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Related Experiment Video

Updated: Dec 21, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

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Published on: March 1, 2022

On a New Algorithm for Removing Repeating Patterns in Similarity Analysis.

¹ACT, Inc., Iowa City, IA, USA.

Educational and Psychological Measurement

|May 20, 2020

Summary

This summary is machine-generated.

A new algorithm effectively identifies repeating response patterns in test data, crucial for accurate similarity analysis and detecting cheating. This method improves test security by removing noise from careless or rapid guessing.

Keywords:

careless responding data cleaning rapid guessing repeating pattern similarity analysis test security

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Area of Science:

Educational Measurement
Psychometrics
Data Science

Background:

Test security relies on detecting answer copying and collusion via response similarity.
Aberrant response data, like repeating patterns from careless responding, can contaminate similarity analyses.
Removing these patterns is essential for valid test security assessments.

Purpose of the Study:

To introduce a novel algorithm for identifying repeating response patterns in test data.
To enhance the accuracy of similarity analyses in test security.
To provide a method for cleaning data prior to psychometric modeling.

Main Methods:

Development of a new algorithm specifically designed to detect repeating answer patterns.
Application and validation of the algorithm on both simulated and real test response data.
Evaluation of the algorithm's performance in terms of detection accuracy and false positive rates.

Main Results:

The algorithm successfully identified 100% of simulated repeating patterns.
A negligible false positive rate was observed in the analysis.
The algorithm demonstrated high efficacy in detecting problematic response patterns.

Conclusions:

The proposed algorithm is highly effective for identifying repeating response patterns in test data.
This method is vital for improving the reliability of similarity analyses in test security.
The algorithm has potential applications beyond similarity analysis, including identifying unmotivated test-takers and data cleaning for item response theory models.