How to Use the Prolific Study Setup Spec Prompt Before You Launch an Online Experiment

Prolific makes it easy to recruit research participants, but a rushed study setup can cost you data, budget, and goodwill. Missing URL parameters mean you cannot link survey responses to submissions. Prescreeners that do not match the protocol bring in ineligible people. Unclear attention check rules lead to rejections that participants dispute. The Prolific Study Setup Spec for an Online Experiment: Prescreeners, Completion Codes, Fair Pay, and Attention Check Rules prompt writes a full setup spec you can check before you press publish.

Why a written spec helps

Most labs set up Prolific studies by clicking through the form and filling things in from memory. That works until a supervisor, an ethics board, or a participant asks why something was done a certain way. A written spec records every decision: who is eligible, how much people are paid and why, which end of survey leads to which code, and what happens to someone who fails a check. It also makes the next study in the same project much faster to set up.

What the prompt produces

1. Listing text with a public title, an internal name, and a description that states duration, device needs, and the task without revealing the manipulation.
2. A study URL with the PROLIFIC_PID, STUDY_ID, and SESSION_ID parameters, plus instructions for capturing them in your survey platform.
3. Prescreeners translated from your eligibility criteria, with any criterion that needs in-study screening flagged.
4. Completion paths for finishing, declining consent, and screening out, each with its own code and action.
5. Reward math from your pilot timing and pay policy, with the hourly equivalent and a budget line.
6. Attention check rules written as instructed response items, with failure thresholds kept inside Prolific's current policy.
7. A launch checklist that starts with a small pilot.

How to fill the inputs

StudyDesign names the platform and the design, such as a Qualtrics survey with two randomized conditions. EligibilityCriteria should come straight from your protocol. Timing needs your sample size and the median completion time from a pilot, not a guess. PayPolicy is your lab or grant rule for hourly pay and screen-out payments.

AttentionChecks lists the checks you plan and where they sit. EthicsNotes gives the protocol number and contact route that should appear to participants. The prompt never asks for names or emails; the Prolific ID is the only identifier it uses.

Walking through the example

The sample is a framing study with 300 participants in the UK and US, a 12 minute pilot median, and a lab rate of 9.00 GBP per hour. The output shows good practice:

• Embedded Data comes first in the Qualtrics Survey Flow, before the randomizer, so IDs are captured for everyone.
• Every eligibility rule maps to a standard filter, including excluding people from the earlier framing study.
• Three distinct codes separate finished, no consent, and failed comprehension, so you can tell them apart in the submissions list.
• The reward is shown with its arithmetic: 9.00 times 12 divided by 60 gives 1.80 GBP per participant.
• Fees and pay minimums are placeholders to confirm on the Prolific site, not numbers stated from memory.
• Rejection only follows failing both instructed checks, and only if that matches Prolific's current policy. Comprehension failure leads to a screen-out, not a rejection.

Running the pilot

Publish with a handful of places first. Download the data and confirm the three ID fields are filled for every row and that both conditions appear. Click through each ending to make sure it lands on the right code. Only then increase places to the full sample. This small step catches most of the problems that are expensive to fix after hundreds of submissions.

Mistakes to avoid

• Paying from an estimated time instead of a pilot median. Underestimates lead to low effective pay and complaints.
• One completion code for every ending. You lose the ability to treat screen-outs fairly.
• Memory based attention checks used for rejection. Use clear instructed items instead.
• Revealing the manipulation in the description. Describe the task, not the hypothesis.
• Changing the description mid-study. Keep the listing stable once data collection starts.

Who it is for

This prompt suits graduate students, postdocs, lab managers, and faculty running online studies on Prolific. It supports good methods but does not replace your ethics board's decisions or Prolific's own documentation.

Related PromptDig links

• Prompt: Prolific Study Setup Spec for an Online Experiment: Prescreeners, Completion Codes, Fair Pay, and Attention Check Rules
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