Q Methodology

Q methodology

• — is amongst the oldest methods in psychology with an explicitly interpretivist focus on the meanings the participants in a study setting attach to their social world

• — was designed expressly to explore the subjective dimension of any issue towards which different points-of-view can be expressed

William Stephenson

Q methodology was first introduced by —

subjectivity

Methodologically speaking, — is made the centre of concern in two related ways that correspond to the two main distinc tive aspects of Q methodology: the collection of data in the form of Q sorts and the subsequent by-person correlation and factor analysis of those sorts.

Q sort

• — as a form of data collection was developed for the purpose by Stephenson, not only to maximize the expression of subjectivity,

• but also to deal with the relatively unusual situ ation of a form of data analysis that, technically speaking, treats participants as variables rather than cases.

• The — as a data-collection form is designed to maximize the expression of qualitative variation and to record it in numerical form

Q methodological studies

— involve a group of participants sorting a sample of items into a configuration (the Q sort) that, taken as a gestalt, reflects a relevant subjective dimen sion (e.g. personal degree of agreement with the items)

• Step 1: Formulating the Research Question

• Step 2: Generating the Set of Items (or Q-set)

• Step 3: Selecting a P-set

• Step 4: Q Sorting

• Step 5: Q Data Analysis

• Step 6: Factor Estimation

• Step 7: Factor Interpretation

step-by-step guide to actually getting a Q methodological study done

concerning meaning

Like most qualitative methods, Q methodological studies are oriented by research questions — rather than by specific testable hypotheses about causal relationships.

Formulating the Research Question

The subjective dimension of any issue towards which different point of view can be expressed, e.g. ‘What is the meaning of ‘quality of life’? ‘What does love mean to you?’ Ethical issues should be considered at this stage.

Generating the Q-set

The Q-set is comprised by numerous items based on your estimation of the concourse. Individually these should express a relevant proposition and together they should cover the ‘concourse’ of what we know to be sayable about the issue in question. Each item should be clearly expressed in ordinary language and randomly numbered.

Selecting a P-set

A typical sampling concern when selecting a P-set of participants to take part in a Q study is to maximize the likelihood of a variety of distinct viewpoints being expressed.

Collecting Data

— from participants in the form of Q sorts with open-ended comments: This typically involves having participants rank order the Q-set into a quasi-normal distribution according to some subjectively relevant dimension such as ‘most disagree’ to ‘most agree’. Through their unique response to the Q-set, each participant expresses their viewpoint on the topic in question. It is the overall pattern, configuration or gestalt of the sort that is of primary interest.

Analysing Q sort data

— involves correlating and factoring the data by-person in order to identify a small set of factors. Each factor will be loaded by a number of Q sorts that have been sorted in a substantially similar way.

Interpreting Q factors

Since the Q sorts loading on a given factor will have been patterned in a similar way, for each factor a single ‘factor array’ is generated by merging the highest loading Q sorts. This factor array can be taken as representing whatever ‘point of view’ is informing the factor. Each factor array is therefore subjected to an interpretation based upon an inspection of the complete set of rankings in combination with any open-ended data provided by the relevant participants. This can also be followed by a ‘cultural analysis’.

1. variables

2. cases

one important aspect of Stephenson’s method is that in a Q study the par ticipants are the 1. — and the items are the 2. —

Q sorting

— is what Brown (1980: 17) calls ‘the technical means whereby data are obtained for factoring’. In practice, it is a convenient means of facilitating the subjective evaluations of the par ticipants. I

P set

participants

Q set

items

Step 6: Factor Estimation

To enable interpretation to take place, an estimate of each factor must first be prepared. This is achieved by merging all the Q sorts that exemplify (or are significantly associated) with the factor in question. This procedure results in the creation of a single ‘factor exemplifying’ Q sort for each factor, sometimes also called a factor array, and it is these factor arrays that are subjected to interpretation.