Description
Book SynopsisStructural equation modeling (SEM) is a very general and flexible multivariate technique that allows relationships among variables to be examined. The roots of SEM are in the social sciences. In writing this textbook, the authors look to make SEM accessible to a wider audience of researchers across many disciplines, addressing issues unique to health and medicine.
SEM is often used in practice to model and test hypothesized causal relationships among observed and latent (unobserved) variables, including in analysis across time and groups. It can be viewed as the merging of a conceptual model, path diagram, confirmatory factor analysis, and path analysis. In this textbook the authors also discuss techniques, such as mixture modeling, that expand the capacity of SEM using a combination of both continuous and categorical latent variables.
Features:
- Basic, intermediate, and advanced SEM topics
- Detailed applications, par
Table of Contents
Part I Introduction to Concepts and Principles of Structural Equation Modeling for Health and Medical Research
1. Introduction and Brief History of Structural Equation Modeling for Health and Medical Research
2. Vocabulary, Concepts and Usages of Structural Equation Modeling
Part II Theory of Structural Equation Modeling
3 The Form of Structural Equation Models
4 Model Estimation and Evaluation
5 Model Identifiability and Equivalence
Part III Applications and Examples of Structural Equation Modeling for Health and Medical Research
6 Choosing Among Competing Specifications
7 Measurement Models for Patient-Reported Outcomes and Other Health-related Outcomes
8 Exploratory Factor Analysis
9 Mediation and Moderation
10 Measurement Bias, Multiple Indicator Multiple Cause Modeling and Multiple Group Modeling
11 Latent Class Analysis
12 Latent Profile Analysis
13 Structural Equation Modeling with Longitudinal Data
14 Growth Mixture Modeling
15 Special Topics
