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What measures do you have in place to ensure that the same participants do not answer all studies?
What measures do you have in place to ensure that the same participants do not answer all studies?
Thanos Kamvysis avatar
Written by Thanos Kamvysis
Updated over a week ago

To ensure account integrity and maintain data quality, Useberry in collaboration with Prolific has implemented several measures and restrictions. These measures include:

Unique Phone Number Verification: Each Prolific account requires a unique non-VOIP (Voice over Internet Protocol) phone number for verification purposes. This helps ensure that each account is associated with a legitimate and distinct individual.

IP and ISP Restrictions: Signups in Prolific are restricted based on IP (Internet Protocol) and ISP (Internet Service Provider) information. Common residential ISPs are allowed, while low-trustworthy IP/ISPs are blocked. This helps prevent fraudulent or suspicious registrations.

Limiting Accounts from the Same IP and Machine: Prolific restricts the number of accounts that can be created using the same IP address and machine. This prevents the creation of duplicate or multiple accounts by the same user.

Limiting Unique IPs per Study: Prolific also limits the number of unique IPs that can participate in a study. This measure helps ensure diverse participant representation and prevents biased or skewed data collection.

Unique PayPal and Circle Accounts: In order to receive payment, participant accounts must have unique PayPal and Circle accounts. This means that if two participant accounts are eligible for payment, they must also have separate PayPal accounts. PayPal and Circle also have their own measures in place to prevent duplicate accounts.

Data Quality Reports and Investigation: Prolific takes data quality reports seriously. We, at Useberry, are constantly reporting participant IDs that raise suspicions, and the Prolific is investigating those individual accounts as well as identify any shared patterns between them.

Monitoring Unusual Usage Patterns: Internal data analysis is conducted to monitor for any unusual usage patterns. This helps identify and address any potential misuse or irregularities within the platform.

By implementing these measures, both Useberry and Prolific aim to maintain the integrity of participant accounts, ensure data quality, and prevent fraudulent or duplicate activities. These safeguards help create a trustworthy and reliable research environment for both researchers and participants.

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