SOC 731
Advanced Design and Data Analysis
University of Nevada, Reno
Fall 2011
***** Update 11/30/2011 *****
Instructor:
Office:
Phone:
Email:
Markus Kemmelmeier, Ph.D.
304 Mack Social Sciences
784-1287
markusk@unr.edu 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).
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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
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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.
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Course schedule & Reading List
August 30
(Session 1)
FH 219
Introduction to the course: Intro to SPSS, basic principles of data
management & data analysis
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
(Session 2)
MIKC 414
Data screening, outliers, & transformations; nonparametric approaches
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
Homework Assignment #2 (for September 13 – due Monday September
12)
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
Homework Assignment #3 (for September 20 – due Monday September
19)
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
Homework Assignment #4 (for September 27 – due Monday September
26)
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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
October 4
(Session 6)
MIKC 414
Minipaper #1 paper is due (ANOVA and regression)
Sequential (“Hierarchical”) linear regression & Mediation analysis
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
(Session 7)
FH 219
October 17
October 18
(Session 8)
MIKC 414
October 24
October 25
(Session 9)
FH 219
More on mediation; reprise interaction effects
http://davidakenny.net/cm/mediate.htm#BK
TBA
Minipaper #2 paper is due
(Regression with interaction terms based on at least two continuous
variables)
MANOVA/Repeated measures designs
Mertler, C. A., & Vannatta, R. A. (2005/2010). Advanced and multivariate
statistical methods: Practical application and interpretation. Los
Angeles, CA: Pyrczak Publishing. Chapter 6
Minipaper #3 is due
(Mediation analysis)
When observations are not independent/Introduction to multilevel designs
Heck, Thomas & Tabata, Chapter 1 & 2
Homework Assignment #6 (for November 1 – due Monday October 31)
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November 1
(Session 10)
MIKC 414
Introduction to multilevel designs
Heck, Thomas & Tabata, Chapter 3
Homework Assignment #7 (for November 8 – due Monday November 7)
November 8
(Session 11)
MIKC 414
Multilevel designs
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
(Session 15)
MIKC 414
Propensity score matching – an approach to observational data
Rudner, L. M., & Peyton, J. (2006). Consider Propensity scores to compare
treatments. GMAC – Graduate Management Admission Council.
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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
(Session 16)
MIKC 414
Propensity score matching – an approach to observational data
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.
Nicotine & 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.edu/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-andmplus-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.
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General Linear Model (GLM)
Department of Mathematics, Central Michigan. University. General Linear Model.
http://www.cst.cmich.edu/users/lee1c/spss/StaProcGLM.htm (watch
presentations).
UCLA Academic Technology Services. MANOVA and GLM.
http://www.ats.ucla.edu/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
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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-sasand-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 Measures
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
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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
resampling. 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)
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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
ability?: 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
juror 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?:
Consequences 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.
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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
variation 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, 455462. (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 parentadolescent intervention to increase sexual risk communication: Results of a randomized
controlled trial. Aids Education and Prevention, 20, 371-383.
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