Department of Cognition, Emotion, and Methods, Faculty of Psychology, Environmental Psychology Unit, University of Vienna, Wächtergasse 1, 1010, Vienna, Austria.
Department of Psychology, Faculty of Arts, Charles University, Prague, Czech Republic.
Behav Res Methods. 2023 Aug;55(5):2320-2332. doi: 10.3758/s13428-022-01876-7. Epub 2022 Jul 18.
Risky-choice and attribute framing effects are well-known cognitive biases, where choices are influenced by the way information is presented. To assess susceptibility to these framing types, the Resistance to Framing scale is often used, although its performance has rarely been extensively tested. In an online survey among university students from Bulgaria (N = 245) and North America (N = 261), we planned to examine the scale's psychometric properties, structural validity, and measurement invariance. However, some of these examinations were not possible because the scale displayed low and mostly non-significant inter-item correlations as well as low item-total correlations. Followingly, exploratory item response theory analyses indicated that the scale's reliability was low, especially for high levels of resistance to framing. This suggests problems with the scale at a basic level of conceptualization, namely that the items may not represent the same content domain. Overall, the scale in its current version is of limited use, at least in university student samples, due to the identified problems. We discuss potential remedies to these problems, as well as provide open code and data ( https://osf.io/j5n6f ) which facilitates testing the scale in other samples (e.g., general population, different languages and countries) to obtain a comprehensive picture of its performance.
风险选择和属性框架效应是众所周知的认知偏差，选择会受到信息呈现方式的影响。为了评估对这些框架类型的易感性，通常使用抗框架量表，但很少对其性能进行广泛测试。在保加利亚（N=245）和北美（N=261）的大学生在线调查中，我们计划检查量表的心理测量特性、结构效度和测量不变性。然而，由于量表的项目间相关性低且大多不显著，以及项目与总分的相关性低，因此无法进行部分检查。随后，探索性项目反应理论分析表明，该量表的可靠性较低，尤其是对于抗框架的高水平。这表明该量表在概念化的基本层面上存在问题，即项目可能无法代表相同的内容领域。总体而言，由于存在这些问题，该量表在当前版本中的用途有限，至少在大学生样本中是如此。我们讨论了这些问题的潜在解决方法，并提供了开放的代码和数据（https://osf.io/j5n6f），以方便在其他样本中（例如，一般人群、不同语言和国家）测试该量表，从而全面了解其性能。
