This document provides template language that researchers can adapt for use in journal manuscripts, ethics/IRB applications, and consent forms. All text is intended as a starting point; researchers should adjust wording to match their specific study design, institutional requirements, and journal guidelines.

The following paragraphs can be adapted for the ‘Methods’ or ‘Participants’ section of a manuscript. Choose the version that best matches your study design and adjust the bracketed fields.

Optional blocks are marked with ▸ and can be added or removed independently.

Participants (N = [NUMBER]) were recruited via Prolific (www.prolific.com), an online participant recruitment platform. Prolific maintains a pre-screened participant pool in which all members have completed identity verification, including a live video selfie and document check, and are subject to ongoing behavioural monitoring, periodic identity re-verification, and performance-based quality tracking. The platform applies 50+ automated checks to every study through its quality assurance system, covering identity verification, fraud detection, and behavioural monitoring. Access to the platform is selective. Participants were [DESCRIBE ELIGIBILITY CRITERIA], and were compensated at a rate of [AMOUNT] per [hour/completion], in accordance with Prolific's minimum pay policy of $8/£6 per hour.

▸ Add if you used demographic quotas: Demographic quotas were applied to [DESCRIBE PURPOSE, e.g., "ensure an equal gender split" / "oversample participants aged 65+" / "match the demographic profile of [TARGET POPULATION]"]. Quotas were set on [VARIABLES, e.g., "age and gender"].

▸ Add if you used a representative sample: A representative sample was used using Prolific’s built-in representative sample feature, stratified to approximate the [COUNTRY] population on key variables. This study used a [TYPE: demographic, political, regional] representative sample.

▸ Add if you used Authenticity Checks: Prolific's Authenticity Checks were enabled during data collection. These include two components: LLM authenticity checks, which detect when participants use AI tools to generate free-text responses by analysing behavioural signals such as copy-pasting and tab-switching (98.7% precision in testing); and bot authenticity checks, which detect when an AI agent or automated script is completing the study, using behavioural analysis across all question types (over 99.9% accuracy in testing). [This study used both components / This study used LLM authenticity checks only / This study used bot authenticity checks only] (see Data Quality section below).

▸ Add for a fuller quality reference: For a detailed description of Prolific's quality assurance infrastructure, see Prolific (2026).

Participants (N = [NUMBER] at baseline) were recruited via Prolific (www.prolific.com) for a [NUMBER]-wave longitudinal study conducted over [DURATION]. Prolific maintains a pre-screened participant pool in which all members have completed identity verification (including a live video selfie and document check) and are subject to ongoing behavioural monitoring and periodic re-verification. The platform applies 50+ automated quality and identity checks before every study, covering identity verification, fraud detection, response quality, and behavioural monitoring. Prolific's platform allows researchers to re-invite participants from prior sessions, supporting longitudinal tracking without requiring researchers to collect personally identifying information.

▸ Add if reporting retention: Retention from [WAVE] to [WAVE] was [PERCENTAGE].

▸ Add eligibility, compensation, quotas, Authenticity Checks, and quality reference blocks as needed (see General template above).

Participants (N = [NUMBER]) were recruited via Prolific (www.prolific.com) using the platform's pre-screening filters. Prolific maintains a pre-screened participant pool in which all members have completed identity verification (including a live video selfie and document check) and are subject to ongoing behavioural monitoring, periodic re-verification, and 50+ automated quality checks per study. Prolific holds verified demographic information for its participant pool, collected during onboarding and subject to periodic re-verification. The following inclusion criteria were applied: [DESCRIBE FILTERS, e.g., "participants who reported a clinical diagnosis of [CONDITION]" or "participants employed full-time in the healthcare sector"]. The exact wording of Prolific's prescreener questions can be viewed when selecting screeners in study setup and via the Audience Checker tool. Researchers are encouraged to use consistent wording when validating eligibility within their survey.

▸ Add if you used custom screening: Custom screening questions were added at the start of the survey to further verify eligibility before participants proceeded to the main study. Participants who did not meet the screening criteria were directed to [DESCRIBE, e.g., "an end-of-survey message" / "a redirect back to Prolific"]. Screened-out participants were [DESCRIBE COMPENSATION APPROACH, e.g., "compensated with a partial payment of [AMOUNT]" / "returned via Prolific so the place could be reassigned to another participant"]. The screening section took approximately [DURATION] to complete. Of [NUMBER] participants who began the screening, [NUMBER] ([PERCENTAGE]) met the eligibility criteria and proceeded to the full study.

▸ Add compensation, Authenticity Checks, and quality reference blocks as needed (see General template above).

This paragraph can be added to any of the templates above when you want to report specifically on data quality measures and exclusions.

Several measures were taken to ensure data quality. At the platform level, all Prolific participants undergo identity verification (including a live video selfie), and are subject to ongoing behavioural monitoring, periodic re-verification, and performance-based quality tracking. Prolific's Protocol system applies 50+ automated checks to every study, covering identity verification, fraud detection, and behavioural monitoring (for full details, see Prolific, 2026).

▸ Add if you used Authenticity Checks: Prolific's Authenticity Checks were enabled during data collection. [LLM authenticity checks / Bot authenticity checks / Both LLM and bot authenticity checks] were used. Of [NUMBER] submissions, [NUMBER] ([PERCENTAGE]) were flagged as low authenticity by the LLM check [and/or] [NUMBER] ([PERCENTAGE]) were flagged as low authenticity by the bot check. In total, [NUMBER] submissions ([PERCENTAGE]) were [excluded from analysis / reviewed manually / describe approach].

▸ Add if you used attention checks: Attention checks were included at [POINTS IN SURVEY]. These consisted of [DESCRIBE TYPE, e.g., "instructed-response items requiring participants to select a specific answer"]. Participants who failed [NUMBER] or more attention checks were [excluded / describe approach]. [IF APPLICABLE: Full attention check items are reported in the Materials section / provided in the Supplementary Materials.]

▸ Add if you used comprehension checks: Comprehension checks were included [DESCRIBE WHERE, e.g., "immediately following the instructions" / "before participants began the main task"] to verify that participants understood the study procedures. These consisted of [DESCRIBE TYPE, e.g., "multiple-choice questions about the study instructions" / "a brief scenario requiring participants to apply the task rules"]. Participants who failed [NUMBER] or more comprehension checks were [DESCRIBE APPROACH, e.g., "excluded from analysis" / "redirected to re-read the instructions before proceeding"]. [IF APPLICABLE: Full comprehension check items are reported in the Materials section / provided in the Supplementary Materials.]

▸ Add if reporting exclusions: [NUMBER] responses ([PERCENTAGE]) were excluded based on [completion time / failed attention checks / low authenticity flags / other criteria], resulting in a final sample of N = [NUMBER].

▸ Add for a fuller quality reference: For full details on Prolific's quality infrastructure, see Prolific (2026).

The following paragraphs address common questions that ethics boards and IRBs raise about online participant recruitment. Adjust to match your institution's application format.

Participants will be recruited through Prolific (www.prolific.com), an online research participation platform. Prolific maintains a pool of pre-screened participants who have completed identity verification (including a live video selfie and document check), phone and email verification, and IP validation. Access to the platform is restricted. Participants are subject to ongoing monitoring, including periodic identity re-verification and behavioural checks.

The researcher is the data controller in relation to study response data collected through [SURVEY PLATFORM, e.g., Qualtrics, Gorilla]. Prolific does not have access to participants' survey responses. Participants are identified to the researcher via pseudonymised Prolific IDs. Prolific retains the link between these IDs and participant identities; the researcher does not have access to this link and cannot directly identify participants. No personally identifying information (e.g., names, email addresses) is shared with the researcher.

▸ If you used Prolific's Survey Builder: Prolific acts as both data processor for participant demographic and account data, and as the hosting platform for study response data collected via the Prolific Survey Builder. As such, Prolific has access to participants' survey responses in this context. The researcher remains the data controller. Participants are identified to the researcher via pseudonymised Prolific IDs; no personally identifying information is shared with the researcher.

Participants will be compensated at a rate of [AMOUNT] per [hour/completion], [meeting / exceeding] Prolific's minimum pay requirement of $8/£6 per hour. Participants may withdraw at any time by returning their submission on Prolific, which releases the place for another participant. Any response data associated with returned submissions [was / will be] deleted and not included in analysis, in line with standard practice for withdrawn consent. [IF APPLICABLE: Partial compensation of [AMOUNT] will be provided for participants who are screened-out due to eligibility criteria / comprehension check fails.]

▸ Add if you used bonus payments: Bonus payments of [AMOUNT / up to AMOUNT] were awarded to participants [DESCRIBE BASIS, e.g., "who completed all sessions of the study"]. Bonuses were distributed through Prolific's built-in bonus payment system.

If your ethics board requires that participants are informed about in-study monitoring, the following language can be adapted for your consent form or information sheet. This wording is based on precedent from researchers who have included Authenticity Checks in ethics-approved studies.

This study uses Prolific's Authenticity Checks, which monitor behavioural patterns during the survey to help detect automated or AI-generated responses.

▸ If using both checks: These checks include monitoring for signs of AI-assisted responding on free-text questions (e.g., tab-switching and copy-paste behaviour) and detection of automated or non-human environments across all question types.

▸ If using LLM checks only: These checks analyse behavioural indicators on free-text questions, such as tab-switching and copy-paste behaviour, to identify responses that may have been generated using AI tools.

▸ If using bot checks only: These checks analyse behavioural patterns across all question types to detect whether the study is being completed by an automated system rather than a human participant.

Importantly, your actual survey responses are not accessed or shared with Prolific through this process, only behavioural signals are analysed. These checks are used to support data quality and do not automatically affect your compensation. Flagged submissions are reviewed by the researcher, who decides whether to approve, return, or reject the submission in line with Prolific’s standard review process.

Note. This is template language only. Requirements vary across ethics boards and institutions. Researchers should consult their own ethics board or IRB for guidance on the specific level of disclosure required. Prolific's Support Team can provide additional technical detail if requested by an ethics board.

When applying for grants or justifying a Prolific budget line to a funder, the argument is less about data quality and more about why paid recruitment is worth the cost. The following paragraph can be adapted for funding applications.

Participant recruitment will be conducted through Prolific (www.prolific.com), a dedicated online research recruitment platform. Prolific provides access to a large, pre-screened, identity-verified participant pool with demographic targeting capabilities, enabling rapid recruitment of samples that meet specific eligibility criteria. Using a paid recruitment platform offers several advantages over unpaid alternatives (e.g., university participant pools, social media recruitment): faster data collection timelines, access to participants beyond the university student population, the ability to recruit specialised or hard-to-reach demographics, and built-in quality assurance infrastructure including identity verification, fraud detection, and behavioural monitoring. See Prolific (2026), How Prolific Protects Data Quality. Participants are compensated at a rate of [AMOUNT] per [hour/completion], meeting or exceeding the platform's minimum pay policy of $8/£6 per hour. The estimated total participant cost for this study is [AMOUNT], based on [NUMBER] participants at approximately [DURATION] per session. This includes participant compensation, Prolific’s platform service fee, and applicable taxes. A breakdown is provided below.

▸ Add cost breakdown:

When referencing Prolific as a recruitment tool, use:

Prolific. (2026). Prolific [Online participant recruitment platform]. Retrieved from https://www.prolific.com

If referencing the data quality or integrity information described in this pack:

Prolific. (2026). How Prolific protects data quality: Quality assurance and integrity documentation. Retrieved [DATE] from https://researcher-help.prolific.com/en/articles/621821-methodological-justification-pack-how-prolific-protects-data-quality.

For supporting evidence on data quality, see the comparative evidence table in "How Prolific Protects Data Quality".

This document is maintained by Prolific and updated as platform features, policies, or recommended practices change. Last updated: 26 March 2026.

For questions about specific wording for ethics applications or journal submissions, contact Support using the icon at the bottom right of this page.