Book contents
- Frontmatter
- Contents
- Preface
- Acknowledgments
- PART I A BEGINNING
- PART II BASIC PRINCIPLES OF STRUCTURAL EQUATION MODELING
- 3 The anatomy of models I: observed variable models
- 4 The anatomy of models II: latent variables
- 5 Principles of estimation and model assessment
- PART III ADVANCED TOPICS
- PART IV APPLICATIONS AND ILLUSTRATIONS
- PART V THE IMPLICATIONS OF STRUCTURAL EQUATION MODELING FOR THE STUDY OF NATURAL SYSTEMS
- Appendix I Example analyses
- References
- Index
4 - The anatomy of models II: latent variables
Published online by Cambridge University Press: 04 December 2009
- Frontmatter
- Contents
- Preface
- Acknowledgments
- PART I A BEGINNING
- PART II BASIC PRINCIPLES OF STRUCTURAL EQUATION MODELING
- 3 The anatomy of models I: observed variable models
- 4 The anatomy of models II: latent variables
- 5 Principles of estimation and model assessment
- PART III ADVANCED TOPICS
- PART IV APPLICATIONS AND ILLUSTRATIONS
- PART V THE IMPLICATIONS OF STRUCTURAL EQUATION MODELING FOR THE STUDY OF NATURAL SYSTEMS
- Appendix I Example analyses
- References
- Index
Summary
Concepts associated with latent variables
What is a latent variable?
As indicated in our overview of structural equation models at the beginning of Chapter 3, in addition to observed variables in structural equation models, another type of variable, referred to as a latent variable, is commonly included. There are a variety of ways latent variables can be defined. Strictly speaking, a latent variable is one that is hypothesized to exist, but that has not been measured directly. Bollen (2002) states that we can simply think of a latent variable as one that is unobserved. In spite of this simple definition, latent variables are typically used to represent concepts. However, there are many types of concept and not all of them correspond to latent variables. Further, the algorithms used to calculate latent variables define their quantitative properties, although how they are interpreted is a matter of theoretical consideration. Finally, latent variables are starting to be used in structural equation models for a wider variety of purposes, such as to represent parameters in latent growth modeling. Because latent variables are often used to represent abstractions, their meaning is not always immediately obvious. For this reason, I believe it will be best to rely on a variety of examples and a discussion of their properties to illustrate the roles latent variables can play in SEM.
- Type
- Chapter
- Information
- Structural Equation Modeling and Natural Systems , pp. 77 - 114Publisher: Cambridge University PressPrint publication year: 2006