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Bayesian Gaussian distributional regression models for more efficient norm estimation
Voncken,Lieke Kneib,Thomas Albers,Casper J. Umlauf,Nikolaus Timmerman,Marieke E.
Voncken,Lieke
Kneib,Thomas
Albers,Casper J.
Umlauf,Nikolaus
Timmerman,Marieke E.
Abstract
A test score on a psychological test is usually expressed as a normed score, representing its position relative to test scores in a reference population. These typically depend on predictor(s) such as age. The test score distribution conditional on predictors is estimated using regression, which may need large normative samples to estimate the relationships between the predictor(s) and the distribution characteristics properly. In this study, we examine to what extent this burden can be alleviated by using prior information in the estimation of new norms with Bayesian Gaussian distributional regression. In a simulation study, we investigate to what extent this norm estimation is more efficient and how robust it is to prior model deviations. We varied the prior type, prior misspecification and sample size. In our simulated conditions, using a fixed effects prior resulted in more efficient norm estimation than a weakly informative prior as long as the prior misspecification was not age dependent. With the proposed method and reasonable prior information, the same norm precision can be achieved with a smaller normative sample, at least in empirical problems similar to our simulated conditions. This may help test developers to achieve cost‐efficient high‐quality norms. The method is illustrated using empirical normative data from the IDS‐2 intelligence test.
Description
Funding Information: Lieke Voncken, Casper J. Albers and Marieke E. Timmerman gratefully acknowledge financial support from the Dutch Research Council (NWO) within research programme Graduate Programme 2013 with project number 022.005.003. Thomas Kneib gratefully acknowledges financial support from the German Research Foundation (DFG) within research project KN 922/9‐1. et al
Date
2021
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Publisher
Research Projects
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Keywords
BAMLSS, KNOTS, SPLINES, continuous test norming, norming efficiency, psychological tests, robustness
Citation
Voncken, L, Kneib, T, Albers, C J, Umlauf, N & Timmerman, M E 2021, 'Bayesian Gaussian distributional regression models for more efficient norm estimation', British Journal of Mathematical and Statistical Psychology, vol. 74, no. 1, pp. 99-117. https://doi.org/10.1111/bmsp.12206
License
info:eu-repo/semantics/openAccess