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

Susan I. Woodruff
Additional contact information
Susan I. Woodruff: San Diego State University

Evaluation Review, 1997, vol. 21, issue 6, 688-697

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.

Date: 1997
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/0193841X9702100603 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:evarev:v:21:y:1997:i:6:p:688-697

DOI: 10.1177/0193841X9702100603

Access Statistics for this article

More articles in Evaluation Review
Bibliographic data for series maintained by SAGE Publications ().