{"title":"Aberrant Responding in Hypothesis Testing: A Threat to Validity or Source of Insight?","authors":"Georgios Sideridis, Mohammed H Alghamdi","doi":"10.3390/bs15030319","DOIUrl":null,"url":null,"abstract":"<p><p>Aberrant responding poses a significant challenge in measurement and validity, often distorting well-established relationships between psychological and educational constructs. This study examines how aberrant response patterns influence the relationship between student-teacher relations and students' perceptions of school safety. Using data from 6617 students from the Saudi Arabia Kingdom from the 2022 Programme for International Student Assessment (PISA), we employed the cusp catastrophe model to evaluate the nonlinear dynamics introduced by aberrant responses, as measured by the U3 person-fit index and the number of Guttman errors. Theoretical and empirical support for the cusp model suggests that aberrance functions as a bifurcation parameter, shifting the relationship between student-teacher relations and perceived school safety from predictable linearity to chaotic instability when exceeding a critical threshold in aberrant responding. Results indicate that both the U3 index and the number of Guttman errors significantly contribute to response distortions, confirming the cusp model's superiority over traditional linear and logistic alternatives. These findings suggest that ignoring aberrant responding risks misinterpreting data structures, while properly accounting for it through catastrophe models provides a more nuanced understanding of nonlinear system behavior in educational assessment. The study highlights the importance of person-fit statistics in psychometric evaluations and reinforces the predictive utility of nonlinear models in handling response distortions in large-scale assessments.</p>","PeriodicalId":8742,"journal":{"name":"Behavioral Sciences","volume":"15 3","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11939625/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Behavioral Sciences","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.3390/bs15030319","RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PSYCHOLOGY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Aberrant responding poses a significant challenge in measurement and validity, often distorting well-established relationships between psychological and educational constructs. This study examines how aberrant response patterns influence the relationship between student-teacher relations and students' perceptions of school safety. Using data from 6617 students from the Saudi Arabia Kingdom from the 2022 Programme for International Student Assessment (PISA), we employed the cusp catastrophe model to evaluate the nonlinear dynamics introduced by aberrant responses, as measured by the U3 person-fit index and the number of Guttman errors. Theoretical and empirical support for the cusp model suggests that aberrance functions as a bifurcation parameter, shifting the relationship between student-teacher relations and perceived school safety from predictable linearity to chaotic instability when exceeding a critical threshold in aberrant responding. Results indicate that both the U3 index and the number of Guttman errors significantly contribute to response distortions, confirming the cusp model's superiority over traditional linear and logistic alternatives. These findings suggest that ignoring aberrant responding risks misinterpreting data structures, while properly accounting for it through catastrophe models provides a more nuanced understanding of nonlinear system behavior in educational assessment. The study highlights the importance of person-fit statistics in psychometric evaluations and reinforces the predictive utility of nonlinear models in handling response distortions in large-scale assessments.