Testing informative hypotheses in SEM increases power: An illustration contrasting classical hypothesis testing with a parametric bootstrap approach

In the present paper, the application of a parametric bootstrap procedure, as described by van de Schoot, Hoijtink, and Dekovi (2010), will be applied to demonstrate that a direct test of an informative hypothesis offers more informative results compared to testing traditional null hypotheses against catch-all rivals. Also, more power can be gained when informative hypotheses are tested directly. In this paper we will (a) compare the results of traditional analyses with the results of this novel methodology; (b) introduce applied researchers to the parametric bootstrap procedure for the evaluation of informative hypotheses; and (c) provide the results of a simulation study to demonstrate power gains when using inequality constraints. We argue that researchers should directly evaluate inequality-constrained hypotheses if there is a strong theory about the ordering of relevant parameters. In this way, researchers can make use of all knowledge available from previous investigations, while also learning more from their data compared to traditional null-hypothesis testing.