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Beyond Average Information: How Q‐Methodology Enhances Course Evaluations in Anatomy
Author(s) -
BrewerDeluce Danielle,
Sharma Bhanu,
AkhtarDanesh Noori,
Jackson Thomas,
Wainman Bruce C.
Publication year - 2019
Publication title -
anatomical sciences education
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.126
H-Index - 51
eISSN - 1935-9780
pISSN - 1935-9772
DOI - 10.1002/ase.1885
Subject(s) - likert scale , course evaluation , course (navigation) , curriculum , diversity (politics) , computer science , medical education , teaching method , psychology , mathematics education , interpretation (philosophy) , higher education , pedagogy , medicine , engineering , developmental psychology , sociology , political science , anthropology , law , programming language , aerospace engineering
Course evaluations can be used for curriculum improvement and have the potential to better the student learning experience. However, because most are based on Likert scales and open‐ended feedback, understanding diversity in student opinion and uncovering optimal options for course change and improvement are often difficult. Alternatively, Q‐methodology can be used to investigate patterns of thought within a group and may offer greater potential for course reform. This manuscript offers a tutorial‐based explanation of the three components of Q‐methodology studies (1) survey instrument development, (2) data collection, and (3) analysis and interpretation, then demonstrates, via case study, the use of Q‐methodology to evaluate a fourth‐year undergraduate pathoanatomy course. The goal of this article is to enable the reader to broadly apply Q‐methodology in other courses to gain insight and feedback beyond that offered by traditional Likert scale methods. As demonstrated through the pathoanatomy case study, Q‐methodology highlights groups (denoted by factors) of like‐minded students that share opinions, preferences, and values. Overall, Q‐methodology analyses support course instructors in identifying areas of course strength and improvement in an evidence‐based way. This alternative to traditional Likert scales represents a promising solution to ongoing course evaluation limitations.