Faculty of Education, University of Macau, Macau, P.R. China.
J Pers Assess. 2020 Mar-Apr;102(2):238-249. doi: 10.1080/00223891.2018.1502194. Epub 2018 Sep 27.
The assumption of population homogeneity in the measurement of dispositional optimism was examined. U.S. and Canadian respondents ( = 591) completed an optimism scale. Possible population heterogeneity was analyzed using factor mixture modeling. Two major results emerged. First, population homogeneity was not supported: A large class of participants had trouble giving consistent answers to optimism items, optimism and pessimism items, or, especially, pessimism items. Second, after the removal of this problematic class, the correlation between optimism and pessimism was found to be .94, a magnitude large enough to support the assumption of unidimensionality. Although psychometric problems with the measurement of optimism have not been identified previously, this study suggests that the typical measurement of dispositional optimism requires substantial revision. The findings showcase the importance of factor mixture modeling in evaluating the psychometric properties of a measurement scale.
本研究旨在检验在测量特质乐观时，是否需要假设群体同质。美国和加拿大的受访者（n=591）完成了乐观量表。使用因素混合模型分析了可能的群体异质性。主要有两个结果。首先，不支持群体同质的假设：有一大类参与者在回答乐观项目、乐观和悲观项目，尤其是悲观项目时，难以给出一致的答案。其次，在去除这个有问题的类别后，发现乐观和悲观之间的相关系数为.94，这个数值足以支持单一维度的假设。虽然之前没有发现特质乐观测量的心理计量学问题，但本研究表明，特质乐观的典型测量需要进行重大修订。研究结果展示了因素混合模型在评估测量量表的心理计量特性方面的重要性。
