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Autor/inn/en | Yuan, Ke-Hai; Zhang, Zhiyong; Zhao, Yanyun |
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Titel | Reliable and More Powerful Methods for Power Analysis in Structural Equation Modeling |
Quelle | 24 (2017) 3, S.315-330 (17 Seiten)Infoseite zur Zeitschrift
PDF als Volltext (1); PDF als Volltext (2) |
Zusatzinformation | Weitere Informationen |
Sprache | englisch |
Dokumenttyp | gedruckt; online; Zeitschriftenaufsatz |
ISSN | 1070-5511 |
DOI | 10.1080/10705511.2016.1276836 |
Schlagwörter | Statistical Analysis; Evaluation Methods; Structural Equation Models; Reliability; Monte Carlo Methods; Effect Size; Robustness (Statistics); Error of Measurement; Maximum Likelihood Statistics; Statistical Distributions; Sample Size; Factor Analysis |
Abstract | The normal-distribution-based likelihood ratio statistic T[subscript ml] = nF[subscript ml] is widely used for power analysis in structural Equation modeling (SEM). In such an analysis, power and sample size are computed by assuming that T[subscript ml] follows a central chi-square distribution under H[subscript 0] and a noncentral chi-square distribution under H[subscript a]. However, with either violation of normality or not a large enough sample size, both empirical and analytical results indicate that the chi-square distribution assumptions are not realistic and consequently methods of power analysis based on such assumptions are not valid. This article describes a Monte Carlo (MC) method for power analysis. A measure of effect size for characterizing the power property of different rescaled statistics is also provided. Robust methods are proposed to increase the power of T[subscript ml] and other statistics. Simulation results show that the MC method reliably controls Type I errors and robust estimation methods effectively increase the power, and their combination is thus recommended for conducting power analysis in SEM. (As Provided). |
Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
Update | 2020/1/01 |