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Data Preparation

Best Practices for Data Preparation

by Audrey Schnell  1 Comment

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 lot 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. [Read more…] about Best Practices for Data Preparation

Tagged With: best practices, data cleaning, data preparation, Missing Data, syntax

Related Posts

  • Best Practices for Organizing your Data Analysis
  • Preparing Data for Analysis is (more than) Half the Battle
  • Three Habits in Data Analysis That Feel Efficient, Yet are Not
  • Member Training: Data Cleaning

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

by Karen Grace-Martin  1 Comment

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.

[Read more…] about Four Weeds of Data Analysis That are Easy to Get Lost In

Tagged With: Data Analysis, data analysis plan, data issues

Related Posts

  • Eight Data Analysis Skills Every Analyst Needs
  • The Difference Between Model Assumptions, Inference Assumptions, and Data Issues
  • Best Practices for Organizing your Data Analysis
  • Three Habits in Data Analysis That Feel Efficient, Yet are Not

Member Training: Data Cleaning

by guest contributer 

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.

[Read more…] about Member Training: Data Cleaning

Tagged With: Data Analysis, Data analysis work flow, data cleaning, data quality, Missing Data, outliers

Related Posts

  • Best Practices for Data Preparation
  • Eight Data Analysis Skills Every Analyst Needs
  • Member Training: A (Gentle) Introduction to k-Nearest Neighbor
  • Member Training: Multiple Imputation for Missing Data

Eight Data Analysis Skills Every Analyst Needs

by Karen Grace-Martin  2 Comments

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.

[Read more…] about Eight Data Analysis Skills Every Analyst Needs

Tagged With: checking assumptions, Data Analysis, data anlyst, data cleaning, data issues, graphs, interpreting, Research Question, researcher, results, Study design

Related Posts

  • Four Weeds of Data Analysis That are Easy to Get Lost In
  • The Right Analysis or the Best Analysis? What to Do When You Can’t Run the Ideal Analysis 
  • Best Practices for Data Preparation
  • Member Training: Data Cleaning

Recoding a Variable from a Survey Question to Use in a Statistical Model

by Jeff Meyer  Leave a Comment

Survey questions are often structured without regard for ease of use within a statistical model.Stage 2

Take for example a survey done by the Centers for Disease Control (CDC) regarding child births in the U.S. One of the variables in the data set is “interval since last pregnancy”. Here is a histogram of the results.

[Read more…] about Recoding a Variable from a Survey Question to Use in a Statistical Model

Tagged With: categorical predictor, continuous predictor, predictor variable, recode, survey, survey questions

Related Posts

  • A Strategy for Converting a Continuous to a Categorical Predictor
  • A Useful Graph for Interpreting Interactions between Continuous Variables
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Member Training: Determining Levels of Measurement: What Lies Beneath the Surface

by TAF Support 

You probably learned about the four levels of measurement in your very first statistics class: nominal, ordinal, interval, and ratio.

Knowing the level of measurement of a variable is crucial when working out how to analyze the variable. Failing to correctly match the statistical method to a variable’s level of measurement leads either to nonsense or to misleading results.

But the simple framework of the four levels is too simplistic in most real-world data analysis situations.

[Read more…] about Member Training: Determining Levels of Measurement: What Lies Beneath the Surface

Tagged With: interval, level of measurement, Likert Scale, nominal variable, ordinal variable, ratio

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  • When a Variable’s Level of Measurement Isn’t Obvious
  • Member Training: Statistical Rules of Thumb: Essential Practices or Urban Myths?
  • Best Practices for Data Preparation
  • Member Training: Data Cleaning

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