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The Cyborg Method: A Method to Identify Fraudulent Responses from Crowdsourced Data.

Matthew Price¹, Johanna E Hidalgo¹, Julia N Kim¹

¹Department of Psychological Science, University of Vermont, 2 Colchester Avenue, Burlington, Vermont, 05405, USA.

Computers in Human Behavior

|May 27, 2024

Summary

This summary is machine-generated.

The Cyborg Method effectively identifies invalid responses in crowdsourced psychological research data. This quality control approach, combining automated and human review, ensures data validity and reduces collection costs.

Keywords:

Crowdsourced data mechanical turk psychology quality control

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Area of Science:

Psychological Research
Data Science

Background:

Crowdsourcing is vital for psychological research data collection.
Concerns exist regarding the validity and quality of crowdsourced data.
Invalid responses in crowdsourced data necessitate robust quality control.

Purpose of the Study:

To evaluate the effectiveness of the Cyborg Method for quality control in crowdsourced data.
To determine if the Cyborg Method can proactively screen invalid responses.
To assess the impact of the Cyborg Method on data collection costs.

Main Methods:

The Cyborg Method was evaluated using two samples.
It involved automated evaluation of participant metadata and review of short answers.
The method was applied both post-collection and during data collection.

Main Results:

The Cyborg Method identified a substantial portion of invalid responses.
Both automated and human evaluation components were crucial for quality control.
Proactive application of the Cyborg Method screened invalid responses effectively.

Conclusions:

The Cyborg Method is a promising approach for collecting high-quality crowdsourced data.
It enhances data validity while reducing per-participant costs.
Empirical evaluation supports the Cyborg Method's utility in psychological research.