Cause and Correlation in Biology A User's Guide to Path Analysis, Structural Equations and Causal Inference with R
Book contents
- Cause and Correlation in Biology
- Cause and Correlation in Biology
- Copyright page
- Dedication
- Contents
- Preface
- 1 Cause from Correlation?
- 2 From Cause to Correlation and Back
- 3 Sewall Wright, Path Analysis, and d-Separation
- 4 Covariance-Based SEM without Explicit Latent Variables
- 5 Statistical Power, AIC Statistics, and Equivalent Models
- 6 Piecewise SEM with Implicit Latent Variables
- 7 Modelling Explicit Latent Variables in Covariance-Based SEM
- 8 Multigroup and Multilevel Structural Equation Models
- 9 Exploratory Structural Equation Modelling
- 10 A Cheat Sheet of Important R Functions
- References
- Index
8 - Multigroup and Multilevel Structural Equation Models
Published online by Cambridge University Press: 18 July 2026
Book contents
- Cause and Correlation in Biology
- Cause and Correlation in Biology
- Copyright page
- Dedication
- Contents
- Preface
- 1 Cause from Correlation?
- 2 From Cause to Correlation and Back
- 3 Sewall Wright, Path Analysis, and d-Separation
- 4 Covariance-Based SEM without Explicit Latent Variables
- 5 Statistical Power, AIC Statistics, and Equivalent Models
- 6 Piecewise SEM with Implicit Latent Variables
- 7 Modelling Explicit Latent Variables in Covariance-Based SEM
- 8 Multigroup and Multilevel Structural Equation Models
- 9 Exploratory Structural Equation Modelling
- 10 A Cheat Sheet of Important R Functions
- References
- Index
Summary
Causal heterogeneity exists when different subsets of your data have been generated via different causal mechanisms. This can be modelled using ‘multigroup’ SEM in both piecewise and covariance-based SEM. Multilevel and mixed-model SEM is used when the data have some nested or cross-classified structure, resulting in partial non-independence of observations.
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- Cause and Correlation in BiologyA User's Guide to Path Analysis, Structural Equations and Causal Inference with R, pp. 261 - 301Publisher: Cambridge University PressPrint publication year: 2026
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