by Jeff Meyer, MPA, MBA

In a previous post we discussed the difficulties of spotting meaningful information when we work with a large panel data set.

Observing the data collapsed into groups, such as quartiles or deciles, is one approach to tackling this challenging task.  We showed how this can be easily done in Stata using just 10 lines of code.

As promised, we will now show you how to graph the collapsed data. [click to continue…]


Converting Panel Data into Percentiles to Observe Trends in Stata (Part 1)

Panel data provides us with observations over several time periods per subject. In this first of two blog posts, I’ll walk you through the process. (Stick with me here. In Part 2, I’ll show you the graph, I promise.)

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Understanding Interaction Between Dummy Coded Categorical Variables in Linear Regression

The concept of a statistical interaction is one of those things that seems very abstract. If you’re like me, you’re wondering: What in the world is meant by “the relationship among three or more variables”?

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September 2016 Membership Webinar: Cox Regression

In this webinar, you’ll see what a hazard function is and describe the interpretations of increasing, decreasing, and constant hazard. Then you’ll examine the log rank test, a simple test closely tied to the Kaplan-Meier curve, and the Cox proportional hazards model.

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Linear Mixed Models for Missing Data in Pre-Post Studies

In the past few months, I’ve gotten the same question from a few clients about using linear mixed models for repeated measures data. They want to take advantage of its ability to give unbiased results in the presence of missing data. In each case the study has two groups complete a pre-test and a post-test measure. Both of these have a lot of missing data…

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August 2016 Membership Webinar: Small Sample Statistics

Despite modern concerns about how to handle big data, there persists an age-old question: What can we do with small samples?

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The Difference Between Relative Risk and Odds Ratios

Odds Ratios and Relative Risks are often confused despite being unique concepts. Why?

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Pros and Cons of Treating Ordinal Variables as Nominal or Continuous

There are not a lot of statistical methods designed just for ordinal variables. But that doesn’t mean that you’re stuck with few options. There are more than you’d think…

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July 2016 Topic Webinar: Working with Truncated and Censored Data

This webinar will discuss what truncated and censored data is and how to identify it…

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June 2016 Topic Webinar: Zero Inflated Models

This webinar will explore two ways of modeling zero-inflated data: the Zero Inflated model and the Hurdle model. Both assume there are two different processes: one that affects the probability of a zero and one that affects the actual values, and both allow different sets of predictors for each process.

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