Work

A Two-Step Method Based on lz* for Identifying Effortful Respondents

Year: 2026

Type: article

Abstract: The likelihood-based person-fit statistic, lz*, is commonly used in educational assessments to distinguish between respondents who are putting in effort and those who are not. However, lz* depends on the estimated item parameters. Item parameter estimates based on data containing non-effortful respondents are biased, thereby undermining the strength of lz*. To address this issue, we propose a two-step method that leverages data mining techniques to obtain more accurate item parameter estimates and then uses them to compute lz*. The results show that the estimates based on the effortful group identified by K-means are more accurate, which improves the performance of lz* in terms of the precision of identifying effortful respondents when non-effort severity is high.

Source: Journal of Intelligence

DOI: 10.3390/jintelligence14020030

Authors: Yilan Chen, Yue Liu, Hongyun Liu

Institutions: Beijing Normal University, Sichuan Normal University

Language: English

Topic: Psychometric Methodologies and Testing

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDG: Quality education

Open Access status: gold

Estimated APC paid: $3,137