Psychometrics - Online Course
A 3-Day Livestream Seminar Taught by
Matthew DiemerWednesday, November 12 –
Friday, November 14, 2025
10:00am-12:30pm (convert to your local time)
1:30pm-3:30pm
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 special education?), 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 12, we are offering this seminar as a 3-day synchronous*, livestream workshop held via the free video-conferencing software Zoom. Each day will consist of two lecture sessions which include hands-on exercises, separated by a 1-hour break. 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; approximately 60% of the seminar will be spent in lecture and small group discussion, with the remaining 40% in instructor-led hands-on learning of psychometric techniques.
*We understand that finding time to participate in livestream courses can be difficult. 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. Captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.
ECTS Equivalent Points: 1
More details about the course content
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.
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.
Computing
The empirical examples and exercises in this course will emphasize R (primarily the lavaan package), but equivalent code and examples will be presented/available for Mplus. To fully benefit from the course, you should have R and RStudio installed, along with the psych, lavaan and lavaanPlot packages. Alternatively, you can use Mplus – the demo version will suffice, but the full version is even better. Whichever package you choose, you should already have a working understanding of the software and be able to complete basic functions in that software.
You should have a copy of SPSS, Stata, R, or some other package that you are familiar with, for (minimal) data manipulation. You are strongly encouraged to bring your own data for hands-on practice, ready to be read into R or Mplus.
If you’d like to use R for this course but don’t yet have much experience with that package, here are some excellent on-line resources for building your R skills.
If you’d like to familiarize yourself with Mplus basics before the seminar begins, we recommend reading through UCLA’s short guide here.
The empirical examples and exercises in this course will emphasize R (primarily the lavaan package), but equivalent code and examples will be presented/available for Mplus. To fully benefit from the course, you should have R and RStudio installed, along with the psych, lavaan and lavaanPlot packages. Alternatively, you can use Mplus – the demo version will suffice, but the full version is even better. Whichever package you choose, you should already have a working understanding of the software and be able to complete basic functions in that software.
You should have a copy of SPSS, Stata, R, or some other package that you are familiar with, for (minimal) data manipulation. You are strongly encouraged to bring your own data for hands-on practice, ready to be read into R or Mplus.
If you’d like to use R for this course but don’t yet have much experience with that package, here are some excellent on-line resources for building your R skills.
If you’d like to familiarize yourself with Mplus basics before the seminar begins, we recommend reading through UCLA’s short guide here.
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.
You should have the equivalent of a two-semester graduate-level sequence in statistics. Familiarity with factor analysis is helpful, but not required. No level of proficiency beyond basic awareness is assumed for mathematical or statistical topics. Learning will entail conceptual understanding as well as hands-on practice conducting and interpreting analyses – with only minimal use of notation and equations.
Registrants will be provided with a list of recommended readings to help them prepare for the seminar.
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.
You should have the equivalent of a two-semester graduate-level sequence in statistics. Familiarity with factor analysis is helpful, but not required. No level of proficiency beyond basic awareness is assumed for mathematical or statistical topics. Learning will entail conceptual understanding as well as hands-on practice conducting and interpreting analyses – with only minimal use of notation and equations.
Registrants will be provided with a list of recommended readings to help them prepare for the seminar.
Seminar outline
Day 1: Introduction, Latent Variable Modeling, Factor Analyses
- A latent variable modeling perspective on psychometrics
- Exploratory Factor Analyses (EFA)
- Factor extraction & factor rotation in EFA
- Best practices: Comparing competing factor solutions
- Interpreting EFA model fit & item loadings
- Best practices: Continuous vs categorical variables in factor analyses
Day 2: Hypothesized Factor Structures, Measurement Bias
- Confirmatory Factor Analyses (CFA)
- Specifying, coding & interpreting CFA models
- Interpreting CFA model fit & item loadings
- Estimating shared error covariances in CFA
- Simple Models, Powerful Claims: MIMIC (Multiple Indicator & MultIple Causes) Models
- Identifying DIF (Differential Item Functioning) with MIMIC Models
- Specifying, coding & interpreting MIMIC Models
- Assessing Scalar Invariance with MIMIC Models
- Addressing items exhibiting DIF
Day 3: Testing Measures across Groups
- Measurement Invariance
- Configural, Metric, and Scalar Invariance
- Assessing “whether measures mean the same thing” across groups
- Specifying, coding & interpreting measurement invariance analyses
- Open Q&A/Consultation time
Day 1: Introduction, Latent Variable Modeling, Factor Analyses
- A latent variable modeling perspective on psychometrics
- Exploratory Factor Analyses (EFA)
- Factor extraction & factor rotation in EFA
- Best practices: Comparing competing factor solutions
- Interpreting EFA model fit & item loadings
- Best practices: Continuous vs categorical variables in factor analyses
Day 2: Hypothesized Factor Structures, Measurement Bias
- Confirmatory Factor Analyses (CFA)
- Specifying, coding & interpreting CFA models
- Interpreting CFA model fit & item loadings
- Estimating shared error covariances in CFA
- Simple Models, Powerful Claims: MIMIC (Multiple Indicator & MultIple Causes) Models
- Identifying DIF (Differential Item Functioning) with MIMIC Models
- Specifying, coding & interpreting MIMIC Models
- Assessing Scalar Invariance with MIMIC Models
- Addressing items exhibiting DIF
Day 3: Testing Measures across Groups
- Measurement Invariance
- Configural, Metric, and Scalar Invariance
- Assessing “whether measures mean the same thing” across groups
- Specifying, coding & interpreting measurement invariance analyses
- Open Q&A/Consultation time
Payment information
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.