How Are We Testing Interactions in Latent Variable Models? Surging Forward or Fighting Shy?

Latent variable models and interaction effects have both been common in the organizational sciences for some time. Methods for incorporating interactions into latent variable models have existed since at least Kenny and Judd, and a great many articles and books have developed these methods further. In the present article, we present an empirical review of the methods that organizational science investigators use to test their interaction hypotheses. We show that it is very common for investigators to use fully latent methods to test additive portions of their models, but to abandon such methods when testing the multiplicative portions of their models. By contrast, investigators whose models do not contain interactions tend to stick with fully latent methods throughout. As there is little rational basis for this pattern, it is likely due to continued discomfort regarding the proper application of existing fully latent methods. Thus, we end by offering R code that implements some of the more sophisticated fully latent approaches, and by offering a sequence of decisions that investigators can follow in order to choose the best analytic approach.