Publications: Methodology

Aldenderfer, M. and R. Blashfield (1984). Cluster Analysis. Newbury Park, CA, SAGE Publications, Inc.

Arruda, J. E., M. D. Weiler, et al. (1996). “A guide for applying principal-components analysis and confirmatory factor analysis to quantitative electroencephalogram data.” International Journal of Psychophysiology 23: 63-81.

Atlas, R. and J. Overall (1994). “Comparative evaluation of two superior stopping rules for hierarchical cluster analysis.” Psychometrika 59: 581-591.

Beaman, J. and J. Vaske (1995). “An ipsative clustering model for analyzing attitudinal data.”Journal of Leisure Research 27: 168-191.

Bentler, P. M. (1990). “Comparative fit indices in structural models.” Psychological Bulletin107: 238-246.

Bullock, H. E. and L. L. Harlow (1994). “Causation issues in structural equation modeling research.” Structural Equation Modeling 1: 253-267.

Campbell, D. T. and J. C. Stanley (1963). Experimental and Quasi-experimental Designs for Research. Boston, Houghton Mifflin Company.

Cattell, R. B. (1966). “The screen test for the number of factors.” Multivariate Behavioral Research 1: 245-276.

Cliff, N. (1983). “Some cautions concerning the application of causal modeling methods.”Multivariate Behavioral Research 18: 115-126.

Cohen, S. and G. M. Williamson (1987). Perceived stress in a probability sample of the United States. Psychology and Health. S. Spacapan and S. Oskamp. Newbury Park, CA, Sage31-67.

Colby, S. M. and W. F. Velicer (?). “A comparison of four alternative procedures for handling missing data in time series analysis.” .

Cronbach, L. J. (1951). “Coefficient alpha and the internal structure of tests.” Psychometrika16: 297-334.

Ding, L., W. F. Velicer, et al. (1995). “Effects of estimation methods, number of indicators per factor, and improper solutions on structural equation modeling fit indices.” Structural Equation Modeling 2(2): 119-144.

Eckes, T. and P. Orlik (1993). “An error variance approach to two-mode hierarchical clustering.” Journal of Classification 10: 51-74.

Fava, J. L. and W. F. Velicer (1992). “The effects of overextraction on factor and component analysis.” Multivariate Behavioral Research 27: 387-415.

Fava, J. L. and W. F. Velicer (1992). “An empirical comparison of factor, image component, and scale scores.” Multivariate Behavioral Research 27: 301-322.

Fava, J. L. and W. F. Velicer (1996). “The effects of underextraction in factor and component analyses.” Educational and Psychological Measurement 56(6): 907-929.

Fitzgerald, T. E. and J. O. Prochaska (1990). “Nonprogressing profiles in smoking cessation: What keeps people refractory to self-change?” Journal of Substance Abuse 2: 87-105.

Guadagnoli, E. and W. F. Velicer (1988). “Relation of sample size to the stability of component patterns.” Psychological Bulletin 103(2): 265-275.

Guadagnoli, E. and W. F. Velicer (1991). “A comparison of pattern matching indices.”Multivariate Behavioral Research 26: 323-343.

Harrop, J. W. and W. F. Velicer (1985). “A comparison of alternative approaches to the analysis of interrupted time series.” Multivariate Behavioral Research 20: 27-44.

Harrop, J. W. and W. F. Velicer (1990). “Computer programs for interrupted time series analysis: I. A quantitative evaluation.” Multivariate Behavioral Research 25(2): 219-231.

Harrop, J. W. and W. F. Velicer (1990). “Computer programs for interrupted time series analysis: II. A quantitative evaluation.” Multivariate Behavioral Research 25: 233-248.

Horn, J. I. (1965). “A rationale and test for the number of factors in factor analysis.”Psychometrika 30: 179-185.

Hughes, S., W. Velicer, et al. (1990). The application of cluster analysis to smoking cessation research. American Psychological Association, Boston, MA.

Johnson, S. (1992). “Methodological issues in diabetes research.” Diabetes Care 15: 1658-1667.

Joreskog, K. G. and Sorbom (1989). LISREL 7: A guide to the program and applications. Chicago, IL, SPSS, Inc.

King, A. C., M. Kiernan, et al. (1997). “Can we identify who will adhere to long-term physical activity? Signal detection methodology as a potential aid to clinical decision making.” Health Psychology 16: 380-389.

Kraemer, H. C. (1988). “Assessment of 2 x 2 associations: Generalization of signal detection methodology.” The American Statistician 42: 37-49.

Lang, J. M. and K. J. Rothman (1998). “That confounded p-value.” Epidemiology 9: 7-8.

Latane, B., A. Nowak, et al. (1994). “Measuring emergent social phenomena: Dynamism, polarization, and clustering as order parameters of social systems.” Behavioral Science 39: 1-24.

Lautenschlager, G. J. (1989). “A comparison of alternatives to conducting Monte Carlo analyses for determining parallel analysis criteria.” Multivariate Behavioral Research 24(3): 265-395.

Levesque, D. and R. Gelles (1997). Battering men: Applying the Transtheoretical model to desistance and change. 5th International Family Violence Research Conference, Durham, NH.

Loftus, G. R. (1996). “Psychology will be a much better science when we change the way we analyze data.” Current Directions in Psychological Science 5: 161-171.

Maddock, J. E., R. G. LaForge, et al. (1997). “Development of a short psychometrically reliable alcohol problem index for college students.” Unpublished manuscript.

Marsh, H. W., J. R. Balla, et al. (1988). “Goodness-of-fit indices in confirmatory factor analysis: The effect of sample size.” Psychological Bulletin 103: 391-410.

Martin, R. A., W. F. Velicer, et al. (1995). “Latent transition analysis applied to the stages of change for smoking cessation.” Addictive Behaviors 20: 67-80.

McDonald, R. P. and H. W. Marsh (1990). “Choosing a multivariate model: Noncentrality and goodness of fit.” Psychological Bulletin 107: 247-255.

Meehl, P. E. (1954). Clinical versus Statistical Prediction: A Theoretical Analysis and Review of the Literature. Minneapolis, MN, University of Minnesota Press.

Milligan, G. W. (1980). “An examination of the effect of six types of error perturbation on fifteen clustering algorithms.” Psychometrika 45: 325-342.

Milligan, G. W. (1981). “A review of Monte Carlo tests of cluster analysis.” Multivariate Behavioral Research 16: 379-407.

Milligan, G. W. and M. C. Cooper (1987). “Methodology review: Clustering methods.” Applied Psychological Measurement 11: 329-354.

Mueller, R. O. (1996). General structural equation modeling. Basic Principles of Structural Equation Modeling. New York, NY, Springer-Verlag New York, Inc.129-178.

Norman, G. (1998). A cluster analytic test of the Transtheoretical model applied to exercise behavior. Psychology. Kingston, RI, University of Rhode Island.

Norman, G. J., W. F. Velicer, et al. (1998). “Dynamic typology clustering with the stages of change for smoking cessation.” Addictive Behaviors 23: 139-153.

Overall, J. and K. Magee (1992). “Replication as a rule for determining the number of clusters in hierarchial cluster analysis.” Applied Psychological Measurement 16: 119-128.

Rodgers, J. and T. Thompson (1992). “Seriation and multidimensional scaling: A data analysis approach to scaling asymmetric proximity matrices.” Applied Psychological Measurement 16: 105-117.

Rossi, J. S. (1982). Meta-analysis, power analysis and artifactual controversy: The case of spontaneous recovery of verbal associations. Eastern Psychological Association, Baltimore, MD.

Rossi, J. S. (1985). “Tables of effect size for z score tests of differences between proportions and between correlation coefficients.” Educational and Psychological Measurement 45: 737-743.

Rossi, J. S. (1987). “One-way ANOVA from summary statistics.” Educational and Psychological Measurement 47: 37-38.

Rossi, J. S., J. O. Prochaska, et al. (1988). A cluster-analytic approach to defining light and heavy smoking. American Psychological Association, Atlanta, GA.

Rossi, J. S. (1988). “ONEWAY: A BASIC program for computing ANOVA from summary statistics.” Behavior Research Methods, Instruments and Computers 20(3): 347-348.

Rossi, J. (1990). “Statistical power of psychological research: What have we gained in 20 years?” Journal of Consulting and Clinical Psychology 58: 646-656.

Rossi, J. S. (1990). How often are our statistics wrong? A statistics class exercise. Teaching Psychology: A Handbook. J. Hartley and W. J. McKeachie. Hillsdale, NJ, Lawrence Erlbaum Associates. 14: 98-101.

Rossi, J. S. (?). Inadequate statistical power and artifactual controversy: Some consequences of statistical significance testing ((Draft). What If There Were No Significance Tests? L. Harlow and S. Mulaik. Hillsdale, NJ, Lawrence Erlbaum.

Sharma, S., S. Durasula, et al. (1989). “Some results on the behavior of alternate covariance structure estimation procedures in the presence of non-normal data.” Journal of Marketing Research 26: 214-221.

SPSS (1990). SPSS Base System (Release 4.0) [Computer software]. Chicago, IL, Author.

Tabachnick, B. G. and L. S. Fidell (1989). Using Multivariate Statistics. New York, NY, Harper Collins Publisher,Inc.

Tucker, L. R. and C. Lewis (1973). “A reliability coefficient for maximum likelihood factor analysis.” Psychometrika 38: 1-10.

VanBuuren, S. and W. Heiser (1989). “Clustering N objects into K groups under optimal scaling of variables.” Psychometrika 54: 699-706.

Velicer, W. F. (1974). “An empirical comparison of the stability of factor analysis, principal component analysis, and image analysis.” Educational and Psychological Measurement 34: 563-572.

Velicer, W. F. (1976). “Determining the number of components from the matrix of partial correlations.” Psychometrika 41: 321-327.

Velicer, W. F. (1976). “The relation between factor score estimates, image scores and principal component scores.” Educational and Psychological Measurement 36: 149-159.

Velicer, W. F. (1977). “An empirical comparison of factor, image, and principal component patterns.” Multivariate Behavioral Research 12: 3-22.

Velicer, W. F. (1978). “Suppressor variables and the semipartial correlation coefficient.”Educational and Psychological Measurement 38: 953-958.

Velicer, W. F., A. C. Peacock, et al. (1982). “A comparison of component and factor patterns: A Monte Carlo approach.” Multivariate Behavioral Research 17: 371-388.

Velicer, W. F. (1982). “Prediction and association for N-way classification tables.” Evaluation Review 6(2): 247-266.

Velicer, W. F. and J. Harrop (1983). “The reliability and accuracy of time series model identification.” Evaluation Review 7(4): 551-560.

Velicer, W. F., C. C. DiClemente, et al. (1984). “Item format and the structure of the personal orientation inventory.” Applied Psychological Measurement 8(4): 409-419.

Velicer, W. F. and R. P. McDonald (1984). “Time series analysis without model identification.”Multivariate Behavioral Research 19: 33-47.

Velicer, W. F., J. M. Govia, et al. (1985). “Item format and the structure of the Buss-Durkee hostility inventory.” Aggressive Behavior 11: 65-82.

Velicer, W. F. and J. L. Fava (1987). “An evaluation of the effects of variable sampling on component, image, and factor analysis.” Multivariate Behavioral Research 22: 193-209.

Velicer, W. F., J. L. Fava, et al. (1988). Component Analysis Extended [Computer Program]. Kingston, RI, University of Rhode Island.

Velicer, W. F. and D. N. Jackson (1990). “Component analysis versus common factor analysis: Some issues in selecting an appropriate procedure.” Multivariate Behavioral Research 25(1): 1-28.

Velicer, W. F. and D. N. Jackson (1990). “Component analysis versus common factor analysis: Some further observations.” Multivariate Behavioral Research 25(1): 97-114.

Velicer, W. F. and R. P. McDonald (1991). “Cross-sectional time series designs: A general transformation approach.” Multivariate Behavioral Research 26(2): 247-254.

Velicer, W. F., S. L. Hughes, et al. (1992). “An empirical Typology of subjects within stages of change (Abstract).” International Journal of Psychology 27: 623.

Velicer, W. F., C. A. Redding, et al. (1992). “A time series investigation of three nicotine regulation models.” Addictive Behaviors 17: 325-345.

Velicer, W. F. (1994). Time Series Models of Individual Substance Abusers. Advances in Data Analysis for Prevention, Intervention and Research. L. M. Collins and L. A. Seitz. Rockeville, MD, National Institute on Drug Abuse264-299.

Velicer, W. V., R. A. Martin, et al. (1996). “Latent transition analysis for longitudinal data.”Addiction 91(Suppl.): S197-S209.

Velicer, W. F. (1996). Stability of well-defined structures, CPRC.

Velicer, W. and G. Norman (1996). “Dynamic typologies: Cluster analysis in the temporal domain.” Submitted for review.

Velicer, W. F. and B. A. Plummer (1998). “Time series analysis in historiometry: A comment on Simonton.” Journal of Personality 66(3): 477-486.

Velicer, W. F. and J. L. Fava (1998). “Effects of variable and subject sampling on factor pattern recovery.” Psychological Methods 3(2): 231-251.

Wallace, R. B. and P. L. Colsher (1990). “Enhancing the utility of quantity-frequency measures of alcohol consumptions with assessments of problem drinking in a population study: A methodologic note.” Annals of Epidemiology 1: 157-165.

Ward, J. H., Jr. (1963). “Hierarchical grouping to optimize an objective function.” Journal of the American Statistical Association 58: 236-244.

Yates, F. (1934). “Contingency tables involving small numbers and the chi-square test.”Journal of the Royal Statistical Society (Series B) 1(Suppl.): 217-235.

Zwick, W. R. and W. F. Velicer (1982). “Factors influencing four rules for determining the number of components to retain.” Multivariant Behavioral Research 17: 253-269.

Zwick, W. R. and W. F. Velicer (1986). “A comparison of five rules for determining the number of components to retain.” Psychological Bulletin 99: 432-442.