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Evaluating and Modifying Covariance Structure Models

A Review and Recommendation

Bibliographic Data

ID19291908
AuthorsD M Kaplan (0000-0002-2333-1481, University of Delaware, corresponding author), David Kaplan (0000-0003-0294-549X)
Year1990
Volume25
Issue2
Pages137-155
Publication date1990-04-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueMultivariate Behavioral Research (JOURNAL)
Journal identifiersISSN: 0027-3171 • E-ISSN: 1532-7906
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1207/s15327906mbr2502_1
PMID26794476
OpenAlexW2070642543
LanguageEN
Citations received59
References cited31

The purpose of this article is to present a strategy for the evaluation and modification of covariance structure models. The approach makes use of recent developments in estimation under non-standard conditions and unified asymptotic theory related to hypothesis testing. Factors affecting the evaluation and modification of these models are reviewed in terms of nonnormality, missing data, specification error, and sensitivity to large sample size. Alternative model evaluation and specification error search strategies are also reviewed. The approach to covariance structure modeling advocated in this article utilizes the LISREL modification index for assessing statistical power, and the expected parameter change statistic for guiding specification error searches. It is argued that the common approach of utilizing alternative fit indices does not allow the investigator to rule out plausible explanations for model misfit. The approach advocated in this article allows one to determine the extent of sample size sensitivity and the effects of specification error by relying on existing statistical theory underlying covariance structure models

Covariance · Data mining · Econometrics · LISREL · Machine learning · Sample size determination · Sensitivity (control systems) · Specification · Statistic · Statistical hypothesis testing · Statistics · Structural equation modeling · Advanced Statistical Methods and Models · Computer Science · Mathematics · Statistical Distribution Estimation and Applications · Statistical Methods and Bayesian Inference

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Unique citing works59
Citations per year1,64
Citation span1990 - 2026 (37)
Citation velocitycurrent
Highly citedNo
Citation typesNeutral: 55
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