How to Preserve Missing Values with Stata’s Collapse Command

You are a code-writing machine.

That 3-day project you started this morning might actually be completed by the end of the day.

As your fingers fly across the keyboard, you think you can hear Stata singing your praise softly in the background.

Then IT happens…

Your programs stops working right. The data begin looking like something from one of Lord Voldemort’s nightmares.

Your finely-tuned debugging skills kick in, and you track down the problem. That -collapse- command you issued a while back did something rather odd. It replaced all of the missing values in your data set with zeros!

But that’s not at all what you wanted! You wanted those to be missing values, not zeros.

Yep, we’ve all been there. Even the most seasoned Stata users get bit by this quirk every once in a while.

In this article, I show three ways Stata can treat missing values when using the -collapse- command and the sum() function.
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Data Analytics and the Three-Headed Monster

three headed dragonA recent theme in the blogosphere centers on how newcomers can get into the field of data science and statistical analysis. What are the necessary qualifications? And how can you go about getting those skills?

Unfortunately, the answers to these questions seem to present a quandary that was eloquently summed up by a comment I read on another blog I seem to have forgotten (perhaps it was Chandoo):

You need experience to get a job as an analyst. But the only way to get experience is to work in a job as an analyst.

Employers today are asking for more from all of their employees. And data analysts are no exception. In fact, the pressure to produce more with less is pushing many employers to merge business functions across smaller workforces.

For the data scientist and more importantly the aspiring marketing researcher or business intelligence analyst there is a three-headed monster to contend with. Each head represents a different role that you will need to fulfill in your career.
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