PSYC 137: Advanced Statistics —
Psychometrics and Multivariate Methods

This Spring 2026, I am teaching a new undergraduate class at Claremont McKenna College covering topics such as reliability and validity, factor analysis (EFA & CFA), structural equation modeling (including mediation & moderation designs), and other topics depending on time and student interest. On this page, I will be posting a “Self Study” version of the class. Anyone can come here to get an overview of the content that will be covered in each module, assigned readings, and even course assignments. I will populate this page throughout the semester, with the goal of creating an evergreen version that anyone can access in the future.

Before you get started, here’s a quick FAQ to answer questions you might be thinking about:

  • Two reasons: First, I would love to receive feedback! This is an advanced undergraduate course that I hope will prepare students for graduate-level research in the social sciences and/or consulting and applied data science careers involving human behavior data. If you’re a faculty or student at other institutions, please feel free to email me if you have thoughts or suggestions on how to improve this class and make it more helpful and applicable for your context.

    Second, I want to inspire others to teach or enroll in classes like this. If you’re a faculty at another undergraduate institution, perhaps you can use these materials to teach your own version of the class. If you’re an undergraduate student, perhaps you can follow along and prepare yourself for future graduate school or careers that require these quantitative methods.

  • Not at all. This is a self-study course, meaning that I am only providing resources, assignments, and slides. I will not be posting actual lecture videos, and I will not review or provide feedback on anyone’s work on the assignments. I hope, however, that this whets your appetite to enroll in an actual class (PSYC 137 at CMC, or a similar class elsewhere) to participate in lectures, discussions, and get live instructor feedback (and graded credit) on assignments!

  • You are welcome to copy, revise, and distribute the materials freely. I only ask that you provide credit pointing people back to this website. Please note that I cannot be held liable for how others choose to use these materials — so use them wisely!

  • I’m certainly not the only one who has done this. There are many other amazing free online resources that I’ve benefited from and would encourage you to explore as well — for example, Dr. Lesa Hoffman’s courses, Dr. Keith McNulty’s people analytics textbook, Dr. Daniel Lakens’ Coursera classes, and many more. One potential difference is that the content on this webpage is geared towards advanced undergraduates. Many other resources are designed for graduate students interested in pursuing research careers in quantitative methods, which requires some mathematical background (e.g., linear algebra and differential equations) that I am not expecting for this course. My hope is that anyone with no more than one semester of experience in introductory statistics can master the material in this class.

  • The on-ground for-credit PSYC 137 course includes:

    1. Lectures: These cover both theory and live demonstrations in R. I will share a public version (i.e., not including in-class activities) of the slides on this site, but there won’t be any lecture recordings.

    2. Readings: I will post links to assigned readings on this site. Some will be freely available online (e.g., blogs, news websites, and other publications). Others (i.e., journal articles) should be accessible via an university library account.

    3. Discussions: Students enrolled in the class are assigned to lead an in-class discussion based on an assigned reading in an advanced topic area related to the content being covered. I will provide links to the discussion topics and readings on this site, but there is no live discussion board. One option if you’re completing this self-study with some friends is to get a group together to discuss!

    4. Quizzes: Students enrolled in the class complete short in-class quizzes to check their understanding of the course material. These will not be shared publicly and will not be made available on this site.

    5. Take-Home Assignments: Students will complete take-home assignments using provided datasets. These will be real-life, messy datasets that require application of course principles in novel ways. I will make the assignments available on this site for public users to use for practice and self-assessment, but answer keys will not be provided.

    6. Final Project: Students complete a final project analyzing their own dataset (e.g., their thesis, an independent research project, a consulting project) using methods learned in class. They will they present their findings in class and submit a written paper. This component of the course will not be included on this site.

  • Unless you are formally enrolled in PSYC 137 at CMC, unfortunately the only credit you will receive is the proverbial pat-yourself-on-the-back!

This self-study course is divided into modules. I will be posting modules as we go throughout the Spring 2026 semester (mid-January through early-May).

Module 1: Review of Introductory Statistics

Prior to starting this class, students should review the content from introductory statistics in R. This class should have covered: basic descriptive statistics, z-scores, the Central Limit Theorem, null hypothesis significance testing, z-tests, t-tests (one-sample, two-sample independent, dependent/paired), ANOVAs (one-way, two-way), correlation (Pearson’s r, Spearman’s rho), linear regression (simple one-predictor, multiple predictors), confidence intervals and effect sizes, chi-square tests of independence and goodness-of-fit, and assumptions of parametric tests. To help review this content, please complete the following:

[ ] Watch the Intro to R video I recorded: Part One and Part Two. You can download the accompanying dataset and slides here. There are many similar “Intro to R” resources available online (several of which I mention in the video), but this is the video I assign to my students so they are familiar with my approach to using R for this class.
[ ] Read Chapter 2 in Zelterman (2015) Applied multivariate statistics with R for a review of basic R programming.
[ ] Watch the Hypothesis Testing in R video I recorded here. You can download the accompanying dataset and R script here. Again, there are several similar resources available online, but this video is geared towards the methods I teach in the Intro Stats class at CMC.
[ ] Review the “PSYC 109 Toolkit” available here. This is a cheat sheet I created summarizing all of the statistical methods in R that I teach in PSYC 109 (the CMC version of intro stats for psychology).

After reviewing the content from intro stats, you can complete the following tasks to formally begin the coursework for PSYC 137:

[ ] Slides for Lecture 1: Review of Intro Stats
[ ] Read Chapter 1 in Zelterman (2015) Applied multivariate statistics with R for an introduction to multivariate methods
[ ] Discuss p-hacking and researcher degrees of freedom based on this article: Wicherts et al. (2016)

Module 2: Data Cleaning and Screening

In this module, we cover the steps it takes to go from messy data (primarily survey data in the social sciences) to a clean dataset ready for analysis, along with screening and checking various assumptions (e.g., multivariate normality, missing data).

[ ] Slides for Lecture 2: Data Cleaning and Screening
[ ] Read De Jonge & Van Der Loo (2013) An introduction to data cleaning with R. You can skip sections 1.2, 2.5, and 3.3.
[ ] Optional: read Ward & Meade (2023) for a detailed overview of careless responding in survey data
[ ] Optional: read Newman (2014) for a detailed overview of missing data
[ ] Discuss methods of dealing with AI-agent fake data in survey research based on this blog post by CloudResearch (2025)

Module 3: Reliability and Validity

In this module, we cover the basics of reliability and validity. Some of this content may be a review from an Intro Stats class, depending on which version of the class you took.

[ ] Slides for Lecture 3-1: Reliability
[ ] Slides for Lecture 3-2: Validity
[ ] Read Chapter 29: Reliability and Validity from Hancock et al. (2018) The reviewer’s guide to quantitative methods in the social sciences (Note: we will read many chapters from this book. I highly recommend purchasing or borrowing a copy!)
[ ] Optional: read Chapter 10: Interrater Reliability and Agreement from Hancock et al. (2018) 
[ ] Discuss psychometric properties of AI-generated multiple-choice exams based on this article: Baudin (2025)
[ ] Complete Take-Home Assignment 1 using the provided dataset here

Module 4: Principal Components Analysis

Module 5: Exploratory Factor Analysis

Module 6: Confirmatory Factor Analysis

Module 7: Test Bias

Module 8: Advanced Regression Methods

Module 9: Moderation and Mediation

Module 10: Structural Equation Modeling

Modules 11-on: TBD

For the remaining one-third of the class, we will cover additional specialized topics depending on the interests of the students enrolled in the class and what they need to learn for their final projects. Potential topics include item response theory, cluster analysis, multilevel modeling, longitudinal data analysis, social network analysis, text analysis, and meta-analysis.

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