Course Description
Models, statistical methods, and computer applications of item response theory (IRT) to educational and psychological test data.
Athena Title
Item Response Theory
Prerequisite
ERSH 8610 or ERSH 8610E
Semester Course Offered
Offered every year.
Grading System
A - F (Traditional)
Student learning Outcomes
- Students will be able to relate item response theory to classical test theory.
- Students will become familiar with models of item response data.
- Students will be able to understand mathematical underpinning of the models in item response theory.
- Students will be able to derive parameter estimation procedures for different item response theory models.
- Students will know the fields of applications - for example, test construction, test equating, differential item functioning, and adaptive testing.
- Students will be able to use computer programs designed to analyze test data under item response theory.
- Students will be able to evaluate current literature of item response theory and its application.
Topical Outline
- Introduction and History
- Item Characteristic Curve Models
- Item Parameter Estimation
- Test Characteristic Curve
- Ability Estimation
- Information Function
- Joint Maximum Likelihood Estimation
- Rasch Model
- Test Equating
- Adaptive Testing, Differential Item Functioning
- Test Construction
- Marginal Maximum Likelihood Estimation
- Bayesian Estimation
- Graded Response Model
- Nominal Response Model
- Computer Lab
- Software and Item Characteristic Curve models
- Item Parameter Estimation via BASIC
- Ability Estimation and Information Function
- Parameter Estimation
- Rasch Software-WINSTEPS, OPLM
- Application Software-EQUATE, IRTDIF
- Marginal Estimation via BILOG
- Polytomous Models via MULTILOG
Syllabus
Public CV
