The table every paper needs and nobody enjoys assembling. How Quanta builds it, what belongs in it, and why it is doing more work than describing your sample.
Quanta builds an APA frequencies table from whichever characteristics you tick: age group, gender, education, role level, tenure, or any categorical field in your data. You select from a checkbox grid rather than constructing the table by hand.
A Group by control sets whether the table describes the full sample or breaks the characteristics down by another variable. Full sample gives you the standard demographics table. Grouping gives you the version that shows whether your conditions were comparable at baseline.
The output is formatted for APA and can be copied or added straight to a report, which removes the step where a hand-built table drifts out of agreement with the numbers it came from.
Anything a reader needs to judge who your results describe. That is the test, rather than convention. If a characteristic could plausibly moderate your findings, it belongs.
Anything you used as a covariate, grouping variable, or exclusion criterion. A reader cannot evaluate an adjusted analysis without seeing the distribution of what you adjusted for.
The total, and the missing count for each characteristic. Percentages that do not sum to 100 with no explanation are a common and avoidable reviewer query.
Not every categorical field you collected. A demographics table listing twenty characteristics buries the four that matter.
A demographics table is where the limits of a study become visible, and it is worth reading yours as a reviewer would.
Small cells are the thing to look for. A category holding three respondents cannot support a group comparison, an adjusted mean, or a subgroup claim, however precisely later output prints it. Seeing that in the demographics table is what stops the subgroup analysis being run in the first place.
Distribution matters as much as size. A sample of 250 with 91 respondents in one age band and 3 in another is not a study of the age range it appears to cover. If you want to test that formally rather than eyeball it, a chi-square goodness of fit test against a population benchmark answers exactly this question.
Build this table early, not while writing up. It changes which analyses are worth running.
Open Descriptives & Explore and find the Demographics table card.
Tick the characteristics you want from the grid. Categorical fields are what belong here; numeric items are summarized separately.
Set Group by to full sample for a standard description, or to a grouping variable to show balance across conditions.
Upload categories as text rather than numeric codes, or a category stored as 1, 2, 3 will be read as a quantity and will not appear as a characteristic.
Use the action bar to add the table to a report, copy it, or copy the APA text.
Give counts and percentages, the total, and the missing count for each characteristic.
Participants (N = 250) were aged 18-24 (n = 19, 7.6%), 25-34 (n = 91, 36.4%), 35-44 (n = 71, 28.4%), 45-54 (n = 42, 16.8%), 55-64 (n = 24, 9.6%), and 65 or over (n = 3, 1.2%).
Report both n and percentage. A percentage alone hides a cell of three.
State how the sample was recruited alongside the table. Composition without provenance leaves a reader unable to judge selection.
Name the imbalances rather than leaving them for a reader to notice. Saying plainly that older respondents were underrepresented, and that findings may not extend to them, is stronger than having it pointed out in review.
Every result carries a ReliCheck Intelligence card with one action: explain this result in plain language. It runs entirely on your Mac with Apple Intelligence. No upload, no API key, nothing to configure. The model only explains the result Quanta has already computed and validated. It never computes a statistic, and it never applies a decision on your behalf.
From the action bar you can add the result to a report, copy the table, or copy the APA text. Exported reports carry the APA table, its note, and a caution specific to that analysis beneath it.
Quanta's engine is pure Swift on Apple's Accelerate framework, with no R or Python at runtime. Every engine is checked against an independent reference rather than internal consistency alone, and the automated suite exceeds 500 checks. Full record on the validation page.
Descriptives & Explore is part of every Quanta subscription, $9.99 a month or $79.99 a year after the 30-day trial. One price for everyone. See pricing.
The analysis, the report builder, and the on-device explanations all work with the network off. Your dataset is never uploaded.
Chi-square goodness of fit testing your sample against a population benchmark · Descriptive statistics numeric summaries · Cross-tabulations two categorical variables together · the complete list on the analyses page.