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Structural Equations with Latent Variables

TLDR
The General Model, Part I: Latent Variable and Measurement Models Combined, Part II: Extensions, Part III: Extensions and Part IV: Confirmatory Factor Analysis as discussed by the authors.
Abstract
Model Notation, Covariances, and Path Analysis. Causality and Causal Models. Structural Equation Models with Observed Variables. The Consequences of Measurement Error. Measurement Models: The Relation Between Latent and Observed Variables. Confirmatory Factor Analysis. The General Model, Part I: Latent Variable and Measurement Models Combined. The General Model, Part II: Extensions. Appendices. Distribution Theory. References. Index.

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Evaluating Goodness-of-Fit Indexes for Testing Measurement Invariance

TL;DR: In this paper, the authors examined the change in the goodness-of-fit index (GFI) when cross-group constraints are imposed on a measurement model and found that the change was independent of both model complexity and sample size.
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Mediation in experimental and nonexperimental studies: New procedures and recommendations.

TL;DR: Efron and Tibshirani as discussed by the authors used bootstrap tests to assess mediation, finding that the sampling distribution of the mediated effect is skewed away from 0, and they argued that R. M. Kenny's (1986) recommendation of first testing the X --> Y association for statistical significance should not be a requirement when there is a priori belief that the effect size is small or suppression is a possibility.
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Sources of Method Bias in Social Science Research and Recommendations on How to Control It

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The job demands-resources model of burnout

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