ACCURATE BY BEING NOISY: A FORMAL NETWORK MODEL OF IMPLICIT MEASURES OF ATTITUDES
ACCURATE BY BEING NOISY: A FORMAL NETWORK MODEL OF IMPLICIT MEASURES OF ATTITUDES
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DOI：
10.1521/soco.2020.38.supp.s26
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发表时间：
2020-11-01
期刊：
影响因子：
1.9
通讯作者：
van der Maas, Han L. J.
中科院分区：
文献类型：
--
作者：
Dalege, Jonas;van der Maas, Han L. J.
在本文中，我们使用我们的态度网络理论对内隐态度测量进行建模。该模型基于一个假设，即隐式测量限制了态度熵的减少，因为隐式测量代表了一种测量结果，即以一种快速而轻松的方式评估态度对象的结果。隐式测量因此评估态度在高熵状态（即，不一致和不稳定的状态）。在模拟中，我们说明了我们的网络理论对隐式测量的影响。这个模拟的结果显示了一个矛盾的结果：与显式测量相比，隐式测量可以更准确地评估对态度对象的冲突评价反应（例如，评价反应与主导评价反应不一致），因为它们以嘈杂和不可靠的方式评估这些属性。我们的结论是，我们的态度网络理论增加了态度的实质性理论化与内隐测量的心理测量特性之间的联系。
In this article, we model implicit attitude measures using our network theory of attitudes. The model rests on the assumption that implicit measures limit attitudinal entropy reduction, because implicit measures represent a measurement outcome that is the result of evaluating the attitude object in a quick and effortless manner. Implicit measures therefore assess attitudes in high entropy states (i.e., inconsistent and unstable states). In a simulation, we illustrate the implications of our network theory for implicit measures. The results of this simulation show a paradoxical result: Implicit measures can provide a more accurate assessment of conflicting evaluative reactions to an attitude object (e.g., evaluative reactions not in line with the dominant evaluative reactions) than explicit measures, because they assess these properties in a noisier and less reliable manner. We conclude that our network theory of attitudes increases the connection between substantive theorizing on attitudes and psychometric properties of implicit measures.