Upload a CSV, pick 3 or more numeric columns, and get a full exploratory factor analysis — latent factors found and named, variance explained, loadings, and what the factors miss. Free.
Free analyses run on up to 10,000 rows. Larger files are randomly sampled to that size — sign up to analyze your full dataset.
Extracting latent factors...
Sent to — named latent factors, variance explained per factor, the full loading matrix, uniqueness ranking, R code, and AI insights.
Analyze another fileThe analysis standardizes your selected columns and fits maximum-likelihood factor models (base R factanal) with varimax rotation, choosing the number of factors via the likelihood-ratio fit test — the smallest count the test cannot reject — with an eigenvalue-based fallback. It reports each factor's loadings, the variance it explains, an auto-generated name from its top-loading variables, and each variable's uniqueness (the share the shared factors leave unexplained).
Use it when many numeric columns are correlated and you suspect a few underlying causes — survey constructs, sentiment, operational load — and you want to know how many there are and what defines them.
Not for pure dimensionality compression before modelling (use PCA), not for categorical items, and not useful when your columns are essentially uncorrelated.
Built for: Researchers, survey analysts, and operators looking for the latent structure behind many correlated measures
Typical data source: A CSV with one row per respondent or period and several numeric item or metric columns
Any table with several numeric columns measuring the same units. For example, weekly business metrics:
Minimum 50 rows · Best with 100-10,000 rows and 4-15 numeric columns
Standard-library analysis: maximum-likelihood exploratory factor analysis with varimax rotation on the numeric columns you choose. Finds the small number of latent factors that explain why many measured variables (survey items, KPIs) move together, names each factor from its top-loading variables, and shows how much of each variable the shared factors do and do not explain. Works on any dataset: map 3 or more numeric columns.
How much of your data's total variation each latent factor carries — the cumulative total is how much the whole factor model explains.
The full loading matrix: which variables define which factor, plus each variable's uniqueness (what the factors leave unexplained).
Each factor auto-named from its strongest variables so you can interpret it in your own domain language.
Variables ranked by how much of them the shared factors miss — high bars are independent signals, not part of the common structure.
Plain-English interpretation — what the numbers mean, what's significant, and what to do next.
What is my survey really measuring?
Map your item columns. The analysis finds the small number of latent constructs behind the answers, names each one from its top-loading items, and shows which items the constructs fail to explain.
See our FAQ for details on pricing, data privacy, and how the analysis works. Every report includes a Methodology section showing the statistical test, assumptions checked, and diagnostics run.
Run any analysis on your own data — validated R analyses, interactive reports, AI insights, and PDF export.
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