0.79∗∗∗ -.46∗∗∗ (0.09) (0.1) (0.13) Interest -.12 -.01 0.70∗∗∗ (0.13) (0.15) (0.23) Interest of Friends 0.59∗∗∗ 0.37∗∗ 0.39 (0.15) (0.18) (0.25) Constant 0.38 -5.78∗∗∗ -4.78∗∗∗ (0.59) (0.76) (1.15) Num. Obs. 407 407 407 Log-Likelihood -207.07 -154.16 -97.5 χ2 111.07 97.89 38.89

∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.10

Table C.4: The Selection of Homogeneous Political Networks: No Controls

227 ∆ PID ∆ DemFeel ∆ RepFeel ∆ ClintonFeel ∆ TrumpFeel Party-ID -.20∗∗∗ ---- (0.04) ∗∗∗ DemFeelT 1 - -.34 --- (0.04) ∗∗∗ RepFeelT 1 - - -.37 -- (0.05) ∗∗∗ ClintonFeelT 1 - - - -.30 - (0.04) ∗∗∗ TrumpFeelT 1 - - - - -.25 (0.04) Join Partisan 0.16 -.65∗∗ 0.07 -.50 0.44 (0.16) (0.26) (0.30) (0.32) (0.29) Choose Friends 0.12 -.16 -.04 0.33 -.16 (0.23) (0.36) (0.40) (0.43) (0.39) Close Friends Reps 0.19 -.32 0.77∗∗ -.92∗∗∗ 0.21 (0.17) (0.27) (0.3) (0.32) (0.30) Close Friends Dems -.37∗∗ 0.59∗∗ -.43∗ 0.40 -.53∗∗ (0.15) (0.24) (0.26) (0.29) (0.25) Constant 0.66∗∗∗ 2.08∗∗∗ 1.31∗∗∗ 2.22∗∗∗ 0.65∗∗∗ (0.18) (0.25) (0.25) (0.24) (0.18) Num. Obs. 286 287 288 284 285 R2 0.09 0.23 0.20 0.15 0.12 F 5.27 17.08 13.69 9.63 7.90 Log-Likelihood -404.15 -543.59 -579.76 -588.90 -562.84

∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.10

Table C.5: Selection Study: Over Time Analysis without Controls

228 ∆ PID ∆ DemFeel ∆ RepFeel ∆ ClintonFeel ∆ TrumpFeel Join Partisan 0.06 -.40 -.28 -.32 0.18 (0.17) (0.29) (0.32) (0.34) (0.30) Choose Friends -0.27 0.43 -0.46 -0.24 -0.08 (0.23) (0.41) (0.44) (0.46) (0.42) Close Friends Reps -.10 0.33 0.1 -.47 -.36 (0.17) (0.29) (0.32) (0.33) (0.30) Close Friends Dems -.19 -.00 0.03 -.13 -.23 (0.15) (0.26) (0.28) (0.30) (0.26) Constant -.06 0.34∗∗ -.23 1.19∗∗∗ 0.12 (0.10) (0.17) (0.18) (0.19) (0.17) Num. Obs. 286 287 288 284 285 R2 0.01 0.02 0.01 0.01 0.01 F 0.66 1.33 0.42 0.85 0.47 Log-Likelihood -415.66 -578.99 -610.20 -609.86 -580.74

∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.10

Table C.6: Selection Study: Over Time Analysis without Controls or Lagged DV

229 Party-ID DemFeel RepFeel ClintonFeel TrumpFeel Join Partisan 0.28 -.50∗ 0.24 -.17 0.56∗ (0.17) (0.28) (0.32) (0.33) (0.31) Choose Friends -0.06 0.42 0.36 0.28 -0.33 (0.22) (0.36) (0.41) (0.43) (0.40) Close Friends Reps 0.19 0.21 0.62∗∗ -.37 -.25 (0.16) (0.26) (0.30) (0.31) (0.29) Close Friends Dems -.26∗ 0.49∗∗ -.03 0.2 -.25 (0.14) (0.23) (0.26) (0.27) (0.25) Party-ID 0.55∗∗∗ -.20 -.03 -.29∗∗ -.03 (0.07) (0.12) (0.13) (0.14) (0.12) ∗∗∗ DemFeelT 1 - 0.49 --- (0.05) ∗∗∗ RepFeelT 1 - - 0.53 -- (0.06) ∗∗∗ ClintonFeelT 1 - - - 0.55 - (0.06) ∗∗∗ TrumpFeelT 1 - - - - 0.64 (0.05) Ideology 0.37∗∗∗ -.26∗∗ 0.37∗∗∗ -.23∗ 0.41∗∗∗ (0.06) (0.11) (0.12) (0.12) (0.12) Interest -.09 -.12 -.20 0.13 -.22∗ (0.07) (0.11) (0.13) (0.13) (0.13) Female -.14 0.55∗∗∗ 0.01 0.92∗∗∗ -.03 (0.12) (0.20) (0.22) (0.23) (0.21) Conflict Avoidance -.10∗ 0.10 -.19∗ 0.1 -.01 (0.05) (0.09) (0.10) (0.10) (0.10) Extraversion -.02 0.08∗∗ 0.02 -.01 0.02 (0.02) (0.03) (0.03) (0.04) (0.03) Conscientiousness -.003 -.04 0.09 -.02 0.09∗ (0.03) (0.05) (0.06) (0.06) (0.05) Emotional Stability -.01 -.01 -.08∗ -.01 0.02 (0.02) (0.04) (0.04) (0.04) (0.04) Openness to Experience -.04 0.11∗∗ -.01 0.09 -.09∗ (0.03) (0.05) (0.06) (0.06) (0.05) Num. Obs. 263 264 264 260 263 R2 0.76 0.72 0.62 0.69 0.71 F 43.40 33.02 20.88 28.22 31.85 Log-Likelihood -336.34 -466.26 -501.90 -500.95 -489.48

∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.10; Due to space constraints, variables not significant across models were removed.

Table C.7: Selection Study: Modeling T2 Attitudes

230 Party-ID DemFeel RepFeel ClintonFeel TrumpFeel Join Partisan 0.16 -.65∗∗ 0.07 -.50 0.44 (0.16) (0.26) (0.30) (0.32) (0.29) Choose Friends -0.12 0.16 0.04 -0.33 0.16 (0.23) (0.36) (0.40) (0.43) (0.39) Close Friends Reps 0.19 -.32 0.77∗∗ -.92∗∗∗ 0.21 (0.17) (0.27) (0.30) (0.32) (0.30) Close Friends Dems -.37∗∗ 0.59∗∗ -.43∗ 0.40 -.53∗∗ (0.15) (0.24) (0.26) (0.29) (0.25) Party-ID 0.80∗∗∗ ---- (0.04) ∗∗∗ DemFeelT 1 - 0.66 --- (0.04) ∗∗∗ RepFeelT 1 - - 0.63 -- (0.05) ∗∗∗ ClintonFeelT 1 - - - 0.70 - (0.04) ∗∗∗ TrumpFeelT 1 - - - - 0.75 (0.04) Constant 0.66∗∗∗ 2.08∗∗∗ 1.31∗∗∗ 2.22∗∗∗ 0.65∗∗∗ (0.18) (0.25) (0.25) (0.24) (0.18) Num. Obs. 286 287 288 284 285 R2 0.69 0.64 0.54 0.59 0.65 F 125.22 100.11 66.08 80.33 102 Log-Likelihood -404.15 -543.59 -579.76 -588.90 -562.84

∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.10

Table C.8: Selection Study: Modeling T2 Attitudes with No Controls

231 Appendix D: Constitution of the College Democrats

232 THE CONSTITUTION of THE COLLEGE DEMOCRATS at THE OHIO STATE UNIVERSITY

ARTICLE I: NAME Section 1: This organization will be known as the College Democrats at The Ohio State University, hereafter referred to as OSU College Democrats.

ARTICLE II: MISSION Section 1: The mission of the College Democrats at The Ohio State University is to elect Democrats, implement Democratic policies, and promote education of and engagement with Democratic ideals.

ARTICLE III: PURPOSE Section 1: OSU College Democrats is committed to fostering a diverse and safe environment. In accomplishing this goal and embracing diversity, discrimination based upon age, race, ethnicity, color, creed, disability, gender identity or expression, national origin, military status, sex, religion, sexual orientation, veteran status and/or political affiliation is hereby prohibited.

ARTICLE IV: AFFILIATION Section 1: OSU College Democrats is chartered and affiliated with the College Democrats of Ohio, the College Democrats of America, the Franklin County Democratic Party, and will work closely with all levels of the Democratic Party.

ARTICLE V: MEMBERSHIP Section 1: All members of this organization must be enrolled at The Ohio State University. Students attending The Ohio State University as part of an exchange program meet this requirement.

Section 2: All members of this organization must abide by the mission statement.

Section 3: All members of this organization shall be considered voting members if they: a. have submitted a valid OSU email address to the Secretary, and b. have attended at least thirty percent (30%) of general body meetings during the current academic year, excluding general election meetings, or c. have received voting status with a unanimous vote of the Executive Board, only in the case of documented time constraints.

Section 4: Members who do not meet the voting requirements shall be considered a non-voting member and unable to vote in any election.

Section 5: Membership audits may be conducted at the discretion of the Executive Board at any time. An audit must occur two weeks before any election to determine the eligible voting members of the organization.

Section 6: Nothing in this article shall be used to revoke the membership of an elected Executive Board member.

ARTICLE VI: ORGANIZATIONAL LEADERSHIP Section 1: The organizational leadership shall be vested in an Executive Board, consisting of the following members in order of precedence: President, Vice President, Treasurer, Political Director, Communications Director, and Secretary. Each Leader shall be responsible for following and enforcing this Constitution.

Section 2: The Executive Board shall hold an official meeting at least once a month during the academic year, separate from scheduled meetings of the general membership. These meetings shall be open to the general membership and minutes from these meeting shall be published publicly.

Section 3: The Executive Board shall reserve the right to hold a private executive session where no minutes are taken. Such an executive session must be noted in the minutes and only elected Officers shall be permitted to attend unless another person's attendance is approved by majority vote.

Section 4: Only elected Officers may vote in Executive Board decisions. Each elected Officer has only one vote and in the instance of a tie, the President’s vote shall be the determining vote.

Section 5: President a. Call, preside, and supervise over the Executive Board and general meetings as Chair; b. Shall serve as the Primary Leader; c. Serve as ex-officio member on all committees; d. Along with the Advisor, shall be considered the primary liaison and point of contact to all outside organizations and Campus Administration unless designated otherwise; e. Nominate a Chief of Staff for approval to the elected Executive Board.

Section 6: Vice President a. Perform all the duties of the President in the President’s absence; b. Assume the Presidency if the President resigns or is impeached; c. Shall be the chief parliamentary officer of the organization and serve as Secondary Leader; d. Shall be the chief interpreter and enforcer of OSU College Democrats Constitution and By-Laws.

Section 7: Treasurer a. Shall be the chief financial officer of the organization and shall control all financial accounts owned by the organization along with the President; b. Report the financial status of the organization at every Executive Board meeting; c. Maintain the finances of this organization in the following ways: i. Collect and record all payments and donations made to this organization; ii. Record all payments made by this organization; iii. Apply for and maintain all operating and programming funding; iv. Advise the Executive Board financially; v. Conduct an annual audit of the finances of this organization; vi. Establish a budget and oversee fundraising operations.

Section 8: Political Director a. Act as the primary liaison to other political organizations; b. Serve as chief organizer of political activism and political communications; c. Report official political activities to the Executive Board; d. In good faith, inform the Executive Board of external political opportunities.

Section 9: Communications Director a. Shall be the chief messaging officer and will coordinate with the President, Political Director, and other Executive Board members on external communications; b. Maintain the organization’s website and social media presence; c. Draft the organization’s communications.

Section 10: Secretary a. Keep, record, and publish all Executive Board meeting minutes; b. Record attendance at all Executive Board meetings and general meetings; c. Administer, record, and publish all roll call votes conducted in Executive Board sessions; d. Collect, maintain, and make public membership, Executive Board, and voting member lists.

Section 11: Appointed Officers a. The Chief of Staff shall be the only required appointed Officer of the Executive Board; b. All other appointed Officers shall be nominated by an elected member that oversees such operations, pending confirmation by the elected members of the Executive Board; c. No appointed Officer shall have the authority to vote in Executive Board meetings; d. Appointed Officers may be dismissed with a majority vote of the Executive Board.

Section 12: In order to be eligible for any of the elected officer positions, students must meet the following qualifications: a. Be a voting member of the organization; b. Meet all Office of Student Life and University requirements for Leadership.

ARTICLE VII: Elections Section 1: General Elections a. Shall be held during a regularly scheduled general membership meeting four to six weeks before the end of Spring Semester; b. The method of voting shall be determined by unanimous decision of the Executive Board;

Section 2: Special Elections a. Will be conducted less than three weeks after a vacancy in the elected Executive Board; b. Shall be conducted during a regularly scheduled meeting determined by a unanimous decision of the Executive Board.

Section 3: The Elections Coordinator a. Shall be nominated and confirmed by a unanimous vote of the elected Executive Board no less than thirty days before a general election and no less than fourteen days before a special or amendment election; b. Shall preside over the election as a neutral facilitator; c. Must be a voting member of the organization; d. Must not be a candidate; e. Shall count all votes twice in the presence of the candidate(s) running; f. Announce all winners to the general membership after votes are counted; g. Ensure a just and fair election.

ARTICLE VIII: IMPEACHMENT AND REMOVAL FROM OFFICE Section 1: An impeachment hearing shall commence whenever two Executive Board members elect to file impeachment proceedings or when twenty percent (20%) of the voting membership petitions to initiate impeachment proceedings.

Section 2: All members of the organization shall be made aware of the impeachment proceedings no later than six days prior to the public proceedings.

Section 3: The impeachment proceedings shall occur as follows: a. The Chief of Staff shall be the presiding Officer. b. The presiding Officer shall have no vote. c. In the absence of the Chief of Staff, the presiding Officer will follow the order of precedence. d. The Complainant shall have five minutes to explain the reason for initiating the proceedings and in response, the Defendant shall have five minutes of defense, followed by a thirty-minute question period; e. The Complainant and Defendant shall both be afforded four minutes for closing remarks. f. A simple majority vote is required to remove an Officer from Office.

ARTICLE IX: ADVISOR Section 1: The Advisor to the College Democrats at The Ohio State University must meet the requirements set forth by the Office of Student Life.

Section 2: The Advisor to the College Democrats at The Ohio State University will be chosen upon a majority vote of the Executive Board. Once chosen, the Advisor must meet with the President no less than twice per year.

ARTICLE X: DISSOLUTION Section 1: This organization shall be dissolved upon a unanimous vote of the elected Executive Board and a three-fourths (3/4) vote of general voting members.

Section 2: In the event of the dissolution of this organization, the Executive Board shall, after payment of all liabilities of the organization, dispose of all assets of the organization in the following fashion: a. Forward the remaining assets to the College Democrats of Ohio and/or College Democrats of America with the request that they should be retained in an account and made available to this organization should it ever be reorganized within one (1) year of dissolution, or; b. Contribute the assets to a nonprofit organization, the purpose of which is to serve the needs and residents of the State of Ohio.

ARTICLE XI: AMENDING THE CONSTITUTION Section 1: Proposed amendments: a. Shall be presented and distributed at one general membership meeting prior to the poll; b. Shall be proposed only by voting members of the organization; c. Shall have two sponsors; d. Shall appear verbatim on the official ballot; e. Approval requires three-fourths of the voting members present.

ARTICLE XII: RATIFICATION Section 1: Ratification of this Constitution requires a majority vote of the general membership.

Appendix E: Constitution of the College Republicans

238 THE CONSTITUTION OF THE COLLEGE REPUBLICANS AT THE OHIO STATE UNIVERSITY

Preamble to the Constitution: This constitution provides for the existence and operation of The Ohio State University chapter of the College Republicans. College Republicans are the sole point of contact on campus for students to access the Republican Party. This chapter shall provide all students equal access to the opportunities that can be afforded by the chapter. The Ohio State University College Republicans shall also function as a constant Republican presence on the campus and communicate the messages of the Republican Party to the university community. It will represent the elected officials and candidates for general election endorsed by the Franklin County Republican Party, Ohio Republican Party, and the Republican National Committee.

Constitution:

Article I: Membership & General Chapter Meetings Section I: This chapter shall qualify as members any person(s) enrolled as undergraduate students at The Ohio State University, Main Campus, who have paid the annual membership dues, to be determined by majority vote in any given academic year. In the case of malicious intent, the Executive Committee may create additional qualifications, including but not limited to registration as a Republican elector and renunciation of membership in other partisan organizations. All members may vote in elections and on amendments or resolutions when present. Section II: Regular chapter meetings shall be held no fewer than five times per academic semester, sans Summer Term. There shall be a regular chapter meeting held in the first week of each semester, members must be notified by the Executive Committee of meeting times no less than forty-eight hours in advance. Regular meetings are to be held as open meetings; non-members may attend all meetings excepting those in which elections are held. Minutes of regular meetings will be made available upon request by members; attendance will be tabulated for each regular meeting. Section III: The regular chapter meetings will be considered a meeting of the Central Committee and shall be conducted by the Executive Committee Chairman. Regular meetings must also provide time for reports from officers including but not limited to: Secretary, Treasurer, Tribunes. Reports from the officers listed above will be provided upon request and delivered in a timely manner. Article II: Executive Committee Officers & Meeting Protocol Section I: There shall be an Executive Committee in the chapter consisting of the following elected officers: Executive Committee Chairman, Executive Committee Vice-Chairman, Secretary, two tribunes, and the Treasurer the following confirmed officers: Sergeant-at-Arms, and Executive Director. Section II: The Executive Committee shall act as the governing body for the chapter and will steer the general activities of the chapter. Meetings of the Executive Committee shall be held no less than four times per academic semester and at least once during Summer Term. All elected and confirmed officers must be present at these meetings; all appointed officers must be present when summoned by the Executive Committee. Meetings of the Executive Committee shall be closed meetings; minutes of these meetings shall be sealed for one full calendar year following the meeting date. Such a seal may be removed by the approval of the chairman and a simple majority vote of the Executive Committee. Section III: The Executive Committee Chairman shall conduct the meetings of the Executive Committee. In voting matters, the Secretary, Treasurer, Tribunes and Vice-Chairman shall each hold one vote. The Chairman has both a vote and the tiebreaking vote. The votes shall be conducted by roll call vote and recorded by the Secretary and the Executive Committee Vice-Chairman, and shall take place in the following order: Treasurer, Secretary, Vice-Chairman, and Chairman, if necessary. Section IV: The appointed positions to the Executive Committee shall have the right to attend meetings and speak. The Chairman may appoint members of the club in good standing the appointment to Executive Committee as an Advisor to the Chairman with full speaking rights. Such an appointment may be rescinded for any reason by Chairman. Section V: The Chairman shall have the power to assemble Ad Hoc Sub-Committees at his discretion with members drawn from any Central Committee member. Such Sub-Committees have authority to only advise the full Committees on the issue for which the Sub-Committee was formed. Section VI: The number of Tribunes may be increased to three under the following circumstances. Either it is a Presidential Election Year or there is a majority voice vote of both committees in favor of it with the approval of the Chairman. Article III: Delineation of Duties and Separation of Powers for Executive Committee Officers and Appointed Officers Section I: The Executive Committee Chairman shall act as the president of the chapter and the chief officer and ambassador to the university community. He shall preside over Central Committee meetings, and shall only vote at said meetings to break a tie, which he must do. His vote and decision will be binding and irrevocable. Presiding over both Central Committee and Executive Committee meetings shall include direction of debate and discussion as well as holding the Chair’s privilege. He shall also notify all members of the Executive Committee of their meeting times. The Chairman shall also appoint temporary replacements for expected absences such as study abroad or extenuating circumstances deemed appropriate by the Chairman and confirmed by the Executive committee. If the Vice Chairman is the position being filled and a vote results in a tie, the Treasurer’s vote shall be binding and irrevocable. Section II: The Executive Committee Vice-Chairman shall act as the deputy officer of the chapter and shall assume the duties of the Executive Committee Chairman in the event of his absence at a Central Committee meeting. He may vote at Central Committee meetings. The Executive Committee Vice-Chairman shall work with all other officers and oversee their work as necessary. In the event of the permanent absence of the Executive Committee Chairman, he shall succeed him in that office, and a special election will be held to fill the office of Vice- Chair. This election will be conducted by the Central Committee and all members. Section III: The Secretary shall hold the following duties at both Executive and Central Committee meetings: record minutes of meetings, records current membership, records vote totals at all meetings, and shall collaborate with the Sergeant-at-Arms to create the list of eligible voters in an election or vote on amendments and resolutions. The Secretary shall also be tasked with notifying all members of the Central Committee meetings no less than forty-eight hours prior to the meeting. Furthermore, the previous Central Committee meeting’s minutes and relevant documents, i.e. PowerPoint, Internship Information, etc., in the communication to the members of the Central Committee prior to the upcoming meeting. The Secretary shall notify all members of Central Committee meetings no less than forty-eight hours prior to the meeting. The Secretary shall maintain and use the email lists for the organization. The Secretary shall be responsible for registering the organization for campus events. Section IV: The Treasurer shall be elected by majority vote of the Central Committee. Duties of the Treasurer will include presiding over and tracking all financial matters concerning the chapter, managing the chapter’s accounts, balancing the checkbook, and giving a report once a semester to the Executive Committee. This report will be available to any member of the Central Committee upon request. During one of the first two Executive Committee meetings of the Fall Semester, a prepared tentative budget shall be presented. This budget shall be available to any member of the Central Committee upon request. Section V: The Executive Director shall be appointed by the Executive Committee Chairman and be confirmed by the balance of the Executive Committee. He shall serve as the de facto Sergeant-at-Arms during all Executive Committee meetings and maintain order as such. The Executive Director has full attendance and speaking rights at Executive Committee meetings. In the absence of the Executive Committee Chairman at these meetings, he shall hold the vote of the Chairman and serve as his proxy. The Executive Director shall be tasked with organization outreach between the Central Committee and other organizations. The Executive Director may in the course of his duties, and with the consent of the Chairman, appoint members of the organization in good standing to aid in outreach efforts and report to the Executive Director and the Chairman. Such an appointment does not grant attendance or speaking rights at Executive Committee meetings and the appointment may be rescinded at the Executive Director’s or Chairman’s discretion. Section VI: The Communications Director shall be responsible for official media relations and social media outreach. Furthermore the Communications Director shall outline to the Executive Committee what the message of the Chapter will be every semester. All other duties shall be at the discretion of the Executive Committee, including but not limited to: advertising speaking guests, promotion of chapter events, social media, websites, and the creation of chapter fund raising letters with the Treasurer. The Communications Director has full attendance and speaking rights at Executive Committee meetings. The Communications Director may in the course of his duties, and with the consent of the Chairman, appoint members of the organization in good standing to aid in outreach efforts and report to the Executive Director and the Chairman. Such an appointment does not grant attendance or speaking rights to Executive Committee meetings and the appointment may be rescinded at the Communication Director’s or Chairman’s discretion. Section VII: The Sergeant-at-Arms shall have four duties: maintain order at all Central Committee meetings, call all meetings to order with the Pledge of Allegiance and/or an invocation, record attendance at Central Committee meetings, and preside over all elections and the filing process for said elections. The Sergeant-at-Arms does not have attendance rights to the Executive Committee meetings. Section VIII: The Tribune shall be the assistant to the Vice Chairman and the Secretary. The Vice Chairman may assign duties to the Tribune with regards to recruitment or involvement events (i.e. door to door events, membership drives). The Tribune will aid the Secretary in signing people in at meetings, the distribution of club materials, and t-shirt sales. The Tribune does have attendance rights and voting rights to the Executive Committee meetings. Section IX: Publication rights and rights to use the name of the organization, including but not limited to titles either appointed or elected, are limited. The Chairman and Communications Director have the right to speak publicly in the organization’s name and others may be extended this right limited to a specific function by a simple majority of the Executive Committee. Written publication in the organization’s name, with the exception of emails to the Central Committee or social media posts corresponding to an assigned position, must be approved by the Executive Committee. Violation of this section is cause for censure. Article IV: Elections, Filing for Elections, and Tie-breaking Rules Section I: Chapter elections shall take place at a chapter meeting during Spring Semester of each academic year, the date to be no later than the first Wednesday of February or CPAC of the American Conservative Union, whichever comes later. All members of the chapter shall be considered to be qualified electors, and elections shall be held in the following order for these Constitutional officers: Tribunes, Treasurer, Secretary, Executive Committee Vice-Chairman, and Executive Committee Chairman. All members must be notified of any election meeting no less than one week in advance. No candidate may vote in the contest for his or her own nominated office. Section II: To be eligible for election as Executive Committee Chairman, the member must have recorded no less than two consecutive prior semesters of membership and previous experience on the Executive Committee; the experience requirement may be waived on petition of the applicant to the Executive Committee and a simple majority of the Executive Committee in support. To be eligible for any other elected office, the member must have recorded no less than one consecutive prior semester of membership. Candidates must also file a signed statement of candidacy with the Sergeant-at-Arms during the annual filing period, to begin no less than fourteen days following the November General Election in that year and will close promptly on the second Wednesday in January following the first official day of class in the Spring Semester. Members may also only declare candidacy for one office. Section III: The Sergeant-at-Arms shall preside over the Election Meeting. Elections shall be held by secret paper ballot, and a simple majority shall signify victory for any candidate. Votes shall be tabulated by an Election Committee, to consist of the Sergeant-at-Arms, the Executive Director, and a Central Committee Member chosen by the Sergeant-at-Arms. In the event that the Sergeant-at-Arms or another member of the Election Committee is a candidate for office, he shall recuse himself during the election for that office. The Executive Committee Chairman shall appoint a replacement member in that case. Results of the election shall be recorded by the Secretary following tabulation, and he shall announce the results, to be confirmed by the Sergeant-at-Arms. Section IV: If a majority is not achieved upon the first ballot, then the candidate with the fewest votes received shall be dropped from the ballot. A second ballot shall be cast, and the process repeated until a majority is achieved. If no majority can be achieved, then the Executive Committee shall vote by roll call in the order provided in Article II, Section III, and they must break the tie. The decision is binding and irrevocable. Section V: In the election for Tribune, the candidate shall be a candidate for the general office. The two or, in the aforementioned case, three highest vote-getters shall be elected to these offices. Section VI: In the case that an office is vacant, excepting that of the Executive Committee Chairman to which the Executive Committee Vice-Chairman shall succeed, a special election shall be held within twenty-eight days of the vacancy occurring. The filing rules shall be those as delineated in Article IV, Section II, excepting that the filing period shall be open for a period of two full weeks prior to the election. All special election protocol shall follow the above Sections of Article IV. Section VII: During the campaign period for any office, any candidate may submit one electronic mail message to the Sergeant-at-Arms to be sent out on the chapter mailing list. Any candidate may also produce no more than one paper advertisement of their candidacy to distribute to chapter members. Section VIII: In the event of the permanent absence of a majority of the elected Executive Committee, the Central Committee shall convene to hold a special election as soon as is possible. In this case, the nominations will be opened at the meeting by a designee of the chapter, and the election will take place at the same meeting. All other election protocol are to follow that of regular elections. Article V: Removal from office Section I: Executive Committee members shall perform all duties as described in Article III. If for any reason a member is unable to fulfill his or her duties, there shall be a vote within the Executive Committee for his or her removal and a two-thirds agreement of the committee. Section II: In the case of a removal, the replacement of the removed committee member will follow the special election protocol outlined in Article IV. Section III: Grounds for removal include but are not limited to: representing the organization in such a way that diminishes the reputation, absence at two or more general committee meetings or one or more Executive Committee meetings per semester, failing to complete the duties stated for the office as described in Article III or severe violations of Section IX of Article III. Section IV: General membership is open to all students at The Ohio State University. Therefore general members shall not be removed from general meetings for any reason unless their behavior is contradictory to university policy. Upon which, the member will be put removed with a majority vote of the Executive Committee. To be considered a voting general member for offices of the Executive Committee, a member must fulfill the requirements stated in Article 1, Section 1. Article VI: Transition between Administrations; Confirmation of the Appointed Officers Section I: There shall be a transition period between the regular election and the swearing in of the Executive Committee-elect that will last from the election date until either the third Wednesday of February or the fulfillment of the Executive Committee-elect. During said transition period, the Executive Committee and Executive Committee-elect shall meet together no less than two times to discuss the smooth and cordial transition of power. The outgoing Executive Committee Chairman shall also conduct no less than two meetings of the Central Committee following the regular election before the swearing in of the Executive Committee-elect. The outgoing Sergeant-at-Arms shall swear in all members of the Executive Committee-elect, and the Executive Committee-elect shall also hold no less than one Executive Committee meeting following swearing in and prior to the final Wednesday in March. Section II: The Executive Committee Chairman shall appoint his choice for the office of Executive Director. The Executive Committee shall hold a roll-call vote to confirm the appointment by simple majority; he may be removed from office by a vote of no-confidence. Section III: The Executive Committee Chairman shall appoint his choice for the office of Sergeant-at-Arms. The Executive Committee shall hold a roll-call vote to confirm the appointment by simple majority; he may be removed from office by a vote of no-confidence. Section IV: The Executive Committee Chairman shall appoint his choice for the office of Communication Director. The Executive Committee shall hold a roll-call vote to confirm the appointment by simple majority; he may be removed from office by a vote of no-confidence. Article VII: Affiliation with the Ohio College Republican Federation, Appointment of Advisors Section I: The Ohio State University chapter of the College Republicans shall be a charter member of the Ohio College Republican Federation. Section II: At the first meeting of the Executive Committee during each Spring Semester, there shall be a debate of the chapter’s due affiliation with the Ohio College Republican Federation. Following debate, the Executive Committee shall hold a vote to re-federate; a simple majority of the Executive Committee shall win. Section III: The Ohio State University chapter of the College Republicans shall appoint a faculty advisor each Spring Semester in accordance with the University’s requirements for such. The term will be one year and shall be approved by the Executive Committee by a majority vote. The advisor may be appointed to consecutive terms by the same procedure. Section IV: The Executive Board shall be given the authority to assist in founding sister chapters at other institutions of higher education in the State of Ohio and shall also be given the authority to collaborate with other existing chapters both affiliated and unaffiliated with the Ohio College Republican Federation. Article VIII: Endorsements Section I: No candidate may be endorsed by the chapter for a primary election. The chapter may award endorsements in elections of the Undergraduate Student Government, Ohio College Republican Federation, and the College Republican National Committee. These allowed endorsements must be debated by the Central Committee at a meeting and may only be awarded by majority vote. Mock elections and straw polls are not considered to be endorsements of the chapter. Article IX: Chapter Governance, Member Rights, and Dissolution Section I: The chapter shall govern in accordance with this Constitution; any un-discussed matters may be resolved through amendment or by the Executive Committee. Section II: Members of the chapter shall have access to minutes of the Central Committee meetings, Executive Committee meetings occurring at least one calendar year prior, a copy of this Constitution, and all meetings of the Central Committee. Members shall not be permitted to attend meetings of the Executive Committee unless summoned and are to receive reports of these meetings via the current Tribune or Secretary. Section III: This chapter may be dissolved by either a 9/10th vote of the current membership or if the chapter fails to meet for two full academic semesters, sans Summer Term. In the event of dissolution, the currently serving elected and confirmed Executive Committee shall share equally both the remaining assets and/or debts/liabilities accrued by the chapter. Section IV: In the event of mishandling of chapter assets, especially finances, the responsibility for the mishandled assets shall be borne by the Executive Committee Chairman and the Treasurer. Section V: All financial transactions by the club are to be handled by reimbursement; the only exceptions are transactions either totaling less than twenty US Dollars or those previously approved by the Executive Committee. The Executive Committee must approve all reimbursements, and it reserves the right to force repayment on any purchases exempted due to being under $20 if the purchase is later deemed to be a misuse of chapter funds. Section VI: Resolutions may be passed by majority vote at a Central Committee meeting. Article X: Amendment and Ratification Procedure Section I: This Constitution may be amended with a 3/4 vote of a present quorum of current membership at a Central Committee meeting. All amendments must be introduced to the Executive Committee and approved by a 2/3 vote no less than two weeks before voting and to the Central Committee meeting no less than one week prior to voting on said amendment. Section II: This Constitution shall be ratified by a 3/4 vote of a present quorum of current membership during Spring Semester of 2016 at a Central Committee meeting. Upon ratification of this Constitution, all prior Constitutions are considered null and void. The current officers of this term shall be retained until the next constitutionally provided election date. Article XI: Non-Discrimination Section I: This organization and its members shall not discriminate against any individual(s) for reasons of age, color, disability, gender identity or expression, national origin, race, religion, sex, sexual orientation, or veteran status. Appendix F: Group Study Survey Instrument, Wave I

251 Appendix F: Group Study Survey Instrument, Wave 1

Q56 CONSENT FOR PARTICIPATION IN SOCIAL AND BEHAVIORAL RESEARCH

Protocol title: Social Network Analysis of Political Student Groups Protocol number: 2014B0364 Principal Investigator: Janet Box-Steffensmeier Co-Investigator: Lauren Ratliff

You are being asked to consent to participating in research entitled: Social Network Analysis of Political Student Groups, which consists of you filling out an online survey. The survey will take approximately 30 minutes to complete, and asks participants to indicate how often they interact with members of a group which they belong to, and also asks a few questions about your attitudes and opinions about politics.

Risks of participating in this research include the possibility that your responses could be released. However, this is highly unlikely given that the servers where your information is held is in a locked and gated area that is supervised 24 hours a day, seven days a week. Another potential risk is a breach of privacy because your survey responses are identifiable by name and e-mail address. However, that information is only available to the researchers on this project and they will use that information only to match participants to their social groups. Additionally, each respondent’s name will be substituted with a numerical code to ensure further confidentiality. The key will be kept separately from the survey data. All e- mail addresses will be permanently deleted once the $10 individual incentives are distributed at the completion of the study. We will work to make sure that no one sees your online responses without approval. But, because we are using the Internet, there is a chance that someone could access your online responses without permission. In some cases, this information could be used to identify you.

Benefits of participating in this research include a better understanding of the way that social influence is spread through naturally occurring social groups. Your participation in this project is completely voluntary and your responses will be kept confidential. Your refusal to participate will involve no penalty or loss of benefits to which you are otherwise entitled, including the $400 group incentive and the $10 gift-card to either Amazon, Kroger, or Target. You may discontinue participation at any time without penalty or loss of benefits to which you are otherwise entitled.

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I consent to participating in the research entitled: Social Network Analysis of Political Student Groups.

By clicking on "I consent" I am indicating that I have read this form or I have had it read to me. I do this freely and voluntarily. If I would like to keep a copy, I can print this from my web browser, or request a copy from Lauren Ratliff at [email protected].

By clicking "I consent" I also acknowledge that I am at least 18 years old. m I consent (4)

Q81 Enter your e-mail address to receive the $10 gift-card. Gift-cards will be distributed to your e-mail inbox once you complete the study. At the end of the study, you will have the opportunity to indicate whether you would like a gift-card from Amazon, Kroger, or Target. All e-mail addresses will be deleted once gift-cards are distributed at the conclusion of the study.

Q1 To begin, we need to know your full name to match you with your OSU Student Activity Group. Please enter your first and last name below.

Q2 Now we would like to know more about your time at the Ohio State University and in the [GROUP].

Q3 What year are you in college? m First year (1) m Second year (2) m Third year (3) m Fourth year (4) m Fifth Year (5) m Sixth year or more (6)

Q4 What is your major(s) in college?

Q5 When did you first first join the [GROUP]? m This semester (Autumn 2015) (1) m Summer 2015 (2) m Spring 2015 (3) m Autumn 2014 (4) m Sometime between Autumn 2013 and Summer 2014 (5) m Prior to Autumn 2013 (6)

Q7 Personally, how committed do you feel to the [GROUP]? m Not at all committed (1) m Slightly committed (2) m Moderately committed (3) m Strongly committed (4) m Very strongly committed (5)

Q8 Please indicate how much you participate in the activities of the [GROUP]. m Participate in all group activities (1) m Participate in most group activities (2) m Participate in some group activities (3) m Participate in very few group activities (4)

Q9 Please tell us the main reasons you decided to join the [GROUP].

Q10 Please indicate whether you agree or disagree with the following statements. Strongly Disagree (2) Neither Agree (4) Strongly disagree (1) Agree nor Agree (5) Disagree (3) I feel uncomfortable when my political views are different m m m m m from those held by other members of the [GROUP]. (1) I often seek out information from other members of m m m m m the [GROUP] before I make a political decision. (2) It is important to me that other members of m m m m m the [GROUP] support my political decisions. (3)

Q11 If you are a member of any other OSU Student Activity Groups in addition to the [GROUP], please write the group name(s) in the provided space below. If not, just skip the question.

Q12 In relation to these other groups, how would you rate your involvement in the [GROUP]? m My involvement in the [GROUP] is MORE important (1) m My involvement in the [GROUP] is AS important (2) m My involvement in the [GROUP] is LESS important (3) m This does not apply to me (4)

Q13 Now we have a few questions about other members of the [GROUP].

Please indicate: - If you know this person

And if you know this person, continue to specify: - [For College Democrats and Republicans Only] Where you met him or her - How often you talk with this person - How often you have personally talked to him or her about politics - Where you would place this person on a political scale

When you arrive at your own name, please select "This is me".

Do you How often Where would you place this person on this How often know do you scale? do you talk him or personally with this her? talk politics person? (If you with him or don't her? know them, please mark 'No' and proceed on to the next individu al) Y N T N So O Str No Ind Ind Ind No Str D N So O e o h e me ft on t epe epe epe t on o e me ft s i v tim e g ver nde nde nde ver g n' v tim e ( ( s er es n De y nt - nt nt - y Re t er es n 1 2 i (1 (2) (3 mo str Clo (4) Clo str pu k (1 (2) (3 ) ) s ) ) cra on ser ser on blic n ) ) m t g to to g an o e (1) De De Rep Re (7) w ( mo mo ubli pu (8 3 cra crat can blic ) ) t (3) (5) an (2) (6) N a m m m m m m m m m m m m m m m m m m e

[For PSL and STEM-EE Scholars Only] Q71 Please list the names of all the members of the [GROUP] in the OLDER cohorts that you talk with most often.

Q58 Is this person a male or female? m Male (1) m Female (2)

Q59 How often do you talk politics with this person? m Never (1) m Sometimes (2) m Often (3)

Q60 Is this person a member of the [GROUP]? m Yes (1) m No (2)

Display This Question: If Please name the member of the [GROUP]. Is Selected Q61 Please name the member of the [GROUP].

Q65 Is this person a male or female? m Male (1) m Female (2)

Q66 How often do you talk politics with this person? m Never (1) m Sometimes (2) m Often (3)

Q67 Is this person a member of the [GROUP]? m Yes (1) m No (2)

Display This Question: If Is this person a member of the [GROUP]? Yes Is Selected Q68 Please name the member of the [GROUP].

Q14 Now we have a few questions about the activities you have engaged in during your life.

Q16 Were you eligible to vote in the 2012 presidential election? m Yes (1) m No (2)

Display This Question: If Were you eligible to vote in the 2012 presidential election? Yes Is Selected Q17 In the 2012 election, did you vote in the general election? m Yes (1) m No (2)

Display This Question: If Were you eligible to vote in the 2012 presidential election? Yes Is Selected And In the 2012 election, did you vote in the general election? Yes Is Selected Q18 For which candidate did you vote for President in the 2012 election? m Gary Johnson (1) m Barack Obama (2) m Mitt Romney (3) m Jill Stein (4) m Other (please specify): (5) ______

Q19 Did you vote in the 2014 Ohio Gubernatorial race? m Yes (1) m No (2) m Not eligible to vote in Ohio (4)

Display This Question: If Do you plan to vote in the 2014 Ohio Gubernatorial race? Yes Is Selected Q20 For which candidate did you vote for Governor in the 2014 midterm elections? m John Kasich (1) m Ed Fitzgerald (2) m Anita Rios (3) m Other (please specify): (5) ______

Q24 How interested were you in the 2012 presidential election/campaign? m Not at all (1) m Very little (2) m Somewhat (3) m Quite a lot (4) m A great deal (5)

Q26 Outside of the [GROUP], are your closest friends Democrats, Republicans, or both? m Almost all Republicans (1) m More Republicans than Democrats (2) m About equally Republicans and Democrats (3) m More Democrats than Republicans (4) m Almost all Democrats (5) m Most do not identify with a particular party (6) m Unsure of what they are (7)

Q27 Outside of the [GROUP], how interested are your closest friends in politics? m Not at all (1) m Very little (2) m Somewhat (3) m Quite a lot (4) m A great deal (5)

Q23 Where would you place the following on this scale? Strong Not Indepen Indepen Indepen Not Strong Do N/ Demo very dent - dent (4) dent - very Republi n't A crat (1) strong Closer Closer strong can (7) kno (9) Demo to to Republi w crat (2) Democr Republic can (6) (8) at (3) an (5) Your self m m m m m m m m m (1) Your Fathe m m m m m m m m m r (2) Your Moth m m m m m m m m m er (3)

Q32 Where do you think things in the United States are generally going? m Right direction (1) m Wrong direction (2) m Don't know (3)

Q33 Do you approve or disapprove of the way the current U.S. Congress has been handling its job? m Strongly approve (1) m Approve (2) m Somewhat approve (3) m Neither approve or disapprove (4) m Somewhat disapprove (5) m Disapprove (6) m Strongly disapprove (7) m Don't know (8)

Q34 Do you approve or disapprove of the way Barack Obama is handling his job? m Strongly approve (1) m Approve (2) m Somewhat approve (3) m Neither approve or disapprove (4) m Somewhat approve (5) m Disapprove (6) m Strongly disapprove (7) m Don't know (8)

Q35 We would like to know your feelings towards some political figures and groups on a scale from 0-10, where '0' is Very unfavorable, '5' is Neutral, and '10' is Very favorable. 0: Very 1 2 3 4 5: 6 7 8 9 10: Very NA: Unfavorab (2 (3 (4 (5 Neutr (7 (8 (9 (10 Favorab Don't le (1) ) ) ) ) al (6) ) ) ) ) le (11) know this perso n (12) The Democrat m m m m m m m m m m m m ic Party (1) The Republica m m m m m m m m m m m m n Party (2) George W. Bush m m m m m m m m m m m m (5) Barack Obama m m m m m m m m m m m m (6) Hillary Clinton m m m m m m m m m m m m (10) Bernie Sanders m m m m m m m m m m m m (11) Jeb Bush m m m m m m m m m m m m (12) Donald Trump m m m m m m m m m m m m (13) Marco Rubio m m m m m m m m m m m m (14) John Kasich m m m m m m m m m m m m (15)

Q37 Now we would like to ask you about your opinions on some of the issues facing the country today.

Q42 Thinking about the relationship between the United States and Israel, what is your opinion about U.S. support of Israel? m Too supportive (1) m Not supportive enough (2) m About right (3) m Don't know (4)

Q78 In general, do you favor or oppose legalizing the possession of small amounts of marijuana for personal use? m Favor (1) m Oppose (2) m Don't know (3)

Q45 In which city and state were you born?

Q46 In which city and state did you live in immediately before you came to the Ohio State University?

Q47 Please indicate your gender. m Male (1) m Female (2)

Q48 Please indicate your age in years.

Q49 Which race do you consider yourself? m American Indian or Alaska Native (1) m Asian (2) m Black or African American (3) m Spanish, Hispanic, or Latino (4) m Native Hawaiian or Pacific Islander (5) m White (6)

Q52 Are you a United States citizen? m Yes (1) m No (2)

Q53 Many people say they belong either to the middle or the working class. Do you ever think of yourself as belonging to one of these classes? m Yes (1) m No (2)

Q54 If you had to make a choice, with which class would you say you belong? m Working class (1) m Lower-middle class (2) m Middle class (3) m Upper-middle class (4) m Upper class (5)

Q55 What about your parents? With which class would you say they belong? m Working class (1) m Lower-middle class (2) m Middle class (3) m Upper-middle class (4) m Upper class (5)

Q77 Which $10 gift-card would you like to receive? m Amazon (1) m Kroger (2) m Target (3)

Appendix G: Group Study Survey Instrument, Wave II

273 Appendix G: Group Study Survey Instrument, Wave II

Q56 CONSENT FOR PARTICIPATION IN SOCIAL AND BEHAVIORAL RESEARCH

Protocol title: Social Network Analysis of Political Student Groups Protocol number: 2014B0364 Principal Investigator: Janet Box-Steffensmeier Co-Investigator: Lauren Ratliff

Risks of participating in this research include the possibility that your responses could be released. However, this is highly unlikely given that the servers where your information is held is in a locked and gated area that is supervised 24 hours a day, seven days a week. Another potential risk is a breach of privacy because your survey responses are identifiable by name and e-mail address. However, that information is only available to the researchers on this project and they will use that information only to match participants to their social groups. Additionally, each respondent’s name will be substituted with a numerical code to ensure further confidentiality. The key will be kept separately from the survey data. All e- mail addresses will be permanently deleted once the $10 individual incentives are distributed at the completion of the study. We will work to make sure that no one sees your online responses without approval. But, because we are using the Internet, there is a chance that someone could access your online responses without permission. In some cases, this information could be used to identify you.

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I consent to participating in the research entitled: Social Network Analysis of Political Student Groups.

By clicking "I consent" I also acknowledge that I am at least 18 years old. m I consent (4)

Q81 Enter your e-mail address to receive the $10 gift-card. Gift-cards will be distributed to your e-mail inbox once you complete the study. At the end of the study, you will have the opportunity to indicate whether you would like a gift-card from Amazon, Kroger, or Target. All e-mail addresses will be deleted once gift-cards are distributed at the conclusion of the study.

Q83 To begin, we need to know if you participated in the first wave of this survey in Fall of 2015. Did you participate in the survey in Fall 2015? m Yes (1) m No (2) m Don't remember (4)

Q1 We need to know your full name to match you with your OSU Student Activity Group. Please enter your first and last name below.

Q2 Now we would like to know more about your time at the Ohio State University and in the [GROUP].

Q4 What is your major(s) in college?

Q7 Personally, how committed do you feel to the [GROUP]? m Not at all committed (1) m Slightly committed (2) m Moderately committed (3) m Strongly committed (4) m Very strongly committed (5)

Q11 If you are a member of any other OSU Student Activity Groups in addition to the [GROUP], please write the group name(s) in the provided space below. If not, just skip the question.

Q13 Now we have a few questions about other members of the [GROUP].

Please indicate: - If you know this person

When you arrive at your own name, please select "This is me".

Do you How often Where would you place this person on this How often know do you scale? do you talk him or personally with this her? talk politics person? (If you with him or don't her? know them, please mark 'No' and proceed on to the next individu al) No No T Ind Ind t t D h epe epe Str ver ver Str o i nde nde Y N N O on y y on n' N O s So nt - Ind nt - So e o e ft g str str g t e ft i me Clo epe Clo me s v e De on on Re k v e s tim ser nde ser tim ( ( er n mo g g pu n er n m es to nt to es 1 2 (1 (3 cra De Re blic o (1 (3 e (2) De (4) Rep (2) ) ) ) ) t mo pu an w ) ) ( mo ubli (1) cra blic (7) (8 3 crat can t an ) ) (3) (5) (2) (6) N a m m m m m m m m m m m m m m m m m m e