A modified hidden Markov model for detecting insufficient effort responses in questionnaires.

Hangqi Xu, Jiawei Xiong, Feiming Li

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In one paragraph

Article in Behavior research methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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3 authors.

Hangqi XuZhejiang Philosophy and Social Science Laboratory for the Mental Health and Crisis Intervention of Children and Adolescents, Zhejiang Normal University, Jinhua, 321004, Zhejiang, China.

Jiawei XiongCurriculum Associates, North Billerica, MA, 01862, USA.

Feiming LiZhejiang Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua, 321004, Zhejiang, China. feimingli@zjnu.edu.cn.

Funding

This research was supported by a grant from the Humanities and Social Sciences Research Project of Ministry of Education, China 23YJA880022

6 · The paper itself

Abstract

Insufficient effort response (IER) significantly compromises the quality of questionnaire data, affecting the validity of resulting inferences. Traditional methods for detecting IER often fail to adequately capture various types of IER or consider participants' internal state transitions. This study expanded the hidden Markov model for analyzing participants' response strategies by reconstructing response and response time (RT) models that target the identification of IER in the context of questionnaires. The method takes into account the characteristics of IER in terms of response and RT, with the aim of dynamically detecting various types of IER. The simulation study demonstrated that a modified hidden Markov model (M-HMM) effectively recovers parameters, with its detection sensitivity primarily influenced by the prevalence of IER, differences in RT distributions between insufficient and effortful responses, and variations in IER severity and type among participants. Utilizing the M-HMM to analyze empirical data allowed for a deeper understanding of IER occurrences and improved item quality assessment, offering valuable insights for practitioners.

Indexed as

Markov ChainsComputer SimulationHidden Markov ModelsHumansReaction TimeSurveys and QuestionnairesHidden Markov modelInsufficient effort responseItem response theoryMixture modelResponse time

Identifiers

PMID41254452

DOI10.1007/s10862-005-9004-7

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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.