MICRO-MACRO AND MACRO-MICRO EFFECT ESTIMATION IN SMALL SCALE LATENT VARIABLE MODELS WITH CROON'S METHOD

Psychological theories and research often incorporate and investigate how macro- and micro-level processes cooperate to convey, provision, and envelope effects. A historical challenge in these multilevel contexts has been incorporating micro-macro, bottom-up, or emergent effects alongside the more common top-down or macro-micro effects. Although multilevel structural equation modeling provides a framework for such analyses, a persistent issue is the large sample size requirements necessary to reliably estimate parameters. In this study, we outline extensions to the recently developed Croon-based estimator for multilevel structural equation models. We then evaluate the performance of Croon's approach under a method-of-moments corrected maximum likelihood estimator to probe models that integrate micro-macro and macro-micro effects. The results suggest that Croon's method often outperforms maximum likelihood in terms of convergence, bias, and root mean-squared error and represents a useful complementary estimator. We provide R code that applies the estimator to an example using the lavaan package.