Abstract
| Original language | English |
|---|---|
| Place of Publication | Newtown |
| Publisher | LSAC |
| Number of pages | 26 |
| Publication status | Published - 2012 |
Publication series
| Name | LSAC Research Report Series |
|---|---|
| Publisher | LSAC |
| No. | 12-03 |
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Keywords
- METIS-291704
- IR-83910
Cite this
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Estimating and testing the extended testlet model. / Glas, Cornelis A.W.
Newtown : LSAC, 2012. 26 p. (LSAC Research Report Series; No. 12-03).Research output: Book/Report › Report › Professional
TY - BOOK
T1 - Estimating and testing the extended testlet model
AU - Glas, Cornelis A.W.
PY - 2012
Y1 - 2012
N2 - Executive Summary A set of test questions (i.e., items) grouped around a common stimulus is often referred to as a testlet. Testlets are found on the Law School Admission Test in the Analytical Reasoning and Reading Comprehension sections. This research extends current testlet theory by identifying and modeling the relationship between testlet features and test-taker response behavior on testlets. Several estimation procedures are developed, and a small simulation study shows that the procedures evaluated produce similar results. One of the purposes of this project is to select the testlet features with the best prediction properties using text mining. This requires estimating the testlet model in the presence of a very large number of testlets and respondents. To demonstrate the feasibility of the estimation methods, a large-scale example is computed using data from 49,256 test takers who were administered a subset from 594 items nested within 100 testlets.
AB - Executive Summary A set of test questions (i.e., items) grouped around a common stimulus is often referred to as a testlet. Testlets are found on the Law School Admission Test in the Analytical Reasoning and Reading Comprehension sections. This research extends current testlet theory by identifying and modeling the relationship between testlet features and test-taker response behavior on testlets. Several estimation procedures are developed, and a small simulation study shows that the procedures evaluated produce similar results. One of the purposes of this project is to select the testlet features with the best prediction properties using text mining. This requires estimating the testlet model in the presence of a very large number of testlets and respondents. To demonstrate the feasibility of the estimation methods, a large-scale example is computed using data from 49,256 test takers who were administered a subset from 594 items nested within 100 testlets.
KW - METIS-291704
KW - IR-83910
M3 - Report
T3 - LSAC Research Report Series
BT - Estimating and testing the extended testlet model
PB - LSAC
CY - Newtown
ER -