Method of the month: Q methodology

Once a month we discuss a particular research method that may be of interest to people working in health economics. We’ll consider widely used key methodologies, as well as more novel approaches. Our reviews are not designed to be comprehensive but provide an introduction to the method, its underlying principles, some applied examples, and where to find out more. If you’d like to write a post for this series, get in touch. This month’s method is Q methodology.


There are many situations when we might be interested in people’s views, opinions or beliefs about an issue, such as how we allocate health care resources or the type of care we provide to dementia patients. Typically, health economists may think about using qualitative methods or preference elicitation techniques, but Q methodology could be your new method to examine these questions. Q methodology combines qualitative and quantitative techniques which allow us to first identify the range of the views that exist on a topic and then describe in-depth those viewpoints.

Q methodology was conceived as a way to study subjectivity by William Stephenson and is detailed in his 1953 book The Study of Behaviour. A more widely available book by Watts and Stenner (2012) provides a great general introduction to all stages of a Q study and the paper by Baker et al (2006) introduces Q methodology in health economics.


There are two main stages in a Q methodology study. In the first stage, participants express their views through the rank-ordering of a set of statements known as the Q sort. The second stage uses factor analysis to identify patterns of similarity between the Q sorts, which can then be described in detail.

Stage 1: Developing the statements and Q sorting

The most important part of any Q study is the development of the statements that your participants will rank-order. The starting point is to identify all of the possible views on your topic. Participants should be able to interpret the statements as opinion rather than facts, for example, “The amount of health care people have had in the past should not influence access to treatments in the future”. The statements can come from a range of sources including interview transcripts, public consultations, academic literature, newspapers and social media. Through a process of eliminating duplicates, merging and deleting similar statements, you want to end up with a smaller set of statements that is representative of the population of views that exist on your topic. Pilot these statements in a small number of Q sorts before finalising and starting your main data collection.

The next thing to consider is from whom you are going to collect Q sorts. Participant sampling in Q methodology is similar to that of qualitative methods where you are looking to identify ‘data rich’ participants. It is not about representativeness according to demographics; instead, you want to include participants who have strong and differing views on your topic. Typically this would be around 30 to 60 people. Once you have selected your sample you can conduct your Q sorts. Here, each of your participants rank-orders the set of statements according to an instruction, for example from ‘most agree to most disagree’ or ‘highest priority to lowest priority’. At the end of each Q sort, a short interview is conducted asking participants to summarise their opinions on the Q sort and give further explanation for the placing of selected statements.

Stage 2: Analysis and interpretation

In the analysis stage, the aim is to identify people who have ranked their statements in a similar way. This involves calculating the correlations between the participants Q sorts (the full ranking of all statements) to form a correlation matrix which is then subject to factor analysis. The software outlined in the next section can help you with this. The factor analysis will produce a number of statistically significant solutions and your role as the analyst is to decide how many factors you retain for interpretation. This will be an iterative process where you consider the internal coherence of each factor: i.e. does the ranking of the statements make sense, does it align with the comments made by the participants following the Q sort as well as statistical considerations like Eigen Values. The factors are idealised Q sorts that are a complete ranking of all statements, essentially representing the way a respondent who had a correlation coefficient of 1 with the factor would have ranked their statements. The final step is to provide a descriptive account of the factors, looking at the positioning of each statement in relation to the other statements and drawing on the post Q sort interviews to support and aid your interpretation.


There are a small number of software packages available to analyse your Q data, most of which are free to use. The most widely used programme is PQMethod. It is a DOS-based programme which often causes nervousness for newcomers due to the old school black screen and the requirement to step away from the mouse, but it is actually easy to navigate when you get going and it produces all of the output you need to interpret your Q sorts. There is the newer (and also free) KenQ that is receiving good reviews and has a more up-to-date web-based navigation, but I must confess I like my old time PQMethod. Details on all of the software and where to access these can be found on the Q methodology website.


Q methodology studies have been conducted with patient groups and the general public. In patient groups, the aim is often to understand their views on the type of care they receive or options for future care. Examples include the views of young people on the transition from paediatric to adult health care services and the views of dementia patients and their carers on good end of life care. The results of these types of Q studies have been used to inform the design of new interventions or to provide attributes for future preference elicitation studies.

We have also used Q methodology to investigate the views of the general public in a range of European countries on the principles that should underlie health care resource allocation as part of the EuroVaQ project. More recently, Q methodology has been used to identify societal views on the provision of life-extending treatments for people with a terminal illness.  This programme of work highlighted three viewpoints and a connected survey found that there was not one dominant viewpoint. This may help to explain why – after a number of preference elicitation studies in this area – we still cannot provide a definitive answer on whether an end of life premium exists. The survey mentioned in the end of life work refers to the Q2S (Q to survey) approach, which is a linked method to Q methodology… but that is for another blog post!


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