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

## TL;DR

This paper introduces a two-step method to improve the accuracy of identifying effortful respondents in educational assessments.

## Contribution

The novel approach combines data mining with the lz* statistic to enhance item parameter estimation and respondent identification.

## Key findings

- Using K-means clustering improves the accuracy of item parameter estimates.
- The two-step method enhances lz* performance in identifying effortful respondents under high non-effort severity.

## 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.