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A reassessment of the Resistance to Framing scale.

Sandra J GeigerJáchym VintrNikolay R Rachev
Published in: Behavior research methods (2022)
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.
Keyphrases
  • psychometric properties
  • healthcare
  • machine learning
  • risk assessment
  • cross sectional
  • data analysis
  • human health