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$ 1,395.00

IN PERSON – 5-day Statistics Short Course

Seminar Overview:

We’re all looking to measure something, but we first need to understand what how that measurement process works! Psychometrics is the underlying science behind all of your tests and measurements used to evaluate psychological attributes, whether those are ability, aptitude, achievement, attitudes, interests, personality, cognitive functioning, and mental health. This course will introduce you to the measurement and statistical concepts that are central to psychometrics, as well as the psychometric fundamentals of factor analysis and Item Response Theory.

Seminar Topics:

  • Measurement and statistical concepts specific to psychometrics
  • The latent variable modeling perspective
  • Reliability – classical and modern approaches
  • Validity – conceptual and statistical aspects
  • Factor analysis – exploratory and confirmatory
  • Instrument development & validation process
  • Measurement bias & strategies to address bias (e.g., MIMIC models)
  • Multi-group analyses & measurement invariance testing
  • Missing data
  • Item Response Theory: 1PL (Rasch) & 2PL & 3PL models
  • How to use MPlus for psychometric analyses

Seminar Description:

Psychometrics is the science of how we measure things, such as the psychological attributes of people. These psychological attributes include abilities, aptitudes, achievement, attitudes, interests, personality traits, cognitive functioning, and mental health. Psychometrics, theoretically-informed and precise measurement, is an essential component of many of the things we hold dear. 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 are all affected by psychometrics. This seminar emphasizes the conceptual understanding of and the application of psychometric principles.

Instructor: Larry Price, Ph.D.

Larry Price, Ph.d. is a Professor of Psychometrics & Statistics and Director of the Office of Data Analytics & Methodology at Texas State University. Between 1999 and 2002, Dr. Price was employed at The Psychological Corporation in San Antonio as a Senior Psychometrician/Statistician where his work focused on improving the psychometric properties of the Wechsler Scales of Intelligence Memory (e.g., WISC-III, WISC-IV, WAIS-III, WMS-III, and WPPSI-III), and Achievement (WIAT-II) and other psychological measures such as the Beck Depression Inventory (BDI) and Clinical Evaluation of Language Fundamentals (CELF-IV). His research interest includes the theoretical development and testing of Bayesian and non-Bayesian psychometric models in psychological and neuropsychological research (neuroimaging network analysis), theoretical development, testing, and refinement of classical and modern psychometric methods in the behavioral sciences, development of dynamic multivariate time series models for the psychological, social and neurosciences. Prior to working at Psychological Corporation, he worked at Emory University from 1986 to 1999 as a Biostatistician and Psychometrician in the School of Medicine. Funding mechanisms for Dr. Price’s work include NIH, NSF, DOE, and private organizations.

APA Continuing Education Credits:

This course offers 24 hours of Continuing Education Credits. Stats Camp Foundation is approved by the American Psychological Association to sponsor continuing education for psychologists. Stats Camp Foundation maintains responsibility for this program and its content.

Seminar Includes:

Materials, downloads, recorded course video viewable for up to one year.

Learning Objectives:

After engaging in course lectures and discussions as well as completing the hands-on practice activities with real data, participants will be able to:

  • Apply a latent variable perspective to psychometrics
  • Examine how psychometrics fits into the scientific enterprise and educational policy
  • Identify construct validity as the “one ring to rule them all” and where other dimensions of validity fit in
  • Examine why Cronbach’s Alpha is biased (a recent article was titled “Thanks Coefficient Alpha, we’ll take it from here”)
  • Understand why other reliability estimates (e.g., mean inter-item correlations, Coefficient H, Omega total) are preferred, and how to calculate and interpret them
  • Code, analyze, and interpret exploratory factor analyses (EFA)
  • Code, analyze, and interpret confirmatory factor analyses (CFA)
  • Synthesize the preceding ideas to understand the instrument development & validation process
  • Understand the difference between “true score” bias and item bias
  • Code, analyze, and interpret Multiple Indicator and Multiple Causes (MIMIC) models
  • Code, analyze, and interpret measurement invariance analyses
  • Understand the differences between configural, metric & scalar invariance
  • Understand why Item Response Theory (IRT) is useful to assess ability & shorten scales
  • Code, analyze, and interpret 1PL, 2PL, 3PL IRT models
  • Use figures to communicate the psychometric properties of measures

Seminar Prerequisites:


  • Intermediate proficiency in basic statistical theory as would be gained in a 1st year graduate course.

Not required but advantageous:

  • Limited experience (e.g., graduate-level course) with classical measurement theory and concepts.

No level of proficiency beyond basic awareness is assumed for skills related to:

  • Advanced mathematical or statistical topics such as matrix algebra.

Software and Computer Support:

It is important to bring a notebook computer to the seminar, so you can run the programs and see how their output corresponds with the presentation material.  Please install the software before arriving at the seminar. We’ll be estimating the examples in R language using the packages rstan and brms. The latter will be the main package in this course. You can read more about brms package: https://mc-stan.org/users/interfaces/brms.

Seminar Audience:

The intended audience is advanced students, faculty, and other researchers, from all disciplines, who want a ground-floor introduction to doing Bayesian data analysis.

Seminar Files

Below are links to seminar files for those who enrolled in the seminar. Please download these files onto your computer before the first day of the seminar. The files are password protected to respect the intellectual property rights of the instructors. By using your login information you agree not to share your login information or the content protected by it.

Instructor will provide password on first day of seminar:
Click Here to Access Bayesian Data Analysis Seminar Files

All statistical software used at Stats Camp will be available, free to participants, on our SMORS (statistical modeling on remote servers) system for the duration of camp.


Summer Stats Camp 2024: Psychometrics
Monday  June 10, 2024
9:00-9:30 Welcome and introduction.
9:30-10:30 A latent variable modeling perspective on psychometrics.
10:30-10:45 Rest Break
10:45-12:30 Coefficient Alpha: Widely known, poorly understood
12:30-1:30 Rest Break
1:30-3:00 Intro to MPlus code.

Bringing latent variables into focus: Exploratory Factor Analyses (EFA).

3:00-3:15 Rest Break
3:15-5:00 Construct validity: “One ring to rule them all”
Tuesday  June 11, 2024
9:00-10:45 Testing hypothesized factor structures: Confirmatory Factor Analyses (CFA)
10:45-11:00 Rest Break
11:00-12:30 CFA: Hands-on practice & statistical power
12:30-1:30 Rest Break
1:30-3:00 Bias & Fairness in Psychometrics: Multiple Indicators and Multiple Causes (MIMIC) models
3:00-3:15 Rest Break
3:15-4:00 Review, integration & catch-up time
4:00-5:00 Individual Consultations
Wednesday  June 12, 2024
9:00-10:30 Multi-group models & Measurement Invariance
10:30-10:45 Rest Break
10:45-12:30 Measurement Invariance (continued)
12:30-1:30 Rest Break
1:30-3:00 Best practices: Instrument development & validation
3:00-3:15 Rest Break
3:15-4:00 Review, integration & catch-up time
4:00-5:00 Individual Consultations
Thursday  June 13, 2024
9:00-10:45 Introduction to Item Response Theory (IRT)
10:45-11:00 Rest Break
11:00-12:30 IRT: Discrimination (1PL), Difficulty (2PL) & Guessing (3PL) models
12:30-1:30 Rest Break
1:30-3:00 IRT: The Graded Response Model
3:00-3:15 Rest Break
3:15-4:00 Review, integration & catch-up time
4:00-5:00 Individual Consultations
Friday  June 14, 2024
9:00-10:30 Missing data: Focus on the auxiliary variables strategy
10:30-10:45 Rest Break
10:45-12:00 Individual consultation
12:00 End of Workshop
12:30-1:30 Rest Break


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