Summarising the underlying dimensions and themes
in big and complex data
Principle Components Analysis summarises dimensions of variables within data.
Data reduction techniques, such as Principle Components Analysis or Latent Class Factor Analysis, are extremely useful summarising the underlying dimensions among MANY variables.
They enable us to derive a FEW composite variables which explain the correlations and patterns of responses in the data. These techniques are very useful as a means of summarising the key themes and as an initial analysis prior to using other procedures.
Rather than comparing variables two at a time for MANY variables, it enables you to create a FEW composite variables, which explain most of the correlations within the underlying data. These are used to summarise and simplify data or to understand where redundancy exists among the items.
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