The Teacher, the Physician and the Person: Exploring Causal Connections between Teaching Performance and Role Model Types Using Directed Acyclic Graphs
نویسندگان
چکیده
BACKGROUND 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 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. METHODS This 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. RESULTS The 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. CONCLUSIONS Different 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.
منابع مشابه
Analysis of the role of teachers performance appraisal in school effectiveness with mediating role of job enthusiasm, justice perception and organizational trust
رThe purpose of this study was to analyse the role of teachers performance appraisal in school effectiveness with mediating role of job enthusiasm, justice perception and organizational trust. . This research was an applied research in terms of the aim and it was descriptive correlational of causal type research in terms of method The statistical population were all s...
متن کاملThe Teacher, the Physician and the Person: How Faculty's Teaching Performance Influences Their Role Modelling
OBJECTIVE Previous studies identified different typologies of role models (as teacher/supervisor, physician and person) and explored which of faculty's characteristics could distinguish good role models. The aim of this study was to explore how and to which extent clinical faculty's teaching performance influences residents' evaluations of faculty's different role modelling statuses, especially...
متن کاملInvestigating the Manifestation of Teaching Expertise Feature among Novice and Experienced EFL Teachers
The present study was an attempt to investigate the manifestation of teaching expertise of EFL teachers in Iranian formal educational context. More specifically, it was intended to study how teachers of English in Iranian high schools and General English instructors in a state university manifest features of teaching expertise. The study also compared the expertise features of novice teachers w...
متن کاملAn Introduction to Inference and Learning in Bayesian Networks
Bayesian networks (BNs) are modern tools for modeling phenomena in dynamic and static systems and are used in different subjects such as disease diagnosis, weather forecasting, decision making and clustering. A BN is a graphical-probabilistic model which represents causal relations among random variables and consists of a directed acyclic graph and a set of conditional probabilities. Structure...
متن کاملLearning Bayesian Network Structure using Markov Blanket in K2 Algorithm
A Bayesian network is a graphical model that represents a set of random variables and their causal relationship via a Directed Acyclic Graph (DAG). There are basically two methods used for learning Bayesian network: parameter-learning and structure-learning. One of the most effective structure-learning methods is K2 algorithm. Because the performance of the K2 algorithm depends on node...
متن کامل