The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Applications of Structural Equation Modeling in Psychological Research
Abstract
This chapter presents a review of applications of structural equation modeling (SEM) published in psychological research journals in recent years. We focus first on the variety of research designs and substantive issues to which SEM can be applied productively. We then discuss a number of methodological problems and issues of concern that characterize some of this literature. Although it is clear that SEM is a powerful tool that is being used to great benefit in psychological research, it is also clear that the applied SEM literature is characterized by some chronic problems and that this literature can be considerably improved by greater attention to these issues.
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Cites
Hu, L., & Bentler, P. M. (1999) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705519909540118 · Cited by 108,788
Alternative Ways of Assessing Model Fit
Browne, M. W., & Cudeck, R. (1992) · Sociological Methods & Research · doi:10.1177/0049124192021002005 · Cited by 27,200
Convergent and discriminant validation by the multitrait-multimethod matrix.
Campbell, D. T., & Fiske, D. W. (1959) · Psychological Bulletin · doi:10.1037/h0046016 · Cited by 17,116
Hu, L.-T., & Bentler, P. M. (1998) · Psychological Methods · doi:10.1037/1082-989x.3.4.424 · Cited by 12,298
Sample size in factor analysis.
MacCallum, R. C., Widaman, K. F., Zhang, S., & Hong, S. (1999) · Psychological Methods · doi:10.1037/1082-989x.4.1.84 · Cited by 5,497
Self-citationMeasurement Invariance, Factor Analysis and Factorial Invariance
Meredith, W. (1993) · Psychometrika · doi:10.1007/bf02294825 · Cited by 4,536
Methodology for Genetic Studies of Twins and Families
Neale, M. C., & Cardon, L. R. (1992) · doi:10.1007/978-94-015-8018-2 · Cited by 4,005
Factor analysis in the development and refinement of clinical assessment instruments.
Floyd, F. J., & Widaman, K. F. (1995) · Psychological Assessment · doi:10.1037/1040-3590.7.3.286 · Cited by 3,970
A practical and theoretical guide to measurement invariance in aging research
Horn, J. L., & Mcardle, J. J. (1992) · Experimental Aging Research · doi:10.1080/03610739208253916 · Cited by 1,592
Taking Account of Time Lags in Causal Models
Gollob, H. F., & Reichardt, C. S. (1987) · Child Development · doi:10.2307/1130293 · Cited by 572
Development and validation of the pain response inventory for children
Walker, L. S., Smith, C. A., Garber, J., & Van Slyke, D. A. (1997) · Psychological Assessment · doi:10.1037/1040-3590.9.4.392 · Cited by 292
Cited by
Principles and practice in reporting structural equation analyses.
McDonald, R. P., & Ho, M.-H. R. (2002) · Psychological Methods · doi:10.1037/1082-989x.7.1.64 · Cited by 4,734
A practical guide to factorial validity using PLS-graph: tutorial and annotated example
Gefen, D., & Straub, D. (2005) · Communications of the Association for Information Systems · doi:10.17705/1cais.01605 · Cited by 3,280
Bias in cross-sectional analyses of longitudinal mediation
Maxwell, S. E., & Cole, D. A. (2007) · Psychological Methods · doi:10.1037/1082-989x.12.1.23 · Cited by 3,401
MacKenzie, S. B., Podsakoff, P. M., & Podsakoff, N. P. (2011) · MIS Quarterly · doi:10.2307/23044045 · Cited by 3,165
An empirical comparison of the efficacy of covariance-based and variance-based SEM
Reinartz, W., Haenlein, M., & Henseler, J. (2009) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2009.08.001 · Cited by 2,906
Metadata
- Authors
- MacCallum, R. C., & Austin, J. T.
- Venue
- Annual Review of Psychology
- Publisher
- Annual Reviews
- Published
- 2000-02
- Volume
- 51
- Issue
- 1
- Pages
- 201-226
- ISSN
- 0066-4308 (ISSN-L)
- 1545-2085
- OpenAlex
- W2169294797
- Cited by
- 2,623
- References
- 11
- FWCI
- 8.89
- Type
- journal article
- Language
- en
- Open access
- closed
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