Advanced Design and Data Analysis

SOC 731 Advanced Design and Data Analysis University of Nevada, Reno Fall 2011

***** Update 11/30/2011 *****

Instructor: Markus Kemmelmeier, Ph.D. Office: 304 Mack Social Sciences Phone: 784-1287 Email: [email protected] or WebCampus email (best way to contact me!)

Times: Tuesday 1–3:45 Location: Frandsen Humanities 219, but see calendar, announcement re computer labs! Office hours: By appointment

Course description This course has four goals: Goal 1: This course provides practical data-analytic training. Whereas most advanced graduate students in the social and behavioral sciences have had their fair share of statistical theory, this course emphasizes both theory and application. It helps you deal with a range of challenges in everyday data analysis, including data management, selection of an analysis strategy, selection of specific statistical procedures and the ‘how to’ of carrying out the analyses needed. Goal 2: This course provides training in some advanced data-analytical techniques that are important in current research in the social and behavioral science, but which are not taught elsewhere on campus. This is true in particular for multi-level modeling. Goal 3: This course relies heavily on the statistics package SPSS, one of the most widely used data analysis programs in the social and behavioral sciences. While this course expects a rudimentary knowledge of SPSS, such knowledge is easily acquired. As we use this program, you will gain greater skill with it and learn some of the more advanced techniques. Should you be already committed to one of the other statistics packages (e.g., STATA, SAS), you may wish to continue using that, though you will need to see me to have the various syntax examples (in SPSS) translated into your respective programming language. Goal 4: This course will also focus on the fact that statistical analyses do not necessarily provide self-evident answers to questions that interest a researcher. Rather, data and statistical analyses serve as arguments that help us build a case for or against a particular conclusion. Therefore, an important aspect of this class will be the interpretation of statistical results as well as communicating our findings (and their implications) to others.

Prerequisites This course is designed for advanced graduate students. It is expected that students in this course have had at least one graduate course in regression analysis (e.g., STAT 757).

1 Literature This course relies on the following textbooks. Both are not available for purchase from the ASUN bookstore, but can be purchased at a more attractive price online, either directly from the publisher or from a bookseller like ecampus.com, amazon.com, bn.com, borders.com etc.: Mertler, C. A., & Vannatta, R. A. (2005/2010). Advanced and multivariate statistical methods: Practical application and interpretation (3rd / 4th ed.). Los Angeles, CA: Pyrczak Publishing. [textbook] Heck, R. H., Thomas, S. L., & Tabata, L. N. (2007). Multilevel and longitudinal modeling with IBM SPSS. New York: Routledge. All other readings are available either via the Knowledge Center’s eReserve system, or via WebCampus. Readings for the first few weeks are also available on WebCampus.

Website/WebCampus This course uses WebCampus, an online system that allows you to access additional course materials. To get access to WebCampus, go to http://webcampus.unr.edu and log in using your net id and password. There is also info on how to use the system, but it is pretty self-explanatory. Check WebCampus regularly as announcements, instructions for assignments, practice questions etc. will be posted there. (If there is any change, I will contact you via WebCampus email).

Data Feel free to complete all assignments using your own data. In my experience, this ensures that you get the most out of this class. If you do not have any data, or data that do not lend themselves for a particular purpose, I will be happy to provide you with appropriate data. It is also recommended that you find data relating to your particular research interests in publicly available data archives, including the Inter-university Consortium of Political and Social Research, www.icpsr.umich.edu.

Requirements Mini papers. You are being asked to generate five short papers that describe data analyses using a particular technique. Each paper should begin with a description of the question that you hope to answer. The inclusion of an extensive literature review is optional as long as you communicate the point of your analysis in one page or so. The remainder of your paper should roughly resemble the methods and results section of a journal article in the sense that they describe the nature of the data you work with and the analyses themselves. Compared to a journal article, however, you should include more statistical detail, including checks of the assumptions of the analytical techniques as well as justifications for the various steps you chose in carrying out your analyses. You should also provide tables summarizing your findings (figures are optional). Homework assignments. In order to enhance learning transfer from theory to practice, there will be some weekly assignments involving practical exercises with SPSS. These assignments should help you apply what we read for and reviewed in class. By sending in your analyses to me prior to the next class, I have a chance to evaluate your analyses and provide you with relevant feedback. Send in (a) your data; (b) your syntax (SPSS or whatever program you are using). No need to send in SPSS output as I will simply re-run your analyses. (The issue with output is also that, depending on the program version, the SPSS output for SPSS Mac is not

2 necessarily compatible with the SPSS output for SPSS PC, and I am a Mac user.) Add comments into your syntax where necessary to facilitate my understanding of your analyses. Where request, also include a verbal description of what you find. Please send in your materials by Monday 12 PM; earlier submissions are encouraged because of the number of students and the relatively short turnaround time. Participation. Your active participation is required both in class and outside of it: it makes your and my experience (and the course overall) better. Besides: the more you invest, the more you get out of this class. This is particularly true with regard to the various assignments (practice, practice, practice!).

Grading Each assignment will be graded using a letter grade. Using UNR’s GPA equivalents (A = 4.0, A- = 3.7, B+ = 3.3, B = 3.0 etc.) final grades will be computed based on the following weights: In-class participation 10% 10 homework sets (3% each) 30% 5 mini papers (12% each) 60% Total 100%

Assistance from me If you require any particular arrangements (e.g., because of unavoidable absences during the semester), please inform me immediately. It is your responsibility to seek assistance when you are having difficulty understanding the course material. I will gladly help out as long there is time to actually get together with you, e.g. not necessarily on the day that an assignment is due.

Peer assistance An important part of your graduate education is that you hone your communication skills, especially as they concern writing. And, as with any kind of learning process, feedback is crucially important if you want to get better. In order to enhance your writing skills, you are explicitly encouraged to use your peers as a resource—if they are willing. In other words, I encourage you to exchange (mini)papers and comment on each other’s work in an effort to make your papers better. Two things to keep in mind, though: (1) Any kind of peer review (whether here in class or at the level of professional journals) is ultimately a mechanism on reciprocity, where you are expected to contribute your time and energy at least at the same level at which you benefit from the time and energy of others; (2) Even if you happen to work on the same data sets and even if you enlist the editorial help of some of your peers, I expect you to write your own papers.

3 Course schedule & Reading List

August 30 Introduction to the course: Intro to SPSS, basic principles of data (Session 1) management & data analysis FH 219 Judd, C. M., McClelland, G. H., & Culhane, S. E. (1995). Data analysis: Continuing issues in the everyday analysis of psychological data. Annual Review of Psychology, 46, 433-465.

Homework Assignment #1 (for September 6 – due Monday September 5)

September 6 Data screening, outliers, & transformations; nonparametric approaches (Session 2) MIKC 414 McClelland, G. H. (2000). Nasty data: Unruly, ill-mannered observations can ruin your analysis. In H. T. Reis & C. M. Judd (Eds.), Handbook of research methods in social and personality psychology (pp. 393-411). New York: Cambridge University Press. Mertler, C. A., & Vannatta, R. A. (2005/2010). Advanced and multivariate statistical methods: Practical application and interpretation. Los Angeles, CA: Pyrczak Publishing. Chapter 3

September 13 ANOVA: Univariate and two-way designs: Diagnosing interactions; post-hoc (Session 3) comparisons and planned comparisons; communicating interactions FH 219 Mertler, C. A., & Vannatta, R. A. (2005/2010). Advanced and multivariate statistical methods: Practical application and interpretation. Los Angeles, CA: Pyrczak Publishing. Chapter 4

September 20 Regression: Correlation and regression, diagnostics, and interactions, simple (Session 4) effects FH 219 Mertler, C. A., & Vannatta, R. A. (2005). Advanced and multivariate statistical methods: Practical application and interpretation. Los Angeles, CA: Pyrczak Publishing. Chapter 7

4 September 27 More regression: Interaction effects simple effects analysis; graphing (Session 5) regression interactions MIKC 414 Cohen, Cohen, Aiken & West (2003): Chapter 7 Aiken & West (1991): Chapter 7 Kristopher Preacher's primer: http://quantpsy.org/interact/interactions.htm Interactive Calculation tools: http://quantpsy.org/interact/index.html

October 3 Minipaper #1 paper is due (ANOVA and regression)

October 4 Sequential (“Hierarchical”) linear regression & Mediation analysis (Session 6) MIKC 414 Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173-1182. http://davidakenny.net/cm/mediate.htm#BK

Homework Assignment #5 (for October 11 – due Monday October 10)

October 11 More on mediation; reprise interaction effects (Session 7) FH 219 http://davidakenny.net/cm/mediate.htm#BK TBA

October 17 Minipaper #2 paper is due (Regression with interaction terms based on at least two continuous variables)

October 18 MANOVA/Repeated measures designs (Session 8) MIKC 414 Mertler, C. A., & Vannatta, R. A. (2005/2010). Advanced and multivariate statistical methods: Practical application and interpretation. Los Angeles, CA: Pyrczak Publishing. Chapter 6

October 24 Minipaper #3 is due (Mediation analysis)

October 25 When observations are not independent/Introduction to multilevel designs (Session 9) FH 219 Heck, Thomas & Tabata, Chapter 1 & 2

Homework Assignment #6 (for November 1 – due Monday October 31)

5 November 1 Introduction to multilevel designs (Session 10) MIKC 414 Heck, Thomas & Tabata, Chapter 3

Homework Assignment #7 (for November 8 – due Monday November 7)

November 8 Multilevel designs (Session 11) MIKC 414 Heck, Thomas & Tabata, Chapter 4 Norušis, M. J. (2007). SPSS 15.0: Advanced statistical procedures. Upper Saddle River, NJ: Prentice Hall. (Chapter 10)

Homework Assignment #8 (for November 15 – due Monday November 14)

November 15 Multilevel designs (Session 12) FH 219 Heck, Thomas & Tabata, Chapter 5 Peugh, J. L., & Enders, C. K. (2005). Using the SPSS Mixed Procedure to Fit Cross-Sectional and Longitudinal Multilevel Models. Educational and Psychological Measurement, 65, 717-741.

November 21 Minipaper #4 is due (Multilevel analysis)

November 22 Mixed models: Random-coefficient models & growth-curve modeling (Session 13) MIKC 414 Heck, Thomas & Tabata, Chapter 6 Peugh, J. L., & Enders, C. K. (2005). Using the SPSS Mixed Procedure to Fit Cross-Sectional and Longitudinal Multilevel Models. Educational and Psychological Measurement, 65, 717-741.

Homework: Happy Thanksgiving!

November 29 Logistic regression: Main effects models & interactions (Session 14) MIKC 121 Mertler, C. A., & Vannatta, R. A. (2005). Advanced and multivariate statistical methods: Practical application and interpretation. Los Angeles, CA: Pyrczak Publishing. Chapter 11

Homework Assignment #9 (for December 6 – due Monday December 5)

December 6 Propensity score matching – an approach to observational data (Session 15) MIKC 414 Rudner, L. M., & Peyton, J. (2006). Consider Propensity scores to compare treatments. GMAC – Graduate Management Admission Council.

6 Austin, P. C. (2011). An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies. Multivariate Behavioral Research, 46, 399-424. Timberlake, D. S., Huh, J., & Lakon, C. M. (2009). Use of propensity score matching in evaluating smokeless tobacco as a gateway to smoking. Nicotine & Tobacco Research, 11, 455-462.

Homework Assignment #10 (for December 13 – due Monday December 12)

December 13 Propensity score matching – an approach to observational data (Session 16) MIKC 414 Rudner, L. M., & Peyton, J. (2006). Consider Propensity scores to compare treatments. GMAC – Graduate Management Admission Council. Austin, P. C. (2011). An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies. Multivariate Behavioral Research, 46, 399-424. Timberlake, D. S., Huh, J., & Lakon, C. M. (2009). Use of propensity score matching in evaluating smokeless tobacco as a gateway to smoking. Nico- tine & Tobacco Research, 11, 455-462.

December 14 Minipaper #5 is due (Repeated-measures mixed model, propensity score analysis)

References Overview/General Abelson, R. (1995). Statistics as Principled Argument. Erlbaum: Hillsdale, NJ. Dallal, G. E. The Little Handbook of Statistical Practice http://www.tufts.edu/~gdallal/LHSP.HTM Judd, C. M., McClelland, G. H., & Culhane, S. E. (1995). Data analysis: Continuing issues in the everyday analysis of psychological data. Annual Review of Psychology, 46, 433-465. UCLA Academic Technology Services. Statistical Computing http://www.ats.ucla.e- du/stat/overview.htm (links to tutorials, seminars, and syntax examples for SPSS, SAS and STATA). Data imputation Myers, T. A. (in press). Goodbye listwise deletion: Presenting hotdeck imputation as an easy and effective tool for handling missing data. Access via http://www.afhayes.com/spss-sas-and- mplus-macros-and-code.html#modmed (8/28/2011 bottom of page) Shafer, J. L. & Olson, M. K. (1998). Multiple imputation for multivariate missing-data problems: A data analysts perspective. Multivariate Behavioral Research, 33, 545-571.

7 General Linear Model (GLM)

Department of Mathematics, Central Michigan. University. General Linear Model. http://www.cst.cmich.edu/users/lee1c/spss/StaProcGLM.htm (watch presenta- tions). UCLA Academic Technology Services. MANOVA and GLM. http://www.ats.ucla.e- du/stat/spss/library/sp_glm.htm Dichotomizing continuous variables Fitzsimons, G. A. (2008). Death to dichotomizing. Journal of Consumer Research, 35, 5-8. Irwin, J. R., & McClelland, G. H. (2001). Misleading Heuristics and Moderated Multiple Regression Models. Journal of Marketing Research, 38, 100–109. Irwin, J. R., & McClelland, G. H. (2003). Negative Consequences of Dichotomizing Continuous Predictor Variables. Journal of Marketing Research, 40, 366–371. Maxwell, S. E., & Delaney, H. D. (1993). Bivariate Median Splits and Spurious Statistical Significance. Psychological Bulletin, 113, 181–90. Multiple regression: General Burk, T. E., website Regression Refresher http://mallit.fr.umn.edu/fr5218/reg_refresh/index.html Cohen, J., Cohen, P., West, S. G., Aiken, L. S. (2003). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences (3rd ed.). Mahwah, NJ: Erlbaum. Osborne, J. W., & Waters, E. (2002). Four assumptions of multiple regression that researchers should always test. Practical Assessment, Research & Evaluation, 8(2). http://pareonline.net/getvn.asp?v=8&n=2 Pedhazur, E. J. (1997). Multiple Regression in Behavioral Research (3rd Ed.). Harcourt Brace: San Diego, CA. Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics. New York: Harper Collins. 5th Edition. Multiple regression: Interactions Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Thousand Oaks: Sage. Bauer, D. J., & Curran, P. J. (2005). Probing interactions in fixed and multilevel regression: Inferential and graphical techniques. Multivariate Behavioral Research, 40, 373-400. Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences, 3rd ed. Hillsdale: Erlbaum. Kenny, D. A.'s moderation webpage http://davidakenny.net/cm/moderation.htm Preacher, K. J.'s moderation and mediation webpage http://quantpsy.org/medn.htm Preacher, K. J., Curran, P. J., & Bauer, D. J. (2006). Computational tools for prbing interaction effects in multiple linear regression, multilevel modeling, and latent curve analysis. Journal of Educational and Behavioral Statistics, 31, 437-448. http://quantpsy.org/pubs/preacher_curran_bauer_2006.pdf

8 West, S. G., Aiken, L. S., & Krull, J. L. (1996). Experimental personality designs: Analyzing categorical by continuous variable interactions. Journal of Personality, 64, 1-48. Mediation Hayes, A. J.'s “My Macros and Code for SPSS and SAS” http://www.afhayes.com/spss-sas- and-mplus-macros-and-code.html#modmed Kenny, D. A.'s mediation webpage http://davidakenny.net/cm/mediate.htm#BK MacKinnon, D. P.'s RIPL website http://www.public.asu.edu/~davidpm/ripl/mediate.htm Preacher, K. J.'s moderation and mediation webpage http://quantpsy.org/medn.htm Repeated measures analysis UCLA Academic Technology Services. Statistical Computing Seminars: Repeated Mea- sures Analysis with SPSS. http://www.ats.ucla.edu/stat/spss/seminars/Repeated_Measures/default.htm Beaumont, R. Modern repeated measures analysis using mixed models in SPSS (1) http://www.youtube.com/watch?v=dIv5imb4yqI Multilevel modeling/Linear mixed modeling Atkins, D. C. (2005). Using Multilevel Models to Analyze Couple and Family Treatment Data: Basic and Advanced Issues. Journal of Family Psychology, 19, 98-110. Bickel, R. (2007). Multilevel analysis for applied research: It's just regression! New York: Guilford. Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks: Sage. Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: modeling change and event occurrence. New York: Oxford University Press. UCLA Academic Technology Services. SPSS topics: Multilevel modeling. http://www.ats.ucla.edu/stat/spss/topics/MLM.htm UCLA Academic Technology Services. Standalone tutorial: Linear mixed models. http://www.ats.ucla.edu/stat/spss/library/spssmixed/mixed.htm West, B. T., Welch, K. B., & Galecki, A. T. Linear Mixed Models: A Practical Guide Using Statistical Software. http://www-personal.umich.edu/~bwest/almmussp.html Yaffee, R. A.'s ANOVAs and MIXED models with SPSS. www.nyu.edu/its/socsci/Docs/SPSSMixed.ppt Logistic regression Lea., S.'s website on logistic regression and discriminant analysis http://people.exeter.ac.uk/SEGLea/multvar2/disclogi.html Propensity score matching Dehejia, R. H., & Wahba, S. (2002). Propensity score-matching methods for nonexperimental causal studies. The Review of Economics and Statistics, 84, 151–161. www.nber.org/~rdehejia/papers/matching.pdf

9 Guo, S., Barth, R., & Gibbons, C. (2004). Introduction to Propensity Score Matching: A new device for program evaluation. ssw.unc.edu/VRC/Lectures/PSM_SSWR_2004.pdf Heinrich, C., Maffioli, A., & Gonzalo, V. (2010). A Primer for Applying Propensity-Score Matching. Office of Strategic Planning and Development Effectiveness, Inter-American Development Bank. Impact-Evaluation Guidelines Technical Notes No. IDB-TN-161. www.iadb.org/document.cfm?id=35320229 Lecture Hall @ School of Social Work, UNC http://ssw.unc.edu/VRC/Lectures/index.htm (access to SPSS macro) Rosenbaum, P. R., & Rubin, D. R. (1983). The Central Role of the Propensity Score in Observational Studies for Causal Effects. Biometrika, 70, 41–55. Austin, P. C. (2011). An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies. Multivariate Behavioral Research, 46, 399-424. SPSS Macro on Raynald Levesque's SPSS website: http://www.spsstools.net/Syntax/RandomSampling/MatchCasesOnBasisOfPropensityScores.txt Elizabeth Stuart's Propensity Score Software Page: http://www.biostat.jhsph.edu/~estuart/propensityscoresoftware.html Generalized Linear Models (GzML) Neal, D. J., & Simons, J. S. (2007). Inference in regression models of heavily skewed alcohol use data: A comparison of ordinary least squares, generalized linear models, and bootstrap resam- pling. Psychology of Addictive Behaviors, 21, 441-452. Generalized Estimation Equations Hanley, J. A., Negassa, A., Edwardes, M. D. deB., & Forrester, J. E. (2003). Statistical analysis of correlated data using generalized estimating equations: An orientation. American Journal of Epidemiology, 157, 364-375. SPSS Algina, J.'s website includes links to explanatory documents re SPSS basics http://plaza.ufl.edu/algina/ Levesque, R.'s SPSS Tools website www.spsstools.net SPSS. Linear Mixed-Effects Modeling in SPSS: An Introduction to the MIXED Procedure. Technical report. (Google) UCLA Academic Technology Services. Resources to help you learn and use SPSS. http://www.ats.ucla.edu/stat/spss/default.htm Controversies/problems with statistical analyses in published research (original & critique) Roese, N. J., & Olson, J. M. (1994). Attitude importance as a function of repeated attitude expression. Journal of Experimental Social Psychology, 30, 39-51. (original) Bizer, G., & Krosnick, J. A. (2000). Exploring the structure of strength-related attitude features: The relation between attitude importance and attitude accessibility. Journal of Personality and Social Psychology, 81, 566-586. (footnote re conversation with original authors) Igou, E. R., & Bless, H. (2005). The conversational basis for the dilution effect. Journal of Language and Social Psychology, 24, 25-35. (original)

10 Kemmelmeier, M. (2006). Does the dilution effect have a conversational basis? Journal of Language and Social Psychology. (critique) Igou, E. R. (2007). Additional thoughts on conversational and motivational sources of the dilution effect. Journal of Language and Social Psychology, 26, 61-68. Kemmelmeier, M. (2007). Is diagnostic evidence on the dilution effect weakened when nondiagnostic objections are added? A response to Igou (2007). Journal of Language and Social Psychology, 26, 69-74. Steinberg, L., & Monahan, K. C. (2011). Adolescents’ exposure to sexy media does not hasten the initiation of sexual intercourse. Developmental Psychology, 2, 562-576. Collins, R. L., Martino, S. C., & Elliot, M. N. (2011). Propensity scoring and the relationship between sexual media and adolescent sexual behavior: Comment on Steinberg & Monahan 2011 paper. Developmental Psychology, 2, 577-579. Collins, R. L., Martino, S. C., Elliot, M. N., & Miu, A. (2011). Relationship between adolescent sexual outcomes and exposure to sex in media: Robustness to propensity-based analyses. Developmental Psychology, 2, 580-581. Steinberg, L., & Monahan, K. C. (2011).Premature dissemination of advice undermines our credibility as scientists: Response to Brown and to Collins et al. Developmental Psychology, 2, 582-584. Brown, J. (2011). Brown comment on Steinberg, L., & Monahan, K. (2011). Adolescents’ exposure to sexy media does not hasten the initiation of sexual intercourse. Developmental Psychology, 2, 585-591.

Reporting Examples Hierarchical/Sequential regression Kemmelmeier, M. (2008). Is there a relationship between political orientation and cognitive abil- ity?: A test of three hypotheses in two studies. Personality and Individual Differences, 45, 767- 772. Regression with a two-way interaction involving a categorical and a continuous variables Kemmelmeier, M. (2010). Gender moderates the impact of need for structure on social beliefs: Implications for ethnocentrism and authoritarianism. International Journal of Psychology, 45, 202–211. Regression with a two-way interaction involving two continuous variables Kemmelmeier, M. (2005). The effects of race and social dominance orientation in simulated ju- ror decision making. Journal of Applied Social Psychology, 35, 1030-1045. Analysis of Variance Kemmelmeier, M. (2003). Individualism and attitudes toward affirmative action: Evidence from priming studies. Basic and Applied Social Psychology, 25, 111-119. Kemmelmeier, M., & Winter, D. G. (2008). Sowing patriotism, but reaping nationalism?: Conse- quences of exposure to the American flag. Political Psychology, 29, 859-879. Uz, I., Kemmelmeier, M., & Yetkin, E. (2009). Effects of Islamist terror in Muslim students: Evidence from Turkey in the wake of the November 2003 attacks. Behavioral Sciences of Terrorism and Political Aggression, 1, 111–126.

11 Multivariate Analysis of Variance Maker, A. H., Kemmelmeier, M., & Peterson, C. (1999). Parental sociopathy as a predictor of childhood sexual abuse. Journal of Family Violence, 14, 47-59. Linear Mixed Model/Multilevel model (cross-sectional) Hayward, R. D., & Kemmelmeier, M. (2007). How competition is viewed across cultures: A test of four theories. Cross-Cultural Research, 41, 364-395. Linear Mixed Model/Multilevel model (longitudinal/repeated measures) Kemmelmeier, M., Danielson, C., & Basten, J. (2005). What's in a grade?: Academic success and political orientation. Personality and Social Psychology Bulletin, 31. 1386-1399. Kemmelmeier, M., Jambor, E., & Letner, J. (2006). Individualism and good works: Cultural vari- ation in giving and volunteering across the United States. Journal of Cross-Cultural Psychology, 37, 327-344. Miller, G. D., Iverson, K. M., Kemmelmeier, M., MacLane, C., Pistorello, J., Fruzzetti, A. E., Crenshaw, K. Y., Erikson, K. M., Katrichak, B. M., Oser, M., Pruitt, L. D., & Watkins, M. (2010). A pilot study of psychotherapist trainees’ alpha-amylase and cortisol levels during treatment of recently suicidal clients with borderline traits. Professional Psychology: Research and Practice, 41, 228–235. Logistic regression Jehle, A., Miller, M. K., & Kemmelmeier, M. (2009). The influence of accounts and remorse on mock jurors’ judgments of offenders. Law and Human Behavior, 33, 393-404. Propensity Score Matching Timberlake, D. S., Huh, J., & Lakon, C. M. (2009). Use of propensity score matching in evaluating smokeless tobacco as a gateway to smoking. Nicotine & Tobacco Research, 11, 455-462. (STATA) Crosnoe, R. (2009). Low-Income Students and the Socioeconomic Composition of Public High Schools. American Sociological Review, 74, 709-730. (STATA) Generalized Estimation Equations Penner, L. A., Dovidio, J. F., West, T. V., Gaertner, S. L., Albrecht, T. L., Dailey, R. K., & Markova, T. (2010). Aversive racism and medical interactions with Black patients: A field study. Journal of Experimental Social Psychology, 46, 436-440. Villarruel, A. M., Cherry, C. L., Cabriales, E. G., Ronis, D. L., & Zhou, Y. (2008). A parent-adolescent intervention to increase sexual risk communication: Results of a randomized controlled trial. Aids Education and Prevention, 20, 371-383.