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2018 | OriginalPaper | Chapter

3. Path Analysis and Structural Equation Models

Author : Patrick Mair

Published in: Modern Psychometrics with R

Publisher: Springer International Publishing

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Abstract

The first part of this chapter introduces simple path analysis structures, not involving any latent variables. Regression-based approaches such as multivariate regression, mediator models, moderator models, and extensions in terms of combined moderator-mediator path models are presented. The second part of this chapter is dedicated to path models with latent variables: structural equation models. This part builds heavily on elaborations on confirmatory factor analysis from the previous chapter. A special focus is on multigroup structural equation models which allow researchers to test hypotheses on group-specific parameters. Within this context non-nested model comparison is illustrated as well. Finally, latent growth models are introduced, an approach for studying changes over time.

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previous chapter Factor Analysis
next chapter Item Response Theory
Footnotes
2
We use a slightly nonstandard notation for the regression parameters in order to make it consistent with the symbols used in the moderator/mediator literature.
 
3
The packages mediation and lavaan treat missing values differently.
 
4
Note that in the syntax we use the symbol c instead of c′ as in Eq. (3.4).
 
5
Note that the authors obtained slightly different results since, apart from including covariates, they had special missing value treatments and did centering.
 
6
This notation is called “LISREL all-y” notation and is used by lavaan internally. There are several other options for SEM formulation (see Bollen, 1989; Kline, 2016).
 
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Metadata
Title
Path Analysis and Structural Equation Models
Author
Patrick Mair
Copyright Year
2018
Book
Modern Psychometrics with R

Print ISBN: 978-3-319-93175-3

Electronic ISBN: 978-3-319-93177-7

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-93177-7_3

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