In regression and ANOVA, an interaction occurs when the effect of one independent variable on the dependent variable is different at different levels of another independent variable. When one or both of the independent variables is categorical, then two common strategies for dealing with interactions are stratifying and adding an interaction term. A somewhat less common method is classification and regression trees. Each has its advantages and disadvantages.
If you often have regression problems in which you have a great many independent variables, partial least squares is a technique you should know about.
Many times, researchers will categorize continuous variables. For example, birth weight of human infants is often categorized as “low birth weight” vs. “normal”; sometimes it is “very low birth weight”, “low birth weight” and normal. The cutoff for low birth weight is usually 2.5 kg. IQ tests are categorized with labels such as “gifted”. Depression tests are categorized. And so on. This rarely makes sense, either statistically or substantively. Read more!
This is a talk that I will give at NESUG in the fall.
Specialties: Regression, logistic regression, cluster analysis, statistical graphics, quantile regression.
When dealing with ordinal data, many methods require you to assign a number or score to each level of a variable. For instance, if you ask people about their political orientation and whether it is very conservative, somewhat conservative, moderate, somewhat liberal or very liberal, you might assign these scores of 1, 2, 3, 4 and 5, respectively. But that is somewhat arbitrary.
One alternative was suggested by Bross (1958) and brought to my attention in reading Alan Agresti’s excellent book: Analysis of Ordinal Categorical Data . Read more!