Course

Course Summary
Credit Type:
Course
ACE ID:
STAT-0047
Organization:
Statistics.com
Location:
Online
Length:
4 weeks
Dates Offered:
Credit Recommendation & Competencies
Level Credits (SH) Subject
Upper-Division Baccalaureate 1 Item Response Theory
Description

Objective:

The course objective is to introduce students to the fundamentals of Item Response Theory (IRT) and its application in analyzing data from complex sample designs, such as stratified cluster sampling. Students learn how to estimate variances, fit linear and logistic regression models to survey data, and assess the effectiveness of measurement instruments in various fields. 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.

Learning Outcomes:

  • Learn to handle and analyze data from complex sampling designs.
  • Apply IRT models to dichotomous and polytomous response data.
  • Evaluate the fit and effectiveness of items and scales, exploring advanced topics like Differential Item Functioning (DIF) and Computerized Adaptive Testing (CAT).
  • Understand the principles and history of IRT and Classical Test Theory.

General Topics:

  • Historical development of IRT
  • Traits, items, scales, and scores
  • Measuring Dichotomous Responses
  • 1-, 2-, and 3-Parameter models
  • Measuring Polytomous Responses and the Graded Response Model
  • Evaluating item and scale effectiveness: Dimensionality, standard errors, and information function
  • Introduction to Differential Item Functioning (DIF) and Computerized Adaptive Testing (CAT)
Instruction & Assessment

Instructional Strategies:

  • Discussion
  • Textbook readings

Methods of Assessment:

  • Other
  • Graded practical exercises and discussion forums

Minimum Passing Score:

73%
Supplemental Materials