[WORK IN PROGRESS - last updated Dec 2025]

Research Methods in Cognitive Psychology

In this course, the most important learning happens as students wrestle with the process of doing research. Answering even the simplest questions requires the researcher to make critical decisions. What will you manipulate? What will you measure? How long should each step take? What stimuli will you use? What comparisons will you make? These questions do not have clear-cut right and wrong answers; instead, they require students to connect their high-level research question to these nitty-gritty concrete details. The heart of research methods lies in asking students to think across levels of abstraction, working from a conceptual question to a detailed study design to a statistical analysis, and from the statistical output back up to a concept-level answer.

My other learning goals for students wrap around this core. Students in my course strengthen their skills in reading the primary literature, thinking algorithmically, troubleshooting and reading documentation, communicating scientific results, programming in R, and working in collaborative groups. They spend the first half of the semester working through a series of scaffolded lab assignments that I developed, and the second half designing, conducting, analyzing, and presenting a final research project on any cognitive psychology topic of their choice.

With support from a CMU Libraries grant for open eductional resources, I redesigned CMU’s 85-310 Research Methods in Cognitive Psychology to better support these learning goals. The course materials are released below under a Creative Commons attribution non-commercial share-alike license (CC BY NC SA 4.0). I created many new readings (adapted from existing OERs), entirely new assignments, and new student support material.

Course outline

Course overview, syllabus review, introductions (Week 1)

This week is for previewing the course as a whole, getting to know each other, and setting expectations. Students read a holistic refresher on why quantitative methods are valuable, and review R and data analysis skills from the prerequisite course.

Lab 1 Skittles Tasting (Week 2-3)

This lab introduces factorial experimental designs with categorical predictors. We step away from computerized tasks to do something in the "real world". Students read chapters reviewing research design, descriptive and inferential statistics, *t*-test and ANOVA, and the communication of research results. They work as a class to develop an experimental protocol and collect data; then analyze that data (using R Notebooks) to compute summary statistics and a factorial ANOVA, and submit a lab report.

Students also are assessed on four learning outcomes: research questions, operational definitions, reliability and validity, descriptive statistics (these assessments are not shared here).

Lab 2 Visual Search (Weeks 4-5)

This lab introduces a computerized experiment control platform (Gorilla), continuous predictors, and interaction terms in linear regression. Students read chapters reviewing linear regression and scientific writing. They participate in an instructor-developed experiment and analyze that data; then work in small groups to ask a related research question, modify the experiment to answer it, collect and analyze data, and report their results.

Lab 3 Visual Working Memory (Weeks 6-7)

Sources

Materials are adapted from a number of sources, including:

• The PsyTeachR project (based at the University of Glasgow): https://psyteachr.org
• Danielle Navarro’s Learning Statistics With R: https://learningstatisticswithr.com/