Bring public views, community priorities and written explanations into a research process your team can repeat. Use consistent questionnaires, review the collected records and compare the groups your study actually reached. Give researchers, public service teams and NGOs a clearer basis for planning the next consultation or programme decision.
A headline percentage needs a clear connection to the questionnaire, respondents and assumptions behind it.
Keep collected answers, approved group comparisons and methodology available when presenting a finding.
Community priorities and programme feedback arrive through separate notes and inconsistent questions.
Collect comparable views and supporting explanations in a shared project before planning the next activity.
Consultation comments are difficult to organise and revisit when colleagues discuss service priorities.
Use agreed codes, source responses and filtered reports to explain the views your consultation recorded.
Use these existing workflows to connect your team’s task with a result it can review and use.
Ask consistent questions about local needs, capture the relevant respondent attributes with consent and collect written reasons behind the priorities.
Give programme teams inspectable community evidence when deciding what to investigate or plan next.
Publish a focused consultation form, share its link or QR code with your chosen audience and organise comments using a shared codebook.
Explain the recorded service concerns with the original responses available to the reviewing team.
Define relevant respondent groups, review sample sizes and use the supported statistics tools with documented assumptions and case weights where appropriate.
Discuss differences within the collected sample with the methodology available for review.
Reuse the agreed questionnaire, define compatible periods and compare collected answers and reviewed comments after your team takes action.
Evaluate how recorded feedback changes without losing the scope of each consultation.
Your workflow brings analysis and quality review together in the same workspace. Match the published plan allowances and optional AI credits to the work your team needs.
Ask questions across approved fields, compare relevant groups and organise written responses. Review field meanings, filters, statistical assumptions and supporting answers, then save analyses for the next team discussion.
Compare approved questionnaire fields and inspect sampled text themes alongside source responses. Where configured, AI-assisted dataset questions can help prepare an analysis for researcher review.
Provider-assisted dataset questions require workspace enablement, a configured provider, permission and credits. Review the analysis and sampled text themes before using them in a decision.
Investigate configured quality findings, assign follow-up and record review decisions. Apply approved corrections in tracked batches, retain the original submissions and reconsider derived corrections through rollback.
Investigate questionable or inconsistent consultation responses, record review decisions and retain the original answers behind approved corrections.
Quality flags guide investigation and depend on the available collection metadata. Corrected values are retained separately; submission exports currently contain the original answers.
Repeated consultations need capacity for projects, researchers, reviewers and incoming responses. Compare Team for this shared workload and retain reusable questionnaires, codebooks and reports in the same workspace. Your organisation provides the respondents and research design; the subscription provides the collection, review and analysis tools.
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Use these measures to evaluate your own improvement. They are not promised customer results.
Agree the research question, audience, consent, respondent selection and reporting scope. Pilot the questions before publication.
Distribute the form, monitor incoming records, investigate quality findings and organise written responses with shared codes.
Compare approved fields, review sample sizes and assumptions, and prepare reports or authorised exports for the next community or programme decision.
Results describe the respondents your study reached. Collection alone does not establish population representativeness, a valid probability sample or causal impact. Jorip does not recruit a respondent panel or verify every respondent identity. Your researchers remain responsible for sampling, privacy and interpretation.
Choose the service, compare the current plan and test the workflow with your team.
Discuss your team’s requirementsBuild a form for your next survey, application or feedback request. Collect responses in one workspace and give your team a clear place to review them.