POWER ANALYSIS AND DETERMINATION OF SAMPLE-SIZE FOR COVARIANCE STRUCTURE MODELING

A framework for hypothesis testing and power analysis in the assessmen t of fit of covariance structure models is presented. We emphasize the value of confidence intervals for fit indices, and we stress the rela tionship of confidence intervals to a framework for hypothesis testing . The approach allows for resting null hypotheses of not-good fit, rev ersing the role of the null hypothesis in conventional tests of model fit, so that a significant result provides strong support for good fit . The approach also allows for direct estimation of power, where effec t size is defined in terms of a null and alternative value of the root -mean-square error of approximation fit index proposed by J. PI. Steig er and J. M. Lind (1980). It is also feasible to determine minimum sam ple size required to achieve a given level of power for any test of fi t in this framework. Computer programs and examples are provided for p ower analyses and calculation of minimum sample sizes.