Free, browser-based tools for every stage of the quantitative research workflow — from feasibility and sample design through fieldwork, statistical analysis, and data quality. Built with React and real-world methodology by a 20-year insights industry veteran. No logins, no paywalls, everything runs in your browser.
Estimate how common your target audience is across the U.S. population. This calculator cross-references over 15 demographic dimensions — age, gender, ethnicity, income, education, employment, marital status, household size, and more — against U.S. Census American Community Survey (ACS) data spanning 200+ Metropolitan Statistical Areas. Use it to set realistic expectations for survey feasibility before you write a single screener question.
Assess the feasibility of reaching healthcare professionals and patient populations for clinical and pharma research. Covers specialty physician counts (oncologists, cardiologists, PCPs, etc.), rare disease patient prevalence, diagnosis rates, and treatment adoption curves. Factors in panel penetration rates, professional verification requirements, and the dramatically higher cost-per-interview typical of HCP studies to give you a realistic timeline and budget before you go to field.
Estimate the reachable universe for business-to-business research targets. Define your audience by industry vertical (NAICS), company size, revenue tier, job title seniority, and functional department — then see how many qualified respondents actually exist in major B2B panels. Accounts for the notoriously low incidence rates and high CPIs that make B2B studies fundamentally different from consumer research, and flags when your quotas are likely to require supplemental recruiting or extended field timelines.
Determine the minimum number of survey completions needed to achieve your desired confidence level and margin of error. Goes beyond basic sample size formulas by incorporating quota subgroup requirements and finite population correction (FPC) for smaller universes. Particularly useful when you need to ensure statistical reliability at the subgroup level — not just for the total sample — which is where most off-the-shelf calculators fall short.
Check whether your demographic and behavioral quota targets are achievable before you launch a study. This tool compares your quota cells against Census population distributions to identify which groups will fill naturally and which will require oversampling, extended field time, or supplemental panel sources. Flags impossible or near-impossible cells early — before they become the bottleneck that delays your entire project by two weeks.
Visualize how multiple target audiences intersect using interactive Venn diagrams. Combine up to five audience segments and see the overlap regions with estimated population sizes and feasibility scores. Essential for multi-segment studies where you need to understand whether your target groups are mostly distinct populations or heavily overlapping — which directly impacts your total sample requirement and per-segment cost.
Select any combination of demographic attributes and instantly see an 18-dimension index profile of that population segment. The profiler calculates index scores (where 100 = national average) across age, income, education, geography, household composition, and more — revealing where your target audience over- or under-indexes relative to the general population. A fast way to build audience understanding and identify natural sample skews before fielding.
Build a complete project budget using vendor-neutral cost-per-interview (CPI) benchmarks across consumer, B2B, and healthcare methodologies. Input your sample size, audience complexity, survey length, and incidence rate to get a realistic total project cost estimate. Breaks down line items including sample costs, programming, incentives, and project management — so you can present a defensible budget to clients or internal stakeholders without waiting for vendor bids.
Paste your survey questions and let AI classify each one by type — single select, multi-select, grid/matrix, open-end, ranking, MaxDiff, conjoint, etc. — then estimate the total length of interview (LOI) in minutes. Accurate LOI estimation is critical because it directly drives your CPI, respondent drop-off rate, and data quality. This tool replaces the back-of-napkin "3 questions per minute" rule with question-type-specific timing benchmarks powered by Claude AI.
Model the full invite-to-complete respondent journey through your screener questions. Define each screening criterion with its pass rate, and see a waterfall visualization of how many panelists survive at each stage. This reveals the true effective incidence rate of your study — which is almost always lower than the topline IR — and helps you calculate how many invitations you actually need to send to hit your completion target.
Model the complete sample pipeline from initial panel invitation through survey completion, accounting for every stage where respondents are lost — email non-delivery, non-opens, click-through abandonment, screener termination, mid-survey dropout, and quality disqualification. Unlike the Screener Funnel Simulator which focuses on qualification logic, this tool maps the entire operational pipeline so you can identify which stage is your biggest bottleneck and optimize accordingly.
Calculate the margin of error for any sample size and proportion at standard confidence levels (90%, 95%, 99%). Includes visual confidence interval bands that show the range of uncertainty around your survey results, plus a reference table comparing MOE across different sample sizes. Helps researchers and clients understand what their data can and cannot claim with statistical rigor — especially important when making business decisions based on small subgroups.
Run statistical significance tests across multiple groups simultaneously — the kind of column-proportion z-tests you see annotated with letters (A, B, C, D) in cross-tabulation reports. Input your sample sizes and percentages for each group, and get p-values, effect sizes (Cohen's h), and statistical power for every pairwise comparison. Replaces the manual process of running significance tests one pair at a time in SPSS or Excel.
Apply Random Iterative Method (RIM) weighting — also known as iterative proportional fitting or raking — to adjust your survey sample to match known population targets. The tool calculates individual respondent weights, reports the design effect (DEFF), effective sample size, weight efficiency, and the full weight distribution. Essential for any study where your achieved sample doesn't perfectly match your target demographics, which in practice means nearly every study.
Forecast how long your survey will take to reach its completion target. Uses response rate decay modeling to project daily completions, accounting for the natural slowdown that occurs as your most responsive panelists complete early and you're left fielding to less engaged segments. Includes soft launch milestones, daily completion forecasts, and early warning indicators for studies that are trending behind schedule.
Fit exponential decay curves to your actual field response data to understand how quickly your response rate is declining. Calculates the half-life of your response rate — the number of days until your daily completions drop by 50% — and detects whether you're on track or heading for a shortfall. Input your daily completion counts and the tool fits the curve, shows the projected trajectory, and flags when a remedial action (reminder send, new sample release, incentive bump) is warranted.
Paste your survey questions and get instant AI analysis for common questionnaire design flaws — leading questions, double-barreled items, loaded language, assumption violations, unbalanced scales, and ambiguous wording. For each flagged issue, the tool explains the specific bias type, rates its severity, and generates a rewritten version that eliminates the problem while preserving your research intent. Think of it as a senior methodologist reviewing your draft at machine speed.
Upload a survey data CSV file and get an automated data quality assessment — all processing happens locally in your browser, so your data never leaves your machine. Detects speeders (respondents who completed suspiciously fast), straightliners (identical answers across grid questions), duplicate entries, outliers, and missing data patterns. Generates a composite quality score with a detailed breakdown so you can make informed decisions about which respondents to flag or remove before analysis.