ANALYSIS OF CLUSTERED DATA IN COMMUNITY PSYCHOLOGY - WITH AN EXAMPLE FROM A WORKSITE SMOKING CESSATION PROJECT

Although it is common in community psychology research to have data at both the community, or cluster, and individual level, the analysis of such clustered data often presents difficulties for many researchers. Since the individuals within the cluster cannot be assumed to be inde pendent, the use of many traditional statistical techniques that assum es independence of observations is problematic. Further, there is ofte n interest in assessing the degree of dependence in the data resulting from the clustering of individuals within communities. In this paper, a random-effects regression model is described for analysis of cluste red data. Unlike ordinary regression analysis of clustered data, rando m-effects regression models do not assume that each observation is ind ependent, but do assume data within clusters are dependent to some deg ree. The degree of this dependency is estimated along with estimates o f the usual model parameters, thus adjusting these effects for the dep endency resulting from the clustering of the data. Models are describe d for both continuous and dichotomous outcome variables and available statistical software for these models is discussed An analysis of a da ta set where individuals are clustered within firms is used to illustr ate features of random-effects regression analysis, relative to both i ndividual-level analysis which ignores the clustering of the data, and cluster-level analysis which aggregates the individual data.