Psychology 701 Statistical Reasoning Winter, 2010
Instructor: Kevin E. O'Grady
Office: Zoology-Psychology 3147F
Office Hours: M-F 11-12
Office Phone: 301-405-5927
e-mail: ogrady@psyc.umd.edu
Web page: www.kevin-e-ogrady.umd.edu/psyc701.htm
BlackBoard: none
Course Description:
This course provides a general introduction and overview of the use of multivariate statistics in the behavioral sciences. It is designed to introduce you to statistical techniques appropriate for research involving multiple independent and/or dependent variables. Specifically, we will examine multiple regression (predicting scores on one variable from scores on several other variables), univariate analysis of variance and covariance, Hotelling's T2 and two-group discriminant analysis (examining differences between two groups on a battery of dependent measures), multivariate analysis of variance and multiple groups discriminant analysis (testing differences among several groups on several measures), multivariate multiple regression, and canonical correlation. It is assumed that you have had at least one other statistics course, and that you are familiar with simple regression and basic analysis of variance methods.
Course Objectives:
The approach taken in this course falls in between the math-stat emphasis on distributions and derivations, and the black box approach in which numbers are fed into a computer program as input and read off as output with minimal attention to what's happening in between. The focus in the course is on understanding the goals of each technique and through this gaining an understanding of properties of the technique. We will pick up the small amount of matrix algebra needed to analyze small demonstration problems (2 or 3 variables and 5-10 subjects) so as to thoroughly "dissect" the procedure. Armed with this understanding, we'll also use computer programs to handle the otherwise impossibly time-consuming arithmetic needed to conduct multivariate analyses on problems of practical size. And we'll get some practice interpreting multivariate analyses reported by others.
Text:
A text is optional. Recommended texts can be found below, in the Readings.
Assignments:
There are no mandatory class assignments. Homework assignments for Psychology 701 are available at the class web page.
Final Exam:
You are to write a 3-5 page single-spaced paper, in which you either: (1) critique the use of a multivariate statistical method (multiple regression through canonical correlation) in a published article; or (2) describe how the statistical analysis in a published article could have better been conducted with a multivariate statistical method (multiple regression through canonical correlation). In both cases, you would need to be clear about what was both right and wrong with the statistical analysis(ses) performed, and exactly why a multivariate statistical method as you describe it would have been the preferred approach. You are required to submit a copy of the published article along with your critique to my email address.
Deadline for submission of the paper is midnight, F, 22 January.
Computer Usage:
You are expected to have access to a computer system running the SAS System, SAS 9.2. In addition to the optional homework assignments, I strongly encourage you to experiment with various data sets, either those I give you for assignment, or others of your own you might have.
Grading:
Your grade will be based on your attendance and a final exam. In order to get a B in the course, you must have no unexcused absences from class, and you must get at least a C on the final exam. A grade of B on the final exam would result in a course grade of A, assuming you have no unexcused absences from class. [Any absence due to a professional conference, event, or undertaking, or any meeting conducted by your area would by definition be excused, as would any absence due to illness or injury. All other absences would need to be discussed with your instructor prior to missing a class.]
Policy on Handling Official Schedule Adjustments due to Inclement Weather:
UMCP has available a campus website (www.umd.edu) and a snow phone line (301-405-SNOW) that will announce closings and delays. The class will follow the university's policy in regard to class closing(s) and delay(s).
In the event I am unable to show up for a scheduled class due to bad weather or other unplanned events, when the university is otherwise open and classes are being held, I will either: (1) teach the class through webcam to BP 1140 computers; and/or (2) reschedule class time to the optional lab period.
Matrix Algebra:
Searle, S.R. (1982). Matrix algebra useful for statistics. New York: Wiley.
Carroll, J.D., Green, P.E., & Chaturvedi, A. (1997). Mathematical tools for applied multivariate analysis (Revised Edition). New York: Academic.
Harville, D. A. (1997). Matrix algebra from a statistician's perspective. Springer-Verlag.
Wickens, T.D. The geometry of multivariate statistics. Hillsdale, NJ: Erlbaum.
Books:
** Harris, R.J. (2001). A primer of multivariate statistics (Third Edition). Hillsdale, NJ: Erlbaum.
** Stevens, J.P. (2009). Applied multivariate statistics for the social sciences (Fifth Edition). Hillsdale, NJ: Erlbaum.
* Timm, N.H., & Mieczkowski, T.A. (1997). Univariate and multivariate general linear models: Theory and applications using SAS software. Cary, NC: SAS Institute.
* Timm, N.H. (1975). Multivariate analysis. Belmont, CA: Brooks/Cole.
Johnson, R.A., & Wichern, D.W. (1998). Applied multivariate statistical analysis (Fourth Edition). New York: Prentice-Hall.
Marcoulides, G.A., & Hershberger, S.L. (1997). Multivariate statistical methods: A first course. Hillsdale, NJ: Erlbaum.
Morrison, D.F. (1976). Multivariate statistical methods. New York: McGraw-Hill.
Cooley, W.W., & Lohnes, P.R. (1971). Multivariate data analysis. New York: Wiley.
Overall, J.E., & Klett, C.J. (1972). Applied multivariate analysis. New York: McGraw-Hill.
Press, S.J. (1972). Applied multivariate analysis. San Francisco: Holt.
Tatsuoka, M.M. (1988). Multivariate analysis: Techniques for educational and psychological research (Second edition). New York: MacMillen.
Cliff, N. (1987). Analyzing multivariate data. San Diego: Harcourt Brace Jovanovich.
Marascuilo, L.A., & Levin, J.R. (1983). Multivariate statistics in the social sciences: A researchers guide. Monterey, CA: Brooks/Cole.
Veldman, D.J. (1969). FORTRAN programming for the behavioral sciences. New York: Holt, Rinehart and Winston.
* Cohen, J., & Cohen, P. (1983). Applied multiple regression/correlation analysis for the behavioral sciences (Second edition). Hillsdale, N.J.: Elrbaum.
Pedhazur, E. (1997). Multiple regression in behavioral research (Third Edition). New York: Harcourt.
Hamilton, L.C. (1992). Regression with graphics: A second course in applied statistics. Belmont, CA: Wadsworth.
Huitema, B. E. (1980). The analysis of covariance and alternatives. New York: Wiley.
* Bock, R.D. (1975). Multivariate statistical methods in behavioral research. New York: McGraw-Hill.
Finn, J.D. (1974). A general model for multivariate analysis. New York: Holt, Rinehart and Winston.
* Cohen, J. (1988). Statistical power analysis for the behavioral sciences (Second Edition). New York: Academic Press.
Miller, R.G. (1981). Simultaneous statistical inference (Second Edition). New York: Springer.
General:
Cattell, R.B. (1966). Guest editorial: Multivariate behavioral research and the integrative challenge. Multivariate Behavioral Research, 1, 4-23.
Burt, C. (1966). The early history of multivariate techniques in psychological research. Multivariate Behavioral Research, 1, 24-42.
Weinberg, H.I. (1979). Statistical adjustments and uncontrolled studies. Psychological Bulletin, 86, 1149-1164.
Harris, R.J. (1983). Multivariate statistics: When will experimental psychology catch up? In S. Koch & D.E. Leary (Eds.), A century of psychology as a science. New York: McGraw-Hill.
Green, B.F., Jr. (1977). Parameter sensitivity in multivariate methods. Multivariate Behavioral Research, 12, 263-288.
** Borsboom, D. (2006). The attack of the psychometricians. Psychometrika, 71, 425-440.
** Steiger, J.H. (2004). Beyond the F test: Effect size confidence intervals and tests of close fit in the analysis of variance and contrast analysis. Psychological Methods, 9, 164-182.
Cumming, G. & Maillardet, R. (2006). Confidence intervals and replication: Where will the next mean fall? Psychological Methods, 11, 217-227.
Bonnett, D.G. (2009). Estimating standardized linear contrasts of means with desired precision. Psychological Methods, 14, 1-5.
Correlation:
Havlicek, L.L., & Peterson, N.L. (1977). Effects of the violation of assumptions upon significance levels of the Pearson r. Psychological Bulletin, 84, 373-377.
Birnbaum, M.H. (1973). The devil rides again: Correlation as an index of fit. Psychological Bulletin, 79, 239-242.
Alf, E.F., & Abrahams, N.M. (1974). Let's give the devil his due: A response to Birnbaum. Psychological Bulletin, 81, 72-73.
Shanteau, J. (1977). Correlation as a deceiving measure of fit. Bulletin of the Psychonomic Society, 10, 134-136.
** Larzelere, R. E., & Mulaik, S. A. (1977). Single sample tests for many correlations. Psychological Bulletin, 84, 557-569.
Steiger, J.H., & Hakstian, A.R. (1982). The asymptotic distribution of elements of a correlation matrix: Theory and application. British Journal of Mathematical and Statistical Psychology, 35, 208-215.
* Steiger, J.H. (1980). Tests for comparing elements of a correlation matrix. Psychological Bulletin, 87, 245-251.
Hakstian, A.R., Whalen, T.E., & Masson, M.E. (1976). A K-sample procedure for comparatively assessing multivariate association. Psychological Bulletin, 83, 922-927.
* Fowler, R.L. (1987). Power and robustness in product-moment correlation. Applied Psychological Measurement, 11, 419-428.
** Olkin, I., & Finn, J.D. (1995). Correlations redux. Psychological Bulletin, 118, 155-164.
Regression:
* Darlington, R.B. (1968). Multiple regression in psychological research and practice. Psychological Bulletin, 69, 161-182.
* Cohen, J. (1968). Multiple regression as a general data-analytic system. Psychological Bulletin, 70, 426-443.
** Dawes, R.M. & Corrigan, B. (1974). Linear models in decision-making. Psychological Bulletin, 81, 95-106.
** Wainer, H. (1976). Estimating coefficients in linear models: It don't make no nevermind. Psychological Bulletin, 83, 213-217.
Laughlin, J.E. (1978). Comment on "Estimating coefficients in linear models: It don't make no nevermind". Psychological Bulletin, 85, 247-253.
Pruzek, R.M., & Frederick, B.C. (1978). Weighting predictors in linear models: Alternative to least-squares and limitation of equal weights. Psychological Bulletin, 85, 254-266.
Wainer, H. (1978). On the sensitivity of regression and regressors. Psychological Bulletin, 85, 267-273.
Browne, M.W. (1975). Predictive validity of a linear regression equation. British Journal of Mathematical and Statistical Psychology, 28, 79-87.
Cattin, P. (1978). A predictive-validity-based procedure for choosing between regression and equal weights. Organizational Behavior and Human Performance, 22, 93-102.
** Bobko, P., & Schemmer, F.M. (1980). Note on standardized regression estimators. Psychological Bulletin, 88, 233-236.
Daganais, F., & Marascuilo, L.A. (1973). The effect of factor scores, Guttman scores, and simple sum scores on the size of F ratios in an analysis of variance design. Multivariate Behavioral Research, 8, 491-502.
Schmitt, N., Coyle, B.W., & Rauschenberger, J. (1977). A Monte Carlo evaluation of three formula estimates of cross-validated multiple correlation. Psychological Bulletin, 84, 751-758.
Rozeboom, W.W. (1978). Estimation of cross-validated multiple correlation: A clarification. Psychological Bulletin, 85, 1348-1351.
Cattin, P. (1980). Note on the estimation of the squared cross-validated multiple correlation of a regression model. Psychological Bulletin, 87, 63-65.
Wilkinson, L. (1979). Tests of significance in stepwise regression. Psychological Bulletin, 86, 168-174.
Rogosa, D. (1980). Comparing nonparallel regression lines. Psychological Bulletin, 88, 307-321.
Stevens, J.P. (1984). Outliers and influential data points in regression analysis. Psychological Bulletin, 95, 334-344.
Chatterjee, S., & Yilmaz, M. (1992). A review of regression diagnostics for behavioral research. Applied Psychological Measurement, 16, 209-227.
Kennedy, E. (1988). Estimation of the squared cross-validity coefficient in the context of best subset regression. Applied Psychological Measurement, 12, 231-237.
Lubinski, D., & Humphreys, L.G. (1990). Assessing spurious "moderator effects": Illustrated substantively with the hypothesized ("synergistic") relation between spatial and mathematical ability. Psychological Bulletin, 107, 385-393.
Shepperd, J.A. (1991). Cautions in assessing spurious "moderator effects". Psychological Bulletin, 110, 315-317.
* Mauro, R. (1990). Understanding L.O.V.E. (Left Out Variables Error): A method for estimating the effects of omitted variables. Psychological Bulletin, 108, 314-329.
Jaccard, J., Wan, C.K., & Turrisi, R. (1990). The detection and interpretation of interaction effects between continuous variables in multiple regression. Multivariate Behavioral Research, 25, 467-478.
* Paunonen, S.V., Gardner, R.C. (1991). Biases resulting from the use of aggregated variables in psychology. Psychological Bulletin, 109, 520-523.
* Hagerty, M.R., & Srinivasan, V. (1991). Comparing the predictive powers of alternative multiple regression models. Psychometrika, 56, 77-85.
Johnson, J.W. (2000). A heuristic method for estimating the relative weight of predictor variables in multiple regression. Multivariate Behavioral Research, 35, 1-20.
** Waller, N.G. (2008). Fungible weights in multiple regression. Psychometrika, 73, 691-703.
Grömping, U. (2009). Variable importance assessment in regression: Linear regression versus random forest. American Statistician, 63, 308-319.
Nonorthogonal ANOVA:
Overall, J.E., & Spiegel, D.K. (1969). Concerning least squares analysis of experimental data. Psychological Bulletin, 72, 311-322.
Joe, G.W. (1971). Comments on Overall and Speigel's "Least squares analysis of experimental data." Psychological Bulletin, 75, 364-366.
Smith, I.L. (1972). Comment on Joe's "Comment on Overall and Spiegel's 'Least squares analysis of experimental data.'" Psychological Bulletin, 77, 170-171.
Rawlings, R.R., Jr. (1973). Note on nonorthogonal analysis of variance. Psychological Bulletin, 77, 373-374.
Williams, J.D. (1972). Two-way fixed effects analysis of variance with disproportionate cell frequencies. Multivariate Behavioral Research, 7, 67-83.
Overall, J. & Spiegel, D.K. (1973). Comments on Rawlings' nonorthogonal analysis of variance. Psychological Bulletin, 79, 311-322.
Rawlings, R.R., Jr. (1973). Comments on the Overall and Spiegel paper. Psychological Bulletin, 79, 168-169.
Gocka, E.F. (1973). Regression analysis of proportional cell data. Psychological Bulletin, 80, 25-27.
Overall, J.E., & Spiegel, D.K. (1973). Comments on "Regression analysis of proportional cell data." Psychological Bulletin, 80, 28-30.
* Timm, N.H., & Carlson, J.E. (1975). Analysis of variance through full rank models. Multivariate Behavioral Research Monographs, 75-1.
** Appelbaum, M.I., & Cramer, E.M. (1974). Some problems in the nonorthogonal analysis of variance. Psychological Bulletin, 81, 335-343.
Carlson, J.E., & Timm, N.H. (1974). Analysis of nonorthogonal fixed-effects designs. Psychological Bulletin, 81, 563-570.
Overall, J.E., Spiegel, D.K., & Cohen, J. (1975). Equivalence of orthogonal and nonorthogonal analysis of variance. Psychological Bulletin, 82, 182-186.
Keren, G., & Lewis, C. (1976). Nonorthogonal designs: Sample versus population. Psychological Bulletin, 83, 817-826.
Lewis, C., & Keren, G. (1977). You can't have your cake and eat it too: Some considerations of the error term. Psychological Bulletin, 84, 1150-1154.
** Herr, D.G., & Gaebelein, J. (1978). Nonorthogonal two-way analysis of variance. Psychological Bulletin, 85, 207-216.
** Cramer, E.M., & Appelbaum, M.I. (1980). Nonorthogonal analysis of variance -- once again. Psychological Bulletin, 87, 51-57.
Spector, P.E. (1980). Handling nonorthogonal analysis of variance: A review of techniques. Evaluation Review, 4, 843-855.
Spector, P.E., Voissem, N.H., & Cone, W.L. (1981). A Monte Carlo study of three approaches to nonorthogonal analysis of variance. Journal of Applied Psychology, 66, 535-540.
* Milligan, G.W., Wong, D.S., & Thompson, P.A. (1987). Robustness properties of nonorthogonal analysis of variance. Psychological Bulletin, 101, 464-470.
** Green, S.B., Marquis, J.G., Hershberger, S.L., Thompson, M.S., & McCollam, K.M. (1999). The overparameterized analysis of variance model. Psychological Methods, 4, 214-233.
ANOVA and Regression:
Wolf, G., & Cartwright, B. (1974). Rules for coding dummy variables in multiple regression. Psychological Bulletin, 81, 173-179.
Bogartz, W. (l975). Coding dummy variables is a waste of time: Reply to Wolf and Cartwright, among others. Psychological Bulletin, 82, 180.
Wolf, G., & Cartwright, B. (1975). A timely reply to Bogartz. Psychological Bulletin, 82, 181.
Keren, G., & Lewis, C. (1977). A comment on coding in nonorthogonal designs. Psychological Bulletin, 84, 346-348.
** O'Grady, K.E., & Medoff, D.R. (1988). Categorical variables in multiple regression: Some cautions. Multivariate Behavioral Research, 23, 243-260.
Cohen, J. (1980). Trend analysis the easy way. Educational and Psychological Measurement, 40, 565-568.
Glass, G.W. (1968). Correlations with products of variables: Statistical formulation and implication for methodology. American Educational Research Journal, 5, 721-728.
Humphreys, L.G., & Fleishman, A. (1974). Pseudo-orthogonal and other analysis of variance designs involving individual difference variables. Journal of Educational Psychology, 66, 464-472.
Sockloff, A.L. (1976). Spurious product correlation. Educational and Psychological Measurement, 36, 33-44.
Sockloff, A.L. (1976). The analysis of nonlinearity via linear regression with polynomial and product variables: An examination. Review of Educational Research, 46, 267-291.
** Cohen J. (1978). Partial products are interactions: partialed powers are curve components. Psychological Bulletin, 85, 858-866.
* Busemeyer, J.R., & Jones, L.E. (1983). Analysis of multiplicative combination rules when the causal variables are measured with error. Psychological Bulletin, 93, 549-562.
Dretzke, B.J., Levin, J.R., & Serlin, R.C. (1982). Testing for regression homogeneity under variance heterogeneity. Psychological Bulletin, 91, 376-383.
* Lane, P.M. (1981). Testing main effects of continuous variables in nonadditive models. Multivariate Behavioral Research, 16, 499-510.
Tate, R.L. (1981). Multivariate ATI analysis. Multivariate Behavioral Research, 16, 243-259.
Cronbach, L.J. (1987). Statistical tests for moderator variables: Flaws in analyses recently proposed. Psychological Bulletin, 102, 414-417.
Dunlap, W.P., & Kemery, E.R. (1987). Failure to detect modering effects: Is multicollinearity the problem? Psychological Bulletin, 102, 418-420.
- Rosnow, R.L., & Rosenthal, R. (1989). Definition and interpretation of interaction effects. Psychological Bulletin, 105, 143-146.
Meyer, D. L. (1991). Misinterpretation of interaction effects: A reply to Rosnow and Rosenthal. Pyschological Bulletin, 110, 571-573.
Maxwell, S.E., & Delaney, H.D. (1992). Bivariate median splits and spurious statistical significance. Psychological Bulletin, 113, 181-190.
Overton, R.C. (2001). Moderated multiple regression for interactions involving categorical variables: A statistical control for heterogeneous variance across two groups. Psychological Methods, 6, 218-233.
ANOVA and Dichotomization of Predictor Variables:
** MacCallum, R.C., Zhang, S., Preacher, K.J., & Rucker, D.D. (2002). On the practice of dichotomization of quantitative variables. Psychological Methods, 6, 19-40.
Preacher, K.J., Rucker, D.D., MacCallum, R.C., & Nicewander, W.A. (2005). Use of the extremet groups approach: A critical reexamination and new recommendation. Psychological Methods, 10, 178-192
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Measurement of Change:
Cronbach, L.J., & Furby, L. (1970). How should we measure "change" -- or should we? Psychological Bulletin, 74, 68-80.
O'Connor, E.F., Jr. (1972). Response to Cronbach and Furby's "How should we measure 'change' -- or should we?". Psychological Bulletin, 78, 159-160.
Hummel-Ross, B., & Weinberg, S.L. (1975). Practical guidelines in applying current theories to the measurement of change. Journal Supplement Abstract Service, #916.
Overall, J.E., & Woodward, J.A. (1975). Unreliability of difference scores: A paradox for measurement of change. Psychological Bulletin, 82, 85-86.
Fleiss, J.L. (1976). Comment on Overall and Woodward's asserted paradox concerning the measurement of change. Psychological Bulletin, 83, 774-775.
Overall, J.E., & Woodward, J.A. (1976). Reassertion of the paradoxical power of tests of significance based on unreliable difference scores. Psychological Bulletin, 83, 776-777.
Nicewander, W.A., & Price, J.M. (1978). Dependent variable reliability and the power of significance tests. Psychological Bulletin, 85, 405-409.
Sutcliffe, J.P. (1980). On the relationship of reliability to statistical power. Psychological Bulletin, 88, 509-515.
** Nicewander, W.A., & Price, J.M. (1983). Reliability of measurement and the power of statistical tests: Some new results. Psychological Bulletin, 94, 524-533.
Williams, R.H., & Zimmerman, D.W. (1977). The reliability of difference scores when errors are correlated. Educational and Psychological Measurement, 37, 679-689.
Zimmerman, D.W., & Williams, R.H. (1982). Gain scores in research can be highly reliable. Journal of Educational Measurement, 19, 149-154.
Zimmerman, D.W., & Williams, R.H. (1982). The relative error magnitude in three measures of change. Psychometrika, 47, 141-147.
Orwin, R.G. (1981). Measuring percentage change: Assumptions underlying unbiased treatment estimates. Journal of Applied Psychology, 66, 671-676.
Tucker, L.R., Damarin, F., & Messick, S. (1966). A base-free measure of change. Psychometrika, 31, 457-473.
Bond, L. (1979). On the base-free measure of change proposed by Tucker, Damarin, and Messick. Psychometrika, 44, 317-355.
Messick, S. (1981). Denoting the base-free measure of change. Psychometrika, 46, 215-217.
Humphreys, L.G., & Drasgow, F. (1989). Some comments on the relationship between reliability and statistical power. Applied Psychological Measurement, 13, 419-425.
Humphreys, L.G. (1991). The relationship of power of statistical tests to range of talent: A correction and amplification. Applied Psychological Measurement, 15, 267.
Overall, J.E. (1989). Contradictions can never a paradox resolve. Applied Psychological Measurement, 13, 426-428.
Humphreys, L.G., & Drasgow, F. (1989). Paradoxes, contradictions, and illusions. Applied Psychological Measurement, 13, 429-431.
Overall, J.E. (1989). Distinguishing between measurements and dependent variables. Applied Psychological Measurement, 13, 432-433.
** Wainer, H. (1991). Adjusting for differential base rates: Lord's Paradox again. Psychological Bulletin, 109, 147-151.
** Williams, R.H., & Zimmerman, D.W. (1996). Are simple gain scores obsolete? Applied Psychological Measurement, 20, 59-70.
Analysis of Covariance:
Evans, S.H., & Anastasio, E.J. (1968). Misuse of analysis of covariance when treatment effect and covariate are confounded. Psychological Bulletin, 69, 225-234.
Sprott, D.A. (1970). Note on Evans and Anastasio on the analysis of covariance. Psychological Bulletin, 73, 303-306.
Harris, D.R., Brisbee, C.T., & Evans, S.H. (1971). Further comments -- misuse of analysis of covariance. Psychological Bulletin, 75, 220-222.
Maxwell, S.A., & Cramer, E.M. (1975). A note on analysis of covariance. Psychological Bulletin, 82, 187-190.
** Overall, J.E., & Woodward, J.A. (1977). Common misconceptions concerning the analysis of covariance. Multivariate Behavioral Research, 12, 171-186.
** Overall, J.E., & Woodward, J.A. (1977). Nonrandom assignment and analysis of covariance. Psychological Bulletin, 84, 588-594.
Cuervorst, R.W. & Stock, W.A. (1978). Comments on the analysis of covariance with repeated measures designs. Multivariate Behavioral Research, 13, 509-513.
* Delaney, H.A., & Maxwell, S.A. (1981). On using analysis of covariance in repeated measures designs. Multivariate Behavioral Research, 16, 105-124.
Algina, J. (1982). Remarks on the analysis of covariance in repeated measures designs. Multivariate Behavioral Research, 17, 117-130.
Delaney, H.A., & Maxwell, S.A. (1980). The use of analysis of covariance in tests of attribute-by-treatment interactions. Journal of Educational Statistics, 5, 191-207.
Maxwell, S.E., Delaney, H.D., & Dill, C.A. (1984). Another look at ANCOVA versus blocking. Psychological Bulletin 95, 136-147.
Repeated Measures Analyses:
** Greenwald, A.G. (1976). Within-subjects designs: To use or not to use? Psychological Bulletin, 83, 314-320.
** Namboodiri, N.K. (1972). Experimental designs in which each subject is used repeatedly. Psychological Bulletin, 77, 54-64.
** Erlebacher, A. (1977). Design and analysis of experiments contrasting the within- and between-subjects manipulation of the independent variable. Psychological Bulletin, 84, 212-219.
Pedhazur, E.J. (1977). Coding subjects in repeated measures designs. Psychological Bulletin, 84, 298-305.
Huynh, H., & Feldt, L.S. (1970). Conditions under which mean square ratios in repeated measurements designs have exact F-distributions. Journal of the American Statistical Association, 65, 1582-1589.
Huynh, H. (1978). Some approximate tests for repeated measures designs. Psychometrika, 43, 161-175.
Huynh, H., & Mandeville, G.K. (1979). Validity conditions in repeated measures designs. Psychological Bulletin, 86, 964-973.
Rogan, J.C., Keselman, H.J., & Mendoza, J.L. (1979). Analysis of repeated measurements. British Journal of Mathematical and Statistical Psychology, 32, 269-286.
Keselman, H.J., Rogan, J.C., Mendoza, J.L., & Breen, L.J. (1980). Testing the validity conditions of repeated measures F tests. Psychological Bulletin, 87, 479-481.
Keselman, H. J., Rogan, J.C., & Games, P. (1981). Robust tests of repeated measures means in educational and psychological research. Educational and Psychological Measurement, 41, 163-171.
Keselman, H.J. (1982). Multiple comparisons for repeated measures means. Multivariate Behavioral Research, 17, 87-92.
Mitzel, H.C., & Games, P.A. (1981). Circularity and multiple comparisons in repeated measure designs. British Journal of Mathematical and Statistical Psychology, 34, 253-259.
Huck, S.W., & McLean, R.A. (1975). Using a repeated measures ANOVA to analyze the data from a pretest-posttest design: A potentially confusing task. Psychological Bulletin, 82, 511-518.
** Boik, R.J. (1981). A priori tests in repeated measures designs: Effects of nonsphericity. Psychometrika, 46, 241-156.
Greenhouse, S.W., & Geisser, S. (1959). On methods in the analysis of profile data. Psychometrika, 24, 95-112.
Bock, R.D. (1963). Multivariate analysis of variance of repeated measurements. In C.W. Harris (Ed.), Problems in measuring change. Madison, WI: University of Wisconsin Press.
Finn, J.D. (1969). Multivariate analysis of repeated measures data. Multivariate Behavioral Research, 4, 39-413.
Pothoff, R.F., & Roy, S.N. (1964). A generalized multivariate analysis of variance model for growth curve problems. Biometrika, 51, 313-326.
** Davidson, M.L. (1972). Univariate versus multivariate tests in repeated measures designs. Psychological Bulletin, 77, 446-452.
Poor, D.D.S. (1973). Analysis of variance for repeated measures designs: Two approaches. Psychological Bulletin, 80, 204-209.
* McCall, R.B., & Appelbaum, M.I. (1973). Bias in analysis of repeated measures designs: Some alternative approaches. Child Development, 44, 401-415.
Groff, M.G. (1983). A scheme for conducting two-sample profile analysis. Multivariate Behavioral Research, 18, 169-181.
Dickson, T.L., & Wolins, L. (1974). Analysis of repeated measures and other designs. Multivariate Behavioral Research, 8, 353-371.
Mendoza, J.L., Toothaker, L.E., & Nicewander, W.A. (1974). A Monte Carlo comparison of the univariate and multivariate methods for the groups by trials repeated measures design. Multivariate Behavioral Research, 8, 165-177.
Woodward, J.A., & Overall, J.E. (1976). Nonorthogonal analysis of variance in repeated measures experimental designs. Educational and Psychological Measurement, 36, 855-859.
Scheifleg, V.M., & Schmidt, W.H. (1978). Analysis of repeated measures data: A simulation study. Multivariate Behavioral Research, 13, 347-362.
Dretzke, B.J., Levin, J.R. & Serlin, R.C. (1982). Testing for regression homogeneity under variance heterogeneity. Psychological Bulletin, 9, 776-383.
Lewis, C., & van Knippenberg, C. (1984). Estimation and model comparisons for repeated measures data. Psychological Bulletin, 96, 182-194.
** O'Brien, R.G., & Kaiser, M.K. (1985). MANOVA method for analyzing repeated measures designs: An extensive primer. Psychological Bulletin, 97, 316-333.
Grieve, A.P. (1984). Tests of sphericity of normal distributions and the analysis of repeated measures designs. Psychometrika, 49, 257-267.
** Boik, R.J. (1988). The mixed model for multivariate repeated measures: Validity conditions and an approximate test. Psychometrika, 53, 469-486.
Rasmussen, J.L., Heumann, K.A., Heumann, M.T., & Botzum, M. (1989). Univariate and multivariate groups by trials analysis under violation of variance-covariance and normality assumptions. Multivariate Behavioral Research, 24, 93-105.
Vallejo, G., Fidalgo, A., & Fernandez, P. (2001). Effects of covariance heterogeneity on three procedures for analyzing multivariate repeated measures designs. Multivariate Behavioral Research, 36, 1-28
.
Power:
Wilson, W.R., & Miller, H. (1964). A note on the inconclusiveness of accepting the null hypothesis. Psychological Review, 71, 238-242.
Cohen, J. (1962). The statistical power of abnormal-social psychological research: A review. Journal of Abnormal and Social Psychology, 65, 145-153.
Boruch, G., & Godbaut, R. (1974). Extreme groups designs and the calculation of statistical power. Educational and Psychological Measurement, 34, 663-675.
Chase, L., & Chase, R. (1976). A statistical power analysis of applied psychological research. Journal of Applied Psychology, 61, 234-237.
Cook, T.J., & Poole, W.K. (1982). Treatment implementation and statistical power: A research note. Evaluation Review, 6, 425-430.
Ohls, J.C. (1980). The power of hypothesis tests in a regression context. Evaluation Review, 4, 623-635.
* Koele, P. (1982). Calculating power in analysis of variance. Psychological Bulletin, 92, 513-516.
* Stevens, J.P. (1980). Power of the multivariate analysis of variance tests. Psychological Bulletin, 88, 728-737.
Sedlmeier, P., & Gigerenzer, G. (1989). Do studies of statistical power have an effect on the power of studies? Psychological Bulletin, 105, 309-316.
Gatsonis, C., & Sampson, A.R. (1989). Multiple correlation: Exact power and sample size calculations. Psychological Bulletin, 106, 516-524.
** Green, S.B. (1991). How many subjects does it take to do a regression analysis? Multivariate Behavioral Research, 26, 499-510.
** Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155-159.
Algina, J., & Olejnik, S. (2000). Determining sample size for accurate estimation of the squared multiple correlation coefficient. Multivariate Behavioral Research, 35, 119-136.
** Maxwell, S.E. (2000). Sample size and multiple regression analysis. Psychological Methods, 5, 434-458.
Jo, B. (2002). Statistical power in randomized intervention studies with noncompliance. Psychological Methods, 7, 178-193.
Venter, A., Maxwell, S.E., & Bolig, E. (2002). Power in randomized group comparisons: The value of adding a single intermediate time point to a traditional prestest-posttest design. Psychological Methods, 7, 194-209.
** Shieh, G. (2003). A comparative study of power and sample size calculation for multivariate general linear models. Multivariate Behavioral Research, 38, 285-307.
* Kelley, K. & Maxwell, S.E. (2003). Sample size for multiple regression: Obtaining regression coefficients that are accurate, not simply significant. Psychological Methods, 8, 305-321.
Maxwell, S.E. (2004). The persistence of underpowered studies in psychological research: Causes, consequencies, and remedies. Psychological Methods, 9, 147-163.
Measures of Explained Variance:
Friedman, H. (1968). Magnitude of experimental effect and a table for its rapid estimation. Psychological Bulletin, 70, 245-251.
Breen, L., & Gaito, J. (1970). Comments on Friedman's rm procedure. Psychological Bulletin, 73, 309-310.
Kelley, T.L. (1935). An unbiased correlation ratio measure. Proceedings of the National Academy of Sciences, 21, 554-559.
Kennedy, J.J. (1970). The eta coefficient in complex ANOVA designs. Educational and Psychological Measurement, 30, 885-889.
Cohen, J. (1973). Eta-squared and partial eta-squared in fixed factor ANOVA designs. Educational and Psychological Measurement, 33, 107-112.
Dodd, D.H., & Schultz, R.F., Jr. (1973). Computational procedures for estimating magnitude of effect for some analysis of variance designs. Psychological Bulletin, 79, 391-395.
Fleiss, J.L. (1969). Magnitude of experimental effect and a table for its rapid estimation. Psychological Bulletin, 72, 273-276.
Whimbey, A., Vaughn, G.M., & Tatsuoka, M.M. (1967). Fixed effects versus random effects: Estimating variance components from mean squares. Perceptual and Motor Skills, 25, 668.
Vaughn, G.M., & Corballis, M.C. (1969). Beyond tests of significance: Estimating strength of effects in selected ANOVA designs. Psychological Bulletin, 72, 204-213.
Dwyer, J.H. (1974). Analysis of variance and the magnitude of effects: A general approach. Psychological Bulletin, 81, 731-737.
Gaebelein, J.W., Soderquist, D.R., & Powers, W.A. (1976). A note on variance explained in the mixed analysis of variance model. Psychological Bulletin, 83, 1110-1112.
Glass, G., & Hakstian, A. (1969). Measures of association in comparative experiments: Their development and interpretation. American Educational Research Journal, 6, 403-414.
* Levin, J.R. (1967). Comment: Misinterpreting the significance of "explained variation." American Psychologist, 2, 675-676.
Mitchell, C., & Hartman, D.P. (1981). A cautionary note on the use of omega squared to evaluate the effectiveness of behavioral treatments. Behavioral Assessment, 3, 93-100.
Smith, I.L. (1972). The eta coefficient in MANOVA. Multivariate Behavioral Research, 7, 361-372.
Huberty, C.J. (1972). Multivariate indices of strength of association. Multivariate Behavioral Research, 7, 523-516.
Stevens, J.P. (1972). Global measures of association in multivariate analysis of variance. Multivariate Behavioral Research, 7, 373-378.
** Cramer, E.M., & Nicewander, W.A. (1979). Some symmetric, invariant measures of multivariate association. Psychometrika, 44, 43-54.
Serlin, R.C. (1982). A multivariate measure of association based on the Pillai-Bartlett procedure. Psychological Bulletin, 9, 413-417.
Yeaton, W.H., & Sechrest, L. (1981). Meaningful measures of effect. Journal of Consulting and Clinical Psychology, 49, 766-767.
Sechrest, L., & Yeaton, W.H. (1982). Magnitudes of experimental effects in social science research. Evaluation Review, 6, 579-599.
Cooper, H.M. (1981). On the significance of effects and the effects of significance. Journal of Personality and Social Psychology, 41, 1013-1018.
** O'Grady, K.E. (1982). Measures of explained variance: Cautions and limitations. Psychological Bulletin, 92, 766-777.
Ozen, D.J. (1985). Correlation and the coefficient of determination. Psychological Bulletin, 97, 307-315.
* Abelson, R.P. (1985). A variance explanation paradox: When a little is a lot. Psychological Bulletin, 97, 129-133.
Steiger, J.H., & Ward, L.M. (1987). Factor analysis and the coefficient of determination. Psychological Bulletin, 101, 471-474.
Haase, R.F. (1991). Computational formulas for multivariate strength of association from approximate F and chi-square tests. Multivariate Behavioral Research, 26, 227-245.
Prentice, D.A., & Miller, D.T. (1992) When small effects are impressive. Psychological Bulletin, 112, 160-164.
Algina, J. (1999). A comparison of methods for constructing confidence intervals for the squared multiple correlation coefficient. Multivariate Behavioral Research, 34, 493-504.
Algina, J., Moulder, B.C., & Moser, B.K. (2002). Sample size requirement for accurate estimation of squared semi-partial correlation coefficients. Multivariate Behavioral Research, 37, 37-57.
* Steyn H.S., Jr. & Ellis, S.M. (2009). Estimating an effect size in one-way multivariate analysis of variance (MANOVA). Multivariate Behavioral Research, 44, 106-129.
Statistical Significance:
Rozeboom, W.W. (1960). The fallacy of the null hypothesis significance test. Psychological Bulletin, 57, 416-428.
Anderson, N.H., & Shanteau, J. (1977). Weak inference with linear models. Psychological Bulletin, 84, 1155-1170.
Lykken, D.T. (1968). Statistical significance in psychological research. Psychological Bulletin, 70, 151-159.
Stevens, S.S. (1968). Measurement, statistics, and the schemapiric view. Science, 161, 849-856.
Morrison, D.E., & Henkel, R.E. (1970). The significance test controversy: A reader. Chicago: Aldine.
Lick, J. (1973). Statistical vs. clinical significance in research on the outcome of psychotherapy. International Journal of Mental Health, 22, 26-37.
Carver, R.P. (1978). The case against statistical significance testing. Harvard Educational Review. 48, 378-399.
Hugdahl, K., & Ost, L-G. (1981). On the difference between statistical and clinical significance. Behavioral Assessment, 3, 289-295.
Chow, S.L. (1988). Significance test or effect size? Psychological Bulletin, 103, 105-110.
** Nickerson, R.S. (2000). Null hypothesis significance testing: A review of an old and continuing controversy. Psychological Methods, 5, 241-301.
Post hoc tests:
Ryan, T.A. (1959). Multiple comparisons in psychological research. Psychological Bulletin, 56, 26-47.
* Games, P.A. (1971). Multiple comparisons of means. American Educational Research Journal, 8, 531-565.
Carmer, S.G., & Swanson, M.R. (1973). An evaluation of ten multiple comparison procedures by Monte Carlo methods. Journal of the American Statistical Association, 68, 66-74.
* Boik, R.J. (1979). A note on the rationale of Scheffé's method and the simultaneous test procedure. Educational and Psychological Measurement, 39, 49-56.
Marascuilo, L.A., & Levin, J.R. (1970). Appropriate post hoc comparisons for interaction and nested hypotheses in analysis of variance designs: The elimination of Type IV errors. American Educational Research Journal, 7, 397-421.
Levin, J.R., & Marascuilo, L.A. (1972). Type IV errors and interactions. Psychological Bulletin, 78, 368-374.
Games, P.R. (1973). Type IV errors revisited. Psychological Bulletin, 80, 304-307.
Levin, J.R. & Marascuilo, L.A. (1973). Type IV errors and Games. Psychological Bulletin, 80, 308-309.
Marascuilo, L.A., & Levin, J.R. (1976). The simultaneous investigation of interaction and nested hypotheses in two-factor analysis of variance designs. American Educational Research Journal, 13, 61-65.
Levin, J.R., & Marascuilo, L.A. (1977). Post hoc analysis of repeated measures interactions and gain scores: Whither the inconsistency? Psychological Bulletin, 84, 247-248.
Keselman, H.J., Games, P.A., & Rogan, J.C. (1979). Protecting the overall rate of Type I errors for pairwise comparisons with an omnibus test statistic. Psychological Bulletin, 68, 884-888.
Ryan, T.A. (1980). Comment on "Protecting the overall rate of Type I errors for pairwise comparisons with an omnibus test statistic". Psychological Bulletin, 88, 354-355.
* Boik, R.J. (1979). Interactions, partial interactions, and interaction contrasts in the analysis of variance. Psychological Bulletin, 86, 1084-1089.
** Berger, M.P.F. (1978). A note on the use of simultaneous test procedures. Psychological Bulletin, 85, 895-897.
Spector, P.E. (1977). What to do with significant effects in multivariate analysis of variance. Journal of Applied Psychology, 62, 158-163.
* Bird, K.D., & Hadzi-Pavlovic, D. (1983). Simultaneous test procedures and the choice of a test statistic in MANOVA. Psychological Bulletin, 93, 167-178.
Jaccard, J., Becker, M.A., & Wood, G. (1984). Pairwise multiple comparison procedures: A review. Psychological Bulletin, 96, 589-596.
Seaman, M.A., Levin, J.R., & Serlin, R.C. (1991). New developments in pairwise multiple comparisons: Some powerful and practicable procedures. Psychological Bulletin, 110, 577-586.
Levin, J.R., Serline, R.C., & Seaman, M.A. (1994). A controlled, powerful multiple-comparison strategy for several situations. Psychological Bulletin, 115, 153-159.
Kowalchuk, R.K., & Keselman, H.J. (2001). Mixed-model pairwise multiple comparisons of repeated measures means. Psychological Methods, 6, 282-296.
** Ramsey, P.H. (2002). Comparison of closed testing procedures for pairwise testing of means. Psychological Methods, 7, 504-523.
** Dayton, C.M. (2003). Information criteria for pairwise comparisons. Psychological Methods, 8, 61-71.
Missing Data:
Timm, N.H. (1970). The estimation of variance-covariance and correlation matrices from incomplete data. Psychometrika, 35, 417-437.
Gleason, T.C., & Staelin, R. (1975). A proposal for handling missing data. Psychometrika, 40, 229-252.
Frane, J.W. (1976). Some simple procedures for handling missing data in multivariate analysis. Psychometrika, 41, 409-415.
Koopman, R.F. (1976). Fast regression estimates of missing data. Psychometrika, 41, 277.
West, S.G. (2001). New approaches to missing data in psychological research: Introduction to the special section. Psychological Methods, 6, 315-316.
Sinharay, S., Stern, J.S., Russell, D. (2001). The use of multiple imputation for the analysis of missing data. Psychological Methods, 6, 317-329.
Collins, L.M., Schafer, J.L., & Kam, C-K. (2001). A comparison of inclusive and restrictive strategies in modern missing data procedures. Psychological Methods, 6, 330-351.
Enders, C.K. (2001). The impact of nonnormality on full information maximum-likelihood estimation for structural equation models with missing data. Psychological Methods, 6, 352-370.
** Schafer, J.L., & Graham, J.W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 6, 147-177.
MANOVA:
Bock, R.D., & Haggard, E.A. (1968). The use of multivariate analysis of variance in behavioral research. In D.K. Whitla (Ed.), Handbook of measurement and assessment in the behavioral sciences. Reading, MA: Addison Wesley.
Wiersma, W., & Hall, C. (1973). The geometric construct of multivariate analysis of variance. Educational and Psychological Measurement, 33, 341-350.
Woodward, J.A., & Overall, J.E. (1975). Multivariate analysis of variance by multiple regression methods. Psychological Bulletin, 82, 21-32.
Zinkgraf, S.A. (1983). Performing factorial multivariate analysis of variance using canonical correlation analysis. Educational and Psychological Measurement, 43, 63-68.
Olson, C.L. (1974). Comparative robustness of six tests in multivariate analysis of variance. Journal of the American Statistical Association, 69, 894-968.
** Olson, C.L. (1976). On choosing a test statistic in multivariate analysis of variance. Psychological Bulletin, 83, 579-586.
Stevens, J.P. (1979). Comment on Olson: Choosing a test statistic in multivariate analysis of variance. Psychological Bulletin, 86, 355-360.
Olson, C.L. (1979). Practical considerations in choosing a MANOVA test statistic: A rejoinder to Stevens. Psychological Bulletin, 86, 1350-1352.
* Harris, R.J. (1976). The invalidity of partitioned U-tests in canonical correlation and multivariate analysis of variance. Multivariate Behavioral Research, 11, 353-365.
Woehlke, P.L. (1976). Robustness of MANOVA when there are unequal cell sizes and variances and different correlations between dependent variables. Journal Supplement Abstract Service, #1347.
Wilkinson, L. (1977). Confirmatory rotation of MANOVA canonical variates. Multivariate Behavioral Research, 12, 487-494.
Wilkinson, L. (1975). Response variable hypotheses in the multivariate analysis of variance. Psychological Bulletin, 82, 408-412.
Hummel, T.J., & Sligo, J.R. (1971). Empirical comparison of univariate and multivariate analysis of variance procedures. Psychological Bulletin, 76, 49-57.
Huberty, C.J., & Smith J.D. (1982). The study of effects in MANOVA. Multivariate Behavioral Research, 17, 417-432.
Roy, J. (1958). Step-down procedure in multivariate analysis. Annals of Mathematical Statistics, 29, 1177-1187.
Stevens, J.P. (1972). Four methods of analyzing between variation for the k-group MANOVA problem. Multivariate Behavioral Research, 7, 499-522.
Stevens, J.P. (1973). Step-down analysis and simultaneous confidence intervals in MANOVA. Multivariate Behavioral Research, 8, 391-402.
Kaplan, R.M., & Litrownik, A.J. (1977). Some statistical methods for the assessment of multiple outcome criteria in behavioral research. Behavior Therapy, 8, 383-392.
O'Grady, K.E. (1978). Comments on "Some statistical methods for the assessment of multiple outcome criteria in behavioral research". Behavioral Therapy, 9, 471-473.
Kaplan, R.M., & Litrownik, A.J. (l978). Further comments on multivariate methods in behavioral research. Behavioral Therapy, 9, 474-476.
Turner, R.M. (1978). Multivariate assessment of therapy outcome research. Journal of Behavior Therapy and Experimental Psychology, 9, 309-314.
Leary, M.R., & Altmaier, E.M. (1980). Type I error in counseling research: A plea for multivariate analyses. Journal of Counseling Psychology, 27, 611-615.
Larrabee, M.J. (1982). Reexamination of a plea for multivariate analyses. Journal of Counseling Psychology, 29, 180-188.
Strahan, R.F. (1982). Multivariate analysis and the problem of Type I error. Journal of Counseling Psychology, 29, 175-179.
Huberty, C.J., & Morris, J.D. (1989). Multivariate analysis versus multiple univariate analyses. Psychological Bulletin, 105, 302-308.
Algina, J., & Oshima, T.C. (1990). Robustess 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.
van den Burg, W. (1990). Testing multivariate partial, semipartial and bipartial correlation coefficients. Multivariate Behavioral Research, 25, 335-340.
** Yuan, K-H. & Bentler, P.M. (2006). Mean comparison: Mainfest variable versus latent variable. Psychometrika, 71, 139-159.
Discriminant Analysis:
Huberty, C.J. (1975). Discriminant analysis. Review of Educational Research, 45, 543-598.
Frank, R.E., Massy, W.F., & Morrison, D.G. (1965). Bias in multiple discriminant analysis. Journal of Marketing Research, 2, 250-258.
Morrison, D.G. (1969). On the interpretation of discriminant analysis. Journal of Marketing Research, 6, 156-163.
* Green, B.F. (1979). The two kinds of linear discriminant functions and their relationship. Journal of Educational Statistics, 4, 247-263.
Borgen, F.H., & Seling, M.J. (1978). Uses of discriminant analysis following MANOVA: Multivariate statistics for multivariate purposes. Journal of Applied Psychology, 63, 689-697.
McKay, R.J., & Campbell, N.A. (l982). Variable selection techniques in discriminant analysis: I. Description. British Journal of Mathematical and Statistical Psychology, 35, 1-29.
McKay, R.J., & Campbell, N.A. (l982). Variable selection techniques in discriminant analysis: II. Allocation. British Journal of Mathematical and Statistical Psychology, 35, 30-41.
Huberty, C.J., Wisenbaker, J.M., & Smith, J.C. (1987). Assessing predictive accuracy in discriminant analysis. Multivariate Behavioral Research, 22, 307-328.
Canonical Correlation:
Darlington, R.B., Weingerg, S.L., & Walberg, H.J. (1973). Canonical variate analysis and related techniques. Review of Educational Research, 43, 433-454.
Wood, D.A. (1972). Toward the interpretation of canonical dimensions. Multivariate Behavioral Research, 7, 477-482.
* Stewart, D., & Love, W. (1968). A general canonical correlation index. Psychological Bulletin, 70, 160-163.
Nicewander, W.A., & Wood, D.A. (1974). Comments on "A general canonical correlation index." Psychological Bulletin, 81, 92-94.
Nicewander, W.A. & Wood, D.A. (1975). On the mathematical bases of the general canonical correlation index: Rejoinder to Miller. Psychological Bulletin, 82, 210-212.
Miller, J.K. (1975). In defense of the general canonical correlation index: Reply to Nicewander and Wood. Psychological Bulletin, 82, 207-209.
* van den Wollenberg, A.L. (1977). Redundancy analysis: An alternative for canonical correlation analysis. Psychometrika, 42, 207-219.
Barcikowski, R.S., & Stevens, J.P. (1975). A Monte Carlo study of the stability of canonical correlations, canonical weights, and canonical variate-variable correlations. Multivariate Behavioral Research, 10, 353-364.
Thorndike, R.M. (1976). Studying canonical analysis: Comments on Barcikowski and Stevens. Multivariate Behavioral Research, 11, 249-253.
Barcikowski, R.S. & Stevens, J.P. (1976). Studying canonical analysis: A reply to Thorndike's comments. Multivariate Behavioral Research, 11, 255-258.
Huberty, C.J. (1984). Issues in the use and interpretation of discriminant analysis. Psychological Bulletin, 95, 156-171.
* Harris, R.J. (1989). A canonical cautionary. Multivariate Behavioral Research, 24, 17-39.
Lazraq,A., & Cleroux, R. (2002) Testing the significance of the successive components in redundancy analysis. Psychometrika, 67, 411-420.
Factor Analysis:
Dzuiban, C.D., & Shirley, E.C. (1974). When is a correlation matrix appropriate for factor analysis: Some decision rules. Psychological Bulletin, 81, 358-361.
Bejar, I.I. (1978). Comment on Dzuiban and Shirkey's decision rules for factor analysis. Psychological Bulletin, 85, 325-326.
Armstrong, J.S., & Soelberg, P. (1968). On the interpretation of factor analysis. Psychological Bulletin, 70, 361-364.
Stogdill, R.M. (1966). Some possible uses of factor analysis in multivariate studies. Multivariate Behavioral Research, 1, 387-389.
Comrey, A.L. (1978). Common methodological problems in factor analytic studies. Journal of Consulting and Clinical Psychology, 46, 648-659.
Tobias, S., & Carlson, J.E. (1969). Bartlett's test of sphericity and chance findings in factor analysis. Multivariate Behavioral Research, 4, 375-377.
Gorsuch, R.L. (1973). Using Bartlett's significance test to determine the number of factors to extract. Educational and Psychological Measurement, 33, 361-364.
Lou, R. (1978). Note on tests of significance in programs for factor analysis. Perceptual and Motor Skills, 47, 1101-1102.
Cattell, R.B. (1966). The scree test for the number of factors. Multivariate Behavioral Research, 1, 245-276.
Hakstian, A.R., & Rogers, W.T. (1982). The behavior of number-of-factors results with simulated data. Multivariate Behavioral Research, 17, 193-220.
Zwick, W.R., & Velicer, W.F. (1982). Factors influencing four rules for determining the number of components to retain. Multivariate Behavioral Research, 17, 253-269.
Harris, C.W. (1978). Note on the squared multiple correlation as a lower board to communality. Psychometrika, 43, 283-284.
Aleamoni, C.M. (1973). Effect of size of sample on eigenvalues, observed communalities, and factor loadings. Journal of Applied Psychology, 58, 266-264.
Shirkey, E.C., & Dziuban, C.D. (1976). A note on some characteristics of the measure of sampling adequacy (MSA). Multivariate Behavioral Research, 11, 125-128.
Kaiser, H. F. (1981). A revised measure of sampling adequacy for factor analytic data matrices. Educational and Psychological Measurement, 41, 379-381.
Cerny, B.A., & Kaiser, H.F. (1977). A study of a measure of sampling adequacy for factor analytic correlation matrices. Multivariate Behavioral Research, 12, 43-47.
Horn, J.L. (1969). On the internal consistency reliability of factors. Multivariate Behavioral Research, 4, 115-125.
Carver, R.P. (1968). On the danger involved in the use of tests which measure factors. Multivariate Behavioral Research, 3, 509-512.
Greene, V.L. (1978). Simultaneous optimization of factor assessibility and representativeness: An old solution to a new problem. Psychometrika, 43, 273-275.
Lykken, D.T. (1971). Multiple factor analysis and personality research. Journal of Experimental Research in Personality, 5, 161-170.
Howarth, E. (1972). Methodological note: Factor analysis has only just begun to fight -- A reply to Lykken. Journal of Experimental Research in Personality, 6, 268-272.
Meehl, P.E., Lykken, D.T. Schofield, W., & Tellegen, A. (1971). Recaptured-item technique (RIT): A method for reducing somewhat the subjective element in factor naming. Journal of Experimental Research in Personality, 5, 171-190.
Mulaik, S.A. (1987). A brief history of the philosophical foundations of exploratory factor analysis. Multivariate Behavioral Research, 22, 267-305.
Guadagnoli, E., & Velicer, W.F. (1988). Relation of sample size to the stability of component patterns. Psychological Bulletin, 103, 265-275.
Cliff, N. (1988). The eigenvalues-greater-than-one rule and the reliability of components. Psychological Bulletin, 103, 276-279.
Snook, S.C., & Gorsuch, R.L. (1989). Component analysis versus common factor analysis: A Monte Carlo study. Psychological Bulletin, 106, 148-154.
Fava, J.L., & Velicer, W.F. (1992). An empirical comparison of factor, image, component and scale scores. Multivariate Behavioral Research, 27, 301-322.
Fava, J.L., & Velicer, W.F. (1992). The effects of overextraction on factor and component analysis. Multivariate Behavioral Research, 27, 387-416.
Ichikawa, M, & Konishi, S. (1995). Application of the bootstrap methods in factor analysis. Psychometrika, 60, 77-94.
Browne, M.W. (2001). An overview of analytic rotation in exploratory factor analysis. Multivariate Behavioral Research, 36, 111-150.
Grice, J.W. (2001). Computing and evaluating factor scores. Psychological Methods, 6, 430-450.
MacCallum, R.C., Widaman, K.F., Preacher, K.J., & Hong, S. Sample size in factor analysis: The role of model error. Multivariate Behavioral Research, 36, 611-638.
Jennrich, R.I. (2002). A simple general method for oblique rotation. Psychometrika, 67, 7-20.
Krijnen, W.P. (2002). On the construction of all factors of the model for factor analysis. Psychometrika, 67, 161-172.