Evaluating Reddit as a Crowdsourcing Platform for Psychology Research Projects
Bibliographic record
Abstract
Background: Online crowdsourcing platforms, such as Amazon Mechanical Turk (MTurk), have become popular alternatives to the ubiquitous student samples used in psychology research. r/SampleSize, an alternative pool on the website Reddit, allows for online participant recruitment without compulsory or immediate payment, making it potentially useful for students, research trainees, and course instructors. Objective: The current study sought to assess the viability of using r/SampleSize as a participant pool by comparing its data characteristics to MTurk and existing lab samples. Method: Two hundred and fifty-six MTurk workers and 277 r/SampleSize participants completed identical questionnaires on demographics, participation motivations, and standard psychology scales. Results: Participants recruited through r/SampleSize reported diverse ages, education levels, income, and employment, although White ethnic background and US residence were predominant. r/SampleSize participants were more internally motivated than MTurk to participate in research and had greater need for cognition but did not differ significantly in altruism or motivation to gain self-knowledge. r/SampleSize data reliability and quality were comparable to MTurk and lab samples across most analyses. Teaching Implications: r/SampleSize can be used to recruit relatively large and diverse samples for undergraduate research projects with minimal setup, labor, and cost. Conclusion: The findings suggest that r/SampleSize is a diverse and viable participant pool.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.050 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".