Research Screener: A Fill-In Template for India

A research screener is a questionnaire, form or call script that decides whether someone qualifies for a study before anyone spends a session on them. Someone who meets the criteria "screens in". Someone who does not "screens out", which means the screener ends for them.

Four terms matter throughout this page:

A short usability-test screener may need five questions. A recruiter-run qualitative screener also carries quotas and a recontact step (a short follow-up call to confirm key answers), which is what the template below covers.

Use one question per criterion, put the cheap screen-out questions first, and ask for contact details last, so people who will never qualify leave before you hold their data. Replace the bracketed text with your study's behaviours. This structure draws on the fieldwork experience of Think Design Research Services, which operates Qualfacto, and the full recruitment process is set out in our guide on how to recruit participants for a study in India.

Two rules keep the template honest. Do not tell the person which answer qualifies them. And never let a single yes/no question decide eligibility: a guesser passes half the time, so use choices that describe different behaviours.

Then add quotas, for example city tier, age band and gender, and close a group once it is full.

Screen the number of seats, divided by one minus the no-show rate, divided by the incidence. This is our model, not an official figure, and it shows how a single criterion can change the workload many times over.

Formula: people to screen = seats ÷ (1 − no-show rate) ÷ incidence.

Inputs: eight seats and a 20% no-show rate, which is our assumption (use your own history). Over-recruit to 8 ÷ 0.8 = 10 qualified people for 8 seats. Incidence is the share of households owning a car. Results are rounded up.

Urban, rural and all-India shares come from the National Statistics Office's Household Consumption Expenditure Survey (HCES) 2023-24 (as reported by Data for India; the survey covered 2,61,953 households per the official factsheet). The Goa and Bihar shares come from the National Family Health Survey (NFHS-5, fieldwork 2019-21, as reported by ThePrint). The two surveys differ in year and method, so read the state rows as a guide to the gap between states, not as a continuation of the national rows. That gap is about 23 times (500 ÷ 22).

Add one more criterion and the number climbs again. Suppose, purely for illustration, that 1 in 10 car-owning urban households bought within the last year. Incidence becomes 14% × 10% = 1.4%, and the count becomes 10 ÷ 0.014, about 715 people.

Compare a criterion most people meet. More than 60% of households own a motorcycle or a scooter (HCES 2023-24, via Data for India, above), so the same maths gives at most 17 people to screen.

Treat the table as the shape of the problem, not a quote. Household ownership is not the same as your target group, and a recruiter's contact list is not a random sample of households. For how this feeds a budget, see our worksheet on user research recruitment cost in India.

Keep the core behaviour and the industry exclusions fixed. Relax city and age quotas first, then widen the recency window as a last resort. This is our recommended order when the count to screen is more than your recruiter can reach in the timeline.

Rank every criterion as must-have or nice-to-have before writing the screener. Relaxing a nice-to-have is a budget decision, and relaxing a must-have changes the study.

Ask about past behaviour, hide the qualifying answer, include a decoy option and check the answer twice. People who answer to qualify rather than to be accurate cost you whole sessions, not just a row in a spreadsheet.

One research firm, KNow Research, reported a 19% uptick in bad actors in its virtual fieldwork, and said mid-fieldwork fraud incidents fell from 19% to 2.7% after it added safeguards (Quirks, 1 November 2023; sample size not stated). That is one vendor's account of its own fieldwork, and it is not an Indian figure.

Our recommended tactics:

Over-screening has a cost too. A screener that interrogates everyone loses real participants, so apply the strictest checks to the core behaviour and keep the rest light.

Put a plain-language consent notice first, collect only what you will use, and do not process anyone under 18 without verifiable parental or guardian consent. The main duties (notice, consent, children's data) start around 13 May 2027, eighteen months after the Digital Personal Data Protection (DPDP) Rules were notified in November 2025. The Ministry of Electronics and Information Technology (MeitY) proposed shortening that period to 12 months in January 2026, and as of late September 2026 no amending notification had been issued (Mirvo Legal). The consent-manager rule starts around 13 November 2026.

The Rules require every organisation that decides why and how personal data is used to issue a separate, clear consent notice that explains the specific purpose. Data minimisation is one of the Act's seven principles, and a child's data needs verifiable consent from a parent or guardian, with exceptions for services such as healthcare and education (Press Information Bureau, Government of India).

For a screener, that means:

• The notice is question 1, written in simple language.
• Do not ask for income, employer or contact details unless a quota or exclusion needs them. The template asks for household industry only to apply exclusions, and for contact details only at the end.
• Screen out under-18s at question 2.

This is a practical reading, not legal advice. Check your own obligations with counsel.

We combine a participant profile with a study-specific screener, and we manage the screening and coordination ourselves. Qualfacto is a qualitative research participant panel in India, operated by Think Design Research Services, and our focus is paid qualitative studies, not random surveys.

Members complete a profile of about five minutes covering demographics, location, education, employment, household assets and language, and can record an optional 30 to 60 second video introduction. Automated checks on the video cover speech, face visibility, framing, lighting and clarity. They do not judge appearance, accent, emotion, personality or honesty, so the study screener still does the qualifying. Our privacy policy sets out what we collect, and members can decline a study, withdraw optional consent or ask us to delete their information. Rewards vary by study, and reward details are shared before participation. To judge any panel's quality, use our panel quality checklist.

To commission a study, share your brief. We review the objective, method, audience, geography, sample size and timelines, assess feasibility and send a tailored proposal. Pricing is set per project, since it depends on method, audience and recruitment complexity. We can also offer a pilot or introductory first project, depending on scope. Our coverage spans metros, Tier 1 and Tier 2 cities (larger and mid-sized cities), and urban, semi-urban and rural locations, in multiple Indian languages assessed per project.

Share your research brief with us and we will tell you the feasibility of your audience before you commit.