Cronbach's alpha estimates the internal consistency of measurement items by averaging their inter-item covariance or correlation, returning a coefficient from 0 to 1. section 6.3.1
Use the measure as a practical workflow:
- Identify variables hypothesized to measure the same underlying concept or process. section 6.3.1
- Inspect correlations using an appropriate coefficient, such as Pearson's for continuous data or Spearman's for ordinal data. section 6.3.1
- Refine the composite measure by removing unrelated variables or adding related ones. section 6.3.1
Weather variables
A correlation matrix for weather.csv shows strong temperature relationships but weaker or negative relationships among other variables. Applying pingouin.cronbach_alpha returns alpha = 0.545 with a confidence interval of 0.501–0.586, suggesting relatively low internal consistency. section 6.3.1
