Built for experiments.
A survey platform built around experimental research — factorial designs, condition-aware pages and questions, and built-in data-quality checks. Also works well for plain surveys.
Designing for conditions
Conditions are first-class. Use a factorial design when your manipulation has a clean structure, or just list out the conditions you need. Either way, condition assignment is handled with block randomization and per-cell quotas.
Factorial designs without the spreadsheet
List your independent variables and their levels; ThisStudy generates every cell of the factorial automatically. Set per-cell quotas if you need balance — participants are assigned with concurrency-safe block randomization, so no cell over-fills.
Each participant carries a condition signature, which lets you rebuild conditions mid-study without losing the data you've already collected.
Or just a flat list of conditions
Not every design is factorial. When you need a one-off set of conditions — a control plus three primes, a between-subjects vignette set, an unbalanced design — list them out directly. Same assignment, same quotas, same per-condition targeting downstream.
You can also combine the two: factorial mains plus extra conditions that sit alongside.
Show a page — or a question — only to specific conditions
Every page and every question has a "show for" setting. Tap the conditions that should see it; leave the rest off. No scripting, no expression language, no separate logic tab.
The same condition picker is used everywhere conditions are referenced, so the control doesn't change shape as you move around the study.
Reusable scales and consent forms
Validated scales can be dropped into any study from a built-in catalog. Consent forms, end pages, and other reusable content can be saved as personal templates and used across studies. The catalog grows over time.
Drop in a validated scale, ready to use
Pick from the catalog and a scale arrives pre-built: the items, the response options, the question naming, and the codebook labels are all in place.
Need a scale we don't have? Save it once as a personal template — it's available to every study in your account from then on.
Selected scales.
- Ames, D. R., Rose, P., & Anderson, C. P. (2006). The NPI-16 as a short measure of narcissism. Journal of Research in Personality, 40(4), 440–450.
- Arnett, J. (1994). Sensation seeking: A new conceptualization and a new scale. Personality and Individual Differences, 16(2), 289-296.
- Cacioppo, J. T., Petty, R. E., & Kao, C. F. (1984). The efficient assessment of need for cognition. Journal of Personality Assessment, 48(3), 306–307.
- Campbell, J. D., Trapnell, P. D., Heine, S. J., Katz, I. M., Lavallee, L. F., & Lehman, D. R. (1996). Self-concept clarity: Measurement, personality correlates, and cultural boundaries. Journal of Personality and Social Psychology, 70(1), 141-156.
- Cools, E., & Van den Broeck, H. (2007). Development and validation of the Cognitive Style Indicator. Journal of Psychology, 141(4), 359-387.
- Cross, S. E., Bacon, P., & Morris, M. (2000). The relational-interdependent self-construal and relationships. Journal of Personality and Social Psychology, 78(4), 791-808.
- Davis, M. H. (1980). A multidimensional approach to individual differences in empathy. JSAS Catalog of Selected Documents in Psychology, 10, 85.
- Diab, D. L., Gillespie, M. A., & Highhouse, S. (2008). Are maximizers really unhappy? The measurement of maximizing tendency. Judgment and Decision Making, 3(5), 364-370.
- Diener, E., Emmons, R. A., Larsen, R. J., & Griffin, S. (1985). The Satisfaction with Life Scale. Journal of Personality Assessment, 49(1), 71–75.
- Duckworth, A. L., & Quinn, P. D. (2009). Development and validation of the Short Grit Scale (Grit-S). Journal of Personality Assessment, 91(2), 166-174.
…and more, with new scales added on request.
Per-condition analytics and study controls
A dashboard for the lifecycle of the study — recruitment, condition balance, completion, data quality — broken out per condition.
See what's happening, condition by condition
Total enrollment, completion rate, mean duration, and quality flags are summarized for each condition. Drill in to see individual participants, response histories, and per-page timing — or filter the whole view down to one cell of your factorial.
The same page also surfaces the study-wide settings — recruitment, redirects, quotas, condition rebuild, sharing — so there's one place to manage the study while it's running.
Built-in checks for bad data.
Each response is checked against a handful of quality signals. Honeypot fields, no-JS verification, and duplicate-text checks run on submit. Speeder verdicts, click-through patterns, and IP-cluster signals are computed at read time.
Nothing is auto-deleted — flags surface in the dashboard so you decide what to do with them.
Three-minute walkthrough
Building a factorial experiment from a blank study.
Everything else you'd expect.
The features that probably aren't why you'd switch, but matter once you have.
Why it's free
ThisStudy is run by academic researchers. There's no subscription, no per-response fee, and no plan to add one. We're in invite-only beta while we stabilize things. If you want to try it on a real study, ask.
Request access