Some but not all dyadic measures in shared decision making research have satisfactory psychometric properties

Objective: To assess the psychometric properties of dyadic measures for shared decision making (SDM) research. Study Design and Setting: We conducted an observational cross-sectional study in 17 primary care clinics with physician-patient dyads. We used seven subscales to measure six elements of SDM: (1) defining the problem, presenting options, and discussing pros and cons; (2) clarifying the patient's values and preferences; (3) discussing the patient's self-efficacy; (4) drawing on the doctor's knowledge; (5) verifying the patient's understanding; and (6) assessing the patient's uncertainty. We assessed the reliability and invariance of the factorial structure and considered a measure to be dyadic if the factorial structure of the patient version was similar to that of the physician version and if there was equality of loading (no significant chi-square). Results: We analyzed data for 264 physicians and 269 patients. All measures except one showed adequate reliability (Cronbach alpha, 0.70-0.93) and factorial validity (root mean square error of approximation, 0.000-0.06). However, we found only four measures to be dyadic (P > 0.05): the values clarification subscale, perceived behavioral subscale, information-verifying subscale, and uncertainty subscale. Conclusion: The subscales for values clarification, perceived behavioral control, information verifying, and uncertainty are appropriate dyadic measures for SDM research and can be used to derive dyadic indices.