Structural Equation Modeling. Introduction
Warsaw International Studies in Psychology
Social Research Specialization
Psychological constructs such as intelligence, neuroticism, or trust cannot be
observed directly – instead, their presence is inferred from manifest variables, such as item responses. Structural equation modeling (SEM) is a statistical technique that is designed to test the relationships between observed and latent variables. Combining the features of factor analysis (the measurement part of a model) and linear regression (the structural part of a model), SEM allows for separating measurement error and testing complex relationships between latent variables.
This course aims to introduce students to SEM – its underlying logic, assumptions, and applications. Throughout the consecutive classes, participants will be presented with different kinds of questions that may be asked and answered with SEM. We will start with an overview of SEM applications. Next, we will learn about Confirmatory Factor Analysis (CFA), path models, and full SEM. The classes would involve a combination of
lectures and lab sessions focusing on the specification, estimation, and interpretation of structural equation models. All analyses would be performed in R.
Topics
1. Structural Equations Modeling – introduction. Using lavaan
Readings:
Kline, Ch. 1
Gana & Broc: Ch. 2
Brown: Ch. 1
2. Confirmatory Factor Analysis
Readings:
Kline: Ch. 9 & 13
Gana & Broc, Ch. 3.5
Brown: Ch. 3, 4, & 8
3. Path analysis. Testing mediations
Readings:
Kline, Ch. 6-7
4. Full SEM I. Test
5. Full SEM II. Final assignment due
Main texts:
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Brown, T. A. (2006). Confirmatory Factor Analysis for applied research. New York: Guilford Press.
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Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R. London: Sage.
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Gana, K., & Broc, G. (2019). Structural Equation Modeling with lavaan. Hoboken, NJ: Wiley.
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Kline, R. B. (2015). Principles and practice of Structural Equation Modeling. New York: Guilford Press.