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Thesis advisor: Michael Russell / Intersectionality theory garners increased attention from researchers interested in understanding the many ways in which oppression impacts lived experiences. In any given present and evolving context, oppression leads to advantages for some social positions and disadvantages for others (Collins & Bilge, 2016; Crenshaw, 1989). Quantitative researchers have attempted to adapt statistical modeling methods to reflect intersectional identities as a proxy for oppression and advantage in their models (Bauer et al., 2021; Schudde, 2018). This dissertation expanded on existing knowledge about the statistical limitations of three methods of modeling intersectional analyses on a continuous outcome variable: 1) Interaction, 2) Categorical, and 3) MAIDHA (multilevel analysis of individual heterogeneity and individual accuracy). Using a Monte Carlo simulation, four demographic data characteristics were manipulated to explore the three models under different scenarios which manipulated: a) the number of demographic categories (and thus intersections); b) the proportion of the sample represented by each demographic group; c) the within-intersectional-group variance in the outcome variable of interest; d) overall sample size. Each scenario and model were replicated 1000 times; results summarized performance of the intersection estimates and effect detection using the outcomes: bias, accuracy, power, type 1 error, and confidence interval coverage. The fundamental questions that guided this dissertation were: 1. What are the statistical advantages and disadvantages of each model under different demographic data characteristics? 2. In what ways does each model perform differently from one another under each demographic data characteristic condition? The findings of this dissertation contribute to intersectional quantitative research methods by providing greater insight into how each model performs under more complex data scenarios. / Thesis (PhD) — Boston College, 2024. / Submitted to: Boston College. Lynch School of Education. / Discipline: Measurement, Evaluation, Statistics & Assessment.

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Yes / To assess whether magnetic resonance imaging (MRI)-based measurements of T2, fat fraction, diffusion tensor imaging, and muscle volume can detect differences between the muscles of myositis patients and healthy controls, and to identify how they compare with semi-quantitative MRI diagnosis. Sixteen myositis patients and 16 age- and gender-matched healthy controls underwent MRI of their thigh. Quantitative MRI measurements and radiologists' semi-quantitative scores were assessed. Strength was assessed using an isokinetic dynamometer. Fat fraction and T2 values were higher in myositis patients whereas muscle volume was lower compared to healthy controls. There was no difference in diffusion. Muscle strength was lower in myositis patients compared to healthy controls. In a subgroup of eight patients, scored as unaffected by radiologists, T2 values were still significantly higher in myositis patients. Quantitative MRI measurements can detect differences between myositis patients and healthy controls. Changes in the muscles of myositis patients, undetected by visual, semi-quantitative scoring, can be detected using quantitative T2 measurements. This suggests that MRI T2 values may be useful for the management of myositis patients. / National Institute for Health Research (NIHR) Leeds (BRC) and Health Education England