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Methods for the Analysis of Pair-Matched Binary Data from School-based Intervention Studies

Columbia University School of Public Health

In the evaluation of school-based intervention programs, students' knowledge, behavior, and attitudes about a particular issue are typically assessed before and after the intervention. The effectiveness of the intervention can then be gauged by comparing these "pre-treatment" and "post-treatment" responses. In the most rigorous evaluations, students are randomized to the intervention or control group. However, instead of randomizing individual students to treatment, most school-based studies rely on clustered randomization schemes. This is generally operationalized as the assignment of schools to treatment condition, although students within schools serve as the units of observation. Because there tends to be positive correlation between responses from students at the same school, the assumption of statistical independence is violated; hence, application of statistical tests that ignore this correlation can result in biased significance levels. This paper explores two statistical methods that account for this correlation in analyzing binary data. A proposal for adapting these methods for application to matched pairs data is presented. The performance of the methods is evaluated via simulation study.

Key Words: clustered data • intracluster correlation • random effects model • school-based research

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