The DOI matches its Crossref record; 261 of the 261 references it cites were found in a registry or an index.
Structural Equation Modeling
Abstract
The first half of this chapter presents an overview of the type hypotheses, statistical theory, and major issues relevant to specifying, estimating, evaluating, and interpreting structural equation models. Following this brief SEM overview, a complete example of a simple SEM model of risky behavior using a large dataset of responses from preteen children is presented. This presentation includes examples of output from EQS 6.1. The second half of chapter is devoted to a detailed presentation of the current issues and important future research directions in structural equation modeling.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“SEM was chosen as the method to examine the data above the use of multiple regression or simple correlations, as the method allowed for the latent modelling of variables with measurement error taken into account (Ullman & Bentler, 2012).”
From Sources and effects of foreign language enjoyment, anxiety, and boredom: A structural equation modeling approach (2023) · cites this paper as Ullman & Bentler, 2012
Citations
Cites
Hu, L., & Bentler, P. M. (1999) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705519909540118 · Cited by 108,788
Self-citationStructural Equations with Latent Variables
Bollen, K. A. (1989) · Wiley series in probability and statistics · doi:10.1002/9781118619179 · Cited by 17,277
Hu, L.-T., & Bentler, P. M. (1998) · Psychological Methods · doi:10.1037/1082-989x.3.4.424 · Cited by 12,298
Self-citationMissing 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
Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1080/10705510701575396 · Cited by 11,415
Power analysis and determination of sample size for covariance structure modeling.
MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996) · Psychological Methods · doi:10.1037/1082-989x.1.2.130 · Cited by 10,418
Costello, A. B., & Osborne, J. (2005) · University of Massachusetts Amherst · doi:10.7275/jyj1-4868 · Cited by 10,297
A comparison of methods to test mediation and other intervening variable effects
MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., & Sheets, V. (2002) · Psychological Methods · doi:10.1037/1082-989x.7.1.83 · Cited by 9,406
A test of missing completely at random for multivariate data with missing values
Little, R. J. A. (1988) · Journal of the American Statistical Association · doi:10.1080/01621459.1988.10478722 · Cited by 8,963
Statistical Analysis with Missing Data
Little, R. J. A., & Rubin, D. B. (2002) · Wiley Series in Probability and Statistics · doi:10.1002/9781119013563 · Cited by 8,473
Research works printed but not linked: 43
- Bentler P. M., 1982, Systems under indirect observation: Causality, structure, prediction, 101
- Bentler P. M., 2005, Zeitreihenanalysen, 13
- Bentler P. M., 2008, EQS 6 structural equations program manual
- Bentler P. M., 1985, Multivariate analysis VI, 9
- Brown T. A., 2006, Confirmatory factor analysis for applied research
- Browne M. W., 1993, Testing structural models
- Byrne B. M., 2006, Structural equation modeling with EQS: Basic concepts, applications, and programming
- Curran P. J., 2000, Multivariate applications in substance use research: New methods for new questions, 1
- Ayala R. J., 2009, The theory and practice of item response theory
- de Leeuw J., 2008, Handbook of multilevel modeling
- Duncan T. E., 2006, An introduction to latent variable growth curve modeling
- Duncan T. E., 1999, An introduction to latent variable growth curve modeling: Concepts, issues, and applications
- Enders C. K., 2010, Applied missing data analysis
- Hancock G. R., 2008, Advances in latent variable mixture models
- Harrington D., 2009, Confirmatory factor analysis
- Hayashi K., 2008, Handbook of statistics 27 Epidemiology & medical statistics, 395
- Headrick T. C., 2010, Statistical simulation: Power method polynomials and other transformations
- 10.1007/s11336-010-9175-3 · DOI
- Jamshidian M. Yuan K. &Le P.(in press).Using confidence intervals to test for non‐ignorable non‐response.
- 10.1037/0033-2909.96.1.201 · DOI · Paper page
- Khoo S.‐T., 2000, Multivariate applications in substance use research: New methods for new questions, 43
- Kline R. B., 2010, Principles and practice of structural equation modeling
- Lee S.‐Y., 2007, Handbook of latent variable and related models
- Lin J. &Bentler P. M.(2010).A new goodness of fit test statistic for covariance structure analysis and its comparative performance. Under review.
- Liu J.(2007).Multivariate ordinal data analysis with pairwise likelihood and its extension to SEM.Unpublished dissertation UCLA.
- Lubke G. H., 2010, The reviewer's guide to quantitative methods in the social sciences
- 10.1037/0033-2909.100.1.107 · DOI · Paper page
- MacKinnon D. P., 2008, Introduction to statistical mediation analysis
- Mair P. Satorra A. &Bentler P. M.(2011).Nonnormal multivariate data from copulas: Applications to SEM. Under editorial review.
- 10.1037/1082-989X.5.1.23 · DOI · Paper page
- Millsap R. E., 2011, Statistical approaches to measurement invariance
- Mulaik S. A., 2010, Foundations of factor analysis
- Muthén B., 2009, Longitudinal data analysis, 143
- Satorra A., 1988, Proceedings of the American Statistical Association, 308
- Satorra A., 1994, Latent variables analysis: Applications for developmental research, 399
- Skrondal A., 2011, Generalized latent variable modeling: Multilevel, longitudinal, and structural equation models
- Ullman J. B., 2007, Using multivariate statistics, 676
- Ullman J. B., 2000, Multivariate applications in substance use research: New methods for new questions, 203
- Wu E. J. C., 2011, EQSIRT—A user‐friendly IRT program
- Yuan K.‐H., 1998, Sociological methodology, 363
- 10.2307/1165323 · DOI
- Yuan K.‐H., 2007, Sociological methodology 2007, 53
- Yuan K.‐H., 2010, Sociological Methodology 2010, 191
Cited by
Dewaele, J.-M., Botes, E., & Greiff, S. (2023) · Studies in Second Language Acquisition · doi:10.1017/s0272263122000328 · Cited by 215
Caputo, F., Cillo, V., Candelo, E., & Liu, Y. (2019) · Management Decision · doi:10.1108/md-07-2018-0833 · Cited by 199
Bewersdorff, A., Hornberger, M., Nerdel, C., & Schiff, D. S. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100340 · Cited by 148
Sustaining Economic Growth: E-Service Quality’s Role in Fostering Customer Loyalty in Nigeria SMEs
Madueke, C. J., & Eyupoglu, S. (2024) · Sustainability · doi:10.3390/su16219175 · Cited by 4
Metadata
- Authors
- Ullman, J. B., & Bentler, P. M.
- Venue
- Handbook of Psychology, Second Edition
- Publisher
- Wiley
- Published
- 2012-09-26
- Cited by
- 330
- References
- 261
- Language
- en
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Structural Equation Modeling. https://app.cochat.ai/papers/10.1002/9781118133880.hop202023
- The paper
- Ullman, J. B., & Bentler, P. M. (2012). Structural Equation Modeling. Handbook of Psychology, Second Edition. https://doi.org/10.1002/9781118133880.hop202023
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.