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- All Subjects: minority stress
- All Subjects: Three-level
- Creators: Grimm, Kevin J.
- Creators: LeMaster, Benny
- Status: Published
The purpose of this study is to examine the social and communicative barriers LGBTQIA+ students face when seeking healthcare at campus health and counseling services at Arizona State University. Social barriers relate to experiences and internalizations of societal stigma experienced by sexual and gender minority individuals as well as the anticipation of such events. Communication between patient and provider was assessed as a potential barrier with respect to perceived provider LGBTQIA+ competency. This study applies the minority stress model, considering experiences of everyday stigma and minority stress as a predictor of healthcare utilization among sexual and gender minority students. The findings suggest a small but substantial correlation between minority stress and healthcare use with 23.7% of respondents delaying or not receiving one or more types of care due to fear of stigma or discrimination. Additionally, communication findings indicate a lack of standardization of LGBTQIA+ competent care with experiences varying greatly between respondents.
To date, the literature has only focused on the theoretical and algorithmic work
required to implement three-level imputation using the joint model (JM) method of
imputation, leaving relatively no work done on fully conditional specication (FCS)
method. Moreover, the literature lacks any methodological evaluation of three-level
imputation. Thus, this thesis serves two purposes: (1) to develop an algorithm in
order to implement FCS in the context of a three-level model and (2) to evaluate
both imputation methods. The simulation investigated a random intercept model
under both 20% and 40% missing data rates. The ndings of this thesis suggest
that the estimates for both JM and FCS were largely unbiased, gave good coverage,
and produced similar results. The sole exception for both methods was the slope for
the level-3 variable, which was modestly biased. The bias exhibited by the methods
could be due to the small number of clusters used. This nding suggests that future
research ought to investigate and establish clear recommendations for the number of
clusters required by these imputation methods. To conclude, this thesis serves as a
preliminary start in tackling a much larger issue and gap in the current missing data
literature.