The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Structural Equation Modeling: Strengths, Limitations, and Misconceptions
Abstract
Because structural equation modeling (SEM) has become a very popular data-analytic technique, it is important for clinical scientists to have a balanced perception of its strengths and limitations. We review several strengths of SEM, with a particular focus on recent innovations (e.g., latent growth modeling, multilevel SEM models, and approaches for dealing with missing data and with violations of normality assumptions) that underscore how SEM has become a broad data-analytic framework with flexible and unique capabilities. We also consider several limitations of SEM and some misconceptions that it tends to elicit. Major themes emphasized are the problem of omitted variables, the importance of lower-order model components, potential limitations of models judged to be well fitting, the inaccuracy of some commonly used rules of thumb, and the importance of study design. Throughout, we offer recommendations for the conduct of SEM analyses and the reporting of results.
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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
Baron, R. M., & Kenny, D. A. (1986) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.51.6.1173 · Cited by 91,532
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Alternative Ways of Assessing Model Fit
Browne, M. W., & Cudeck, R. (1992) · Sociological Methods & Research · doi:10.1177/0049124192021002005 · Cited by 27,200
Comparative fit indexes in structural models.
Bentler, P. M. (1990) · Psychological Bulletin · doi:10.1037/0033-2909.107.2.238 · Cited by 25,266
Multiple Imputation for Nonresponse in Surveys
Rubin, D. B. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316696 · Cited by 20,701
Hu, L.-T., & Bentler, P. M. (1998) · Psychological Methods · doi:10.1037/1082-989x.3.4.424 · Cited by 12,298
Missing data: our view of the state of the art
Schafer, J. L., & Graham, J. W. (2002) · Psychological Methods · doi:10.1037/1082-989x.7.2.147 · Cited by 11,638
Agresti, A. (2002) · Wiley Series in Probability and Statistics · doi:10.1002/0471249688 · Cited by 6,746
Marsh, H. W., Hau, K.-T., & Wen, Z. (2004) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1207/s15328007sem1103_2 · Cited by 6,691
Cited by
Latent Variable Modeling of Differences and Changes with Longitudinal Data
McArdle, J. J. (2009) · Annual Review of Psychology · doi:10.1146/annurev.psych.60.110707.163612 · Cited by 1,952
Best-practice recommendations for defining, identifying, and handling outliers
Aguinis, H., Gottfredson, R. K., & Joo, H. (2013) · Organizational Research Methods · doi:10.1177/1094428112470848 · Cited by 1,341
Yes, but what’s the mechanism? (don’t expect an easy answer).
Bullock, J. G., Green, D. P., & Ha, S. E. (2010) · Journal of Personality and Social Psychology · doi:10.1037/a0018933 · Cited by 1,063
Wood, A. M., Maltby, J., Gillett, R., Linley, P. A., & Joseph, S. (2008) · Journal of Research in Personality · doi:10.1016/j.jrp.2007.11.003 · Cited by 761
Maslowsky, J., Jager, J., & Hemken, D. (2015) · International Journal of Behavioral Development · doi:10.1177/0165025414552301 · Cited by 659
Metadata
- Authors
- Tomarken, A. J., & Waller, N. G.
- Venue
- Annual Review of Clinical Psychology
- Publisher
- Annual Reviews
- Published
- 2005-04-01
- Volume
- 1
- Issue
- 1
- Pages
- 31-65
- ISSN
- 1548-5943 (ISSN-L)
- 1548-5951
- OpenAlex
- W2112759395
- Cited by
- 882
- References
- 33
- FWCI
- 8.12
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
