Factor score path analysis: An alternative for SEM?

By Ines Devlieger, Yves Rosseel. 2017

138 citations recorded by the source corpus.

Abstract. Theoretical researchers consider Structural Equation Modeling (SEM) to be the preferred method to study the relationships among latent variables. However, SEM has the disadvantage of requiring a large sample size, especially if the model is complex. Furthermore, since SEM estimates all parameters simultaneously, one misspecification in the model may influence the whole model. For these reasons, applied researchers often use a two-step Factor Score Regression (FSR) approach. In the first step, factor scores are calculated for the latent variables, which are used to perform a linear regression in the second step. However, this method results in incorrect regression coefficients. Croon (2002) developed a method that corrects for this bias. We combine this method of Croon (2002) with path analysis, resulting in Factor Score Path Analysis. This method results in correct path coefficients and has some advantages over SEM: it requires smaller sample sizes, can handle more complex models and the method ...

Subfields: Computer Networks and Communications, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/8ba9fcae5687b9a6afdfb68b8973d41f87fa67ca

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Zhiyong Zhang
2. Yujiao Mai
3. Zhonglin Wen
4. Milica Miočević
5. R. van de Schoot

In Management Science and Operations Research

1. Zhiyong Zhang
2. Yujiao Mai
3. Zhonglin Wen
4. Milica Miočević
5. R. van de Schoot
6. Herbert W. Marsh
7. Eldad Davidov
8. Carlo Salvato

In Statistics and Probability

1. Milica Miočević
2. Zhiyong Zhang
3. Yujiao Mai
4. Zhonglin Wen
5. R. van de Schoot
6. B. Muthén
7. Daniel M. McNeish
8. T. Asparouhov

Citations in the corpus

• Comparing Exploratory Structural Equation Modeling and Existing Approaches for Multiple Regression with Latent Variables (2018, methodology): “In terms of model convergence and proper solutions, SEM was more likely to have problems than MRA particularly when the sample size was small (Devlieger et al., 2016; Devlieger & Rosseel, 2017; Ledgerwood & Shrout, 2011).”
• Small Sample Size Solutions (2020, methodology): “For example, Croon (2002) devised a correction that removes the bias, and several studies have shown that this method works remarkably well (Devlieger, Mayer, & Rosseel, 2016; Devlieger & Rosseel, 2017).”