Journal ArticleDOI
Multivariate data analysis
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This chapter discusses Structural Equation Modeling: An Introduction, and SEM: Confirmatory Factor Analysis, and Testing A Structural Model, which shows how the model can be modified for different data types.Abstract:
I Introduction 1 Introduction II Preparing For a MV Analysis 2 Examining Your Data 3 Factor Analysis III Dependence Techniques 4 Multiple Regression Analysis 5 Multiple Discriminate Analysis and Logistic Regression 6 Multivariate Analysis of Variance 7 Conjoint Analysis IV Interdependence Techniques 8 Cluster Analysis 9 Multidimensional Scaling and Correspondence Analysis V Moving Beyond the Basic Techniques 10 Structural Equation Modeling: Overview 10a Appendix -- SEM 11 CFA: Confirmatory Factor Analysis 11a Appendix -- CFA 12 SEM: Testing A Structural Model 12a Appendix -- SEM APPENDIX A Basic Statsread more
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A new criterion for assessing discriminant validity in variance-based structural equation modeling
TL;DR: In this paper, the heterotrait-monotrait ratio of correlations is used to assess discriminant validity in variance-based structural equation modeling. But it does not reliably detect the lack of validity in common research situations.
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PLS-SEM: Indeed a Silver Bullet
TL;DR: The authors conclude that PLS-SEM path modeling, if appropriately applied, is indeed a "silver bullet" for estimating causal models in many theoretical models and empirical data situations.
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The measurement and antecedents of affective, continuance and normative commitment to the organization
Natalie J. Allen,John P. Meyer +1 more
TL;DR: In this article, the authors proposed a three-component model of organizational commitment, which integrates emotional attachment, identification with, and involvement in the organization, and the normative component refers to employees' feelings of obligation to remain with the organization.
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A caution regarding rules of thumb for variance inflation factors.
TL;DR: In this article, the authors examined the effect of the variance inflation factor (VIF) on the results of regression analyses, and found that threshold values of the VIF need to be evaluated in the context of several other factors that influence the variance of regression coefficients.
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A model of (often mixed) stereotype content: Competence and warmth respectively follow from perceived status and competition.
TL;DR: Contrary to antipathy models, 2 dimensions mattered, and many stereotypes were mixed, either pitying (low competence, high warmth subordinates) or envying (high competence, low warmth competitors).