Literature DB >> 31709521

A hierarchical latent response model for inferences about examinee engagement in terms of guessing and item-level non-response.

Esther Ulitzsch1, Matthias von Davier2, Steffi Pohl1.   

Abstract

In low-stakes assessments, test performance has few or no consequences for examinees themselves, so that examinees may not be fully engaged when answering the items. Instead of engaging in solution behaviour, disengaged examinees might randomly guess or generate no response at all. When ignored, examinee disengagement poses a severe threat to the validity of results obtained from low-stakes assessments. Statistical modelling approaches in educational measurement have been proposed that account for non-response or for guessing, but do not consider both types of disengaged behaviour simultaneously. We bring together research on modelling examinee engagement and research on missing values and present a hierarchical latent response model for identifying and modelling the processes associated with examinee disengagement jointly with the processes associated with engaged responses. To that end, we employ a mixture model that identifies disengagement at the item-by-examinee level by assuming different data-generating processes underlying item responses and omissions, respectively, as well as response times associated with engaged and disengaged behaviour. By modelling examinee engagement with a latent response framework, the model allows assessing how examinee engagement relates to ability and speed as well as to identify items that are likely to evoke disengaged test-taking behaviour. An illustration of the model by means of an application to real data is presented.
© 2019 The Authors. British Journal of Mathematical and Statistical Psychology published by John Wiley & Sons Ltd on behalf of British Psychological Society.

Keywords:  engagement; guessing; item response theory; missing responses; response times

Year:  2019        PMID: 31709521     DOI: 10.1111/bmsp.12188

Source DB:  PubMed          Journal:  Br J Math Stat Psychol        ISSN: 0007-1102            Impact factor:   3.380


  4 in total

1.  Automated Bot Detection Using Bayesian Latent Class Models in Online Surveys.

Authors:  Zachary Joseph Roman; Holger Brandt; Jason Michael Miller
Journal:  Front Psychol       Date:  2022-04-27

2.  A Response-Time-Based Latent Response Mixture Model for Identifying and Modeling Careless and Insufficient Effort Responding in Survey Data.

Authors:  Esther Ulitzsch; Steffi Pohl; Lale Khorramdel; Ulf Kroehne; Matthias von Davier
Journal:  Psychometrika       Date:  2021-12-02       Impact factor: 2.290

3.  A Multilevel Mixture IRT Framework for Modeling Response Times as Predictors or Indicators of Response Engagement in IRT Models.

Authors:  Gabriel Nagy; Esther Ulitzsch
Journal:  Educ Psychol Meas       Date:  2021-09-13       Impact factor: 3.088

4.  On the Treatment of Missing Item Responses in Educational Large-Scale Assessment Data: An Illustrative Simulation Study and a Case Study Using PISA 2018 Mathematics Data.

Authors:  Alexander Robitzsch
Journal:  Eur J Investig Health Psychol Educ       Date:  2021-12-14
  4 in total

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