Random-Effects Models for Analyzing Clustered Data From a Nutrition Education Intervention

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Abstract
When evaluating the effects of public health interventions, larger units, or clusters, of individuals are often the unit of randomization and implementation. Ignoring dependency in the data due to clustering can misrepresent intervention effects. Random-effects models (REMs) may be a useful way to analyze such data. The present study compares results of analyses of data from a nutrition intervention program using four different methods: (a) usual multiple regression analysis using indivtdual subject data, (b) usual multiple regression analysis using the classroom cluster as the unit of analysis, (c) two-level REM model with subjects clustered within class rooms, and (d) two-level REM model with subjects clustered within sites.

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