## Abstract

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 independent, the use of many traditional statistical techniques that assumes independence of observations is problematic. Further, there is often 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 clustered data. Unlike ordinary regression analysis of clustered data, random-effects regression models do not assume that each observation is independent, but do assume data within clusters are dependent to some degree. The degree of this dependency is estimated along with estimates of the usual model parameters, thus adjusting these effects for the dependency resulting from the clustering of the data. Models are described for both continuous and dichotomous outcome variables, and available statistical software for these models is discussed. An analysis of a data set where individuals are clustered within firms is used to illustrate fetatures of random-effects regression analysis, relative to both individual-level analysis which ignores the clustering of the data, and cluster-level analysis which aggregates the individual data.

Original language | English (US) |
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Pages (from-to) | 595-615 |

Number of pages | 21 |

Journal | American Journal of Community Psychology |

Volume | 22 |

Issue number | 5 |

DOIs | |

State | Published - Oct 1 1994 |

## Keywords

- correlated data
- intraclass correlation
- nested design
- random-effects model
- unit of analysis

## ASJC Scopus subject areas

- Health(social science)
- Applied Psychology
- Public Health, Environmental and Occupational Health