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Frequentist Model Averaging in Structural Equation Modelling

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  • Shaobo Jin

    (Uppsala University)

  • Sebastian Ankargren

    (Uppsala University)

Abstract

Model selection from a set of candidate models plays an important role in many structural equation modelling applications. However, traditional model selection methods introduce extra randomness that is not accounted for by post-model selection inference. In the current study, we propose a model averaging technique within the frequentist statistical framework. Instead of selecting an optimal model, the contributions of all candidate models are acknowledged. Valid confidence intervals and a $$\chi ^2$$ χ 2 test statistic are proposed. A simulation study shows that the proposed method is able to produce a robust mean-squared error, a better coverage probability, and a better goodness-of-fit test compared to model selection. It is an interesting compromise between model selection and the full model.

Suggested Citation

  • Shaobo Jin & Sebastian Ankargren, 2019. "Frequentist Model Averaging in Structural Equation Modelling," Psychometrika, Springer;The Psychometric Society, vol. 84(1), pages 84-104, March.
    • Handle: RePEc:spr:psycho:v:84:y:2019:i:1:d:10.1007_s11336-018-9624-y
    DOI: 10.1007/s11336-018-9624-y
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    Cited by:

    1. Shaobo Jin, 2022. "Frequentist Model Averaging in Structure Equation Model With Ordinal Data," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 1130-1145, September.
    2. Francisco Javier Blanco-Encomienda & Elena Rosillo-Díaz, 2021. "Quantitative evaluation of the production and trends in research applying the structural equation modelling method," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1599-1617, February.

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