A prediction-oriented specification search algorithm for generalized structured component analysis

Enthalten in
Structural equation modeling 
Volume
29
Issue
4
Start Page
611
End Page
619
Citation
Structural Equation Modeling: A Multidisciplinary Journal 29 (4): 611-619 (2022)
Publisher DOI
10.1080/10705511.2022.2057315
Scopus ID
2-s2.0-85129310657
Publisher
Psychology Press, Taylor & Francis Group
Generalized structured component analysis (GSCA) is used for specifying and testing the relationships between observed variables and components. GSCA can perform model selection by comparing theoretically established models. In practice, however, theories may not always completely and unambiguously specify the relationships between variables in the model. In such situations, a specification search strategy allows for exploring potential relationships between variables in a data-driven manner. A specification search based on prediction of unseen observations is attractive as it does not require the provision of theoretically plausible models. To date, GSCA has not been equipped with such a specification search strategy. Addressing this limitation, we propose a prediction-oriented specification search algorithm for GSCA, which reveals the best combination of predictors that minimizes each target variable’s prediction error. We conduct a simulation study to examine the new algorithm’s performance and apply it to real data to further investigate and demonstrate its practical usefulness.
DDC Class
330: Wirtschaft