So the only way to include cases with more than 50% observed data would be to impute them in a separate step before you run the reliability analysis. And while you could impute the mean, I highly recommend you do not. While mean imputation maintains the mean of each separate variable, it does not maintain the relationships among variables.

Two designs commonly used in epidemiology are the cohort and case-control studies. Both study causal relationships between a risk factor and a disease. What is the difference between these two designs? And when should you opt for the one or the other?

In a previous post, Interpreting Interactions in Regression, I said the following: In our example, once we add the interaction term, our model looks like: Height = 35 + 4.2*Bacteria + 9*Sun + 3.2*Bacteria*Sun Adding the interaction term changed the values of B1 and B2. The effect of Bacteria on Height is now 4.2 + […]

Time-to-event outcomes have common characteristics, some of which make linear models untenable: 1. The main outcome is measuring likelihood of the occurrence of a specific event, if the event has not already occurred. This event is usually something that takes the individual from one state to another, and the research question is about how predictor variables relate to the propensity for the event to occur.

Do you find quizzes irresistible?  I do. Here’s a little quiz about working with missing data: True or False? 1. Imputation is really just making up data to artificially inflate results.  It’s better to just drop cases with missing data than to impute. 2. I can just impute the mean for any missing data.  It […]

In my last post, I gave a little quiz about missing data.  This post has the answers. If you want to try it yourself before you see the answers, go here. (It’s a short quiz, but if you’re like me, you find testing yourself irresistible). True or False? 1. Imputation is really just making up data […]

Where do you find your resources? If you are not in academia, have access to a top-notch library, or receive the industry publications of interest, you may need to get creative if you do not want to pay for each article. (In a pinch, I have paid up to $36 for an article, which can add up if you are conducting a comprehensive literature review!)

Would you build your house without a foundation? Of course not! However, many people skip the first step of any empirical-based project--conducting a literature review. Like the foundation of your house, the literature review is the foundation of your project.

You’ve probably experienced this before. You’ve done a statistical analysis, you’ve figured out all the steps, you finally get results and are able to interpret them. But the statistical results just look…wrong. Backwards, or even impossible—theoretically or logically. This happened a few times recently to a couple of my consulting clients, and once to me. […]

I received received a question about controlling for inflated Type I error through Bonferroni corrections in nonparametric tests.  Here’s the specific question and my quick answer: My colleague is applying non parametric (Kruskal-Wallis) to check for differences between groups. There are 12 groups and test showed that there is significant difference in the groups. However, […]