Abstract
Structural Equation Modeling (SEM) is a method for modeling whole sets of complex interrelations between observed and/or latent variables. In its most common form, SEM combines confirmatory factor analysis (CFA with another method named path analysis). Just like CFA, SEM relates observed variables to latent variables that are measured by those observed variables and, as path analysis does, SEM allows for a wide range of regression-type relations between sets of variables (both latent and observed). This chapter presents an introduction to SEM, an integrated strategy for conducting SEM analysis that is well-suited for educational sciences, and a tutorial on how to carry out an SEM analysis in R.
Original language | English |
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Title of host publication | Learning analytics methods and tutorials |
Subtitle of host publication | A practical guide using R |
Editors | M. Saqr, S. López-Pernas |
Publisher | Springer |
Pages | 705-721 |
Number of pages | 17 |
ISBN (Electronic) | 978-3-031-54464-4 |
ISBN (Print) | 978-3-031-54463-7 |
DOIs | |
Publication status | Published - 2024 |
Keywords
- Learning analytics
- SEM
- Structural equation modeling