Data Preparation

Issues in Coding Missing Values

October 11th, 2023 by

There’s no mincing words here. Missing values can cause problems for every statistician. That’s true for a lot of reasons, but it can start with simple issues of choices stage 1made when coding missing values in a data set. Here are a few examples.

Example 1: The Null License Plate

Researcher Joseph Tartaro thought it would be funny to get the following California vanity license plate: (more…)


Best Practices for Formatting Date Variables

March 9th, 2023 by

Formatting Date Variables seems like it should be straightforward, but sadly, it’s not.

If you are given data that includes dates, expect confusion. Dates can be represented in many different ways. (more…)


Best Practices for Data Preparation

October 4th, 2021 by

If you’ve been doing data analysis for long, you’ve probably had the ‘AHA’ moment where you realized statistical practice is a craft and not just a science. As with any craft, there are best practices that will save you a stage 1lot of pain and suffering and elevate the quality of your work. And yet, it’s likely that no one may have taught you these. I know I never had a class on this. (more…)


Four Weeds of Data Analysis That are Easy to Get Lost In

January 18th, 2021 by

Every time you analyze data, you start with a research question and end with communicating an answer. But in between those start and end points are twelve other steps. I call this the Data Analysis Pathway. It’s a framework I put together years ago, inspired by a client who kept getting stuck in Weed #1. But I’ve honed it over the years of assisting thousands of researchers with their analysis.

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Member Training: Data Cleaning

June 1st, 2020 by

Data Cleaning is a critically important part of any data analysis. Without properly prepared data, the analysis will yield inaccurate results. Correcting errors later in the analysis adds to the time, effort, and cost of the project.

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Eight Data Analysis Skills Every Analyst Needs

October 24th, 2019 by

It’s easy to think that if you just knew statistics better, data analysis wouldn’t be so hard.

It’s true that more statistical knowledge is always helpful. But I’ve found that statistical knowledge is only part of the story.

Another key part is developing data analysis skills. These skills apply to all analyses. It doesn’t matter which statistical method or software you’re using. So even if you never need any statistical analysis harder than a t-test, developing these skills will make your job easier.

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