The Analysis

Qsorted and Risqibiznis are available with three levels of input: standalone materials and instructions, summary report on data supplied, or a fully bespoke research project investigating, for example, impact of interventions or campaigns.

The data analysis is performed by a bespoke software programme designed for Q Methodology. This produces factorial analysis on common clusters of belief to build up a detailed picture of who shares what perspectives on the topic.

We provide default instructions for those wanting to standardise their work in order to compare against results from wider studies, but can advise on alternatives where required.

Data processing is usually achievable in around 12 working days, and a short report is made with a brief technical element, but a larger summary interpretation.

Q Methodology

Q methodology, Q sorting or simply Q as it is sometimes called, is a form of data collection and analysis that has a long pedigree, though is not as well known as questionnaires and interviews. There are numerous websites where the technical and scientific details of Q are discussed in depth, for example at this website.

Our team have been using Q method in our research for almost 30 years, on peer-popularity, sexual aggression, resilience, special needs, bullying, professionals’ beliefs on inclusion, and pathways into higher education. The activity is very engaging, and most people get deeply involved in the process, much more than they do with traditional approaches.

The participants are asked to evaluate a set of statements or ideas or physical objects according to a specific instruction, and to express the evaluation on a purpose-made grid.

The outcomes, the arrangement of items on the grid, are then fed into bespoke analytic software that generates a set of correlations in the form of clusters of shared viewpoints.

These clusters are expressed as statistically significant factors, and allow interpretation of the shared perspectives. The readouts can look like this example from a study on girls’ popularity and aggression:

Factors were accepted as statistically significant (P < 0.01), if they exceed the standard error of a zero order correlation, obtained from the following formula described in Brown (1993):

Factor 1 ‘Sometimes girls can be popular even though they are not actually liked by many others. Fear of becoming a target oneself can be the driver’. This factor accounted for the largest shared perspective, 48% of the participants. Those belonging to this viewpoint tended to be older girls (31 of 42) and from the girls-only schools in the study. A strong overlap with Factor 3, below, was evident, indicating a consensus that social exclusion from an ‘in’ group was a powerful tool in popular girls’ aggression.

Based on Z scores ≥ 1, popularity is associated (in order) with: being really pretty; very fashionable; popular with boys; very loud; many friends; having a certain type of make-up or hairstyle; quite thin; and going out with older boys. By contrast, based on Z scores ≤ 1, unpopular girls may be lesbian; very quiet; well behaved in school; liked by teachers; quite fat; miss a lot of school; and religious.

Similar, but more extensive, data on youth safety, and their interpretations, would be very valuable for those tasked with policy and strategy in supporting safer communities. The findings can illuminate patterns of belief and behaviour across age, type of school, postcode and gender.

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