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Improving Measurement Precision of Hierarchical Latent Traits Using Adaptive Testing

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  • Chun Wang

Abstract

Many latent traits in social sciences display a hierarchical structure, such as intelligence, cognitive ability, or personality. Usually a second-order factor is linearly related to a group of first-order factors (also called domain abilities in cognitive ability measures), and the first-order factors directly govern the actual item responses. Because only a subtest of items is used to measure each domain, the lack of sufficient reliability becomes the primary impediment for generating and reporting domain abilities. In recent years, several item response theory (IRT) models have been proposed to account for hierarchical factor structures, and these models are also shown to alleviate the low reliability issue by using in-test collateral information to improve measurement precision. This article advocates using adaptive item selection together with a higher order IRT model to further increase the reliability of hierarchical latent trait estimation. Two item selection algorithms are proposed—the constrained D-optimal method and the sequencing domain method. Both are shown to yield improved measurement precision as compared to the unidimensional item selection (by treating each dimension separately). The improvement is more prominent when the test length is short and when the correlation between dimensions is high (e.g., higher than .64). Moreover, two reliability indices for hierarchical latent traits are discussed and their use for quantifying the reliability of hierarchical traits measured by adaptive testing is demonstrated.

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  • Chun Wang, 2014. "Improving Measurement Precision of Hierarchical Latent Traits Using Adaptive Testing," Journal of Educational and Behavioral Statistics, , vol. 39(6), pages 452-477, December.
    • Handle: RePEc:sae:jedbes:v:39:y:2014:i:6:p:452-477
    DOI: 10.3102/1076998614559419
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    References listed on IDEAS

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    1. Chun Wang & Hua-Hua Chang, 2011. "Item Selection in Multidimensional Computerized Adaptive Testing—Gaining Information from Different Angles," Psychometrika, Springer;The Psychometric Society, vol. 76(3), pages 363-384, July.
    2. Robert Gibbons & Donald Hedeker, 1992. "Full-information item bi-factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 57(3), pages 423-436, September.
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    5. Lihua Yao, 2012. "Multidimensional CAT Item Selection Methods for Domain Scores and Composite Scores: Theory and Applications," Psychometrika, Springer;The Psychometric Society, vol. 77(3), pages 495-523, July.
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    8. Hua-Hua Chang & William Stout, 1993. "The asymptotic posterior normality of the latent trait in an IRT model," Psychometrika, Springer;The Psychometric Society, vol. 58(1), pages 37-52, March.
    9. Chun Wang & Hua-Hua Chang & Keith Boughton, 2011. "Kullback–Leibler Information and Its Applications in Multi-Dimensional Adaptive Testing," Psychometrika, Springer;The Psychometric Society, vol. 76(1), pages 13-39, January.
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    Cited by:

    1. Ping Chen & Chun Wang, 2016. "A New Online Calibration Method for Multidimensional Computerized Adaptive Testing," Psychometrika, Springer;The Psychometric Society, vol. 81(3), pages 674-701, September.
    2. Chun Wang & David J. Weiss & Zhuoran Shang, 2019. "Variable-Length Stopping Rules for Multidimensional Computerized Adaptive Testing," Psychometrika, Springer;The Psychometric Society, vol. 84(3), pages 749-771, September.

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