The Pure and the Overarching: An Application of Bifactor Model to Safety Attitudes Questionnaire

Although this article is long, we believe that it is worth reading. It answers the most fundamental question that patient safety professionals have asked: How do we measure the overall safety attitudes score of each person? Broadly speaking, we showed how to structuralize various safety-related traits in one’s mind, using the Safety Attitudes Questionnaire-Korean version as an example. We applied the bifactor model, which explicitly contains a general safety attitudes dimension that governs all SAQ-K items as well as six original SAQ-K domains. The major finding regarding the model structure is that several items might not fall under a specific SAQ domain, although they are still largely governed by safety attitudes in general; yet the stress recognition domain might not be part of the general construct—namely, safety attitudes. However, the more important information that we intended to share was that the bifactor model can effectively take control of the seemingly inadequate items or domains and calculate domain-specific scores and general safety attitudes score by providing different weights for each item. Thus, we can obtain much more purified domains scores from the data, compared to the traditional mean score approach. In addition, the item response theory-based approach used in this article gives more solid theoretical strength in handling the ordinal data and also offers the possibility of adding or dropping items or even a domain, while still allowing a longitudinal analysis— comparing scores from different versions of safety attitudes questionnaire. In addition to these theoretical strengths, the bifactor model provides exceptional computational efficiency compared to the other models we have tried, thereby allowing us to unlock an extremely large and complex dataset that could not be analyzed thus far. We hope the approach introduced in this article can help all the patient safety professionals achieve more precise and valid information from their already collected safety attitudes data as well as data collected in the future, ultimately saving more lives.