1996
DOI: 10.1037/0022-006x.64.5.919
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Analysis of prevention program effectiveness with clustered data using generalized estimating equations.

Abstract: Experimental studies of prevention programs often randomize clusters of individuals rather than individuals to treatment conditions. When the correlation among individuals within clusters is not accounted for in statistical analysis, the standard errors are biased, potentially resulting in misleading conclusions about the significance of treatment effects. This study demonstrates the generalized estimating equations (GEE) method, focusing specifically on the GEE-independent method, to control for within-cluste… Show more

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Cited by 89 publications
(59 citation statements)
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“…Therefore, each analysis was run using the generalized estimating equations (GEE) approach in SAS PROC GENMOD (SAS Institute, 2005) in order to adjust the estimated standard error to account for the within-cluster correlation. This approach generally provides for a more conservative test of the hypothesis when a positive ICC is present (Norton, Bieler, Ennett, & Zarkin, 1996).…”
Section: Discussionmentioning
confidence: 99%
“…Therefore, each analysis was run using the generalized estimating equations (GEE) approach in SAS PROC GENMOD (SAS Institute, 2005) in order to adjust the estimated standard error to account for the within-cluster correlation. This approach generally provides for a more conservative test of the hypothesis when a positive ICC is present (Norton, Bieler, Ennett, & Zarkin, 1996).…”
Section: Discussionmentioning
confidence: 99%
“…The estimated parameters from the linear probability model are unbiased and consistent, but not efficient due to heteroskedasiticity. We estimate White robust standard errors, which account for the heteroskedasticity and are consistent [23].…”
Section: Illustrative Examplesmentioning
confidence: 99%
“…That is, the error terms of clients within therapist are correlated, thus violating the assumption of independent observations (Wampold & Serlin, 2000). Ignoring clustering effects can lead to biased standard errors of parameter estimates and inflated Type I error rates when using ordinary least squares regression (Norton, Bieler, Ennett, & Zarkin, 1996;Zucker, 1990). Procedures are available for diagnosing clustering effects and adjusting significance tests accordingly (e.g., Hedeker, Gibbons, & Flay, 1994;Wampold & Serlin, 2000).…”
Section: Part 3: Process-outcome Analysesmentioning
confidence: 99%
“…Procedures are available for diagnosing clustering effects and adjusting significance tests accordingly (e.g., Hedeker, Gibbons, & Flay, 1994;Wampold & Serlin, 2000). However, the current study does not contain a sufficient number of average clients per therapist to calculate reliable intraclass correlation coefficients (Hedeker et al, 1994;Norton et al, 1996), a necessary first step in correctly diagnosing significant therapist clustering. Step 2) and the interaction term (Step 3) predict change in the given outcome.…”
mentioning
confidence: 99%