| Abstract
| - Using generalizability theory as a guide, this study discusses statistical problems and strategies of analyzing longitudinal rating data involving multiple raters—a common type of data issue frequently encountered in social work evaluations. To disentangle raters' bias from clients' true change, the study shows the importance of looking into the multifaceted structure of measurement error. To analyze data containing nonnegligible variability associated with raters, this study proposes using a three-level hierarchical linear model. It demonstrates that the three-level model produces a better model fit to the data, smaller sample residual, and more accurate significance testing than the popular two-level model when analyzing rating data with nonnegligible raters' influences.
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