Reflections on “Statistical Power and Structural Equation Models in Business Research”
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This article reflects on McQuitty’s 2004 paper, which describes the importance of statistical power for hypothesis testing and, more specifically, the evaluation of structural equation models (SEMs). McQuitty also explains the use of what was at the time a new method developed by MacCallum, Browne, and Sugawara for estimating the power associated with the RMSEA fit statistic, and then applies the method to SEMs used in marketing journals. A sizeable portion of the published models had too little power to reject false models (a Type II error) or too much power, which leads to the over rejection of correct models (a Type I error). A brief discussion of the impact of McQuitty and ongoing research of power for SEMs follows.
Abstract
This article reflects on McQuitty’s 2004 paper, which describes the importance of statistical power for hypothesis testing and, more specifically, the evaluation of structural equation models (SEMs). McQuitty also explains the use of what was at the time a new method developed by MacCallum, Browne, and Sugawara for estimating the power associated with the RMSEA fit statistic, and then applies the method to SEMs used in marketing journals. A sizeable portion of the published models had too little power to reject false models (a Type II error) or too much power, which leads to the over rejection of correct models (a Type I error). A brief discussion of the impact of McQuitty and ongoing research of power for SEMs follows.
참고문헌 (18)
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