Here - Tilburg University

Presenter
Bennink, Margot; Dept. Methodology and Statistics, Tilburg School of Social
and Behavioral Sciences
Authors
Margot Bennink; Marcel A. Croon; Jeroen K. Vermunt; Dept. Methodology and
Statistics, Tilburg School of Social and Behavioral Sciences
Title
Micro-Macro analysis for discrete outcomes
Abstract
This study deals with models for predicting outcomes at the higher level (e.g.
team performance) from explanatory variables at the lower level (e.g.
employee’s motivation and skills). This “reversed” multilevel analysis problem is
rather common in social sciences, and is sometimes referred to as micro-macro
analysis. Recently, Croon and Van Veldhoven proposed a statistical model for
micro-macro multilevel analysis which involves using a factor analytic structure
in which the scores of the lower-level units are seen as indicators of latent
factors at the group level. The key is that the outcome variable is not regressed
on the aggregated group mean(s) of the micro-level predictor(s) but on the
latent macro-level variable(s). The aim of the project, from which the current
study is a part, is to generalize this approach so that it can also be applied when
the explanatory and/or outcome variables are discrete instead of continuous and
normally distributed. Two new models for micro-macro relations between
discrete variables are presented; a simple 1-2 model in which a dichotomous
micro-level variable affects a dichotomous macro-level outcome variable, and a
more complex 2-1-2 model in which a dichotomous macro-level variable has a
direct effect on a dichotomous macro-level outcome variable and an indirect
effect on the outcome through a dichotomous mediating variable defined at the
micro-level. In both models the latent variable at the group level is defined to be
discrete (latent classes). We present the theoretical background of the models, a
simulation study in which their performance is evaluated, as well as an empirical
application.