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Abstract Details
Activity Number:
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42
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Type:
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Contributed
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Date/Time:
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Sunday, July 31, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Section for Statistical Programmers and Analysts
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Abstract - #302027 |
Title:
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Linear Logistic Test Model: Using SASĀ® to Simulate the Decomposition of Item Difficulty by Algorithm, Sample Size, Cognitive Component, and Time-to-Convergence
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Author(s):
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George T. MacDonald*+ and Jeffrey Kromrey
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Companies:
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University of South Florida and University of South Florida
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Address:
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David C. Anchin Center, Tampa, FL, 33620,
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Keywords:
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LINEAR LOGISTIC TEST MODEL ;
PROC NLMIXED ;
LLTM
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Abstract:
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Fischer (1973) introduced a model called The Linear Logistic Test Model (LLTM) that is capable of bridging cognitive processing models and psychometric models. In his mathematics study, he found that differentiating calculus items could be explained by eight basic cognitive operations. He postulated that item difficulty could be re-parameterized to express these operations. LLTM can be coded in SAS using PROC NLMIXED. Clustering items within persons is considered by many to be a multi-level approach. Because no algorithm method exists that always finds the global optimum, and given the array of optimization algorithms available, coders may well want to know which algorithms work best under various test conditions. To provide some answers to these questions, a simulation study was undertaken to determine the utility of the algorithm methods available in PROC NLMIXED according to varying s
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