ࡱ> proo@ ;hbjbj p p &koo8hhh|8<T|<TpF F F 5555;6"92<$=R'@V<9h,B F ,,V<h<&2&2&2,>8h5&2,5&2&262V5@Ph5H P &/>Y5 5<0<e50@d1@5||@h5,F #&2&(TF F F V<V<|| 2||Advanced Statistics and Research Methods for Psychology II Psychology 612 Spring 2005PRIVATE  Class: Thursday 4:30-7:10 Science & Tech 131 Instructors: June Tangney Jeff Stuewig Deb Mashek 2007 David King 2007 David King 2007 David King Th 2:30-4, F 2-3 or by appt F 2-3:30 993-1365 jtangney@gmu.edu  HYPERLINK "mailto:jstuewig@gmu.edu" jstuewig@gmu.edu dmashek@gmu.edu Labs: M 10:30am-12:20pm F 10:30am-12:20pm W 6:00-7:50 (DK 1005) M 12:30pm-2:20pm F 12:30pm-2:20pm W 8:00-9:50 Teaching Assistants: Beth Kikta, Mike Ford Beau Abar Robinson B 209 #3 Robinson B 209 #3 David King 1030 M 2:30-3:30 or by appt M 12-1 or by appt W 12-4 or by appt 571-334-8907 993-4097  HYPERLINK mailto:kincalca@gmu.edu bkikta@gmu.edu  HYPERLINK mailto:smarsh@gmu.edu mford3@gmu.edu  HYPERLINK mailto:ssrokows@gmu.edu babar@gmu.edu Description of Course: This course is the second part of a twocourse sequence concerning the fundamentals of applied social science research. It is designed to help you develop skills that will enable you to effectively evaluate the research of others and to design, conduct, and report on research of your own. You will be exposed to the logic underlying the research process as well as a broad range of design and assessment methods. Throughout the course there will be an emphasis on both conceptual understanding and the development of practical "howto" skills. Traditionally, psychology as a discipline has made use of an unusually broad range of research methods and analytical strategies to address questions of interest. Because each approach to answering research questions involves tradeoffs, researchers have often found it necessary to employ a combination of methods to reach any firm conclusions. A major goal of this course is to facilitate decision making within these constraints. If your goal is to do quality work, whether in a research or applied domain, then you will need tools to help you make sense of your data or of the effectiveness of your chosen approach to a problem. You will become familiar with methods ranging from classical experimental paradigms, to quasiexperimental methods, to field/correlational approaches. After developing the conceptual foundation for conducting research, we will develop a basic understanding of research methods and data interpretation. From there, we will move to a variety of more advanced statistical tools, examining the pros, cons, and assumptions associated with each. We have structured this course in an integrated fashion to provide a clear bridge between theoretical, statistical, and methodological issues and the conclusions that can be drawn from research endeavors. Throughout the year, you will gain handson experience through a number of different projects, learning how to draw statistical and substantive conclusions from the results of various analyses. You will often be asked to prepare a written summary of results using APA style. As part of the course, doctoral students are required (and masters students are invited) to identify a substantive area of interest, conduct a review of the relevant theoretical and empirical literature, formulate a specific research question, and develop a detailed research plan, culminating in a written research proposal. Course Requirements: The course requirements for this second semester include: (1) participation in laboratory sessions; (2) a series of computer assignments and brief writeups of the results in APA format; (3) three midterm exams, of which one can be dropped; (4) one final exam; and for students participating in the research proposal portion of the course (see syllabus for Psych 611) (5) a research proposal. Grades for all doctoral students as well as any masters students participating in the proposal will be determined as follows: 60% There will be three midterms, of which the two highest will count, each 20%. The final also counts 20% and is not optional. Due to the nature of the material, each midterm exam is cumulative, although it will focus primarily on the material covered since the last exam. The final exam will evaluate the mastery of materials covered throughout the course. 20% Laboratory participation, including evaluation of the assigned projects. 20% Research proposal Grades for all other students will be determined as follows: 75% There will be three midterms, of which the two highest will count, each 25%. The final also counts 25% and is not optional. Due to the nature of the material, each exam is cumulative, although it will focus primarily on the material covered since the last exam. 25% Laboratory participation, including evaluation of the assigned projects. There will be no make-ups for any midterm exams. If extenuating circumstances prevent you from taking a midterm during your scheduled lab time, then this is the exam you drop. Any subsequent missed exams result in a grade of zero. Projects are to be turned in on time. If projects are turned in late, but within a week of the due date, they will count for half the points possible. If projects are turned in more than a week late, they will not be worth any points. You will find the required reading list attached to this syllabus. Readings other than those associated with the required texts from last semester will be made available in the second week of class for individual copying. They will also be on reserve at the Johnson Center library. Please note that the readings listed in the course outline are to be read before the next class. Honor Code: All students in this course are to become familiar with and follow the Universitys honor code, which does not tolerate any form of cheating and attempted cheating, plagiarism, lying, and stealing. For more information on the Honor Code please visit:  HYPERLINK "http://www.gmu.edu/catalog/apolicies/#Anchor13" http://www.gmu.edu/catalog/apolicies/#Anchor13 Student Disabilities: Any student concerned about a disability and needing special arrangements please contact June Tangney. Required Texts: Howell, D. (2001). Statistical methods for psychology (5th Ed.). Belmont, CA: Duxbury Press. Kerlinger, F. N. & Lee, H. B. (2000). Foundations of Behavioral Research (4th Ed.). New York, NY: Holt, Rinehart & Winston. Optional Texts: American Psychological Association. (2001). Publication manual (5th edition). Washington, D. C.: American Psychological Association. (APA) Cohen, J., Cohen, P., West, S., G. & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral science (Third edition). Hillsdale NJ: Erlbaum. Cook, T. D., & Campbell, D. T. (1979). Quasiexperimentation: Design and analysis issues for field settings. Boston, MA: Houghton Mifflin. Overview of Topics: 1/2. Jan 27/Feb 3 Review of Key Concepts from 611 Good variance, bad variance, and partitioned variance Anatomy of the test statistic -- Probability and the normal curve z test for a sample mean - Sampling distributions and the standard error of the mean - t distribution and single sample t test - Type I and Type II error Power -- Variables and Constructs - Reliability and Validity Power Revisited Readings: Review 611 Notes and Readings Labs 1 and 2: Project 1: Linking Course Material to Personal Interests and Goals Q&A 3. Feb 10 Multiple Regression Review of Basic Regression Model Multiple Predictors Semi-partial regression weights Forward, Backward, Forced Entry, and the Perils of Stepwise Hierarchical regression and model testing Change in R-square Multicollinearity Thoughtful Regression Readings: Kerlinger & Lee Ch.32 (pp.755-785) Cohen et al. pp.1-10, 64-90, 151-175 Lab 3&4: Project 2: Multiple Regression 4. Feb 17 - Significance Testing of Correlations and Regression Shrinkage and Adjusted R - Hypothesis testing of r - Testing two independent rs Testing dependent rs - Confidence intervals on r - Dilemma of Outliers  Testing R2 - Testing b weights - Testing two independent bs - Diagnostics - Suppressor variables - Curvilinear Regression - Artificial dichotomization Readings: Tabachnick & Fidell  Ch. 4 (pp.56-110) Cohen, J. (1983). The cost of dichotomization. Applied Psychological Measurement, 7, 249-253. Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155-159. Lab 5: Exam 1 5/6. Feb 24/Mar 3- Regression: Mediation, Moderation, and Suppression Effects Mediation - Moderation - Testing mediation and moderation with multiple regression Suppression -- Interpreting interactions in multiple regression - Assumptions of regression - Introduction to path analysis Readings: 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. Paulhus, D. L., et al. (2004). Two replicable suppressor situations in personality research. Multivariate Behavioral Research, 39, 301-326. MacKinnon, D. P. (2000). Technical assistance report: Mediation analysis.  HYPERLINK "http://www.public.asu.edu/~davidpm/ripl/Mediation_Analysis.PDF" http://www.public.asu.edu/~davidpm/ripl/Mediation_Analysis.PDF Frazier, P. A., et al. (2004). Testing moderator and mediator effects in counseling psychology research. Journal of Counseling Psychology, 51, 115-134. Lab 6: Project 3: Mediated and Moderated Regression 7. Mar 10 Longitudinal and Repeated Measures Designs Evaluating Change Review of within subjects designs (pretest - postest design) - Comparison of between-groups and repeated measures designs. Using subjects as their own control - t test of repeated measures - Repeated measures ANOVA and source table Longitudinal approaches more generally -- Change scores Assumptions, violations, and analysis Readings: Kerlinger & Lee Ch.15 (pp.387-412) Rogosa, D. (1995). Myths and methods: Myths about longitudinal research plus supplemental questions. In A. Gottman, J. M. (Ed). The Analysis of Change (pp.3-66). Mahwah, NJ: Lawrence Erlbaum Associates. Lab 7: Project 4: Analysis of Change Mar 17 Spring Break (Yea!) UPDATED LIT REVIEWS AND RESEARCH QUESTIONS DUE MAR 21 TO TA & ADVISOR 8. Mar 24 Its Magic: Regression and ANOVA are the pretty much the same thing! The GLM Coding of categorical IVs - comparing ANOVA with regression Ancova and GLM Holding variables constant - Assumptions, violations, and analysis Readings: Kerlinger & Lee Ch.33 (pp.787-799) Cook & Campbell Ch. 4 (147-206) Lab 8: Project 5: ANOVA = GLM 9/10. Mar 31/Apr 7 Factor Analytic Techniques and Structural Equation Modeling Exploratory factor analysis - Principal components - Principal factors - Rotations - Factor selection - Confirmatory factor analysis - SEM - Goodness of fit indices - Testing alternative models Readings: Russell, D. W. (2002). In search of underlying dimensions: The use (and abuse) of factor analysis in Personality and Social Psychology Bulletin. Personality and Social Psychology Bulletin, 28, 1626-1646. McArdle, J. J. (1996). Current directions in structural factor analysis. American Psychological Society, 5, 11-18. Hox, J. J. & Bechger, T. M. (1998). An introduction to structural equation modeling. Family Science Review, 11, 354-373. Lab 9: Exam 2 Lab 10: Project 6: Exploratory Factor Analysis 11. Apr 14 Other Multivariate Techniques MANOVA - Type I and Type II error - MANCOVA - Discriminant analysis Canonical Correlations - Multidimensional Scaling Cluster Analysis Readings: Kerlinger & Lee Ch. 33 (pp.799-803) Cole, D. A., et al. (1993). Multivariate group comparisons of variable systems: MANOVA and structural equation modeling. Psychological Bulletin, 114, 174-184. Haase, R. F. & Ellis, M. V. (1987). Multivariate analysis of variance. Journal of Counseling Psychology, 34, 404-413. Lab 11: Project 1: Linking Course Material to Personal Interests and Goals - Revisited 12. Apr 21 - Levels of Analysis Repeated measures revisited - Nested effects - Levels of analysis - Aggregation issues - Analytical Approaches Readings: Klein, K. J., Dansereau, F., & Hall, R. J. (1994). Levels issues in theory development, data collection and analysis. Academy of Management Review, 19, 195-229. Thorndike, E. L. (1939). On the fallacy of imputing the correlations found for groups to the individuals of smaller groups composing them. American Journal of Psychology, 52, 122-124. Robinson, W. S. (1950). Ecological correlations and the behavior of individuals. American Sociological Review, 15, 351-357. Lab 12: Exam 3 13. Apr 28 - Qualitative Analysis Coding systems - Research using secondary data - Analysis using nominal data - Nonparametric tests - Chi-square and the phi-coefficient - Odds-ratios - Logistic regression Readings: Howell Ch.6 (pp. 141-176) Howell Ch.15 (pp.583-593) Lab 13: Presentations 14. May 5 Effective Lit. Reviews, Meta-Analysis, and the Publication Process Problems with Null Hypothesis Significance Testing approaches Circumventing Common Problems in Narrative Review Meta-analysis Estimating effect size Identifying moderators Publish and Perish? Q&A with Journal Editors and Reviewers Readings: Arvey, R. D., & Campion, J. E. (1998). Being there: Writing the highly cited article. Personnel Psychology, 51, 845-848. Draft, R. L. (1995). Why I recommend that your paper manuscript be rejected and what you can do about it. In L. L. Cummings & P. J. Frost (Eds.), Publishing in the Organizational Sciences (pp. 164-182). Thousand Oaks, CA: Sage. Fine, M.A., & Kurdek, L.A. (1993). Reflections on Determining Authorship Credit and Authorship Order on Faculty-Student Collaborations. American Psychologist, 48(11), 1141-1147. Schmidt, F. L. (1992). What do data really mean? Research findigs, meta-analysis, and cumulative knowledge in psychology. American Psychologist, 47, 1173-1181. Wilkinson, L., & the Task Force on Statistical Inference (1999). Statistical Methods in Psychology Journals. 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