Pre-screen, one language
Up to 40 items in the language they were written in.
You send
You receive
- Turnaround
- 48 hours from receipt of the file, on working days.
- Scope ceiling
- One questionnaire, up to 40 items, one language.
- Price
- 290 EUR
Fixed price, 48 hours
Send a draft questionnaire for expert review before your pilot. Within two working days, receive a signed report with item-level concerns, suggested revisions and a provenance check. The second option adds translation-risk notes. Every item is reviewed by a named psychometrician; the review does not predict pilot statistics or establish cross-language equivalence.
Why before the pilot
A weak item costs as much to field as a good one, and a pilot finds it only after you have paid for it. Most drafts carry a few: a double question, a stem that assumes a job the respondent does not have, an idiom no translator can carry. The pre-screen puts a second pair of eyes on the draft while it is still cheap to change.
How it works
Two options
Both options cover one questionnaire of up to 40 items and end in the same signed report. The second adds a translation-risk pass for each target language you name.
Up to 40 items in the language they were written in.
You send
You receive
The one-language pre-screen plus a translation-risk pass for each language you name.
You send
You receive
Prices exclude VAT; Montenegrin VAT of 21 percent is added where it applies. Longer questionnaires and more languages are quoted on request.
What the AI pass is, and is not
We tested the AI critique before selling it. On the 332 items of a large international field trial, where a real expert review had dropped 140, its cut calls were items the review had dropped in 16 of 23 cases, against a base rate of 42 percent (odds ratio 2.78, 95 percent confidence interval 1.76 to 4.40). On 162 published adult self-report items the same pass did not predict which items were statistically weakest. So the claim is exactly this: the AI pass reads the way an expert panel reads, and it is a first read only. The reviewer's judgement is the product.
Maeda, H. and Lu, Y. (2026). Automated item evaluation: Predicting item acceptance and rejection using LLM-generated critiques. arXiv:2608.06609. 52,759 items, 34 percent rejected from operational use; adding LLM critiques to the item text improved rejection prediction (fusion model AUC .80).
Why an AI first read is worth having.
arXiv:2608.06609Lukauskas, M. and Sarkauskaite, V. (2026). Plausible but not valid: A psychometric audit of LLMs as synthetic survey respondents. arXiv:2608.14606. 37 models, every one with a strong acquiescence shift (+0.84 SD); not a drop-in replacement for human respondents.
Why the pre-screen never simulates a respondent.
arXiv:2608.14606Limitations, in plain words
The same limitations page is printed in every report, so the people who read it after you know what it can and cannot carry.
Sample report
An anonymised pre-screen of a 75-item work-behaviour questionnaire with three target languages: the summary, the revise-and-cut rows with rewrites, the provenance and translation-risk tables, and the limitations page.
The sample is a real run with item ids and scale codes relabelled. A client receives the same document on their own questionnaire.
Order
Fill in the form and paste the items, or email the file. We confirm receipt and the price by return, and the report follows within 48 hours on working days.
Or send the questionnaire directly to
milos@adriamont.me