CAT Field-Test Item Calibration Sample Size: How Large is Large under the Rasch Model?

Wei He

CAT Field-Test Item Calibration Sample Size: How Large is Large under the Rasch Model?

Wei He * §

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CAT Field-Test Item Calibration Sample Size: How Large is Large under the Rasch Model?

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Abstract

This study was conducted in an attempt to provide guidelines for practitioners regarding the optimal minimum calibration sample size for pretest item estimation in the computerized adaptive test (CAT) under WINSTEPS when the fixed-person-parameter estimation method is applied to derive pretest item parameter estimates. The field-testing design discussed in this study is a form of seeding design commonly used in the large-scale CAT programs. Under such as seeding design, field-test (FT) items are stored in an FT item pool and a predetermined number of them are randomly chosen from the FT item pool and administered to each individual examinee. This study recommends focusing on the valid cases (VCs) that each item may end up with given a certain calibration sample size, when the FT response data are sparse, and introduces a simple strategy to identify the relationship between VCs and calibration sample size. From a practical viewpoint, when the minimum number of valid cases reaches 250, items parameters are recovered quite well across a wide range of the scale. Implications of the results are also discussed.

References

17 Cites in Article

Reference Format

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Wei He. 2015. "CAT Field-Test Item Calibration Sample Size: How Large is Large under the Rasch Model?". Global Journal of Human-Social Science - G: Linguistics & Education GJHSS-G Volume 15 (GJHSS Volume 15 Issue G1).

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GJHSS Volume 15 Issue G1

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Journal Specifications

Crossref Journal DOI 10.17406/GJHSS

Print ISSN 0975-587X

e-ISSN 2249-460X

Keywords

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Classification

GJHSS-G Classification FOR Code: 139999

200499

Submission ReceivedJune 5, 2015
Peer Review Double Blind
Handling Editor
Accepted July 5, 2015
Published July 15, 2015

Version of record

v1.2

Issue date

February 19, 2015

Language

English

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Article Matrices

Total Score: 131

Country: United States

Subject: Global Journal of Human-Social Science

Authors: Wei He (PhD/Dr. count: 0)

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