Keywords: power analysis, structural equation The SEM based power analysis approach may prove useful for researchers designing research in the health and medical spheres. An analysis based on the chi-square of this model can provide estimates of the sample size required for different levels of power to reject the null hypothesis. A model (representing the null hypothesis) is then tested in a structural equation model, using the population parameters as input. This represents the hypothesised population effects. The covariance matrix, along with a vector of means, relating to the alternative hypothesis is generated. The SEM approach can be equivalent to other multivariate statistical tests, and therefore the Satorra and Saris approach to power analysis can be used. Whilst the Satorra and Saris approach is familiar to researchers who use the structural equation modelling approach, it is less well known amongst other researchers. Satorra and Saris developed a method for estimating the power of the likelihood ratio test for structural equation models.
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For many complex multivariate designs that are increasingly being employed, power analyses can be difficult to carry out, because the software available lacks sufficient flexibility. This paper demonstrates how structural equation modelling (SEM) can be used as a tool to aid in carrying out power analyses.
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