Intraclass Correlation for Measures from a Middle School Nutrition Intervention Study: Estimates, Correlates, and Applications

This article presents the first estimates of school-level intraclass correlation for dietary measures based on data from the Teens Eating for Energy and Nutrition at School study. This study involves 3,878 seventh graders from 16 middle schools from Minneapolis–St. Paul, Minnesota. The sample was 66...

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Bibliographic Details
Published in:Health education & behavior 2001-12, Vol.28 (6), p.666-679
Main Authors: Murray, David M., Phillips, Glenn A., Birnbaum, Amanda S., Lytle, Leslie A.
Format: Article
Language:English
Subjects:
Adolescent
Biological and medical sciences
Child
Correlation analysis
Diet - statistics & numerical data
Feeding Behavior
Female
Fruit
Health Education
Humans
Male
Medical sciences
Middle school students
Minnesota - epidemiology
Models, Statistical
Nutrition
Nutritional Sciences - education
Prevention and actions
Public health. Hygiene
Public health. Hygiene-occupational medicine
Regression Analysis
School Health Services
Specific populations (family, woman, child, elderly...)
Students - statistics & numerical data
Studies
Vegetables
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Summary:This article presents the first estimates of school-level intraclass correlation for dietary measures based on data from the Teens Eating for Energy and Nutrition at School study. This study involves 3,878 seventh graders from 16 middle schools from Minneapolis–St. Paul, Minnesota. The sample was 66.8% White, 11.2% Black, and 7.0% Asian; 48.8% of the sample was female. Typical fruit and vegetable intake was assessed with a modified version of the Behavior Risk Factor Surveillance System questionnaire. Twenty-four-hour dietary recalls were conducted by nutritionists using the Minnesota Nutrition Data System. Mixed-model regression methods were used to estimate variance components for school and residual error, both before and after adjustment for demographic factors. School-level intraclass correlations were large enough, if ignored, to substantially inflate the Type I error rate in an analysis of treatment effects. The authors show how to use the estimates to determine sample size requirements for future studies.
ISSN:1090-1981
1552-6127
DOI:10.1177/109019810102800602