by Jeff Meyer
As mentioned in a previous post, there is a significant difference between truncated and censored data.
Truncated data eliminates observations from an analysis based on a maximum and/or minimum value for a variable.
Censored data has limits on the maximum and/or minimum value for a variable but includes all observations in the analysis.
As a result, the models for analysis of these data are different. (more…)
by Jeff Meyer, MBA, MPA
One of the most important concepts in data analysis is that the analysis needs to be appropriate for the scale of measurement of the variable. The focus of these decisions about scale tends to focus on levels of measurement: nominal, ordinal, interval, ratio.
These levels of measurement tell you about the amount of information in the variable. But there are other ways of distinguishing the scales that are also important and often overlooked.
Suppose you are asked to create a model that will predict who will drop out of a program your organization offers. You decide to use a binary logistic regression because your outcome has two values: “0” for not dropping out and “1” for dropping out.
Most of us were trained in building models for the purpose of understanding and explaining the relationships between an outcome and a set of predictors. But model building works differently for purely predictive models. Where do we go from here? (more…)
Most analysts would respond, “a mixed model,” but have you ever heard of latent growth curves? How about latent trajectories, latent curves, growth curves, or time paths, which are other names for the same approach?
Even with a few years of experience, interpreting the coefficients of interactions in a regression table can take some time to figure out. Trying to explain these coefficients to a group of non-statistically inclined people is a daunting task.
For example, say you are going to speak to a group of dieticians. They are interested (more…)
We’ve looked at the interaction effect between two categorical variables. Now let’s make things a little more interesting, shall we?
What if our predictors of interest, say, are a categorical and a continuous variable? How do we interpret the interaction between the two? (more…)