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Continuous Online Item Calibration: Parameter Recovery and Item Utilization

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  • Hao Ren

    (Pacific Metrics)

  • Wim J. van der Linden

    (Pacific Metrics)

  • Qi Diao

    (Pacific Metrics)

Abstract

Parameter recovery and item utilization were investigated for different designs for online test item calibration. The design was adaptive in a double sense: it assumed both adaptive testing of examinees from an operational pool of previously calibrated items and adaptive assignment of field-test items to the examinees. Four criteria of optimality for the assignment of the field-test items were used, each of them based on the information in the posterior distributions of the examinee’s ability parameter during adaptive testing as well as the sequentially updated posterior distributions of the field-test item parameters. In addition, different stopping rules based on target values for the posterior standard deviations of the field-test parameters and the size of the calibration sample were used. The impact of each of the criteria and stopping rules on the statistical efficiency of the estimates of the field-test parameters and on the time spent by the items in the calibration procedure was investigated. Recommendations as to the practical use of the designs are given.

Suggested Citation

  • Hao Ren & Wim J. van der Linden & Qi Diao, 2017. "Continuous Online Item Calibration: Parameter Recovery and Item Utilization," Psychometrika, Springer;The Psychometric Society, vol. 82(2), pages 498-522, June.
    • Handle: RePEc:spr:psycho:v:82:y:2017:i:2:d:10.1007_s11336-017-9553-1
    DOI: 10.1007/s11336-017-9553-1
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    References listed on IDEAS

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    1. Martijn Berger, 1992. "Sequential sampling designs for the two-parameter item response theory model," Psychometrika, Springer;The Psychometric Society, vol. 57(4), pages 521-538, December.
    2. Martha Stocking, 1990. "Specifying optimum examinees for item parameter estimation in item response theory," Psychometrika, Springer;The Psychometric Society, vol. 55(3), pages 461-475, September.
    3. Douglas Jones & Zhiying Jin, 1994. "Optimal sequential designs for on-line item estimation," Psychometrika, Springer;The Psychometric Society, vol. 59(1), pages 59-75, March.
    4. Wim Linden & Hao Ren, 2015. "Optimal Bayesian Adaptive Design for Test-Item Calibration," Psychometrika, Springer;The Psychometric Society, vol. 80(2), pages 263-288, June.
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    Cited by:

    1. Yinhong He & Ping Chen, 2020. "Optimal Online Calibration Designs for Item Replenishment in Adaptive Testing," Psychometrika, Springer;The Psychometric Society, vol. 85(1), pages 35-55, March.
    2. Hyeon-Ah Kang & Yi Zheng & Hua-Hua Chang, 2020. "Online Calibration of a Joint Model of Item Responses and Response Times in Computerized Adaptive Testing," Journal of Educational and Behavioral Statistics, , vol. 45(2), pages 175-208, April.
    3. Mahmood Ul Hassan & Frank Miller, 2019. "Optimal Item Calibration for Computerized Achievement Tests," Psychometrika, Springer;The Psychometric Society, vol. 84(4), pages 1101-1128, December.
    4. Ul Hassan, Mahmood & Miller, Frank, 2021. "An exchange algorithm for optimal calibration of items in computerized achievement tests," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    5. Wim J. van der Linden & Bingnan Jiang, 2020. "A Shadow-Test Approach to Adaptive Item Calibration," Psychometrika, Springer;The Psychometric Society, vol. 85(2), pages 301-321, June.
    6. Frank Miller & Ellinor Fackle-Fornius, 2024. "Parallel Optimal Calibration of Mixed-Format Items for Achievement Tests," Psychometrika, Springer;The Psychometric Society, vol. 89(3), pages 903-928, September.

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