# Frequentist Model Averaging in Structural Equation Modelling

Frequentist Model Averaging in Structural Equation Modelling 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Psychometrika Springer Journals

# Frequentist Model Averaging in Structural Equation Modelling

Psychometrika, Volume 84 (1) – Jun 4, 2018
21 pages

/lp/springer_journal/frequentist-model-averaging-in-structural-equation-modelling-Glk2vuSqY8
Publisher
Springer Journals
Subject
Psychology; Psychometrics; Assessment, Testing and Evaluation; Statistics for Social Sciences, Humanities, Law; Statistical Theory and Methods
ISSN
0033-3123
eISSN
1860-0980
D.O.I.
10.1007/s11336-018-9624-y
Publisher site
See Article on Publisher Site

### 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.

### Journal

PsychometrikaSpringer Journals

Published: Jun 4, 2018

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