Introduction to Item Response Theory (IRT)
This course will teach you the statistical basis for analyzing multiple-choice survey or test data – item response theory (IRT).
Overview
Complex sample designs such as stratified cluster sampling make it possible to extract maximum information at minimum cost, but they are typically harder to work with than simple random samples. How do you analyze the resulting data – in particular, how do you determine margins of error? This course teaches you how to estimate variances when analyzing survey data from complex samples, and also how to fit linear and logistic regression models to complex sample survey data.
- Introductory
- 4 Weeks
- Expert Instructor
- Tuiton-Back Guarantee
- 100% Online
- TA Support
Learning Outcomes
After taking this course you should be able to understand how IRT is used, which models are appropriate for different contexts, how to construct scales, and how to understand output from IRT analyses.
- Define traits, items, scales and scores
- Add item parameters: 1-, 2- and 3-Parameter models
- Construct appropriate dichotomous and polytomous scales Interpret scores
- Assess fit, and how well items work
Who Should Take This Course
Researchers, social scientists, and education measurement scientists who want to learn about analyzing and creating better scales, tests, and questionnaires.
Our Instructors
Dr. Karen Schmidt
Dr. Karen Schmidt is a Professor in The Department of Psychology at The University of Virginia, Charlottesville, VA. Dr. Schmidt has been a professor for 24 years, and teaches courses in statistics, research methods, and item response theory (IRT) and Rasch measurement at the undergraduate and graduate level. Dr. Schmidt specializes in psychometrics, with specific focus on Rasch measurement and item response theory (IRT). Her research and interests include scale and test design and analysis, item features experimental design and analysis, and trait measurement in a wide variety of areas, including psychological, educational, health, and medical sciences.
Course Syllabus
Week 1
Introduction, Theory, Concepts
- History of IRT
- Classical test theory and IRT
- Why is effective measurement important?
- Traits, items, scales and scores
Week 2
Measuring Dichotomous Responses
- Adding item parameters: 1-, 2- and 3-Parameter models
- What do the scores mean?
- Dichotomous scale construction considerations
Week 3
Measuring Polytomous Responses
- The Graded Response Model
- What do the scores mean?
- Polytomous scale construction considerations
Week 4
Practical Considerations and Applications of IRT
- Assessing fit: How well do the items work?
- Item and scale effectiveness: Dimensionality, Standard Errors, Information
- Differential Item Functioning (DIF) and Computerized Adaptive Testing (CAT)
Class Dates
2022
Instructors:
Instructors:
2023
Instructors:
Instructors:
Prerequisites
In this course, you will use Excel and R. We recommend using R in conjunction with R Studio. Some familiarity with R is assumed. Exercises and materials will be provided to introduce you to R and R Studio with roughly 3 hours of additional work. Warning: The first week of the course has a comparatively heavy workload of regular course material, so if you are very new to R, be sure to appropriately budget your time.
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Additional Information
Homework
The homework in this course consists of multiple-choice questions on course concepts, ungraded practice exercises using R, and a final project using R.
Course Text
Software
In this course, you will use Excel and R. We recommend using R in conjunction with R Studio. Some familiarity with R is assumed. Exercises and materials will be provided to introduce you to R and R Studio with roughly 3 hours of additional work. Warning: The first week of the course has a comparatively heavy workload of regular course material, so if you are very new to R, be sure to appropriately budget your time.
Supplemental Information
Literacy, Accessibility, and Dyslexia
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