Effects of aggregation on implicit bias measurement
Effects of aggregation on implicit bias measurement
复制标题
DOI：
10.1016/j.jesp.2022.104331
复制
发表时间：
2022-07
影响因子：
3.5
通讯作者：
Jason W. Hannay;B. Payne
中科院分区：
文献类型：
--
作者：
Jason W. Hannay;B. Payne
学者们争论内隐偏见在多大程度上是一种稳定的个人态度，而不是社会背景的一个特征。社会背景观点的主要证据是，群体平均值比个人得分更稳定，与不同结果的关联性更强。然而，群体平均值取决于对许多观察结果的汇总，这就提出了一个问题：群体平均值的明显上级可靠性和有效性是否可能是汇总的统计假象。如果我们只对每个人进行更多的测量，那么个体差异相关性会像基于情境的相关性一样大吗？我们报告两项研究测试聚合重复内隐测试的影响。我们发现，汇总到六个测试增加重测信度有所增加，但增加效度的相关性只有轻微的，并没有三个测试后的好处。结果表明，在上下文水平上的大的相关性不能减少到聚合多个测试的统计效果。
Scholars debate the extent to which implicit bias is a stable individual attitude versus a feature of social contexts. The primary evidence for the social context view is that group averages are more stable, and more strongly associated with disparate outcomes, than individual scores. Group averages, however, depend on aggregating many observations, raising the question of whether their apparently superior reliability and validity may be a statistical artefact of aggregation. Would individual difference correlations be as large as context-based correlations if we only aggregated more measures per person? We report two studies testing the effects of aggregating repeated implicit tests. We find that aggregating up to six tests increases test-retest reliability somewhat, but increases validity correlations only slightly, and has no benefit after three tests. Results suggest that large correlations at the level of contexts cannot be reduced to the statistical effects of aggregating multiple tests.