Understanding GLM, and multiple regression in particular, is one of the requirements to successfully fitting SEM to your data..

There are many types and examples of ordinal variables: percentiles, ranks, likert scale items, to name a few. In this webinar we’re going to lay out all the options and when each is reasonable. There are more options than most people realize.

Stata allows members of the Stata community to share their expertise. There are countless commands written by very, very smart non-Stata employees that are available to all Stata users..

Let’s look at how to investigate the effect of the missing data on the regression models in Stata..

In our last post, we calculated Pearson and Spearman correlation coefficients in R and got a surprising result. So let’s investigate the data a little more with a scatter plot...

Let’s use R to explore bivariate relationships among variables. We use a new version of the data set of tourists from different nations, their gender and numbers of children. Here, we have a new variable – the amount of money they spend while on vacation..

Sometimes when you’re learning a new stat software package, the most frustrating part is not knowing how to do very basic things. This is especially frustrating if you already know how to do them in some other software. Let’s look at some basic but very useful commands that are available in R..

In this webinar we will describe broadly what propensity score matching is along with a discussion about different matching methods that can be used to create balanced samples of "treated" and "non-treated" participants.

Fortunately in Stata it is not a difficult process to use the same sample for multiple models..

An “estimation command” in Stata is a generic term used for statistical models. Examples of statistical models are linear regression, ANOVA, poisson, logit, and mixed. Stata has more than 100 estimation commands to analyze data...