Sample Size Requirement for Unbiased Estimation of Structural Equation Models: A Monte Carlo Study

Détails

ID Serval
serval:BIB_D4CC76E12B01
Type
Actes de conférence (partie): contribution originale à la littérature scientifique, publiée à l'occasion de conférences scientifiques, dans un ouvrage de compte-rendu (proceedings), ou dans l'édition spéciale d'un journal reconnu (conference proceedings).
Collection
Publications
Institution
Titre
Sample Size Requirement for Unbiased Estimation of Structural Equation Models: A Monte Carlo Study
Titre de la conférence
Academy of Management Proceedings
Auteur⸱e⸱s
Bastardoz N., Antonakis J.
Editeur
The Academy of Management
Organisation
Academy of Management, Philadelphia, PA, U.S.A.
ISSN
0065-0668
1543-8643
Statut éditorial
Publié
Date de publication
2014
Peer-reviewed
Oui
Volume
2014
Numéro
1
Pages
13405-13405
Langue
anglais
Résumé
Several rules of thumb for the minimum sample size of structural equation models have been proposed. A widely-accepted ratio of sample size to estimated parameters is N:p = 5:1 (Bentler & Chou, 1987). To examine under what conditions this rule-of-thumb holds, we ran Monte-Carlo simulations for a structural-equation model having three exogenous latent variables that predicted a dependent variable via an endogenous regressor. We varied (a) the number of observations (from 40 to 200); (b) the number of latent variables indicators (from 2 to 6); (c) the correlations between factors (from 0.2 to 0.8) and the degree of endogeneity affecting the endogenous regressor (high or low). Results show that ML estimates are still consistent across-the-board. However, in small sample size conditions model convergence rates were low and the chi-square test of model fit tended to over-reject correctly specified models. We found that a correction to the chi-square proposed by Swain (1976) better approximates the chi-square distribution at small sample sizes and reduced rejection rates close to the Type I error rate (5%). Finally, we found that the Hausman (1978) endogeneity test had very low power at small sample sizes. Based on these results we make several recommendations for applied researchers.
Mots-clé
Monte-Carlo simulations, Sample Size, Structural Equation Model
Création de la notice
23/06/2016 13:08
Dernière modification de la notice
20/08/2019 15:54
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