RSCH-FPX7864 goes deep into quantitative research methodology — experimental and correlational designs, and the statistical tests used to analyze the resulting data — building genuine quantitative research competency.
Quantitative research designs
RSCH-FPX7864 covers experimental designs (with random assignment, supporting causal claims), quasi-experimental designs (without full randomization, common when randomization isn't feasible), and correlational designs (examining relationships without manipulation), teaching students to select the design matching their research question and constraints.
Statistical analysis for quantitative research
The course covers common statistical tests — t-tests, ANOVA, regression, correlation — and, critically, correctly interpreting their results and understanding their underlying assumptions, since misapplying a statistical test or misinterpreting its output are common quantitative research errors.
Key topics in RSCH-FPX7864
- Experimental, quasi-experimental, and correlational designs
- Selecting a research design matched to the question and constraints
- Common statistical tests: t-tests, ANOVA, regression, correlation
- Correctly interpreting statistical test results and assumptions
- Sample size and power analysis considerations
- Common quantitative research design and analysis errors
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Worked example: choosing between experimental and quasi-experimental design
- Research question: Does a new training program improve employee performance?
- Ideal design: A true experiment randomly assigning employees to receive the training or not
- Practical constraint: The organization won't allow randomly denying some employees an evidently beneficial training
- Adapted design: A quasi-experimental design comparing outcomes for the department that received training against a similar department that didn't, without random assignment
- Lesson: Real-world constraints often require adapting from an ideal experimental design to a feasible quasi-experimental one, while explicitly acknowledging the resulting limitation on causal claims
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Frequently asked questions
A true experimental design uses random assignment to a treatment or control condition, which theoretically balances out any other differences between groups (both measured and unmeasured), meaning any observed difference in outcomes can be more confidently attributed to the treatment itself rather than pre-existing group differences. A quasi-experimental design lacks this random assignment, meaning the groups being compared could differ in other ways that also explain the outcome difference — for example, if departments choose whether to adopt a new training program rather than being randomly assigned to it, the departments that chose to adopt it might already differ in motivation or leadership quality in ways that also affect performance, independent of the training itself. RSCH-FPX7864 teaches that this is why quasi-experimental designs, while often necessary due to real-world ethical or practical constraints, require more cautious causal language and explicit acknowledgment of this design limitation compared to a true randomized experiment.
Every statistical test relies on specific underlying assumptions about the data — for example, many common tests assume the data is normally distributed, that variances are roughly equal across groups being compared, or that observations are independent of each other — and violating these assumptions can produce misleading or invalid results even if the test is run correctly in terms of mechanics. RSCH-FPX7864 teaches students to check these assumptions before relying on a given statistical test's output, because a researcher who runs an inappropriate test without checking whether its assumptions are actually met risks drawing an unreliable conclusion that looks statistically sound on the surface but is actually built on a violated foundational assumption — recognizing when a specific test's assumptions aren't met, and knowing what alternative approach to use instead, is a core quantitative research competency this course is designed to build.