Curriculum

The 54-credit PhD program in Nutrition and Dietetics provides broad training in research theory and applications, nutrition specialization, and education for nutrition and dietetics professionals who desire further education to advance in their careers or to transition to academia and research.

Core Course Sequence

You will build your research skills by taking courses in research methodology and statistics, advanced nutrition science, a doctoral seminar, and other topics related to your area of specialization. You will also complete a dissertation proposal and independent research to answer your research questions. The program culminates in a dissertation.

Sample Courses

Advanced Nutrition: Protein, Fats & Carbs

This course explores biochemical, physiological, and clinical factors that affect the role of macronutrients in human health and disease, with an emphasis on critical analysis of recent research studies in this field. It  also includes topics such as cholesterol, fiber, non-nutritive sweeteners, and alcohol.

Advanced Nutrition: Vitamins & Minerals

This course explores biochemical, physiological, and clinical factors that affect the role of micronutrients in human health and disease, with an emphasis on critical analysis of recent research studies in this field. It also includes the role of selected vitamins and minerals in cancer, coronary heart disease, HIV infection, bone loss, and other conditions of topical interest.

Statistics for the Social and Behavioral Sciences I

This class offers an introduction to the basic tools of applied statistics, using statistical software as a platform to achieve hands-on experience with real data. Topics include both descriptive (measures of location, variability, correlation, and simple regression) and inferential (probability, central limit theorem, sampling distributions, hypothesis tests, and confidence intervals) statistics. 

Statistics for Behavioral and Social Sciences II

This course introduces students to an array of inferential techniques (t-tests, one, and two-way anova, simple and multiple regression, nonparametric methods) using statistical software as a platform to achieve hands-on experience with real data.