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Convenience Sampling Method00:55

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population.
Convenience sampling is a non-random method of sample selection; this method selects individuals that are easily accessible and may result in biased data. For example, a marketing...
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Related Experiment Video

Updated: Feb 27, 2026

A Method for Manipulating Blood Glucose and Measuring Resulting Changes in Cognitive Accessibility of Target Stimuli
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A Two-Step Method Based on lz* for Identifying Effortful Respondents.

Yilan Chen1, Yue Liu2, Hongyun Liu1

  • 1Faculty of Psychology, Beijing Normal University, Beijing 100875, China.

Journal of Intelligence
|February 26, 2026
PubMed
Summary
This summary is machine-generated.

This study improves person-fit analysis in educational testing by using data mining to get better item parameter estimates. This helps accurately identify respondents who are not putting in effort, even when their behavior is severe.

Keywords:
effortful respondentsperson-fit statistic l z *two-step methodunsupervised learning algorithms

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Area of Science:

  • Educational Measurement
  • Psychometrics
  • Data Mining

Background:

  • The likelihood-based person-fit statistic (lz*) is vital for detecting non-effortful respondents in educational assessments.
  • lz* accuracy is compromised by biased item parameter estimates when non-effortful respondents are present.

Purpose of the Study:

  • To develop a more accurate method for estimating item parameters for person-fit analysis.
  • To enhance the precision of the lz* statistic in identifying non-effortful respondents.

Main Methods:

  • A two-step approach was proposed, combining data mining with person-fit statistics.
  • K-means clustering was utilized to identify distinct respondent groups (effortful vs. non-effortful).
  • Item parameters were re-estimated using data solely from the identified effortful group.

Main Results:

  • Item parameter estimates derived from the effortful group were found to be more accurate.
  • The enhanced lz* statistic demonstrated improved precision in identifying non-effortful respondents, particularly under high non-effort severity.
  • The proposed method effectively mitigates bias introduced by non-effortful respondents.

Conclusions:

  • The data mining-enhanced, two-step method provides a robust approach to person-fit analysis.
  • This technique improves the reliability of identifying respondents who are not exerting effort in educational assessments.
  • Accurate item parameter estimation is crucial for the validity of person-fit statistics.