Lord–Wingersky Algorithm Version 2.5 with Applications

Sijia Huang and Li Cai ()
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Sijia Huang: Indiana University Bloomington
Li Cai: University of California, Los Angeles (UCLA)

Psychometrika, 2021, vol. 86, issue 4, No 9, 973-993

Abstract: Abstract Item response theory scoring based on summed scores is employed frequently in the practice of educational and psychological measurement. Lord and Wingersky (Appl Psychol Meas 8(4):453–461, 1984) proposed a recursive algorithm to compute the summed score likelihood. Cai (Psychometrika 80(2):535–559, 2015) extended the original Lord–Wingersky algorithm to the case of two-tier multidimensional item factor models and called it Lord–Wingersky algorithm Version 2.0. The 2.0 algorithm utilizes dimension reduction to efficiently compute summed score likelihoods associated with the general dimensions in the model. The output of the algorithm is useful for various purposes, for example, scoring, scale alignment, and model fit checking. In the research reported here, a further extension to the Lord–Wingersky algorithm 2.0 is proposed. The new algorithm, which we call Lord–Wingersky algorithm Version 2.5, yields the summed score likelihoods for all latent variables in the model conditional on observed score combinations. The proposed algorithm is illustrated with empirical data for three potential application areas: (a) describing achievement growth using score combinations across adjacent grades, (b) identification of noteworthy subscores for reporting, and (c) detection of aberrant responses.

Keywords: hierarchical item factor model; summed score; subscore; bifactor model (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s11336-021-09785-y

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