Introduction to Item Response Theory (IRT)

Introduction to Item Response Theory (IRT)

taught by Karen Schmidt

Aim of Course:

This online course, "Introduction to Item Response Theory (IRT)" will introduce you to the statistical basis for analyzing multiple-choice survey or test data - item response theory. This includes both dichotomous (two-outcome) data and polytomous (multiple outcome) data.  After introducing the key foundational concepts of traits, items, scales and scores, the course goes on to cover how to measure and model response data.  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 understand output from IRT analyses.

Course Program:

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)

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.

Introduction to Item Response Theory (IRT)

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.
Level:
Introductory
Prerequisite:
You should be familiar with introductory statistics.  Try these self tests to check your knowledge.
Organization of the Course:

This course takes place online at the Institute for 4 weeks. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Time Requirement:
About 15 hours per week, at times of  your choosing.

Options for Credit and Recognition:
Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:
  1. No credit - You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
  2. Certificate - You may be enrolled in PASS (Programs in Analytics and Statistical Studies) that requires demonstration of proficiency in the subject, in which case your work will be assessed for a grade.
  3. CEUs and/or proof of completion - You may require a "Record of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's).  For those successfully completing the course,  CEU's and a record of course completion will be issued by The Institute, upon request.
  4. Other options - Statistics.com Specializations, INFORMS CAP recognition, and academic (college) credit are available for some Statistics.com courses
Specialization:
Specializations are an easy way for you to demonstrate mastery of a specific skill in statistics and analytics. This course is part of the Rasch & Item Response Theory Specialization which gives a deep dive into statistical methods for calibration and equating of test instruments.  Take any three of the four Statistics.com courses on this topic (this course, plus the courses listed to the right under "related courses," not including conferences).  For savings, use the promo code "rasch-specialization" and register for all three courses at once for  $1197 ($399 per course, not combinable with other tuition savings).  If you register for all four, you'll still receive the discounted rate.
Course Text:

Course subject materials will be provided in each lesson. A suggested text (not required), for those who wish a more rigorous review of the concepts, is Fundamentals of Item Response Theory (Measurement Methods for the Social Science) by Ronald K. Hambleton, available on Amazon or on Sage.

Software:
You will use a hand calculator for some simple calculations (or Excel), and you will use the free R Studio statistical software program for obtaining IRT scores, item parameters, and response probabilities.  Its use is illustrated in the course. Exercises and materials will be provided to help you become introduced to 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 need to learn R, be sure to appropriately budget your time.
Instructor(s):

Dates:

August 18, 2017 to September 15, 2017 February 16, 2018 to March 16, 2018 August 17, 2018 to September 14, 2018

Introduction to Item Response Theory (IRT)

Instructor(s):

Dates:
August 18, 2017 to September 15, 2017 February 16, 2018 to March 16, 2018 August 17, 2018 to September 14, 2018

Course Fee: $589

Do you meet course prerequisites? What about book & software? (Click here to learn more)

We have flexible policies to transfer to another course, or withdraw if necessary (modest fee applies)

Group rates: Click here to get information on group rates. 

First time student or academic? Click here for an introductory offer on select courses. Academic affiliation?  You may be eligible for a discount at checkout.

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