Columnwise neural imputation for incomplete ordinal psychometric data.
TL;DR
This work proposes the columnwise neural imputation (COLNI) algorithm, a artificial neural network-based imputation approach to impute missing ordinal responses in psychometric data, and evaluates its effectiveness in a multidimensional empirical setting.
Abstract
Missing data are pervasive in psychological and educational assessments. Naive remedies, including listwise deletion and item-mean imputation, often degrade research validity and misinform subsequent decisions. Recent advances in artificial neural networks have demonstrated their efficacy in prediction-related tasks by using observed features to infer unknown values. Building on this potential, we propose the columnwise neural imputation (COLNI) algorithm to impute missing ordinal responses in psychometric data. Simulation studies demonstrated that, when benchmarked against conventional methods, COLNI more accurately recovered item means, inter-item correlations, and person and item parameters under the multidimensional graded response model. We further evaluated COLNI using data from the Short Dark Triad test, confirming its effectiveness in a multidimensional empirical setting. We conclude with implementation guidelines and avenues for refining and extending this artificial neural network-based imputation approach in future research. (PsycInfo Database Record (c) 2026 APA, all rights reserved).