Psych. 711, Applied Multivariate Analysis
Fall, 2005
Dr. Hyde
Office hours: Thurs., 11:00-12:00 and by appt.
410 Brogden Psychology Bldg.
262-9522
jshyde@wisc.edu
Course Description
Texts
Grimm, L. W., & Yarnold, P. R. (Eds.) (1995). Reading and understanding multivariate
statistics. Washington, DC: American Psychological Association.
Stevens, J. (2002). Applied multivariate statistics for the social sciences 4th ed. Mahwah, NJ:
Erlbaum.
Reader, available on e-reserve. To access e-reserves, go to http://my.wisc.edu/portal/ and go to
academic. In your list of courses will be a library/reserve link.
Books are available at A Room of One’s Own Bookstore, 307 W. Johnson St.
Optional
Gorsuch, R. L. (1983). Factor analysis. 2d ed. Hillsdale, NJ: Erlbaum.
SPSS for Windows: Base, Advanced Statistics
Other Relevant Texts
Hedges, L.V., & Olkin, I. (1985). Statistical methods for meta-analysis. NY: Academic
Press.
Tabachnick, B. G. & Fidell, L. S. (2001). Using multivariate statistics. 4th ed., New York:
Allyn & Bacon.
Classlist: psych711-1-f05@lists.wisc.edu
SCHEDULE
Week 1
Sept. 6
Introduction to multivariate statistical methods
Matrix algebra
Read: Grimm & Yarnold (G&Y) Ch. 1
Stevens, Chs. 1, 2
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Week 2
Sept. 13
Multiple regression formulated in matrix terms
Cleaning data
Read: G&Y, Ch. 2
Stevens, ch. 3 (read quickly)
Optional: Schafer & Graham (2002)
Week 3
Sept. 20
Multivariate normal distribution, Wishart distribution; Hotelling's
T2, simple MANOVA
Read: G&Y, Ch. 8 to p. 267
Hummel & Sligo (1971) in reader
Stevens, Ch. 4
Huberty & Morris (1989), Algina & Oshima (1990) (read them in
the order listed)
Week 4
Sept. 27
Week 5
Oct. 4 Multivariate analysis of repeated measures data
Read: G&Y, Ch 8, p. 267-end
Stevens, Ch. 13
McCall & Appelbaum (1973), Algina & Keselman (1997),
Leichtman & Ceci (1995) (in that order)
Week 6
Oct. 11 Discriminant analysis, MANCOVA
Read: G&Y, Ch. 9
Stevens Chs. 7, 9
Marche & Howe (1995)
Week 7
Oct. 18
Week 8
Oct. 25 Statistical methods in meta-analysis; methodological issues
Read: Hedges & Becker (1986), Beaman (1991), Kling et al.
(1999)
Week 9
Nov. 1 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
Optional: Chapter 11, Combining estimates of correlation coefficients,
In Hedges, L.V., & Olkin, I. (1985). Statistical methods for
meta-analysis. NY: Academic Press.
Week 10
Nov. 8 Introduction to factor analysis; principal components
Read: G&Y Ch 4
Week 11
Nov. 15
Complex MANOVA
Read: Stevens, Chs. 5, 6, 8
Olson (1976), Urberg et al. (1995)
EXAM 1: Multivariate analysis of variance
Introduction to meta-analysis
Read: G&Y, Ch. 10
Issues in factor analysis: communalities, rotation, number of
factors
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Week 12
Nov. 22
META-ANALYSIS PROJECTS DUE Nov. 15
Read: Stevens, Ch. 11 (through p. 415)
Zwick & Velicer (1986), McKinley & Hyde (1996)
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. 29
Catch up and review
Week 14
Dec. 6 EXAM 2: Factor analysis
Canonical correlation
Read: Stevens Ch. 12
Weiss
Week 15
Dec. 13
Course review
Practice final exam: choosing a multivariate method
Dec. 20
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.
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REFERENCES
Journal Articles to Be Read in This Course (e-reserve)
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.
Beaman, A. L. (1991). An empirical comparison of meta-analytic and traditional reviews.
Personality and Social Psychology Bulletin, 17, 252-257. (This entire issue of
PSPB was devoted to meta-analysis.)
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, 272299.
Hedges, L. V., & Becker, B. J. (1986). Statistical methods in the meta-analysis of research
on gender differences. In J. S. Hyde & M. C. Linn (Eds.), The psychology of
gender: Advances through meta-analysis. (pp. 14-50). Baltimore: Johns Hopkins
University Press.
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.
Kling, K. C., Hyde, J. S., Showers, C. J., & Buswell, B. N. (1999). Gender differences in selfesteem: A meta-analysis. Psychological Bulletin, 125, 470-500.
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.
McCall, R. & Appelbaum, M. (1973). Bias in the analysis of repeated-measures designs: Some
alternative approaches. Child Development, 44, 401-415.
McKinley, N. M. & Hyde, J. S. (1996). The Objectified Body Consciousness Scale:
Development and validation. Psychology of Women Quarterly, 20, 181-216.
Olson, C. L. (1976). On choosing a test statistic in multivariate analysis of variance.
Psychological Bulletin, 83, 579-586. (also see rejoinders following)
Urberg, K. A., Degirmencioglu, S. R., Tolson, J. M., & Halliday-Scher, K. (1995). The structure
of adolescent peer networks. Developmental Psychology, 31, 540-547.
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Weiss, D. J. (1972). Canonical correlation analysis in counseling psychology research. Journal
of Counseling Psychology, 19, 241-252.
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)
Eagly, A.H., & Crowley, M. (1986). Gender and helping behavior: A meta-analytic review
of the social psychological literature. Psychological Bulletin, 100, 283-308.
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 metaanalysis. 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.
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
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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.
Stewart, D., & Love, W. (1968). A general canonical correlation index. Psychological Bulletin,
70, 160-163.
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I wish to include persons with disabilities fully in this course. Please let me know if you need
any special accommodations in the curriculum, instruction, or assessments of this course to
enable you to participate fully. I will try to maintain confidentiality of the information you share
with me.
Where to take complaints about a Teaching Assistant or Course Instructor
Occasionally a student may have a complaint about a T.A. or course instructor. If that
happens, you should feel free to discuss the matter directly with the T.A. or instructor. If the
complaint is about the T.A. and you do not feel comfortable discussing it with him/her, you
should discuss it with the course instructor. If you do not feel the instructor has resolved the
matter to your satisfaction, then you should speak to the Department Chair, Professor Joseph
Newman (room 238 Psychology). You should also speak to him if the complaint is about the
instructor and you do not feel comfortable discussing it directly with him/her.
If you believe the T.A. or course instructor has discriminated against you because of your
religion, race, gender, sexual orientation, or ethnic background, you also may take your
complaint to the Affirmative Action Office (room 175 Bascom Hall). If your complaint has to
do with sexual harassment, you may also take your complaint to Ms. Arlene Davenport, the
Psychology Department sexual harassment contact person.
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