Standard Edition

Principal component analysis and factor analysis PCA with biplots, monoplots, and colour-mapped coefficient matrices — common factor analysis with 12 rotation methods — Cronbach’s alpha for internal consistency.

Make multivariate data interpretable

With many variables, the important structure is hidden by the sheer volume of data. PCA reduces that complexity to a few components that capture most of the variation — but the hard part isn’t extracting the components. It’s understanding what they mean. A table of loadings tells you the numbers. It doesn’t show you that three variables cluster together, that two groups separate clearly on the first component, or that one observation is an outlier pulling the solution.

That takes visualisation most packages don’t offer — biplots that show variables and observations together, colour maps that reveal the loading pattern at a glance, rotation to find the view that makes the structure clear. Plus common factor analysis when the goal is latent constructs rather than dimensionality reduction, and Cronbach’s alpha for scale reliability.

It is powerful enough so I can “play with the data”, which helps me to truly understand the results. Over the years I have tried several statistics programs, some Excel based, some not. But I always came back to Analyse-It, which is powerful enough for my purposes but the easiest to use.
Klaus T.
Chief R&D Officer
Pharmaceuticals

What’s included

See variables and observations together in a biplot

A biplot shows the structure a loading table can’t — which variables move together, which observations are unusual, whether groups separate. Choose between the classic Gabriel biplot (variables as vectors, observations as points) and the Gower & Hand biplot (both as points). Reflect, rotate, and scale until the view makes sense. Colour observations by a factor to see group separation.

Spot the loading pattern without reading every number

The coefficient matrix colour map highlights which variables load strongly on which components — the pattern is visible immediately, even with many variables. The correlation monoplot shows the variable relationships alone when the biplot is too crowded. Eigenvalues, scree plot, and proportion of variance explained for choosing how many components to retain.

Explore the raw relationships before reducing

Before committing to PCA, see the data. Scatter plot matrices show every pairwise relationship, with distribution histograms on the diagonal. Colour observations by a factor to see whether relationships hold across subgroups. Colour-mapped correlation matrix shows the strength and direction of every association at a glance.

Find interpretable latent factors

When the goal is to uncover latent constructs rather than reduce dimensionality, common factor analysis with maximum likelihood extraction gives the underlying structure. Twelve orthogonal and oblique rotation methods — Varimax, Oblimin, and ten others — rotate to the most interpretable solution. Factor pattern and structure matrices with colour maps.

Assess whether a scale measures what it should

Cronbach’s alpha tells you whether the items in a scale are internally consistent. The deleted-alpha for each item shows exactly which items weaken the scale and which strengthen it — essential when developing or refining a questionnaire, checklist, or measurement instrument.
PCA plain

Example analyses

See PCA and factor analysis results in detail — biplots, colour-mapped coefficient matrices, and scree plots.

Multivariate PCA — NYC neighbourhood liveability
50 neighbourhoods × 12 variables. Eigenvalues (PC1 48.5%, PC2 19.9%), correlation monoplot, Gower-Hand biplot coloured by borough.
Correlation Correlation matrix
NYC liveability, 50 observations × 5 variables. Colour-mapped correlation matrix, scatter plot matrix with histograms, Pearson r with Fisher’s Z CIs.

Part of the Standard edition

PCA and factor analysis are one part of a complete statistical analysis toolkit. The Standard edition also includes ANOVA and ANCOVA, simple and multiple regression, logistic regression, descriptive statistics, hypothesis testing, correlation, and categorical data analysis. See everything in the Standard edition →

Validated, reliable, trusted for over 30 years

Validated calculations Every statistic tested against NIST Standard Reference Datasets, published datasets and thousands of internal test-cases. No reliance on Excel'sbuilt-in functions. See how we develop and validate Analyse-it →
Data stays on your PC No cloud processing, no uploads, no third-party access. Your data never leaves your computer — essential when working with sensitive, confidential, or patient-identifiable data.
Standard Excel workbooks Analyses are ordinary Excel workbooks that you can share with colleagues, archive for audit, and open on any machine with Excel — no Analyse-it licence required.
No formulas to break Results contain no formulas, so they can't be accidentally edited or corrupted. The results you reported will be exactly what you find when you reopen the workbook.

Technical details

Correlation / Association

  • Correlation matrix with colour map on coefficients
  • Covariance matrix
  • Scatter plot
  • Scatter plot matrix
  • Vary points by colour based on a factor
  • Pearson r with Fisher’s Z CI
  • Spearman rs with Fisher’s Z CI
  • Kendall τ with Samara-Randles CI
  • Pearson test for linear association
  • Kendall test for monotonic association

PCA new in v3.80

  • Eigenvalues / Eigenvectors
  • Coefficient matrix with colour map
  • Classic Gabriel biplot (variables as vectors, observations as points)
  • Gower-Hand biplot (variables and observations as points)
  • Correlation monoplot
  • Scree plot
  • Reflect, rotate, and scale biplot
  • Predict new observations / variables

Common factor analysis new in v3.90

  • Maximum likelihood factor extraction
  • Factor pattern / structure matrices with colour map
  • 12 factor orthogonal/oblique rotations including Varimax, Oblimin

Item reliability new in v4.80

  • Cronbach’s alpha (standardized and unstandardized)
  • Deleted Cronbach’s alpha for each item