Materials Similar to Motivations for using the item response theory nominal response model to rank responses to multiple-choice items
- 65%: Quantitatively ranking incorrect responses to multiple-choice questions using item response theory
- 56%: Analyzing Multiple-Choice-Multiple-Response Items Using Item Response Theory
- 50%: Linking and comparing short and full-length concept inventories of electricity and magnetism using item response theory
- 48%: Online test administration results in students selecting more responses to multiple-choice-multiple-response items
- 45%: Identifying a preinstruction to postinstruction factor model for the Force Concept Inventory within a multitrait item response theory framework
- 43%: Multidimensional item response theory and the Force Concept Inventory
- 43%: Multidimensional item response theory and the Brief Electricity and Magnetism Assessment
- 42%: Item response theory analysis of the mechanics baseline test
- 41%: Methods for utilizing Item response theory with Coupled, Multiple-Response assessments
- 41%: Multidimensional Item Response Theory and the Conceptual Survey of Electricity and Magnetism
- 40%: Replicating analyses of item response curves using data from the Force and Motion Conceptual Evaluation
- 40%: Validity of Force Concept Inventory evaluated by students’ explanations and confirmation using modified item response curve
- 40%: Multidimensional item response theory and the force and motion conceptual evaluation
- 40%: Detecting the influence of item chaining on student responses to the Force Concept Inventory and the Force and Motion Conceptual Evaluation
- 39%: Examining the relation of correct knowledge and misconceptions using the nominal response model
- 39%: Analysis of Individual Test Of Astronomy STandards (TOAST) Item Responses
- 39%: Multilevel Rasch modeling of two-tier multiple choice test: A case study using Lawson’s classroom test of scientific reasoning
- 38%: Item response theory evaluation of the Light and Spectroscopy Concept Inventory national data set
- 38%: Exploring the CLASS with Item Response Theory
