Basics of Structural Equation Modeling

Abstract

PART ONE: BACKGROUND What Does It Mean to Model Hypothesized Causal Processes with Nonexperimental Data? History and Logic of Structural Equation Modeling PART TWO: BASIC APPROACHES TO MODELING WITH SINGLE OBSERVED MEASURES OF THEORETICAL VARIABLES The Basics Path Analysis and Partitioning of Variance Effects of Collinearity on Regression and Path Analysis Effects of Random and Nonrandom Error on Path Models Recursive and Longitudinal Models Where Causality Goes in More Than One Direction and Where Data Are Collected Over Time PART THREE: FACTOR ANALYSIS AND PATH MODELING Introducing the Logic of Factor Analysis and Multiple Indicators to Path Modeling PART FOUR: LATENT VARIABLE STRUCTURAL EQUATION MODELS Putting It All Together Latent Variable Structural Equation Modeling Using Latent Variable Structural Equation Modeling to Examine Plausability of Models Logic of Alternative Models and Significance Tests Variations on the Basic Latent Variable Structural Equation Model Wrapping up

Keywords

Structural equation modelingComputer scienceMathematicsStatistics

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Year: 1998
Type: book
Citations: 1518
Access: Closed

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APA Style

                            
                                    Geoffrey Maruyama
                                
                            (1998). 
                            Basics of Structural Equation Modeling. 
                            
                            .
                            https://doi.org/10.4135/9781483345109

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DOI: 10.4135/9781483345109