Statistical Thinking Can Organize Qualitative Analysis

W/ my co-presenter, I conducted a whirlwind tour of complex regression models (serial mediation, parallel mediation, multi-level models, and multi-level mediation) to our lab. If you can imagine it, and you have meaningful quantitative data, there’s a model for you! (even if you only use SPSS, there are macros – PROCESS and MLMED) for you.)
When I was driving back from visiting my parents in Raleigh, one of the things I really looked forward to in Columbus was my multi-level modeling class (finding patterns in data when your observations are clustered within an individual, country, media market, school, etc). I was excited to be acquiring new tools – new ways of tackling meaningful questions, systematically. Knowledge is power (limited power, sometimes, but power none the less). 
Statistical tools are not only powerful for measuring complex social situations, but can be powerful for thinking about them as well. I like to joke that stereotypes reflect really simple statistical thinking (mean differences). Intersectionality starts to take different levels of variables into account (regression). Privilege demands thinking in terms of clustering – different people with different traits in different situations (i.e. multi level regressions).

How many articles have you read on topics like privilege that had no guiding framework for thinking about the influence of group-membership, individual traits, overlapping groups, and categories of situations? They tend to fumble. They try to simplify with analogies, but often that simplicity feels artificial. Multi-level regression provides a heuristic framework – a way of organizing how we tackle that complexity. Even if we lack the data for a conclusive analysis – multi-level modeling helps us to articulate our questions, our guesses, and our insights.

It is also something, as the presentation this morning indicated, that can be made accessible in qualitative terms. 


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