A Collection of Numerical Recipes Useful for Building Scalable Psychometric Applications

Harold Doran
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Harold Doran: Human Resources Research Organization

Journal of Educational and Behavioral Statistics, 2023, vol. 48, issue 1, 37-69

Abstract: This article is concerned with a subset of numerically stable and scalable algorithms useful to support computationally complex psychometric models in the era of machine learning and massive data. The subset selected here is a core set of numerical methods that should be familiar to computational psychometricians and considers whitening transforms for dealing with correlated data, computational concepts for linear models, multivariable integration, and optimization techniques.

Keywords: computational psychometrics; Gaussian quadrature; linear models; numerical analysis; psychometric data scientist (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:48:y:2023:i:1:p:37-69

DOI: 10.3102/10769986221116905

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