Workshop on Q methodology

1. Workshop on Q methodology Midwestern Educational Research Association St. Louis, MO 8 – 9:20 AM October 25, 2007 Sue Ramlo Joe Jurczyk

2. Sue Ramlo, PhD Physicist w/ PhD in Curricular & Instructional Studies Presentations of Q studies Published Q studies Editorial board of both Q journals (English) Operant Subjectivity Human Subjectivity Previous Q workshop developer Joe Jurczyk Systems Engineer w/ MBA & ABD in C&I Dissertation – developing & evaluating a versatile on-line Q-sort tool Presentations of Q studies Previous Q workshop presenter About the presenters:

3. Workshop Outline • Introduction to Q methodology • Sorting items onto a normal Gaussian distribution • Concourse of items & the Q sample • Analyzing the sorts using PQ Method – factors & interpretation • Why groups of people? • Examples of Q studies

4. Introduction to Q methodology - Overview • Investigate the views, or perspectives, of a person or a group of people. • Process involves: • Creating a concourse of items (text, sounds, pictures). • Sorting a sample of the items into a normal Gaussian distribution. • Sorts are factor analysized to group people with similar sorts (Note: R FA groups items).

5. Most UNlike my view (~14 statements here) Neutral view about this statement (~14 statements here) MOST like my view (~14 statements here) Sorting items onto a normal Gaussian distribution • Pre-sort into 3 piles • Distribute (& re-distribute) to fit specific normal Gaussian distribution.

6. Most undesirable (~7 items) Neutral view about this statement (~7 items) MOST desirable (~7 items) Now you try it! • Condition of instruction – Because you’ve been working so hard, your boss is going to give you a bonus in the form of a one year vehicle lease & he wants your input. In the envelope you have received, there are pictures of a variety of different vehicles. Rate these items on a scale of “most desired” (+4) to “most undesired” (-4)….” • Pre-sort into 3 piles • Distribute (& re-distribute) to fit specific normal Gaussian distribution.

7. Introduction to Q methodology – historical background • Developed in 1935 by William Stephenson • Physicist-psychologist • Student of Spearman • A Study of Behavior, 1955 • Q for Quantum • Most typically used in fields of psychology, marketing, advertising, political science… • Mixes quantitative & qualitative aspects of research

8. Why not another method to determine views? • Alternatives for determining perspectives are not as powerful as Q (McKeown, 2001). • Likert scale evaluations and rank ordering lead to the loss of meaning (McKeown, 2001) – e.g. aggregate results • Because Q measures personal opinion regarding a concourse of items related to a topic, validity is not a consideration (Brown, 1999).

9. What is meant by subjectivity? • What do you see? Bunny? Duck? • Is one right & the other wrong or are they both just different views?

10. Any Q study starts with a concourse: • Can consist of words / statements, pictures, sounds, smells… • Subjective • Not “It’s raining” • But can be “the rain makes me feel sad” –or- “I love to walk in the rain.” • Items are interpreted by participant – removes the view of the researcher & the issue of validity / reliability. • Select the Q sample from the concourse • Try to “balance” the Q sample • Sample needs to be sufficiently “large” {sample size here is the number of items, not the number of people in the study}

11. Concourse of items – 3 possibilities • “Naturalistic” statements - taken from participants’ oral or written communications. • Interviews • Focus Groups • “Ready made” statements - from sources other than those of the participants’ communications. • Likert survey items • Based on knowledge of researcher w/o interviews • Hybrid - combine both “naturalistic” and “ready made” items. • One is not inherently superior to the other (McKeown & Thomas, 1988). • Researcher selects the type best suited to the project at hand

12. Q sample – select items from the concourse to use in the study. • Example: Selection from a Q sample of 44 (chosen from a concourse of 74)

13. Condition of instruction • Participants sort based upon a condition of instruction (or multiple conditions). • E.g. Sort the following statements as they relate to your views about learning in this class. • The statements are matters of subjective opinion and may mean different things to different people. • Meaning is determined by sorter, not researcher • Reason why validity is not a consideration • e.g. I worked hard in this class.

14. Analyzing Q sorts • SPSS & SAS not really designed for Q sorts – you mess with weightings, etc. • Need software designed for Q methodology • PCQ • PQMethod • QUANAL

15. Factor Analysis • Higher order correlation • Used to determine patterns in a data set • R-factor analysis groups items (people are rows, items are in columns). Factors represent similar items. Objective. • Q-factor analysis groups people (people are in columns, items are in rows). The factors represent people with similar topologies. Objective • Q methodology is not Q FA but does group people based upon their VIEWS on a subject. Factors represent similar views about a topic. Subjective.

16. PQ Method to determine factors & assist in their interpretation • PQMethod • Free download (start at www.qmethod.org) • DOS based • Designed for handling Q sort entry and analyses • Choices • Centroid versus Principal Components factor extraction • Graphical hand rotation versus Varimax • Start PQMethod

17. PQMethod Analyses • Creates print out with: • Factor loadings • Factor correlations • Distinguishing statements • Consensus statements, etc • Example - knowledge Tech Physics sorts Ramlo 2006.lis.

18. Results • Different factors represent the various views within the P-set • More democratic, not simply majority “wins” • Allows further investigation (linear regression, etc) especially if groups not known a priori • Consensus allows researcher to see where there is agreement • Organizational change theory

19. For more on Q methodology: • www.qmethod.org • I4S – International Society for the Scientific Study of Subjectivity; Next conference in Hamilton, ON; Sept/Oct. 2008 • Brown, S. R. (1980). Political subjectivity: Applications of Q methodology in political science. New Haven: Yale University Press. • McKeown, B., & Thomas, D. (1988). Q methodology. Newbury Park, Calif.: Sage Publications. • Stephenson, W. (1955). The study of behavior: Q-technique and its methodology. Chicago: University of Chicago Press.