Abstract
An existing micro---macro method for a single individual-level variable is extended to the multivariate situation by presenting two multilevel latent class models in which multiple discrete individual-level variables are used to explain a group-level outcome. As in the univariate case, the individual-level data are summarized at the group-level by constructing a discrete latent variable at the group level and this group-level latent variable is used as a predictor for the group-level outcome. In the first extension, that is referred to as the Direct model, the multiple individual-level variables are directly used as indicators for the group-level latent variable. In the second extension, referred to as the Indirect model, the multiple individual-level variables are used to construct an individual-level latent variable that is used as an indicator for the group-level latent variable. This implies that the individual-level variables are used indirectly at the group-level. The within- and between components of the (co)varn the individual-level variables are independent in the Direct model, but dependent in the Indirect model. Both models are discussed and illustrated with an empirical data example.
Highlights
Data are collected on individuals that are nested within groups (Goldstein 2011)
Data can be collected on children nested in schools, on employees nested in organizations, or on family members nested in families
In a macro– micro situation, the outcome or dependent variable is measured at the individual level, while in a micro–macro situation, the outcome variable is measured at the group level
Summary
Data are collected on individuals (micro-level units) that are nested within groups (macro-level units) (Goldstein 2011). The analysis of observations from micro–macro designs requires an appropriate methodology that takes into account the measurement and sampling error of the individual scores and neatly separates the between- and within-group association among the variables (Preacher et al 2010). Such techniques have been developed by using a group-level latent variable for the aggregation. The Indirect model can, for example, be used when multiple individual-level items on the satisfaction of employees with respect to their relationships at work are used to construct the individual-level latent variable ηi j that is used as an indicator for ζ j to predict organizational performance measures, such as the level of organizational conflicts. Both methods and their estimation procedures are discussed and applied to empirical data examples
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