We’ve talked a lot around here about the reasons to use syntax — not only menus — in your statistical analyses. Regardless of which software you use, the syntax file is pretty much always a text file. This is true for R, SPSS, SAS, Stata — just about all of them. This is important because it means you can use an unlikely tool to help you code: Microsoft Word. I know what you're thinking. Word? Really? Yep, it's true. Essentially it's because Word has much better Search-and-Replace options than your stat software’s editor. Here are a couple features of Word’s search-and-replace that I use to help me code faster.

There are many rules of thumb in statistical analysis that make decision making and understanding results much easier. Have you ever stopped to wonder where these rules came from, let alone if there is any scientific basis for them? Is there logic behind these rules, or is it propagation of urban legends? In this webinar, we’ll explore and question the origins, justifications, and some of the most common rules of thumb in statistical analysis.

Think of CFA as a process for testing what you already think you know. CFA is an integral part of structural equation modeling (SEM) and path analysis. The hypothesized factors should always be validated with CFA in a measurement model prior to incorporating them into a path or structural model. Because… garbage in, garbage out. CFA is also a useful tool in checking the reliability of a measurement tool with a new population of subjects, or to further refine an instrument which is already in use.

Question: How do we decide whether to have rotated or unrotated factors? Answer: Great question. Of course, the answer depends on your situation. When you retain only one factor in a solution, then rotation is irrelevant. In fact, most software won't even print out rotated coefficients and they're pretty meaningless in that situation. But if you retain two or more factors, you need to rotate. Unrotated factors are pretty difficult to interpret in that situation.

Question: Can you use Principal Component Analysis with a Training Set Test Set Model? Answer: Yes and no. Principal Component Analysis specifically could be used with a training and test data set, but it doesn't make as much sense as doing so for Factor Analysis. That's because PCA is really just about creating an index variable from a set of correlated predictors.

Question: Can we use PCA for reducing both predictors and response variables? In fact, there were a few related but separate questions about using and interpreting the resulting component scores, so I'll answer them together here.

One of the many confusing issues in statistics is the confusion between Principal Component Analysis (PCA) and Factor Analysis (FA). They are very similar in many ways, so it’s not hard to see why they’re so often confused. They appear to be different varieties of the same analysis rather than two different methods. Yet there is a fundamental difference between them that has huge effects on how to use them.

Question: In Principal Component Analysis, can loadings be both positive and negative? Answer: Yes. Recall that in PCA, we are creating one index variable (or a few) from a set of variables. You can think of this index variable as a weighted average of the original variables. The loadings are the weights. The goal of the PCA is to come up with optimal weights. "Optimal" means we're capturing as much information in the original variables as possible, based on the correlations among those variables.

Let’s imagine you have been asked to determine the factors that will help a hospital determine the length of stay in the intensive care unit (ICU) once a patient is admitted. The hospital tells you that once the patient is admitted to the ICU, he or she has a day count of one. As soon as they spend 24 hours plus 1 minute, they have stayed an additional day. Clearly this is count data. There are no fractions, only whole numbers.

One of the biggest challenges that data analysts face is communicating statistical results to our clients, advisors, and colleagues who don’t have a statistics background. Unfortunately, the way that we learn statistics is not usually the best way to communicate our work to others, and many of us are left on our own to navigate what is arguably the most important part of our work.