Abstract
BackgroundIn fledgling areas of research, evidence supporting causal assumptions is often scarce due to the small number of empirical studies conducted. In many studies it remains unclear what impact explicit and implicit causal assumptions have on the research findings; only the primary assumptions of the researchers are often presented. This is particularly true for research on the effect of faculty’s teaching performance on their role modeling. Therefore, there is a need for robust frameworks and methods for transparent formal presentation of the underlying causal assumptions used in assessing the causal effects of teaching performance on role modeling. This study explores the effects of different (plausible) causal assumptions on research outcomes.MethodsThis study revisits a previously published study about the influence of faculty’s teaching performance on their role modeling (as teacher-supervisor, physician and person). We drew eight directed acyclic graphs (DAGs) to visually represent different plausible causal relationships between the variables under study. These DAGs were subsequently translated into corresponding statistical models, and regression analyses were performed to estimate the associations between teaching performance and role modeling.ResultsThe different causal models were compatible with major differences in the magnitude of the relationship between faculty’s teaching performance and their role modeling. Odds ratios for the associations between teaching performance and the three role model types ranged from 31.1 to 73.6 for the teacher-supervisor role, from 3.7 to 15.5 for the physician role, and from 2.8 to 13.8 for the person role.ConclusionsDifferent sets of assumptions about causal relationships in role modeling research can be visually depicted using DAGs, which are then used to guide both statistical analysis and interpretation of results. Since study conclusions can be sensitive to different causal assumptions, results should be interpreted in the light of causal assumptions made in each study.
Highlights
Role modeling research is a relatively new area in the emerging field of medical education research
We introduce the well-established graphical tools directed acyclic graphs (DAGs) that are new to the field of medical education research. [4,5,6] we present our theoretical assumptions about the connections between teaching performance and role modeling
The specific research questions explored in our current study were: 1) what are the potential causal relationships between teaching performance and the three role model types; and 2) how do these different causal assumptions impact the associations between teaching performance and the role model types? We explored the main plausible causal models on this topic, to gain insights into the influence of faculty’s teaching performance on their role modeling as teacher-supervisor, physician, and person
Summary
Role modeling research is a relatively new area in the emerging field of medical education research. Researchers who use quantitative methods like regression models and structural equation models need to make causal assumptions in their analyses These assumptions are usually made early on a study. In fledgling areas of research, evidence supporting causal assumptions is often scarce due to the small number of empirical studies conducted In many studies it remains unclear what impact explicit and implicit causal assumptions have on the research findings; only the primary assumptions of the researchers are often presented. This is true for research on the effect of faculty’s teaching performance on their role modeling. This study explores the effects of different (plausible) causal assumptions on research outcomes
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