Week #5: SOCRMx – moving into analysis

Maybe I simply don’t have enough experience in this area but I have to say that I’m struggling at the moment. I’m still pushing through the MOOC – alongside probably 3 or 4 other people still responding to the activities and posting in the discussion forum – but the lecturers seem to have gone MIA. There is no feedback from them on anything and I think that the rest of the people participating are mainly here because it’s a formal course-credit unit that they are undertaking.

(This is why their posts are so much better written and more deeply considered than mine but that’s ok)

There was a nice discussion of how data gets filtered early on though that I’ll quote:

Hardy and Bryman (2004) argue that some key dimensions of analysis apply across qualitative/quantitative approaches (pp.4-12) – including a focus on answering research questions and relating analysis to the literature; and a commitment to avoiding deliberate distortion, and being transparent about how findings were arrived at. They also discuss data reduction as a core element of analysis:

“to analyze or to provide an analysis will always involve a notion of reducing the amount of data we have collected so that capsule statements about the data can be provided.” (p.4)

So we’re starting to tap into the analysis side of things and have been asked to re-read the papers examined last week with an eye for how they approached analysis. The first is qual and the second is quant.

For what it’s worth, these are my responses.

Questions for discussion:

Why do you think Paddock chose narratives as a way of conveying the main themes in her research?

The research is about lived experiences – “a case study research strategy suits the imperative to explore the dynamic relationships between these sites”

What is the impact for you of the way the interview talk is presented? What is the point of the researcher noting points of laughter, for example? What about filler sounds like ‘erm’?

Helps to convey the voice of the subject and humanise them.

How does Paddock go about building a case for the interpretations she is making? How does she compel you, as a reader, to take her findings seriously? Share a specific example of how you think this is done in this article.

Ties it to theoretical concepts. They’re very uncritical about that sort of things I’m criticising in terms of the consumerist culture, cheap food, not worrying about where the stuff comes from how far it’s come or how it’s produced – is linked directly to Bourdieu’s Cultural Capital.

Interviewees use many emotive words in the excerpts presented here, but Paddock has focused in on the use of the word ‘disgusting’, and developed this through her analysis. How does this concept help her link the data with her theoretical perspective?

Used to differentiate class values

Paddock’s main argument is that food is an expression of social class. Looking just at the interview excerpts presented here, what other ideas or research questions do you think a researcher could explore?

Education, privilege, consumer culture


Overall I struggled with this paper because the author didn’t explicitly describe her analysis process in the paper. She just seemed to dive in to discussing the findings and how the quotes tied in to the theory.

Paper 2: Kan, M-Y., Laurie, H. 2016. Who Is Doing the Housework in Multicultural Britain? Sociology. Available: https://doi.org/10.1177/0038038516674674


The researchers here conducted secondary analysis of an existing dataset (the UK Household Longitudinal Study https://www.understandingsociety.ac.uk . What are some advantages and disadvantages of secondary analysis for exploring this topic? (hint: there are some noted at various points in the paper)

Advantages – Practicality, addressing issues not previously covered by the original researchers,

Disadvantages – data hasn’t been collected to respond specifically to the research questions,

How does the concept of intersectionality allow the researchers to build on previous research in this area?

Offers a new lens to examine relationships in the data


Choose a term you aren’t familiar with from the Analysis Approach section of the article on page 8 and do some reading online to find out more about what it means (for example: cross-sectional analysis;multivariate OLS regressions; interaction effects). Can you learn enough about this to explain it in the discussion forum? (if you are already very familiar with statistical analysis, take an opportunity to comment on some other participants’ definitions)

A cross-sectional analysis explores a broad selection of subjects at a certain point in time while a longitudinal study takes place over a significantly longer period.

How do Kan and Laurie go about building a case for the interpretations they are making? How do they compel you, as a reader, to take their findings seriously? Share a specific example of how you think this is done in this article.

I was concerned that correlation was tied too much to causation. In explaining some of the possible reasons for differences by ethnicity, broad claims were made about the nature of entire cultures that – while perhaps reflective of the quant data – seemed to have no other supporting evidence beyond assertion.