One common reason for running Principal Component Analysis (PCA) or Factor Analysis (FA) is variable reduction. In other words, you may start with a 10-item scale meant to measure something like Anxiety, which is difficult to accurately measure with a single question. You could use all 10 items as individual variables in an analysis--perhaps as predictors in a regression model. But you'd end up with a mess..

A data set can contain indicator (dummy) variables, categorical variables and/or both. Initially, it all depends upon how the data is coded as to which variable type it is. For example, a categorical variable like marital status could be coded in the data set as a single variable with 5 values...

One of the most confusing things about mixed models arises from the way it’s coded in most statistical software.  Of the ones I’ve used, only HLM sets it up differently and so this doesn’t apply. But for the rest of them—SPSS, SAS, R’s lme and lmer, and Stata, the basic syntax requires the same pieces […]

The webinar presented by Yasamin Miller will cover broadly survey design and planning. It will outline the advantages and disadvantages of the various data collection modes, types of samples available to target your population, how to obtain a representative sample, and how to avoid the pitfalls of bad questionnaire design..

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..