A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Misclassification Produced by Rapid-Guessing Identification Methods and Their Suitability Under Various Conditions
Tekijät: Holopainen, Santeri; Metsämuuronen, Jari; Laakso, Mikko-Jussi; Kujala, Janne
Julkaisuvuosi: 2026
Lehti: Educational and Psychological Measurement
Artikkelin numero: 00131644261419426
ISSN: 0013-1644
eISSN: 1552-3888
DOI: https://doi.org/10.1177/00131644261419426
Julkaisun avoimuus kirjaamishetkellä: Avoimesti saatavilla
Julkaisukanavan avoimuus : Osittain avoin julkaisukanava
Verkko-osoite: https://doi.org/10.1177/00131644261419426
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/515789236
Rinnakkaistallenteen lisenssi: CC BY
Rinnakkaistallennetun julkaisun versio: Kustantajan versio
Response Time Threshold Methods (RTTMs) are widely used to identify rapid-guessing behavior (RG) in low-stakes assessments, yet face two key challenges: (a) inevitable misclassifications due to overlapping response time distributions of engaged and disengaged responses, and (b) lack of agreement on which method to use under varying conditions. This simulation study evaluated five RTTMs. Item responses and response times were generated from either a one-component model without RG or a two-component mixture model with RG in the population. Distribution, item, and person parameters were varied. Results showed that when the population contained RG, the mixture lognormal distribution-based method (MLN) was the most robust approach and estimated precise thresholds closest to the time points at which the misclassification rates were minimized, even when bimodality was more difficult to detect. The cumulative proportion method (CUMP) was less robust but also accurate when successful, though less precise. In addition, when the population did not include RG, CUMP was the only method to set thresholds for a notable proportion of cases. The methods were generally more conservative than liberal, though the mixture response time quantile method (MRTQ) was neither. The results are discussed in the light of prior RG research and the methods' characteristics, and future directions are suggested. Ultimately, for practical settings, we recommend a six-step process for RG identification that utilizes both a mixture modeling approach (MLN or MRTQ) and the CUMP method.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
Julkaisussa olevat rahoitustiedot:
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The present study is part of the EDUCA Flagship funded by the Research Council of Finland (#358924, #358947) and the EDUCA-Doc Doctoral Education pilot funded by the Ministry of Education and Culture (Doctoral school pilot #VN/3137/2024-OKM-4).
