At first from a data mining perspective, the French vision of data analysis (analyse des données) has always seen R and Q analysis as not being methodologically different because of mathematical properties (see duality diagrams and eigen-decomposition of Hermitien operator(s)): linking the objectivity (R analysis) with subjectivity (Q analysis).
Nonetheless interpretation coming from highlighting the double associations is qualitative (see also biplot for quantitative appreciation) and one may give the focus to one or the other.
It is the ‘survey angle’ that may entail the Q or R approach therefore in the era of 4V data sources availability (Volume, Velocity, Various, Veracity, i.e., Big Data) what does this change in terms of methods or practice of these methods?
Keeping this in the context of PCA or MCA (reducing obviously the range of Q methods), there are consequences of being able to increase the dimensions of the matrix used: subjects, variables, both, and the consideration of including time and space.
Do we tend to favour an R analysis when increasing the Volume? What kind of subjectivity analysis (Q analysis) one can control by increasing the Various? Velocity and Veracity may simply be seen as adding complexity but the question of ‘snapshot analysis’ as having persistence has to be considered, in the same way as changing opinions and veracity may be also related. Are we faithful to ourselves when using social media to communicate or do we use a ‘range’ of ourselves? is this depending on the space/time ?
This can be extended to other methods in Q methodologies I suppose. Does this 4V reveals our habitus or in the contrary makes it unreachable, noisy, bluring what could have been analysed within a controlled survey (i.e., using the big data as secondary data). Is the solution to mine the survey (its structure) at the same time as mining the data (answers to this survey)?
I must confess that I don't really understand much of what is being asked here and I'm not sure that it has much to do with Q methodology. There have been various Q studies about social media, among the more recent being the following:
Jeffares, Stephen (2014, May 14). Interpreting hashtag politics: Policy ideas in an era of social media. London: Palgrave Macmillan. ISBN: 978-1-137-35773-1, ISBN10: 1-137-35773-8. 200 pp. £58.00 ($95.00) cloth.
In general, I would say that efforts to join Q and R have been at the expense of both Q and R.
If my understanding of the questions are reasonably correct, in my opinion there are some methodological issues and philosophical issues here. I think you do allude here on both. The 4Vs seem to suggest us the nature of knowledge here 'data'. I might argue that this is only an epistemic 'view of big data'. This could be an area to look at differently in a more critical manner. Turning to Q, you do mention two most important advantages of Q: recognising 'the pollution' overcoming the dichotomy of 'I and 'we/us' and subjective/objective co-existance. However Q can be combined with other modern ways of thinking and analysing social phenomena i.e. critical realism. When the 4Vs are considered, I may ask where did the big data come from? how did they first exist/accummulate to even know them as in 4Vs?. I think here Ontology is more crucial and critical reaslist view of the world may provide a very useful analytical categories. The big data (in the cotext of social media) I assume could be a result of individual thought process/experience/feelings which is neither isolated from the social interactions nor wholly decided from these interactions (individual is seen here as emergent from the collective yet different) and structure the individual operates in (in space/time) which then forms our practices (praxis).
What I am briefly trying to say is social reality is stratified as in critical realism (individual>social interactions>structure>praxis) and these stratified social realities maybe understood as 'planars' that make what we call 'big data', therefore its investigation could be enriched if we used this framework without conflating individual (in social media), structure and collective.
How could Q capture this from big data would be an interesting project, but not an impossible one. When I read your feed, I could see your questions actually touch individual self/ social interaction (group influence)/structure/time/space (which is the context, I am thinking). I think these are distinctive socially real planars that you may find unique mechanisms/entities (as in critical realism) at each level. Our Habitus (I am unsure whether you have used it to refer to sociological or psychological habitus or both) in this approach would be not a conflated or reduced habitus but stratified one.
In this case perhaps integrative pluralism could be very useful with interdisciplinary approach. Methodologically this will mean mixing Q with one or more possible methods to probe data at other levels. Conducting a PCA could end up in conflcation/reducibility of different 'things', so can happen with a survey type approach, of course without falling into a-bit-of-that and a-bit-of-this, but one could even use different methods in an integrative way (i.e. see Pawson, 1995). Although not directly related to IT or Q, the idea that I am refering is best explained by Berth Danermark (2007) 'Interdisciplinary Research and Critical Realism: The Example of Disability Research'.
I hope this helpful if not confusing.
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