Congrats to Nicole Morris: A research grant story

The Texas Tech Department of Psychological Sciences hosted the first of its now annual, competitive grant program and each proposal underwent an NIH-style panel review. Each proposal was judged on its feasibility, significance, innovation, and methodological approach. With a strong pool of applications, the department was unable to fund all such proposals. Nicole (@NicoleLemaste10 on the Twitterverse) was one of those who was funded and I’m proud of the proposal that they put together so let me just say….

Nicole’s project examines the effectiveness of the Minnesota Multiphasic Personality Inventory-3 (MMPI-3)’s new Eating Concerns (EAT) scale within sexual minority individuals. Below I have provided the three stated aims of their proposal:

Aim 1: Expand interpretive understanding of EAT score elevations with psychological risk factors for eating pathology (e.g., body image, intuitive eating, etc.) Hypothesis: EAT scale scores will demonstrate strong associations with criterion measures of body image and eating behaviors which are risk indicators for AN, BN, and BED. Analytic Plan. Using correlation and regression analyses, I will identify the associations between criterion measures of disordered eating and EAT scale score and individual EAT items.

Aim 2: Explore differential performance of EAT scale scores between the LGBTQIA+ community and heterosexual individuals. Hypothesis: EAT scores will be higher for those that are within the LGBTQIA+ community. Analytic Plan. I willcontrast descriptive characteristics between sexuality and gender minority groups. Between group comparisons will be conducted using t-test and ANOVA and appropriate post hoc testing and effect size calculations.

Aim 3: Investigate cut-score accuracy for identifying the presence of clinical eating pathology in LGBTQIA+ college students. Hypothesis: Alternative cut-scores will provide better classification accuracy for identifying eating pathology in LGBTQIA+ individuals. Analytic Plan. Using classification accuracy statistics (i.e., sensitivity, specificity, high rate, etc.) I will determine if the standard cut score is appropriate for the LGBTQIA+ community.

I’m looking forward to sharing more of Nicole’s work on EAT as we move forward.