Item Response Theory - Online Course
A 4-Day Livestream Seminar Taught by
Matthew Diemer10:30am-12:30pm (convert to your local time)
1:30pm-3:00pm
Item Response Theory (IRT) methods are best known for their use in large-scale achievement testing, such as (in the U.S.) the SAT and GRE. However, IRT methods have unique—and overlooked—advantages for improving measurement in the social sciences, psychology, education, medicine, business, marketing, and other fields. For example, IRT methods illuminate how precise a measure is across people with low, medium, or high levels of the underlying construct of interest. (In contrast, factor analytic approaches assume an item, subscale, or measure is equally precise for everyone.) Further, IRT is unique in allowing researchers to identify redundant and/or low-information items, making it possible to streamline longer scales to yield more efficient short-form measures.
This workshop provides a hands-on introduction to IRT. It minimizes notation and jargon and instead emphasizes conceptual understanding and application, with a particular emphasis on figures—the “visual language” of IRT. The Graded Response Model, which is designed for analyses of Likert-type items (e.g., strongly disagree to strongly agree), will be the primary focus.
Each day will be roughly divided between lecture and guided analytic practice aimed at learning to apply course concepts. You are encouraged to bring your own data to use during the ‘hands on’ portion of each session. Alternatively, you will be provided with a dataset and analytic problems to work on.
Starting June 3, we are offering this seminar as a 4-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.
*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
Computing
Analyses and syntax will be provided in R and in Mplus. You should have some basic familiarity with either package.
If you’re going to use R for this workshop, please install the ltm, mirt, and lavaan packages prior to the workshop.
If you’re going to use Mplus, at minimum you need to download and install the free demo version of Mplus prior to the start of the workshop. If you have access to the full version, that’s even better.
You should have a copy of SPSS, Stata, R, or some other package that you are familiar with to use for (minimal) data manipulation.
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.
Analyses and syntax will be provided in R and in Mplus. You should have some basic familiarity with either package.
If you’re going to use R for this workshop, please install the ltm, mirt, and lavaan packages prior to the workshop.
If you’re going to use Mplus, at minimum you need to download and install the free demo version of Mplus prior to the start of the workshop. If you have access to the full version, that’s even better.
You should have a copy of SPSS, Stata, R, or some other package that you are familiar with to use for (minimal) data manipulation.
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 seminar is designed for researchers who want to learn how to develop and/or improve measurement in the social sciences, psychology, education, medicine, business, marketing, and other fields. Those who want to gain understanding of how to interpret, analyze, and depict IRT analyses will find this workshop of particular interest.
To benefit from this seminar, you should have a basic understanding of regression analysis and familiarity with the basic principles of measurement, such as reliability and validity. Some knowledge of factor analysis would be helpful, but is not essential.
This seminar is designed for researchers who want to learn how to develop and/or improve measurement in the social sciences, psychology, education, medicine, business, marketing, and other fields. Those who want to gain understanding of how to interpret, analyze, and depict IRT analyses will find this workshop of particular interest.
To benefit from this seminar, you should have a basic understanding of regression analysis and familiarity with the basic principles of measurement, such as reliability and validity. Some knowledge of factor analysis would be helpful, but is not essential.
Seminar outline
Day 1: Introduction to item response theory (IRT)
• What is a latent variable? Theta in IRT methods
• The graphical language of IRT
• Uses of IRT in large-scale achievement
• IRT with dichotomous (correct vs incorrect) response options
• IRT parameters: a, b, c (almost as easy as 1-2-3)
• Overview of IRT models: 1PL (including the Rasch model), 2PL, 3PL
• Estimating IRT models with MPlus and the mirt package, in R
Day 2: Graded response model (GRM)
• Recap & links to Day 1
• ‘Information’ and measurement precision in an IRT framework
• Item information curves (IICs), test information curves (TICs), item characteristic curves (ICCs), and test characteristic curves (TCCs)
• Uses of IRT to assess attitudes, beliefs, behaviors, etc.
• IRT with polytomous (Likert-type, or strongly disagree-strongly agree) response options
• Meaning of IRT parameters with polytomous response options
• Defining thresholds per measurement theory
• Differencing probabilities in the GRM
• Estimating GRM models with MPlus and the mirt package, in R
Day 3: Leveraging Advantages of IRT to Improve Measurement
• Recap & links to Day 2
• Correspondence of IRT ‘information’ to Alpha and more widely known internal consistency estimates
• Unique capacity of IRT to identify low-information, redundant and imprecise items to streamline measures (e.g., create a short form of an existing measure)
• IICs, TICs, ICCs and TCCs to identify & visualize item properties, to improve measurement precision and data quality
• Reducing participant burden & enhancing data quality via shortened measures
• Decision-making processes in refining or shortening measures
Day 4: Differential Item Functioning (DIF)
• Recap & links to Day 3
• Differential Item Functioning (DIF) in IRT
• Uniform DIF vs non-uniform DIF
• Assessing DIF with MPlus and the mirt package, in R
• Assessing DIF: ICCs, anchor items (purification), model- and item-based approaches
• Advanced Topics: Longitudinal IRT to assess change
• Overarching summary and overview
Day 1: Introduction to item response theory (IRT)
• What is a latent variable? Theta in IRT methods
• The graphical language of IRT
• Uses of IRT in large-scale achievement
• IRT with dichotomous (correct vs incorrect) response options
• IRT parameters: a, b, c (almost as easy as 1-2-3)
• Overview of IRT models: 1PL (including the Rasch model), 2PL, 3PL
• Estimating IRT models with MPlus and the mirt package, in R
Day 2: Graded response model (GRM)
• Recap & links to Day 1
• ‘Information’ and measurement precision in an IRT framework
• Item information curves (IICs), test information curves (TICs), item characteristic curves (ICCs), and test characteristic curves (TCCs)
• Uses of IRT to assess attitudes, beliefs, behaviors, etc.
• IRT with polytomous (Likert-type, or strongly disagree-strongly agree) response options
• Meaning of IRT parameters with polytomous response options
• Defining thresholds per measurement theory
• Differencing probabilities in the GRM
• Estimating GRM models with MPlus and the mirt package, in R
Day 3: Leveraging Advantages of IRT to Improve Measurement
• Recap & links to Day 2
• Correspondence of IRT ‘information’ to Alpha and more widely known internal consistency estimates
• Unique capacity of IRT to identify low-information, redundant and imprecise items to streamline measures (e.g., create a short form of an existing measure)
• IICs, TICs, ICCs and TCCs to identify & visualize item properties, to improve measurement precision and data quality
• Reducing participant burden & enhancing data quality via shortened measures
• Decision-making processes in refining or shortening measures
Day 4: Differential Item Functioning (DIF)
• Recap & links to Day 3
• Differential Item Functioning (DIF) in IRT
• Uniform DIF vs non-uniform DIF
• Assessing DIF with MPlus and the mirt package, in R
• Assessing DIF: ICCs, anchor items (purification), model- and item-based approaches
• Advanced Topics: Longitudinal IRT to assess change
• Overarching summary and overview
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.