Psych. 711, Applied Multivariate Analysis

Psych. 711, Applied Multivariate Analysis

Fall, 2008

Dr. Hyde

Office hours: Thurs., 11:00-11:45 and by appt.

410 Brogden Psychology Bldg.

262-9522

Syllabus

Texts

Grimm, L. W., & Yarnold, P. R. (Eds.) (1995). Reading and understanding multivariate statistics. Washington, DC: American Psychological Association.

Lipsey, M. W. & Wilson, D. B. (2001). Practical meta-analysis. Thousand Oaks, CA: Sage.

Stevens, J. (2002). Applied multivariate statistics for the social sciences 4th ed. Mahwah, NJ: Erlbaum.

Journal articles are available from Learn@UW.

Books are available at A Room of One’s Own Bookstore, 307 W. Johnson St.

Other Relevant Texts

Gorsuch, R. L. (1983). Factor analysis. 2d ed. Hillsdale, NJ: Erlbaum.

SPSS for Windows: Base, Advanced Statistics

Tabachnick, B. G. & Fidell, L. S. (2001). Using multivariate statistics. 4th ed., New York: Allyn & Bacon.

Classlist:

SCHEDULE

Week 1 Sept. 2 Introduction to multivariate statistical methods

Matrix algebra

Read: Grimm & Yarnold (G&Y) Ch. 1

Stevens, Chs. 1, 2

Week 2 Sept. 9 Multiple regression formulated in matrix terms

Cleaning data (see also http://www.ats.ucla.edu/stat/sas/library/nesug99/ss123.pdf

Read: G&Y, Ch. 2

Stevens, ch. 3 (read quickly)

Week 3 Sept. 16 Multivariate normal distribution, Wishart distribution; Hotelling's T2, simple MANOVA

Read: G&Y, Ch. 8 to p. 267

Hummel & Sligo (1971)

Stevens, Ch. 4

Huberty & Morris (1989), Algina & Oshima (1990) (read them in the order listed)

Week 4 Sept. 23 Complex MANOVA

Read: Stevens, Chs. 5, 6, 8

Olson (1976), Urberg et al. (1995)

Week 5 Sept. 30 Multivariate analysis of repeated measures data

Read: G&Y, Ch 8, p. 267-end

Stevens, Ch. 13

O’Brien & Kaiser (1985), Algina & Keselman (1997), Leichtman & Ceci (1995) (in that order)

Week 6 Oct. 7 Discriminant analysis, MANCOVA

Read: G&Y, Ch. 9

Stevens Chs. 7, 9

Marche & Howe (1995)

Week 7 Oct. 14 EXAM 1: Multivariate analysis of variance

Introduction to meta-analysis

Read: G&Y, Ch. 10

Week 8 Oct. 21 Statistical methods in meta-analysis; methodological issues

Read: Lipsey & Wilson Chs 1-3

Grabe et al. (2008)

Week 9 Oct. 28 Meta-analysis of correlational data

Other issues: fixed vs. random-effects models; dichotomous outcomes; factorial designs; power of moderator tests

Consultations on student meta-analysis projects

Read: Lipsey & Wilson Chs. 4-8

Week 10 Nov. 4 Introduction to factor analysis; principal components

Read: G&Y Ch 4

Week 11 Nov. 11 Issues in factor analysis: communalities, rotation, number of factors

META-ANALYSIS PROJECTS DUE Nov. 11

Read: Stevens, Ch. 11 (through p. 415)

Zwick & Velicer (1986), McKinley & Hyde (1996)

Week 12 Nov. 18 Other factor-analytic models: maximum likelihood, confirmatory analysis, 2- and 3-mode analysis, cluster analysis

Read: York & John (1992), Fabrigar et al. (1999)

Week 13 Nov. 25 Catch up and review

Week 14 Dec. 2 EXAM 2: Factor analysis

Imputation for missing data

Read: Acock (2005)

Optional: Schafer & Graham (2002)

Canonical correlation

Optional: Shell & Husman (2008)

Week 15 Dec. 9 Course review

Practice final exam: choosing a multivariate method

Dec. 16 Take Home Final Exam, due by noon

GRADING

There will be a total of 500 possible points for the course. Each printout from a computer assignment, done correctly and turned in on time, will count 10 points; points will be deducted for lateness and/or inaccuracy. The matrix algebra homework will count 10 points. Each of the two hourly exams will have 100 possible points. The meta-analysis project is worth a possible 100 points. The comprehensive final is worth 100 points.

COURSE OBJECTIVES

1. To gain an understanding of why and when one would use multivariate statistical methods, including multivariate analysis of variance, factor analysis, and meta-analysis.

2. To develop skills in reading journal articles that present applications of multivariate methods or advances in multivariate methods, e.g., articles in Psychological Bulletin (formerly) and Psychological Methods (currently).

3. To develop skills in using computers to analyze multivariate data, to be able to interpret printouts, and to be able to write up the results for a journal article.

4. To gain a basic (not necessarily advanced) understanding of the theoretical rationale and derivations for multivariate statistics.

REFERENCES

Journal Articles to Be Read in This Course (Learn@UW)

Acock, A. C. (2005). Working with missing values. Journal of Marriage and Family, 67,1012-1028.

Algina, J., & Keselman, H. J. (1997). Detecting repeated measures effects with univariate and multivariate statistics. Psychological Methods, 2, 208-218.

Algina, J., & Oshima, T. C. (1990). Robustness of the independent samples Hotelling's T2 to variance-covariance heteroscedasticity when sample sizes are unequal and in small ratios. Psychological Bulletin, 108, 308-313.

Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4, 272-299.

Grabe, S., Ward., L. M., & Hyde, J. S. (2008). The role of the media in body image concerns among women: A meta-analysis of experimental and correlational studies. Psychological Bulletin, 134, 460-476.

Huberty, C. J., & Morris, J.D. (1989). Multivariate analysis versus multiple univariate analyses. Psychological Bulletin, 105, 302-308.

Hummel, T. J., & Sligo, J. R. (1971). Empirical comparison of univariate and multivariate analysis of variance procedures. Psychological Bulletin, 76, 49-57.

Leichtman, M. D. & Ceci, S. J. (1995). The effects of stereotypes and suggestions on preschoolers' reports. Developmental Psychology, 31, 568-578.

Marche, T. A. & Howe, M. L. (1995). Preschoolers report misinformation despite accurate memory. Developmental Psychology, 31, 554-567.

McKinley, N. M. & Hyde, J. S. (1996). The Objectified Body Consciousness Scale: Development and validation. Psychology of Women Quarterly, 20, 181-216.

O’Brien, R. G., & Kaiser, M. K. (1985). MANOVA method for analyzing repeated measures designs: An extensive primer. Psychological Bulletin, 97, 316-333.

Olson, C. L. (1976). On choosing a test statistic in multivariate analysis of variance. Psychological Bulletin, 83, 579-586. (also see rejoinders following)

Shell, D. F. & Husman, J. (2008). Control, motivation, affect, and strategic self-regulation in the college classroom: A multidimensional phenomenon. Journal of Educational Psychology, 100, 443-459.

Urberg, K. A., Degirmencioglu, S. R., Tolson, J. M., & Halliday-Scher, K. (1995). The structure of adolescent peer networks. Developmental Psychology, 31, 540-547.

York, K.L., & John, O.P. (1992). The four faces of Eve: A typological analysis of women's personality at midlife. Journal of Personality and Social Psychology, 63, 494-508.

Zwick, W. R. & Velicer, W. F. (1986). Comparison of five rules for determining the number of components to retain. Psychological Bulletin, 99, 432-442.

Other Relevant Articles

Bangert-Downs, R.L. (1986). Review of developments in meta-analytic method. Psychological Bulletin, 99, 388-399.

Cliff, N. (1988). The eigenvalues-greater-than-one rule and the reliability of components. Psychological Bulletin, 103, 276-279. (Disputes the statistical basis for Kaiser's rule for the number of factors)

Fiske, D.W. (1983). The meta-analysis revolution in outcome research. Journal of Consulting and Clinical Psychology, 51, 65-70.

Gillett, R. (2003). The metric comparability of meta-analytic effect-size estimators from factorial designs. Psychological Methods, 8, 419-433.

Hakstian, A. R., Roed, J.C., & Lind, J.C. (1979). Two-sample T procedure and the assumption of homogeneous covariance matrices. Psychological Bulletin, 86, 1255-1263. (addresses the robustness of Hotelling's T2)

Hedges, L. V. & Pigott, T. D. (2001). The power of statistical tests in meta-analysis. Psychological Methods, 6, 203-217.

Hedges, L. V., & Pigott, T. D. (2004). The power of statistical tests for moderators in meta-analysis. Psychological Methods, 9, 426-445.

Hedges, L. V. & Vevea, J. L. (1998). Fixed- and random-effects models in meta-analysis. Psychological Methods, 3, 486-504.

Huberty, C.J. (1984). Issues in the use and interpretation of discriminant analysis. Psychological Bulletin, 95, 156-171.

Keselman, H. J., Algina, J., Lix, L. M., Wilcox, R. R., & Deering, K. N. (2008). A generally robust approach for testing hypotheses and setting confidence intervals for effect sizes. Psychological Methods, 13, 110-129.

Kraemer, H.C., & Andrews, G. (1982). A nonparametric technique for meta-analysis effect size calculation. Psychological Bulletin, 91, 404-412.

Morris, S. B. & DeShon, R. P. (1997). Correcting effect sizes computed from factorial analysis of variance for use in meta-analysis. Psychological Methods, 2, 192-199.

Morris, S. B. & DeShon, R. P. (2002). Combining effect size estimates in meta-analysis with repeated measures and independent-groups designs. Psychological Methods, 7,

Orwin, R.G., & Cordray, D.S. (1985). Effects of deficient reporting on meta-analysis: A conceptual framework and reanalysis. Psychological Bulletin, 97, 134-147.

Raudenbush, S.W., Becker, B.J., & Kalaian, H. (1988). Modeling multivariate effect sizes. Psychological Bulletin, 103, 111-120.

Rosenthal, R. (1979). The file drawer problem and tolerance for null results. Psychological Bulletin, 86, 638-661.

Rosenthal, R. (1995). Writing meta-analytic reviews. Psychological Bulletin, 118, 183-192.

Sánchez-Meca, J., Marín-Martínez, F., & Chacón-Moscoso, S. (2003). Effect-size indices for dichotomized outcomes in meta-analysis. Psychological Methods, 8, 448-467.

Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7, 147-177.

Stevens, J. P. (1980). Power of the multivariate analysis of variance tests. Psychological Bulletin, 88, 728-737.

Stewart, D., & Love, W. (1968). A general canonical correlation index. Psychological Bulletin, 70, 160-163.

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