Understand your data with principal component analysis (PCA) and discover underlying patterns

PCA provides valuable insights that reach beyond descriptive statistics and help to discover underlying patterns. Two PCA metrics indicate 1. how many components capture the largest share of variance (explained variance), and 2., which features correlate with the most important components (factor loading). These metrics crosscheck previous steps in the project work flow, such as data collection which then can be adjusted. When a project structure resembles the one below, the prepared dataset is under scrutiny in the 4. step by looking at descriptive statistics. Among the most common ones are means, distributions and correlations taken across all observations or subgroups.