The Estimation of Reliability in Longitudinal Models

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Abstract
Despite the increasing attention devoted to the study and analysis of longitudinal data, relatively little consideration has been directed toward understanding the issues of reliability and measurement error. Perhaps one reason for this neglect has been that traditional methods of estimation (e.g. generalisability theory) require assumptions that are often not tenable in longitudinal designs. This paper first examines applications of generalisability theory to the estimation of m easurement error and reliability in longitudinal research, and notes how factors such as missing data, correlated errors, and true score instability prohibit traditional variance com ponent estimation. Next, we discuss how estimation methods using restricted maximum likelihood can account for these factors, thereby providing m any advantages over traditional estimation methods. Finally, we provide a substantive exam ple illustrating these advantages, and include brief discussions of programming and software considerations.