Can you do factor analysis with ordinal data?
If the model includes variables that are dichotomous or ordinal a factor analysis can be performed using a polychoric correlation matrix. Note that variables used with polychoric may be binary (0/1), ordinal, or continuous, but cannot be nominal (unordered categories).
What is Polychoric PCA?
Polychoric Correlations That alternative is to base the PCA on a different type of correlations: polychoric. Polychoric correlations assume the variables are ordered measurements of an underlying continuum. They are interpreted the same way as Pearson correlations.
Which is an example of an ordinal variable in PCA?
Examples of ordinal variables commonly used in PCA include a wide range o f Likert scales (e.g., a 7-point scale from ‘strongly agree’ through to ‘strongly disagree’; a 5-point scale from ‘never’ to ‘always’; a 7-point scale from ‘not at all’ to ‘very much’; a 5-point scale from ‘not important’ to ‘extremely important’).
How to perform a principal component analysis ( PCA )?
Principal Components Analysis (PCA) using SPSS Statistics Introduction. Principal components analysis (PCA, for short) is a variable-reduction technique that shares many similarities to exploratory factor analysis. Its aim is to reduce a larger set of variables into a smaller set of ‘artificial’ variables, called ‘principal components’, which
What kind of correlations are used in PCA?
Most PCA procedures calculate that first step using only one type of correlations: Pearson. And that can be a problem. Pearson correlations assume all variables are normally distributed. That means they have to be truly quantitative, symmetric, and bell shaped.
Why do we need linear relationship in PCA?
Assumption #2: There needs to be a linear relationship between all variables. The reason for this assumption is that a PCA is based on Pearson correlation coefficients, and as such, there needs to be a linear relationship between the variables.