This study explored the extent to which variations in self-report measures across studies can produce differences in the results obtained from mixture models. Data (N = 854) come from a laboratory analogue study of methods for creating commensurate scores of alcohol- and substance-use-related constructs when items differ systematically across participants for any given measure. Items were manipulated according to 4 conditions, corresponding to increasing levels of alteration to item stems, response options, or both. In Study 1, results from latent class analyses (LCAs) of alcohol consequences were compared across the 4 conditions, revealing differences in class enumeration and configuration. In Study 2, results from factor mixture models (FMMs) of alcohol expectancies were compared across 2 of the conditions, revealing differences in patterns and magnitude of the factor loadings and thresholds. The results suggest that even subtle differences in measurement can have substantively meaningful effects on mixture model results.

中文翻译：

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混合模型对测量变化敏感性的实证评估

本研究探讨了不同研究中自我报告测量的差异在多大程度上会导致从混合模型获得的结果存在差异。数据 (N = 854) 来自一项实验室模拟研究，该研究针对任何给定措施在参与者之间系统地不同时创建与酒精和物质使用相关的构造的相应分数的方法。项目根据 4 种条件进行操作，对应于项目词干、响应选项或两者的改变水平的增加。在研究 1 中，对酒精后果的潜在类别分析 (LCA) 的结果在 4 种情况下进行了比较，揭示了类别枚举和配置的差异。在研究 2 中，对酒精预期的因子混合模型 (FMM) 的结果在 2 个条件下进行了比较，揭示因子载荷和阈值的模式和大小的差异。结果表明，即使是细微的测量差异也会对混合模型结果产生实质性的影响。