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Multilevel researchers have predominantly applied either direct consensus or referent-shift consensus composition models when aggregating individual-level data to a higher level of analysis. This prevailing focus neglects both theory and empirical evidence, suggesting that the variance of group members' responses may complement the absolute mean level of group members' judgments. The goals of this article are to demonstrate the application of dispersion-composition models for capturing variability among group members' collective judgments and highlight the statistical challenges (and inherent constraints) of using group means and variances as predictors of study criteria. To this end, the authors present and illustrate a six-step sequential framework for applying dispersion-composition models using data from two independent field samples. The authors contend that the application of dispersion-composition models not only will strengthen a study’s conclusions by eliminating potential rival data interpretations but may also shed new light on past findings, potentially opening new doors to a more complete understanding of multilevel phenomena. [ABSTRACT FROM PUBLISHER]

The authors reviewed recently published research on small groups and teams to understand how within-team nonresponse is reported and handled. They used Monte Carlo simulation to investigate how data-handling choices affect measurement reliability and hypothesis testing under conditions of random and systematic nonresponse. More complete reporting of nonresponse is recommended by the authors, and they propose guidelines for analyzing team-level constructs using data from teams without full response. [ABSTRACT FROM AUTHOR]