A 3-Day Remote Seminar Taught by
Matthew Diemer , Ph.D.

Psychometrics is the science of how we measure the psychological attributes of people. These attributes include abilities, aptitudes, achievement, attitudes, interests, personality traits, cognitive functioning, and mental health.

The theoretically-informed and precise measurement of such latent phenomena is an essential component of many of the things we hold dear. These include scientific advances (e.g., can I make a claim that I am measuring what I purport to measure?), educational placement decisions (e.g., should a child be placed into a gifted program?), statistical power (e.g., is my measure precise enough to suggest that X predicts Y?), and other key considerations.

This seminar emphasizes the conceptual understanding and application of psychometric principles. These techniques provide a powerful way to identify and remediate bias in measurement, as well as to make equity-informed claims in the language of psychometrics.

Starting November 18, we are offering this seminar as a 3-day synchronous*, remote workshop for the first time. Each day will consist of a live lecture held via the free video-conferencing software Zoom. You are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if you are unable to attend at the scheduled time.

This seminar is thoroughly hands-on. Each session will include hands-on exercises reviewing the content covered.

*We understand that scheduling is difficult during this unpredictable time. If you prefer, you may take all or part of the course asynchronously. The video recordings will be made available within 24 hours of each session and will be accessible for four weeks after the seminar, meaning that you will get all of the class content and discussions even if you cannot participate synchronously.

Closed captioning is available for all live and recorded sessions.


The seminar centers on latent variables as an overarching perspective to understand psychometric concepts, principles, and techniques. Simply put, a latent variable is something unobserved, or not directly measurable, that is of interest (e.g., achievement, well-being, motivation, racial identity). Psychometricians use directly observable measures (e.g., items, subscales, physiological measures) to model the underlying latent constructs of interest.

Conceptually, the core of psychometrics is about using empirical evidence to make claims in support of construct validity, generally regarded as the most central aspect of validity. This evidence can be obtained with key analytic approaches that are covered in this workshop. Specifically, (a) exploratory factor analyses (EFA), (b) confirmatory factor analyses (CFA), (c) MIMIC models, and (d) measurement invariance testing. Importantly, these last two approaches provide powerful strategies to detect and remediate measurement bias, as well as to support claims about whether measures mean the same thing and can be measured in the same way across groups.


Participants must download the free demo version of Mplus ( prior to the start of the workshop, which will be used during the hands-on portion of each session. Syntax will be provided for example models in Mplus. Attendees should have a copy of SPSS, Stata, R, or some other package that they are familiar with, for (minimal) data manipulation. Participants are strongly encouraged to bring their own data for hands-on practice.

WHO SHOULD Register? 

This course is for anyone who is interested in learning more about psychometrics and measurement, particularly in the social sciences and in education. This content is particularly useful for participants who will be developing their own measures. Further, the course will provide direct instruction in how to understand, detect, and remediate bias in measurement, a core equity issue.

Participants should have the equivalent of a two-semester graduate-level sequence in statistics. Familiarity with factor analysis and/or the Mplus software package is helpful, but not required. No level of proficiency beyond basic awareness is assumed for mathematical or statistical topics, such as matrix algebra. Learning will entail conceptual understanding as well as hands-on practice conducting and interpreting analyses – with only minimal use of notation and equations.


Day One: Introduction, Latent Variable Modeling, Factor Analyses

  • Latent variable modeling perspective on psychometrics
  • Introduction to Mplus code
  • Exploratory Factor Analyses (EFA)

Day Two: Hypothesized Factor Structures, Measurement Bias

  • Confirmatory Factor Analyses (CFA)
  • MIMIC (Multiple Indicator & Multiple Causes) Models

Day Three: Testing Measures across Groups

  • Measurement Invariance
  • Open Q&A/Consultation time