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ORIGINAL RESEARCH article

Front. Psychol.
Sec. Quantitative Psychology and Measurement
Volume 15 - 2024 | doi: 10.3389/fpsyg.2024.1482016

Comparison of Different Reliability Estimation Methods for Single-item Assessment: A Simulation Study

Provisionally accepted
Sijun Zhang Sijun Zhang 1*Kimberly Colvin Kimberly Colvin 2*
  • 1 Hunan University, Changsha, China
  • 2 University at Albany, Albany, New York, United States

The final, formatted version of the article will be published soon.

    Single-item assessments have recently become popular in various fields, and researchers have developed methods for estimating the reliability of single-item assessments, some based on factor analysis and correction for attenuation, and others using the double monotonicity model, Guttman’s λ6, or the latent class model. However, no empirical study has investigated which method best estimates the reliability of single-item assessments. This study investigated this question using a simulation study. To represent assessments as they are found in practice, the simulation study varied several aspects: the item discrimination parameter, the test length of the multi-item assessment of the same construct, the sample size, and the correlation between the single-item assessment and the multi-item assessment of the same construct. The results suggest that by using method based on the double monotonicity model and method based on correction for attenuation simultaneously, researchers can obtain the most precise estimate of the range of reliability of a single-item assessment in 94.44% of cases. The test length of a multi-item assessment of the same construct, the item discrimination parameter, the sample size, and the correlation between the single-item assessment and the multi-item assessment of the same construct did not influence the choice of method choice.

    Keywords: single-item assessment, Reliability, simulation study, Correction for attenuation, factor analysis, Double monotonicity model, Guttman's λ6, Latent class model

    Received: 17 Aug 2024; Accepted: 14 Oct 2024.

    Copyright: © 2024 Zhang and Colvin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Sijun Zhang, Hunan University, Changsha, China
    Kimberly Colvin, University at Albany, Albany, 12222, New York, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.