RSCH8625 takes doctoral students from the foundational quantitative methods covered in RSCH7864 into the advanced statistical territory their actual dissertation analysis is likely to require. The course is built around the specific statistical methods that recur most often in quantitative dissertations, building applied statistical software proficiency alongside the conceptual understanding needed to choose, run, and correctly interpret each technique.
Advanced statistical methods for dissertation-level research
Core topics
- Multiple regression: Modeling a continuous outcome as a function of multiple predictor variables simultaneously, interpreting standardized and unstandardized coefficients, and assessing model fit and assumptions — one of the most widely used techniques across quantitative dissertation methodologies
- Factorial analysis of variance (ANOVA): Extending ANOVA to designs with two or more independent variables, examining main effects and interaction effects, and interpreting how multiple factors jointly influence an outcome
- Chi-square: Testing associations between categorical variables, a technique frequently needed when dissertation data involves group membership, classification, or other non-continuous measures
- Logistic regression: Modeling a binary or categorical outcome as a function of predictor variables — essential for dissertations where the outcome of interest is a yes/no, pass/fail, or other categorical distinction rather than a continuous measure
- Exploratory factor analysis for survey selection: Using factor analysis to evaluate and select or validate survey instruments, examining underlying latent constructs and ensuring a chosen instrument's items group together in a statistically defensible, theoretically meaningful way
- Power analysis for sample size determination: Calculating the sample size needed to detect an effect of a given size with adequate statistical power — a calculation every quantitative dissertation must justify before data collection begins
Proficiency with statistical software is expected throughout the course, since each method is taught and applied through hands-on analysis rather than as abstract formulas alone.
RSCH8625 assignments include regression analyses, factorial ANOVA write-ups, and power analysis justifications
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Frequently asked questions
RSCH8625 assumes two things are already in place by the time a student enrolls: a foundational grasp of quantitative research design and basic statistical analysis from RSCH7864, and a developed project framework from RSCH-V8926 that specifies what the dissertation is actually trying to investigate. Without RSCH7864's foundation in research design and basic statistics, students would lack the conceptual vocabulary needed to understand why a particular advanced technique — multiple regression versus logistic regression, for example — is the correct choice for a given research question, and would be limited to mechanically running statistical procedures without understanding what they mean. Without a developed project framework from RSCH-V8926, students would be learning advanced statistical methods in the abstract, disconnected from the specific variables, sample, and research questions their own dissertation will actually involve, which makes the learning far less useful and harder to retain. Sequencing RSCH8625 after both prerequisites ensures students arrive ready to immediately apply each advanced statistical method covered in the course to their own emerging dissertation analysis plan, rather than treating the course as a generic statistics refresher disconnected from their actual doctoral project.