Psychometrics - Online Course
A 3-Day Livestream Seminar Taught by
Matthew DiemerWednesday, March 3,
Friday, March 5,
Saturday, March 6, 2027
10:00am-12:30pm (convert to your local time)
1:30pm-3:30pm
This seminar is part of the Measurement and Psychometrics Certification, a series of 4 expert-led courses designed to provide advanced training and build specialized skills in measurement science. Contact us to learn how to complete your certification and access special pricing.
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 understanding and applying psychometric principles. Psychometric 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 March 3, this seminar will be presented as a 3-day synchronous, livestream workshop via Zoom. Each day will feature two lecture sessions with hands-on exercises, separated by a 1-hour break. Live attendance is recommended for the best experience. If you can’t join in real time, recordings will be available within 24 hours and accessible for four weeks after the seminar.
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.
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 workshop covers four key analytic approaches for obtaining such evidence: (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 workshop covers four key analytic approaches for obtaining such evidence: (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 and 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 software 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, here are some excellent online 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 and 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 software 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, here are some excellent online 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 you if you’re interested in learning more about psychometrics and measurement, particularly in the social sciences and in education. This content is particularly useful if you’ll be developing your own measures. Further, the course will provide direct instruction in how to understand, detect, and remediate bias in measurement.
You should have the equivalent of a two-semester graduate-level sequence in statistics. Familiarity with factor analysis is helpful but not required. Beyond this statistical background, no advanced mathematical proficiency is assumed. Learning will entail conceptual understanding as well as hands-on practice conducting and interpreting analyses – with only minimal use of notation and equations.
You will be provided with a list of recommended readings to help you prepare for the seminar.
This course is for you if you’re interested in learning more about psychometrics and measurement, particularly in the social sciences and in education. This content is particularly useful if you’ll be developing your own measures. Further, the course will provide direct instruction in how to understand, detect, and remediate bias in measurement.
You should have the equivalent of a two-semester graduate-level sequence in statistics. Familiarity with factor analysis is helpful but not required. Beyond this statistical background, no advanced mathematical proficiency is assumed. Learning will entail conceptual understanding as well as hands-on practice conducting and interpreting analyses – with only minimal use of notation and equations.
You will be provided with a list of recommended readings to help you prepare for the seminar.
Seminar outline
Day One: introduction, latent variable modeling, factor analyses
-
- A latent variable modeling perspective on psychometrics
- Exploratory factor analyses (EFA)
- Factor extraction and rotation in EFA
- Best practices: comparing competing factor solutions
- Interpreting EFA model fit and item loadings
- Best practices: continuous vs. categorical variables in factor analyses
Day Two: hypothesized factor structures, measurement bias
-
- Confirmatory factor analyses (CFA)
- Specifying, coding and interpreting CFA models
- Interpreting CFA model fit and item loadings
- Estimating shared error covariances in CFA
- Simple models, powerful claims: MIMIC (multiple indicators, multiple causes) models
- Identifying DIF (differential item functioning) with MIMIC models
- Specifying, coding and interpreting MIMIC models
- Assessing scalar invariance with MIMIC models
- Addressing items exhibiting DIF
Day Three: testing measures across groups
-
- Measurement invariance
- Configural, metric, and scalar invariance
- Assessing “whether measures mean the same thing” across groups
- Specifying, coding and interpreting measurement invariance analyses
- Open Q&A/consultation time
Day One: introduction, latent variable modeling, factor analyses
-
- A latent variable modeling perspective on psychometrics
- Exploratory factor analyses (EFA)
- Factor extraction and rotation in EFA
- Best practices: comparing competing factor solutions
- Interpreting EFA model fit and item loadings
- Best practices: continuous vs. categorical variables in factor analyses
Day Two: hypothesized factor structures, measurement bias
-
- Confirmatory factor analyses (CFA)
- Specifying, coding and interpreting CFA models
- Interpreting CFA model fit and item loadings
- Estimating shared error covariances in CFA
- Simple models, powerful claims: MIMIC (multiple indicators, multiple causes) models
- Identifying DIF (differential item functioning) with MIMIC models
- Specifying, coding and interpreting MIMIC models
- Assessing scalar invariance with MIMIC models
- Addressing items exhibiting DIF
- Confirmatory factor analyses (CFA)
Day Three: testing measures across groups
-
- Measurement invariance
- Configural, metric, and scalar invariance
- Assessing “whether measures mean the same thing” across groups
- Specifying, coding and interpreting measurement invariance analyses
- Open Q&A/consultation time
- Measurement invariance
Payment information
The fee of $995 USD includes all course materials.
PayPal and all major credit cards are accepted.
Need funding from your employer or institution? Use our funding request template.
Our Tax ID number is 26-4576270.
The fee of $995 USD includes all course materials.
PayPal and all major credit cards are accepted.
Need funding from your employer or institution? Use our funding request template.
Our Tax ID number is 26-4576270.