Human studies

Use EDSL to collect responses from people by turning a Survey into a hosted web questionnaire. Participants answer through a link; you retrieve their responses as data in Python. You can conduct a human study on its own or use the same instrument to compare human and model responses. This example asks about an export feature. Participants who have not used it finish after the first question; those who have used it describe a problem and what would resolve it. The code first defines that questionnaire:

from edsl import QuestionYesNo, QuestionFreeText, Survey

used = QuestionYesNo(question_name="used", question_text="Have you used the export feature?")
problem = QuestionFreeText(
    question_name="problem",
    question_text="What problem, if any, did you encounter when exporting?",
)
followup = QuestionFreeText(
    question_name="followup",
    question_text="What would resolve this problem: {{ problem.answer }}?",
)
survey = (
    Survey([used, problem, followup])
    .add_stop_rule(used, "{{ used.answer }} == 'No'")
    .add_targeted_memory(followup, problem)
)

Publish and collect

With Expected Parrot login configured, publish it and retain the two identifiers used in the collection workflow:

info = survey.humanize(human_survey_name="Export feature feedback")
respondent_url = info["respondent_url"]
human_survey_uuid = info["uuid"]

respondent_url is the link participants open. human_survey_uuid identifies this hosted collection when you retrieve responses later. Calling humanize() creates the survey; it does not recruit participants or generate their answers. Use survey.preview() to inspect the questionnaire before sharing its respondent link. See Humanize for presentation settings and Prolific for recruitment.

Retrieve responses

After responses have been collected:

from edsl import Coop, Results

human_results = Coop().get_human_survey_responses(human_survey_uuid)
if not isinstance(human_results, Results):
    raise TypeError("Inspect returned response data before applying Results methods.")
table = human_results.select("answer.*")
table

Each completed response is a row, with columns such as answer.used, answer.problem, and answer.followup. Participants routed past a question have no answer to it. Use table.to_csv("human-responses.csv") to export the selected answers, or table.to_pandas() for analysis in a notebook. The type check handles the service’s fallback to raw response data when conversion to Results is unavailable. To show different material to participants, attach scenarios with survey.by(scenarios).humanize(...) and choose an assignment method. To invite a known panel, attach respondent profiles and email addresses. The Human validation example demonstrates matching human classifications with model predictions. For adaptive interviews with human participants, use Human interviews.

Introduction

Setup and tutorials

EDSL objects

How-to guides

Research examples

Human research

Execution and costs

Saving and sharing

Research and teaching

Expected Parrot platform

Contributing

Publish and collect

Retrieve responses

​Publish and collect

​Retrieve responses

Publish and collect

Retrieve responses