Q methodology
Encyclopedia : Q : QM : QME : Q methodology
Q Methodology is a research method used in psychology and other social sciences to study people's "subjectivity" -- that is, their viewpoint. Q was developed by psychologist William Stephenson. It has been used both in clinical settings for assessing patients, as well as in research settings to examine how people think about a topic.
The name "Q" comes from the form of factor analysis that is used to analyze the data. Normal factor analysis, called "R method," involves finding correlations between variables (say, height and age) across a sample of subjects. Q, on the other hand, looks for correlations between subjects across a sample of variables. Q factor analysis reduces the many individual viewpoints of the subjects down to a few "factors," which represent shared ways of thinking. It is sometimes said that Q factor analysis is R factor analysis with the data table turned sideways. While helpful as a heuristic for understanding Q, this explanation may be misleading, as most Q methodologists argue that for mathematical reasons no one data matrix would be suitable for analysis with both Q and R.
The data for Q factor analysis comes from a series of "Q sorts" performed by one or more subjects. A Q sort is a ranking of variables -- typically presented as statements printed on small cards -- according to some "condition of instruction." For example, in a Q study of people's views of George W. Bush, a subject might be given statements like "He is a deeply religious man" and "He is a liar," and asked to sort them "from most like how I think about George W. Bush, to least like how I think about George W. Bush." The use of ranking, rather than asking subjects to rate their agreement with statements individually, is meant to capture the idea that people think about ideas in relation to other ideas, rather than in isolation.
The sample of statements for a Q sort is drawn from a "concourse" -- the sum of all things people say or think about the issue being investigated. Since concourses do not have clear membership lists (as would be the case in the population of subjects), statements cannot be drawn randomly. Commonly Q methodologists use a structured sampling approach in order to ensure that they include the full breadth of the concourse.
One salient difference between Q and other social science research methodologies, such as surveys, is that it typically uses many fewer subjects. This can be a strength, as Q is sometimes used with a single subject. In such cases, a person will rank the same set of statements under different conditions of instruction. For example, someone might be given a set of statements about personality traits and then asked to rank them according to how well they describe herself, her ideal self, her father, her mother, etc.
External links
- [Q Methodology page] Includes more information on Q, as well as free software for conducting a Q factor analysis.
The Q Method Page (International Society for the Scientific Study of Subjectivity)
Q Methodology takes an inventory of an individual's subjective positions on issues. It aims not to identify mean or average values, but to identify the spectrum of positions. It provides a number of potential uses:
analysis of contributions to dialogues audit the effects of deliberation/participation on social learning filter candidates for delibertive processes for issue representativeness The steps:
Concourse definition is identifing the full breadth of social discussions and discourses surrounding the problem or issue: i.e., the "concourse." The extent of the research is limited only by the constraints of resources and time. Everything from newspaper articles and PR advertising to political speeches and neighbourhood discussions are legitimate sources. Reading, interviews and surveys can all be of value.
Q-set selection is based on the large number, possibly hundreds, of statements on the topic distilled from the concourse. This original sampling is then distilled to a more manageable number, usually no more than sixty, proportionately representative statements: the Q set.
P-sample is the set of participants in the relevant process. Usually the P-sample involves no more than fifty.
Q-sorting involves all members of the P-sample rating the statements in the Q-set on a Likert scale. The distinctive element of Q-sorting is that all statements are ranked and scored together, generating for each respondent a complete partial ordering of all statements scored across the spectrum
Statistical analysis subjects Q-sorts to factor analysis, enabling identification of clusters of Q-sorts containing ranking patterns.
Result interpretation requires careful examination of the rankings assigned to Q-statements by members of each cluster. Some times unanticipated elements emerge that may require researchers to reassess previous assumptions.
Toddi A. Steelman, Understanding Participant Perspectives: Q-Methodology in National Forest Management (online paper)
Steven R. Brown, The History and Principles of Q Methodology in Psychology and the Social Sciences (online paper)
Q Methodology - advantages and the disadvantages of this research method Kelvin Karim
Abstract: Kelvin Karim describes how nurse researchers might use Q methodology, what it is and the advantages and disadvantages of the method.
Kelvin Karim BA, BN, RGN, Dip DN is a Community Macmillan Nurse, Walsall Community NHS Trust, Walsall, West Midlands.
There are a significant number of texts written specifically for nurses and other health care researchers. However, very few of these texts include a description of what Q methodology is and how it may be used by researchers. This article seeks to redress this gap. The advantages and disadvantages of Q methodology will also be discussed. Origins of Q methodology Q methodology was invented in 1935 by physicist and psychologist William Stephenson. Stephenson's ideas were not well received in the 1930’s when influential psychologists such as Cattell, Burt and Eysenck, were deeply rooted in experimental psychometric testing (Brown, 1998) and his ideas found more favour outside the field of psychology, especially in the USA. Brown (1998) suggests that Stephenson's thinking was ahead of his time and the proliferation of research studies involving Q methodology is testament to this. Some 50 years later, the method is increasingly used in marketing, political science, psychology, public administration and a range of more recent intellectual developments such as feminism and women’s issues.
What is it? According to Niemi (1988), Q methodology is a systematic and rigorously quantitative means for examining human subjectivity. It is essentially a method for examining intensively the subjectivity of an individual or group and in recent years has become a popular tool for researchers in social sciences. Q methodology refers to the use of Q sorting, which is a data collection technique and Q factor analysis, which is a procedure for statistical analysis. While Q sorting and Q factor analysis can be used independently, they can also be combined, enabling researchers to benefit from both qualitative and quantitative research approaches. Initially the researcher selects a set of items (stimuli) which are placed on individual Q sort cards. Collectively the cards are called a Q sort deck and may comprise of a series of statements, words, pictures, pieces of art, paintings or photographs. The Q sort deck is essentially a survey or test instrument. The researcher looking at any given subject must ensure that the full range of opinions are represented and that they are presented in an easily understandable way. Typically, the number of cards range from 60 to 100 (Polit & Hungler, 1999). Denzine (1998) cites Kerlinger (1986) as suggesting that at lease 60 cards should be used to have statistical stability and reliability. Participants are then asked to sort the cards according to certain dimensions such as approval/disapproval, most like me/least like me or lowest or highest priority. Denzine (1998) provides the example of a researcher who is seeking student perceptions on the most desirable place to study on a university campus. A Q sort deck would have to include all of the possible study locations and students would be asked to rank the cards using a desirability continuum, placed towards the middle with fewer at the extremes. Thus the middle cards are likely to contain the neutral views and extreme cards will contain the strongest views. It is the strongest views which probably contain the most important information. This is termed a forced sort. In a standard questionnaire format each question is independent of the other. However, Q methodology involves an ipsitive approach which means that each item in the Q sort deck are dependent and interrelated. The participants are less likely to respond to an item which is inconsistent with a previous item because his/her choice is likely to be restricted by the previous response. Denzine (1998) points out that in a forced sort situation, a normal statistical distribution will occur because of the ranking procedure which results in fewer cards being selected to represent the strongest held views of the participants.
Factor analysis Analysis of data obtained through Q sorts can range from the most descriptive statistical procedures such as rank ordering, averages and percentages to highly complex procedures such as factor analysis. Factor analysis, is a procedure designed to reveal the common elements in a set of items (Polit & Hungler, 1999). Put another way, factor analysis is analysis which examines inter-relationships among large numbers of variables and attempts to disentangle those relationships to identify clusters of variables that are most closely linked together (Burns & Grove, 1997). The researcher can do this manually or by using a computer programme such as Q Method software. There is a great deal of complicated mathematics which underlies factor analysis. However, it is highly fortunate that there is, as Brown (1991) suggests, little reason for the researcher to need to understand mathematics. Brown uses the analogy of the car driver not needing to know about the mechanics of a car in order to drive it. Q methodology is about appraising the subjective. Brown (1991) points out that the phenomena with which Q methodology deals is the ordinary conversation, commentary and discourse of every day life backed by a powerful statistical mechanism which can go relatively unnoticed by Q methodology users. There is no right or wrong view. Q may be used in research which seeks to explore individual or group perceptions and attitudes. This seems very appropriate in the area of health care where researchers frequently seek to elicit the view of patients and professionals. Disadvantages Conversely, it can be argued, Q methodology can be time consuming and difficult to administer. Q sorts ideally have to be done face to face and therefore obtaining a geographically diverse sample is complicated. According to Polit and Hungler (1999), the forced procedure of distributing cards according to the researcher’s specifications is the subject of criticism. Critics argue that this artificial procedure tends to exclude information concerning how people would ordinarily distribute their opinions. In addition, standard statistical procedures to Q sort data such as the interpreting data by comparing individual score with the average score for a group (normative measures) cannot be used with Q methodology due to the ipsative nature of participant responses. However, some also argue that this relatively unimportant, especially when the number of items is large (Polit & Hungler, 1999). In planning Q samples to be sorted, the researcher has to generate the statements. This can be time consuming, especially if naturalistic, that is derived from interviews or written narratives. The researcher may, however, use ready made materials. The aim of the researcher is to seek to ensure the fullest range of viewpoints in the Q-sort deck. Practically, the researcher has to make a number of decisions not only about what material to include on each card but how many piles they should be placed in and how many cards should be placed in each pile. The researcher has to decide whether the cards should be ranked in each pile. Researchers unfamiliar with Q methodology are likely to feel that the method is complicated and may lack the confidence to make these decisions. The decisions made may influence the outcomes and the researcher may find it hard to justify why each of the decisions were taken. From a participant point of view, it is not an easy exercise. The participant may struggle to understand what is required of him/her. It is also a time consuming exercise requiring the participant to think. Providing the environment and sufficient time to conduct a Q sort may be problematic for researchers and participants. The method is unlikely to be practical for use with participants who have cognitive difficulties. Researchers may also be tentative about asking people who are disabled by illness to take part. The method may not, for example, be appropriate for use with many people with a terminal illness or with learning difficulties. Popularity Q methodology offers researchers a powerful tool for systematically examining subjective data. Although there are many drawbacks in using Q methodology, there are also a number of key advantages especially for experienced researchers seeking to explore perceptions and attitudes. The method is proving popular in the social sciences and has been used by researchers in subjects as diverse as pornography, political campaigning, religion and oral history. The literature now contains around 1,500 bibliographic entries (Brown, 1986). Nevertheless, as McKeown and Thomas (1990) suggest, ‘Q retains a somewhat fugitive status within the larger scientific community’. However, at the very least, British health care researchers should have the opportunity to become familiar with Q methodology in order to decide for themselves whether or not it is appropriate to their needs. By dismissing or ignoring Q methodology, as many texts seem to, British health care researchers are being denied the opportunities that it presents.
References Brown, S.R. (1986) Q technique and method. In: Berry W.D. and Lewis-Beck M.S. (1986) (Eds) New Tools for Social Scientists. Sage, California. Brown, S.R. (1991) Q Methodology Tutorial. Kent State University, Ohio [On-line]. Available at http://facstaff.uww.edu/ cottlec/qarchive/qindex.htm Brown, S.R. (1998) The History and Principles of Q Methodology in Psychology and the Social Sciences. Kent State University, Ohio [On-line]. Available at http://facstaff .uww.edu/cottlec/qarchive/qindex.htm Burns, N., Grove, S.K. (1997). The Practice of Nursing Research: Conduct, Critique and Utilization. W.B. Saunders, London. Denzine, G.M. (1998) The Use of Q Methodology in Student Affairs Research and Practice. Student Affairs Journal-Online [Online]. Available from http://sajo.org Kerlinger, F. (1986) Foundations of Behavioural Research (3rd ed). New York. Holt, Rinehart and Winston cited Denzine, G.M. (1998) The Use of Q Methodology in Student Affairs Research and Practice. Student Affairs Journal-Online, [Online]. Available from http://sajo.org McKeown, B., Thomas, D. (1988). Q Methodology - Quantitative Applications in the Social Sciences. Sage, California. Niemi, R.G. (1988) Series Editor's Introduction. In: McKeown, B., Thomas, D. (1988) Q Methodology. Sage, California. Polit, D.F., Hungler, B.P. (1999) Nursing Research Principles and Methods. Lippincott, Philadelphia.
From Wikipedia, the Free Encyclopedia. Original article here. Support Wikipedia by contributing or donating.
All text is available under the terms of the GNU Free Documentation License See Wikipedia Copyrights for details.