Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
February 17, 2026
Neural Networks and Rules-based Systems used to Find Rational and
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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.
Wei He. 2015. \u201cCAT Field-Test Item Calibration Sample Size: How Large is Large under the Rasch Model?\u201d. Global Journal of Human-Social Science - G: Linguistics & Education GJHSS-G Volume 15 (GJHSS Volume 15 Issue G1): .
Crossref Journal DOI 10.17406/GJHSS
Print ISSN 0975-587X
e-ISSN 2249-460X
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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.
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