The Potential of Machine Learning Methods in Psychological Assessment 					and Test Construction

Bißantz, Steven; Frick, Susanne; Melinscak, Filip; Iliescu, Dragos; Wetzel, Eunike

doi:10.1027/1015-5759/a000817

European Journal of Psychological Assessment

2024

DOI: 10.1027/1015-5759/a000817

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The Potential of Machine Learning Methods in Psychological Assessment and Test Construction

Steven Bißantz,

Susanne Frick,

Filip Melinscak

et al.

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2024

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Item reduction of the “Support Intensity Scale” for people with intellectual disabilities, using machine learning

González‐Carrasco,

Espinosa Parra,

Álvarez‐Aguado

et al. 2024

Brit J Learn Disabil

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BackgroundThe study focuses on the need to optimise assessment scales for support needs in individuals with intellectual and developmental disabilities. Current scales are often lengthy and redundant, leading to exhaustion and response burden. The goal is to use machine learning techniques, specifically item‐reduction methods and selection algorithms, to develop shorter and more efficient scales.MethodsA data set of 93 participants was analysed using the Supports Needs Scale. Five feature‐selection algorithms were evaluated to create a shortened questionnaire. For each algorithm, a Random Forest model was trained, and performance was assessed using metrics like accuracy, precision, recall and F1‐score to measure how well each model predicted support needs.FindingsThe "Select from Model" algorithm successfully identified key items that could predict the level of Support Needs using the Random Forest model. Only 51 variables, out of the original 147, were needed to maintain predictive accuracy. The reduced questionnaire maintained good reliability and internal consistency compared to the original instrument, with a strong F1 score indicating excellent predictive performance.ConclusionsThe study demonstrates that machine learning techniques are effective in reducing the length of support needs questionnaires while preserving their psychometric properties. These methods can help institutions provide more efficient access to information about support needs without compromising validity or reliability, potentially leading to better resource allocation and improved care for individuals with intellectual disabilities.

show abstract

Item reduction of the “Support Intensity Scale” for people with intellectual disabilities, using machine learning

González‐Carrasco,

Espinosa Parra,

Álvarez‐Aguado

et al. 2024

Brit J Learn Disabil

View full text Add to dashboard Cite

show abstract

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The Potential of Machine Learning Methods in Psychological Assessment and Test Construction

Cited by 1 publication

References 25 publications

Item reduction of the “Support Intensity Scale” for people with intellectual disabilities, using machine learning

Item reduction of the “Support Intensity Scale” for people with intellectual disabilities, using machine learning

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