Knowing the level of measurement of a variable is crucial when working out how to analyze the variable. Failing to correctly match the statistical method to a variable’s level of measurement leads either to nonsense or to misleading results.
Principal Component Analysis is really, really useful.
You use it to create a single index variable from a set of correlated variables.
In fact, the very first step in Principal Component Analysis is to create a correlation matrix (a.k.a., a table of bivariate correlations). The rest of the analysis is based on this correlation matrix.
You don’t usually see this step — it happens behind the scenes in your software.
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 [Read more…] about Principal Component Analysis for Ordinal Scale Items
There are not a lot of statistical methods designed just for ordinal variables.
But that doesn’t mean that you’re stuck with few options. There are more than you’d think. [Read more…] about Pros and Cons of Treating Ordinal Variables as Nominal or Continuous
These are especially hard to know how to analyze–some people treat them as numerical, others emphatically say not to. Everyone agrees nonparametric tests work, but these are limited to testing only simple hypotheses and designs. So what do you do if you want to test something more elaborate?
In this webinar we’re going to lay out all the options and when each is [Read more…] about Member Training: Analysis of Ordinal Variables–Options Beyond Nonparametrics
But even something so fundamental can be tricky once you start working with real data. [Read more…] about When a Variable’s Level of Measurement Isn’t Obvious
When your dependent variable is not continuous, unbounded, and measured on an interval or ratio scale, your model will not meet the assumptions of linear models.
Today I’m going to go into more detail about 6 common types of dependent variables that are not continuous, unbounded, and measured on an interval or ratio scale and the tests that work instead.
Side note: the usual advice is to use nonparametric tests when normality [Read more…] about When Dependent Variables Are Not Fit for Linear Models, Now What?