Q-methodology: Definition and Application in Health Care Informatics
- Affiliation of the authors: University of Illinois at Chicago, College of Associated Health Professions, School of Biomedical and Health Information Sciences, Chicago, IL
- Correspondence and reprints: Annette L. Valenta, DrPH, University of Illinois at Chicago, Health Information Management Program (MC 520), School of Biomedical and Health Information Sciences, 1919 West Taylor Street, Room 811 AHPB, Chicago, IL 60612-7249. E-mail: Valenta{at}uic.edu
- Received 19 September 1996
- Accepted 17 July 1997
Abstract
Objective To introduce the Q-methodology research technique to the field of health informatics. Q-methodology—the systematic study of subjectivity—was used to identify and categorize the opinions of primary care physicians and medical students that contributed to our understanding of their reasons for acceptance of and/or resistance to adapting information technologies in the health care workplace.
Design Thirty-four physicians and 25 medical students from the Chicago area were surveyed and asked to rank-order 30 opinion statements about information technologies within the health care workplace. The Q-methodology research technique was employed to structure an opinion typology from their rank-ordered statements. (The rank-ordered sorts were subjected to correlation and by-person factor analysis to obtain groupings of participants who sorted the opinion statements into similar arrangements.)
Results The typology for this study revealed groupings of similar opinion-types associated with the likelihood of physicians and medical students to adapt information technology into their health care workplace. A typology of six opinions was identified in the following groups: (1) Full-Range Adopters; (2) Skills-Concerned Adopters; (3) Technology-Critical Adopters; (4) Independently-Minded and Concerned; (5) Inexperienced and Worried; and (6) Business-Minded and Adaptive. It is imperative to understand that in the application of Q-methodology, the domain is subjectivity and research is performed on small samples. The methodology is a combination of qualitative and quantitative research techniques that reveals dimensions of subjective phenomena from a perspective intrinsic to the individual to determine what is statistically different about the dimensions and to identify characteristics of individuals who share common viewpoints. Low response rates do not bias Q-methodology because the primary purpose is to identify a typology, not to test the typology's proportional distribution within the larger population.
Conclusion Q-methodology can allow for the simultaneous study of objective and subjective issues to determine an individual's opinion and forecast their likeliness to adapt information technologies in the health care workplace. This study suggests that an organization's system implementers could employ Q-methodology to individualize and customize their approach to understanding the personality complexities of physicians in their organization and their willingness to adapt and utilize information technologies within the workplace.
Footnotes
-
↵* For data entry into statistical software programs, this means that participants are entered as column headings, whereas statements form the rows.
-
↵† To simplify structure and maximize factor loadings, factor extraction is usually followed by varimax and/or judgmental rotations.
-
↵‡ For example, if sorts from three participants were defining one factor with loadings of 0.70, 0.80, and 0.50 respectively, and if these participants (in the same order) had ranked opinion statement #1 as +4, +3, and +4, a weighted average score for this statement would be calculated as: (4 × 0.70 + 3 × 0.80 + 4 × 0.50)/3 = 2.4. This process is repeated in the same fashion for each of the remaining statements (and for each of the identified factors).
-
↵§ The significance of factor loadings is calculated with the formula for zero-order correlation coefficients, i.e., SE = 1/ (sqrt[N]), where SE is the standard error and N is the number of Q-sort statements. 1 2 Since there were 30 statements in this study, the standard error comes out to 0.18 (SE = 1/(sqrt[30]) = 1/5.447 = 0.18). Correlations are considered to be statistically significant at the 0.01 level when they are in excess of 2.58 standard errors (irrespective of sign), or (2.58(SE) = 2.58 (.18) = 0.47).
