Book ChapterDOI
Topics in Applied Multivariate Analysis: COVARIANCE STRUCTURES
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The article was published on 1982-01-01. It has received 300 citations till now. The article focuses on the topics: Covariance & Multivariate statistics.read more
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On the evaluation of structural equation models
Richard P. Bagozzi,Youjae Yi +1 more
TL;DR: In this article, structural equation models with latent variables are defined, critiqued, and illustrated, and an overall program for model evaluation is proposed based upon an interpretation of converging and diverging evidence.
Journal ArticleDOI
Development and validation of a work domain goal orientation instrument.
TL;DR: In this paper, the authors describe the development and validation process for an instrument to assess goal orientation (an individual disposition toward developing or validating one's ability in achievement settings), and the results of exploratory factor analysis, reliability analysis (internal consistency and test-retest), confirmatory factor analyses, and nomological network analysis all support the conclusion that the instrument operationalizes the theorized three-dimensional construct.
Journal ArticleDOI
"How Big Is Big Enough?": Sample Size and Goodness of Fit in Structural Equation Models with Latent Variables.
TL;DR: Tanaka et al. as mentioned in this paper developed the ME2 estimator for moment structures and used it to measure the stability of depression in college students, and found that the maximum entropy measurement error of singular covariance matrices in under-sized samples is larger than that of the full covariance matrix.
Journal ArticleDOI
A Parsimonious Estimating Technique for Interaction and Quadratic Latent Variables
TL;DR: In this article, an alternative estimation technique for quadratic and interaction latent variables in structural equation models using LISREL, EQS, and CALIS is proposed, which specifies these vari...
Journal ArticleDOI
Structural equation modeling: strengths, limitations, and misconceptions.
TL;DR: Several strengths of SEM are reviewed, with a particular focus on recent innovations that underscore how SEM has become a broad data-analytic framework with flexible and unique capabilities.