Correspondence Analysis in Archaeology
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  • Guide by worked examples
    • Aim of Correspondence Analysis
    • Association between rows and columns
    • Number of dimensions useful for data interpretation
    • Interpreting the CA scatterplot: dimensions interpretation
    • Interpreting the CA scatterplot (continued): correlation between row profiles and dimensions
    • Quality of the representation
    • Assembling the whole picture
    • Extension: clustering rows and/or columns
    • Another worked example
  • References
  • CA in R
    • CAinterprTools (R package)
    • R function for various CA scatterplots
    • R function for improved CA scatterplot
    • R function for perceptual-map-like CA scatterplot
    • R function for plotting Pareto chart of categories contribution
    • R Script for CA
    • Additional R Script for CA
    • R Script for the Significance of CA's Dimensions
  • Other Tools for Statistics
    • R package for seriation via CA
    • R function for scalar-stress probability calculation
    • R function for post. prob. for different relations btw 2 Bayesian 14C phases
    • R function for Posterior Probability Density plot
    • R function for binary Logistic Regression
    • R function for binary Logistic Regression internal validation
    • R function for optimism-adjusted AUC
    • R function for Brainerd-Robinson similarity coefficient
    • R function for univariate outliers detection
    • R function for plotting Jenks natural breaks classification
    • R function for permutation-based Chi square test of independence
    • R function for permutation t-test
    • R function for visually displaying Mann-Whitney test
    • R function for visually displaying Kruskal-Wallis test
    • Kruskal-Wallis Excel Template
    • Chi-squared Excel Template
    • Excel Template for Robust Statistics
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Interpreting the CA scatterplot (continued): correlation between row profiles and dimensions

The next step is to consider the correlation between the row profiles and the dimensions. This means that, after having given “names” to the dimensions (i.e., after having located what column category has determined the dimensions), we can understand how row categories (sites, in our example) relate to the dimensions. This can be done by inspecting the bar plot  provided by the script, where the correlation (ranging from 0.0 to 1.0) between row categories and the dimensions is displayed.
Immagine
The reader is referred to Greenacre (2007, 86) for a full coverage of the way in which CA computes these figures. It suffices here to stress that almost all the sites (i.e. row categories) have a strong correlation with the first dimension, with the exception of site 3, 5, and 12. These have a strong correlation with the second dimension instead. The analyst may refer back to the CA scatterplot to have an idea to which pole of the dimensions these correlations refer.
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