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Friday, October 9, 2020 | History

2 edition of Impact of local item dependence on item response theory scoring in CAT found in the catalog.

Impact of local item dependence on item response theory scoring in CAT

Lynda M. Reese

Impact of local item dependence on item response theory scoring in CAT

by Lynda M. Reese

  • 2 Want to read
  • 33 Currently reading

Published by Law School Admission Council in Newtown, PA .
Written in English

    Subjects:
  • Law examinations -- Design and construction.,
  • Law schools -- United States -- Entrance examinations.,
  • Law schools -- United States -- Admission.

  • Edition Notes

    Other titlesLocal item dependence on item response theory scoring in CAT, Item response theory scoring in CAT
    StatementLynda M. Reese
    SeriesLSAC research report series, Law School Admission Council computerized testing report -- 98-08, Computerized testing report (Law School Admission Council) -- 98-08..
    ContributionsLaw School Admission Council.
    The Physical Object
    Paginationi, 13 p. :
    Number of Pages13
    ID Numbers
    Open LibraryOL16638462M

      These simulations (a) provide an indication of the average number of items from the Nicotine Dependence item banks that would be administered under typical CAT conditions, (b) indicate which items would be most routinely selected for CAT administration, and (c) characterize the expected CAT-based score by: Although Demars' IRT can be considered to be an introductory book (and requires almost no math/stats background) it covers a variety of topics about Item Response Theory. As a result of a comprehensive survey of the related literature, the author provides nuggets of information about a wide range of rules of thumb and analysis by:

    Item response theory has become an essential component in the toolkit of every researcher in the behavioral sciences. It provides a powerful means to study individual responses to a variety of stimuli, and the methodology has been extended and developed to cover many different models of interaction. This volume presents a wide-ranging handbook to item response theory - and its applications to 5/5(1). This guide provides information on all pain interference short form and CAT instruments. Whether one uses a short form or CAT, the score metric is Item Response Theory (IRT), a family of statistical models that link individual questions to a presumed underlying trait or concept of pain interference represented by all items in the item Size: KB.

    The Impact of Item Parameter Estimation on Computerized Adaptive Testing with Item Cloning (CT ) by Cees A. W. Glas, University of Twente, Enschede, The Netherlands. Impact of Local Item Dependence on Item Response Theory Scoring in CAT (CT ) by Lynda M. Reese. The Impact of Local Dependencies on Some LSAT Outcomes (SR ) by Lynda. Page 4 Effects of Local Item Dependence Otherwise, an inflated reliability estimate would occur, since these items were inter-dependent and the dependence would spuriously inflate the correlation between the two half-tests.


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Impact of local item dependence on item response theory scoring in CAT by Lynda M. Reese Download PDF EPUB FB2

This study represented a first attempt to evaluate the impact of local item dependence (LID) for Item Response Theory (IRT) scoring in computerized adaptive testing (CAT).

The most basic CAT design and a simplified design for simulating CAT item pools with varying degrees of LID were : Lynda M. Reese. Impact of local item dependence on item response theory scoring in CAT.

Newtown, PA: Law School Admission Council, (OCoLC) Document Type: Book: All Authors / Contributors: Lynda M Reese; Law School Admission Council. Four statistics are proposed for the detection of local dependence (LD) among items analyzed using item response theory. Among them, the X 2 and G 2 LD indexes are of special interest.

Simulated data are used to study the distribution and sensitivity of these statistics under the null condition, as well as under conditions in which LD is by: The CAT was developed using item response theory (IRT), and items were only included if they met stringent statistical criteria showing that together they had reliable measurement properties to quantify the overall impact of COPD on health and wellbeing.

However, COPD is a heterogeneous condition and whilst the CAT was designed to be Author: Sarah Marietta von Siemens, Peter Alter, Johanna I.

Lutter, Hans-Ulrich Kauczor, Bertram Jobst, Robe. The practical effects of the presence of LID on passage-based tests are discussed, as are issues regarding how to calibrate context-dependent item sets using item response theory. +2 Figures. The primary purpose of this study is to investigate the impact of the local item dependence (LID) of testlet items on the performance of the multistage tests (MST) that make pass/fail decisions.

In this study, LID is simulated in testlet items. Testlet items are those that physically share the same stimulus. Computer adaptive testing (CAT) has been shown to shorten the test length and increase the precision of latent trait estimates. Oftentimes, test takers are asked to respond to several items that are related to the same passage.

The purpose of this study is to explore three CAT item selection techniques for items of the same passages and to provide recommendations and guidance for item Author: Lihua Yao. Simultaneous or concurrent calibration using the graded item response theory model placed all of the items on the same scale.

Twenty-two of 28 potential new items were added across the seven scales. A recommended short form was proposed for the Anger scale, and the recommended short forms for the Anxiety and Depressive Symptoms scales were revised.

Using Item Response Theory (IRT) for Developing and Evaluating the Pain Impact Questionnaire (PIQ-6™) Janine Becker, PhD, Dipl-Psych Reprint requests to: Janine Becker, PhD, Dipl-Psych, Department of Psychosomatics and Psychotherapy, Charité Berlin, Humboldt University Hospital, Luisenstraße 13A, Berlin, by: 8 The “New Psychometrics” – Item Response Theory Letting the average ability θ¯ =0 leads to the conclusion that the difficulty of an item for all subjects, δ j, is the logarithm of Q/P divided by the number of subjects, N, δ j = lnQ P N.

() That is, the estimate of ability (Equation ) for items with an average difficulty of. context. For this reason, what is now known as local item dependence (LID) must be considered in the development and scoring of educational tests.

The concept of LID is best understood within the framework of item response theory (IRT). The most popular IRT models specify a single latent trait to account for all statistical dependencies among.

Local item dependence and differential item functioning were assessed, and items were calibrated using the item response theory (IRT) graded response model.

Finally, computer adaptive tests (CATs) and short forms were administered in a new sample (n = ) to assess test-retest reliability and by: Item Response Theory: What It Is and How You Can Use the IRT Procedure to Apply It Xinming An and Yiu-Fai Yung, SAS Institute Inc.

ABSTRACT Item response theory (IRT) is concerned with accurate test scoring and development of test items. You design test items to measure various kinds of abilities (such as math ability), traits (such as.

The effects of violations of unidimensionality on the estimation of item and ability parameters and on item response theory equating of the GRE Verbal Scale. Journal of Educational Measurement, 22, –Cited by: 6. LID has been addressed in one of the following ways in the litrature: (1) Score-based polytomous item response theory models such as the graded response model (Samejima, ), polytomous logistic.

In item response theory (IRT), explanatory item response models (EIRM;) aim to explain the person and/or item side of the assessment data in order to enrich inferential information and enhance the feedback. Among person explanatory, item explanatory, and doubly explanatory models of the EIRM approach, this paper will focus on item explanatory Author: Jinho Kim, Mark Wilson.

This study represented a first attempt to evaluate the impact of local item dependence (LID) for Item Response Theory (IRT) scoring in computerized adaptive testing (CAT). The most basic CAT design and a simplified design for simulating CAT item pools with varying degrees of LID were applied.

A data generation method that allows the LID among items to be. fect on CAT score precision, while local dependence in responses had a substantial effect on score precision, depending on the degree of local dependence present.

Item response theory (IRT) is the foundation upon which computerized adaptive testing (CAT) is built, providing a basis for selecting items and scoring responses. Scoring •IRT-based scoring uses the item parameters to weight each response based on the properties of that particular item.

Each item contributes information to create an overall score. •Each item response provides information about where an individual is likely to be situated on the latent construct being measured. Item response theory (IRT) is used in the design, analysis, scoring, and comparison of tests and similar instruments whose purpose is to measure unobservable characteristics of the respondents.

This entry discusses some fundamental and theoretical aspects of IRT and illustrates these with worked examples. Presentation Overview •IRT Models –Theory of IRT (Reeve) –IRT item, scale, and person properties (Reeve) –Comparison with Classical Test Theory (Reeve) –IRT Assumptions and Model Fit (Orlando Edelen) –IRT Scoring (Orlando Edelen) •Applying IRT to enhancing health outcomes measurement –Designing and evaluating scales (Siemons; Krishnan).When we examined the comparability of items in the CRIS-CAT scales and the CRIS fixed-form scales, we found that 10 items in the CRIS fixed-form Extent scale, 6 items in the Perceived Limitations scale, and 5 items in the Satisfaction scale were omitted from the CRIS-CAT scale item pool.

This loss of items may be attributable to the differences in data analyzed for each study. Item response theory (IRT)-based item banking has been promoted as a powerful solution for overcoming well-known limitations of fixed-length instruments, such as floor and ceiling effects or insensitivity to change [13,14,15].

IRT is a framework for modeling item response data in which items and respondents are located on a common scale [16, 17 Cited by: 2.