3.4.1 The Basic Concept
Q Method is a research method used in psychology and other social sciences to study people's "subjectivity" -- that is, their viewpoint. Q method, first introduced by William Stephenson in 1935, provides a foundation for the systematic study of subjectivity (Brown, 1993). Q method has been used as a research tool in a wide variety of disciplines including nursing, veterinary medicine, public health, transportation, education, rural sociology, hydrology and mobile communication. In Q methodological study, people are presented with a sample of statements about some topic, called the Q-set. Q participants, called the P-set, are asked to rank-order the statements from their individual point of view, according to some preference, judgment or feeling about them, mostly using a quasi-normal distribution (van Exel, J., and G. de Graaf, 2005.)
Q method is also a type of research that integrates qualitative and quantitative techniques to reveal social perspectives. The former is mainly to collect qualitative data with the same effect of in-depth interviews; and the later is to analyze the quantitative data which can be clearly preceded the statistical analysis and comparison. Qualitative research can rich in perspectives and ideas of the individual;
while quantitative research can often make a clear and systematic analysis, so Q method is not only a good research tool, but also a participatory activity.
The advantage of Q method, because of its unique process of operationalization, is to help researchers clarify the issues characterized with subjectivity and complex.
Therefore, it is often used to handle multi-view issues which is helpful for the interviewees to raise the real point of view on the research topic.
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3.4.2 Q and R Methods
Stephenson (1935) defined traditional and general research survey with large samples as "R Method". In R research, respondents are subjects and questions are variables. R researchers look for patterns in responses across the variables for each person. In the analysis of survey data, statistics are used to find patterns in responses across respondents. It is common to compute a correlation coefficient comparing responses. The letter “Q” was selected to emphasize that Q method was different from R method techniques. The comparison of Q and R methodological provides some insight into the key differences in the methodologies. These include (Brown, et al., 2008):
1. Q method seeks to understand how individuals think/cognition about the research topic of interest. R methodology identifies the structure of opinion or attitudes in a population. Thus, the results of Q method will identify how an individual, or individuals with common views, understand an issue; the results of R methods describe the characteristics of a population that are associated statistically with opinions, attitudes, or behavior (e.g., voting) being investigated.
2. Although R methods are intended for the “objective” analysis of research issues, Q methodology is designed to study subjectivity. R methodology is found on logical positivism in which the researcher is an outside objective observer. In contrast, Q methodology is more closely related to postpositivist ideas (Duning, 1999).
3. Q methodology is an intensive method that seeks in-depth understanding of how at least one person thinks about the topic of investigation. As an intensive method, Q methodology requires a small number of well-selected subjects to complete the Q sort. R methods are extensive methods designed to extract an understanding of
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populations through representative samples of them; thus, they require – depending on the population size and sampling techniques- data from a certain percentage of the population of interest.
4. R method involves finding correlations between variables across a sample of subjects. Q method, 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. Following is the key differences between R and Q method refer to Table 3- 3.
Table 3- 3 The comparison between R and Q methodology
R method Q method
variable Survey question Q sort done by a Q participant Subject Respondent Q statement
Population All possible
Analysis Normal Inverted
Source: Webler et al. (2009)
3.4.3 Q Sorting
The people who do the Q sorts are called Q participants. In R studies, the people
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who fill out surveys are called respondents. Q participants are selected to represent the breadth of opinion in a target population, not the distribution of beliefs across the population. Q participants are also chosen because they have different and well-formed opinions. People who have well-formed opinions will find it easier to do the Q sort and are likely to produce a more robust sort (Webler et al., 2009)
Fundamentally, Q methodology provides a foundation for the systematic study of subjectivity, and it is this central feature which recommends it to persons interested in qualitative aspects of human behavior. The Q participants by way of the ranking process of Q method to express their views on the topics of the study. Therefore, the Q participants will be asked some questions which are relevant to the research topics(Q statements)for some advice; thereafter, the arrangement based on their individual opinion from the most agreeable to the most disagree will be arranged, this process called "Q sorting". The statements are matters of opinion only (not fact); there is no right or wrong way to provide "my point of view" about anything. Yet the rankings are subject to factor analysis, and the resulting factors, in as much as they have arisen from individual subjectivities, indicate segments of subjectivity which exist. And since the interest of Q methodology is in the nature of the segments and the extent to which they are similar or dissimilar.
Q sorting gathered from all the Q participants by way of statistical software to conduct correlation or factor analysis, the varied communities can be figured out. In other words, the similar rankings of statements to the Q participants were assembled into a catalog, different catalog known as "Q factor", where each member of a Q factor has a similar viewpoint on the research topic. Kerlinger (1986: 517) argued that Q methodology’s main strength is “its affinity to theory.” “It means that if a theory can be expressed in categories and if items that express the categories can be produced, then Q can be a powerful approach to testing theory”.
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