Abstract: Comparing Bayesian and Non-Bayesian Analytic Strategies for Understanding Intersectionality and Coping in Different Contexts in a Sample of Young Middle Eastern/Muslims (Society for Social Work and Research 23rd Annual Conference - Ending Gender Based, Family and Community Violence)

Comparing Bayesian and Non-Bayesian Analytic Strategies for Understanding Intersectionality and Coping in Different Contexts in a Sample of Young Middle Eastern/Muslims

Saturday, January 19, 2019: 5:30 PM
Union Square 22 Tower 3, 4th Floor (Hilton San Francisco)
* noted as presenting author
Larry Gant, PhD, Professor of Social Work and Professor of Art and Design, University of Michigan-Ann Arbor, Ann Arbor, MI

Analytic strategies for critical intersectionality reflect how one conceptualizes and operationalizes the constructs of positionalities and related categories and processes. (Bowleg, 2012).  Many methods focus on static categories and relationships, assume some similarities within discrete categories, and depend on larger sample sizes to enhance reliability and validity. An alternative Bayesian approach allows us to examine complexity within categories, between categories, historical moments and moments over time, and individual/institutional interactions. This approach is formally identified as Fuzzy Set Qualitative Comparative Analysis (fs(QCA)). This work requires five stages [Lee, 2014] guided both by qualitative analysis and knowledge of current theory. Scores for each condition and outcomes per case are calculated and then compared to find necessary and sufficient conditions for different configurations. In this study we examine intersectionality within a small sample of youth and young adults from a Middle-Eastern /Muslim/Arab American community. Specifically, we investigate how intersecting positionalities and situated contexts are associated with an array of individual-level responses and impacts.


Sample: (<28 years of age, N = 25; 40 per cent male). They completed a survey, with 36 complex questions on different positionalities, experiences with types of discrimination, levels and types of vigilance, and general measures of health and well-being. Fuzzy-set qualitative analysis (fs(QCA)) explored the “causal stories” within patterns of questionnaire responses. We examined the ways participants navigated discrimination, marginalization and hypervigilant responsiveness, within complex environments. Both Kirq and R packages for QCA and Set Methods were used to generate analytic truth tables and enabled both crisp-set and fuzzy-set QCA analysis (Thygeson et al, 2016).


We compare more conventional statistical comparisons with those generated by CQA. For instance, three way statistical comparisons find an age by occupation interaction by for girls and women, but not for boys and men. The fs(QCA) analysis yielded three Boolean truth tables generating three causal combinations of variable configurations that provide substantial  “solution coverage” (percentage of all observations that exhibit the major patterns) and “solution consistency” (how well the model predicts “what if statements”) related to different modes of navigating interactions with discrimination, marginalization and hypervisibility. We will demonstrate several of the visual depictions of patterns across different types of variables, linking these with examples from the focus groups, described in paper one in this symposia.  For instance, during times of heightened anti-Muslim sentiment in the US Arab Muslim men created time and situation dependent stable personas as “modified or invisible Muslims” within work spaces, deliberately cutting beards, appropriating Westernized names and nicknames, and deflecting political discussions with self-depreciating humor. These behaviors required high degrees of effort among the men, and were associated with experiences of fear, high stress, and physical exhaustion.

Discussion. We consider the ways in which fs(QCA) enables configuration of intersectional processes of strength and/or marginalization, and acknowledges and depicts multiple causal paths to outcomes or impact. These patterns then allow us to form narratives and other generated material into interesting proposals for community change (Hancock, 2013).