ArticleRoyal Society open science2025
Reducing the burden of psychological questionnaire measures through selective item re-weighting.
Article in Royal Society open science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
2 authors.
Funding
Abstract
Questionnaire measures are central to many areas of study within the psychological sciences. However, they often place a heavy burden on participants; questionnaires are frequently lengthy and unengaging, and with participants often required to complete multiple measures within a single study, this results in lower data quality, increased cost and a poor participant experience. Here, we introduce a straightforward method for creating short versions of existing measures that are able to accurately determine participants' sum scores, subscale scores or factor scores. Our method, referred to as Factor Score Item Reduction with Lasso Estimator, uses Lasso-regularized regression to select items and weight them such that true scores can be predicted accurately from a reduced item set. We demonstrate the performance of this method on an example dataset, and provide code and guidance for implementing the approach.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.