Dispersion-Composition Models in Multilevel Research
提出并演示了离散-构成模型，用于捕捉群体成员集体判断的变异性，并强调使用组均值和方差作为预测变量时的统计挑战，旨在消除数据解释歧义并深化对多层级现象的理解。
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.