Factor Analysis, PCA and Cluster Analysis Online
Analyse many variables at once: find the factors behind questionnaire items, reduce dimensions, separate known groups, and discover segments in your data.
Exploratory Factor Analysis (EFA) Online
Principal axis factoring with varimax rotation, KMO, Bartlett’s test, scree plot and loadings.
Replaces SPSS FactorPrincipal Component Analysis (PCA) Online
Reduce many variables to a few components, with eigenvalues, loadings and a scree plot.
Replaces SPSS · SASDiscriminant Analysis Online
Linear discriminant functions that separate groups, with Wilks’ lambda and a classification table.
Replaces SPSS DiscriminantCluster Analysis Online
K-means and Ward hierarchical clustering with an elbow plot, silhouette score and dendrogram.
Replaces SPSS · SASExploratory Factor Analysis (EFA) Online
Use EFA to discover how many constructs a set of items measures and which items belong together, typically when developing or adapting a questionnaire.
Principal Component Analysis (PCA) Online
Use PCA to reduce data before further analysis, or to build composite scores. Use EFA instead when you want to model latent constructs.
Discriminant Analysis Online
Use it to predict group membership from continuous predictors, or to describe which variables separate the groups. It complements MANOVA.
Cluster Analysis Online
Use it to find natural groupings, such as customer segments or patient profiles, when no group labels exist.
Other categories
Data cleaning and preparation · Describe and explore · Compare means · Nonparametric tests · Correlation and regression · Bayesian statistics · Categorical data · Reliability and agreement · Structural equation modeling · Econometrics and time series · Panel data and causal inference · Marketing and decision research · Survival, clinical and meta-analysis · Study planning and calculators · PLS-SEM