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A low level of response to alcohol is considered a significant risk factor for alcohol use disorder. Survey measures of this construct assess the number of drinks required to experience various alcohol effects, so data will be missing for effects participants have not experienced. Furthermore, missingness will likely be more common for items with higher means, as more severe effects are likely experienced both less commonly and at higher consumption levels. We explored whether these atypical characteristics of response-to-alcohol survey data cause problems when using conventional person-mean imputation scoring. This scoring approach involves averaging across nonmissing items for each participant, implicitly assuming that missing items have similar distributional properties (e.g., means) as nonmissing items. Analyses used data from the most commonly utilized response-to-alcohol survey measure: The Self-Rating of the Effects of Alcohol Scale (SRE). Results (a) revealed a strong relationship between higher item means and greater item missingness, (b) established that this relation causes person-mean imputation to produce more downwardly biased response-to-alcohol summary scores for participants with more missing data, (c) established that this induced a spurious relationship between higher response-to-alcohol summary scores and higher alcohol-effect endorsement (i.e., the number of SRE alcohol effects experienced), and (d) found that these biases can be reduced with 2 alternative scoring approaches. We discuss these and other potential problems with person-mean imputation, and common and unique advantages of the 2 alternative approaches. We consider generalizability, including how the problems shown here may vary in practical significance across different populations and measures. (PsycINFO Database Record

Two alternative approaches to conventional person-mean imputation scoring of the Self-Rating of the Effects of Alcohol Scale (SRE).