RSCH7864 is the quantitative arm of Capella's doctoral research-methods sequence, building directly on the broad methodological survey provided by RSCH7860. Where RSCH7860 introduces the landscape of research paradigms, RSCH7864 deepens into the specific logic, computation, and interpretation of statistics — the tools that quantitative research relies on to draw conclusions from numerical data.
Logic, computation, and interpretation — in that order
Why statistical understanding requires all three components
- Logic of statistics: Before computing anything, students learn why specific statistical tests exist — what research questions they answer, what assumptions they require, and what conclusions they can and cannot support. This is the most important and often most neglected component of statistical training
- Computation: Students use the JASP statistical program to practice running statistical analyses — t-tests, ANOVA, correlation, regression, chi-square, and other foundational tests — gaining hands-on experience with real data and real software output rather than only textbook examples
- Interpretation: Students learn to read, interpret, and critically evaluate statistical results — both their own and those reported in published research — including understanding p-values, effect sizes, confidence intervals, and the distinction between statistical significance and practical significance
Decision-making skills in the research process
RSCH7864's emphasis on "decision-making skills" reflects a pedagogical philosophy that statistical competency is fundamentally about making defensible choices: selecting the appropriate statistical test for a given research question and data structure, determining appropriate sample sizes, deciding how to handle missing data or assumption violations, and choosing between competing interpretations of results. Doctoral students who emerge from this course should be able to justify their statistical choices to a dissertation committee, peer reviewer, or IRB — not merely perform calculations.
Why Capella uses JASP rather than SPSS
JASP (Jeffreys's Amazing Statistics Program) is an open-source, free statistical software package developed by the University of Amsterdam that provides both classical (frequentist) and Bayesian statistical analyses through a user-friendly graphical interface. Capella's adoption of JASP for RSCH7864 offers students a no-cost tool they can continue using throughout their doctoral work and professional careers without institutional software licenses, while providing the same analytical capabilities as commercial alternatives for the statistical methods covered in this course.
RSCH7864 assignments include JASP analyses, statistical interpretation reports, and quantitative research design papers
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Frequently asked questions
RSCH7864 is designed for doctoral students across disciplines, many of whom do not have undergraduate mathematics or statistics backgrounds. The course emphasizes the logic and interpretation of statistics — understanding what a statistical test does, when to use it, and what its results mean — rather than the mathematical derivation of statistical formulas. Students compute statistics using the JASP software rather than by hand, which means the course focuses on understanding inputs, outputs, and decision-making rather than on mathematical computation itself. That said, students who have not encountered basic statistical concepts (mean, standard deviation, normal distribution, hypothesis testing) since their undergraduate work will benefit from reviewing those fundamentals before or early in the course. The most common challenge students report is not mathematical difficulty but conceptual difficulty — wrapping their heads around what a p-value actually represents, why statistical significance does not necessarily mean practical importance, or why a non-significant result does not mean "no effect." These are conceptual and logical challenges, not mathematical ones, and they are precisely what the course is designed to address through the emphasis on decision-making skills rather than computational technique.