Working with imperfect models

Multivariate Behavioral Research

Volumn 38, Issue 1, 2003, Pages 113-139

  MacCallum, Robert C ^a  

^a UNIVERSITY OF NORTH CAROLINA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0037502887     PISSN: 00273171     EISSN: None     Source Type: Journal    
DOI: 10.1207/s15327906mbr3801_5     Document Type: Conference Paper

References (37)

1. Akaike, H. (1987). Factor analysis and AIC. Psychometrika, 52, 317-332.
2. Botha, J. D., Shapiro, A., & Steiger, J. H. (1988). Uniform indices-of-fit for factor analysis models. Multivariate Behavioral Research, 23, 443-450.
3. Box, G. E. P. (1979). Some problems of statistics and everyday life. Journal of the American Statistical Association, 74, 1-4.
4. Browne, M. W. (1968). A comparison of factor analytic techniques. Psychometrika, 33, 267-334.
5. Browne, M. W. & Arminger, G. (1995). Specification and estimation of mean-and covariance-structure models. In G. Arminger, C. C. Clogg, & M. E. Sobel (Eds.), Handbook of statistical modeling for the social and behavioral sciences (pp. 185-249). New York: Plenum.
6. Browne, M. W. & Cudeck, R. (1989). Single sample cross-validation indices for covariance structures. Multivariate Behavioral Research, 24, 445-455.
7. Browne, M. W. & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136-162). Newbury Park, CA: Sage.
8. Cudeck, R. & Henly, S. J. (1991). Model selection in covariance structures analysis and the "problem" of sample size: A clarification. Psychological Bulletin, 109, 512-519.
9. Fechner, G. T. (1860). Elemente der psychophysic (Vol. 1). Leipzig: Brietkopf & Marterl.
10. Hansen, M. H. & Yu, B. (2001). Model selection and the principle of minimum description length. Journal of the American Statistical Association, 96, 746-774.
11. Jöreskog, K. G. & Sörbom, D. (1996). LISREL 8: User's reference guide. Chicago: Scientific Software.
12. Linhart, H. & Zucchini, W. (1986). Model selection. New York: Wiley.
13. Luce, R. D. (1995). Four tensions concerning mathematical modeling in psychology. Annual Review of Psychology, 46, 1-26.
14. MacCallum, R. C. (1986). Specification searches in covariance structure modeling. Psychological Bulletin, 100, 107-120.
15. MacCallum, R. C., Roznowski, M., & Necowitz, L. B. (1992). Model modification in covariance structure analysis: The problem of capitalization on chance. Psychological Bulletin, 111, 490-504.
16. MacCallum, R. C. & Tucker, L. R (1991). Representing sources of error in the common factor model: Implications for theory and practice. Psychological Bulletin, 109, 502-511.
17. MacCallum, R. C., Tucker, L. R, & Briggs, N. E. (2001). An alternative perspective on parameter estimation in factor analysis and related methods. In R. Cudeck, S. du Toit, & D. Sörbom (Eds.), Structural equation modeling: Present and future. (pp. 39-57). Lincolnwood, IL: SSI.
18. MacCallum, R. C., Widaman, K. F., Preacher, K. J., & Hong, S. (2001). Sample size in factor analysis: The role of model error. Multivariate Behavioral Research, 36, 611-637.
19. MacCallum, R. C., Widaman, K. F., Zhang, S., & Hong, S. (1999). Sample size in factor analysis. Psychological Methods, 4, 84-99.
20. Marsh, H. W., Balla, J. R., & McDonald, R. P. (1988). Goodness-of-fit indexes in confirmatory factor analysis: The effect of sample size. Psychological Bulletin, 103, 391-410.
21. McDonald, R. P. & Marsh, H. W. (1990). Choosing a multivariate model: Noncentrality and goodness of fit. Psychological Bulletin, 107, 247-255.
22. Meehl, P. E. (1990). Appraising and amending theories: The strategy of Lakatosian defense and two principles that warrant it. Psychological Inquiry, 1, 108-141.
23. Miller, G. A. (1964). Mathematics and psychology. New York: Wiley.
24. Myung, I. J. & Pitt, M. A. (1997). Applying Occam's razor in modeling cognition. A Bayesian approach. Psychonomic Bulletin and Review, 4, 79-95.
25. Pitt, M. A., Myung, I. J., & Zhang, S. (2002). Toward a method of selecting among computational models of cognition. Psychological Review, 109, 472-491.
26. Preacher, K. J. & MacCallum, R. C. (2002). Exploratory factor analysis in behavior genetics research: Determinants of good factor recovery. Behavior Genetics, 32, 153-161.
27. Ratcliff, R. & Tuerlinckx, F. (in press). Estimating parameters of the diffusion model: Approaches to dealing with contaminant reaction times and parameter variability. Psychonomic Bulletin and Review.
28. Roberts, S. & Pashler, H. (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107, 358-367.
29. Shapiro, A. & Browne, M. W. (1989). On the asymptotic bias of estimators under parameter drift. Statistics and Probability Letters, 7, 221-224.
30. Steiger, J. H. (1990). Structural model evaluation and modification: An interval estimation approach. Multivariate Behavioral Research, 25, 173-180.
31. Steiger, J. H. & Lind, J. M. (1980, May). Statistically-based tests for the number of common factors. Paper presented at Psychometric Society Meeting, Iowa City, Iowa.
32. Thissen, D. (2001). Psychometric engineering as art. Psychometrika, 66, 473-486.
33. Thurstone, L. L. (1930). The learning function. Journal of General Psychology, 3, 469-478.
34. Tucker, L. R, Koopman, R. F., & Linn, R. L. (1969). Evaluation of factor analytic research procedures by means of simulated correlation matrices. Psychometrika, 34, 421-459.
35. Tukey, J. W. (1961). Discussion, emphasizing the connection between analysis of variance and spectrum analysis. Technometrics, 3, 191-219.
36. Weber, E. H. (1834). De pulsu, resorptione, auditu et tactu: Annotationes anatomical et physiological. Leipzig: Koehler.
37. Zucchini, W. (2000). An introduction to model selection. Journal of Mathematical Psychology, 44, 41-61.