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data issues

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

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  • 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

The Difference Between Model Assumptions, Inference Assumptions, and Data Issues

by Karen Grace-Martin 4 Comments

Have you ever compared the list of model assumptions for linear regression across two sources? Whether they’re textbooks, lecture Stage 2notes, or web pages, chances are the assumptions don’t quite line up.

Why? Sometimes the authors use different terminology. So it just looks different.

And sometimes they’re including not only model assumptions, but inference assumptions and data issues. All are important, but understanding the role of each can help you understand what applies in your situation.

[Read more…] about The Difference Between Model Assumptions, Inference Assumptions, and Data Issues

Tagged With: Assumptions, data issues, inference

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  • Four Weeds of Data Analysis That are Easy to Get Lost In
  • Best Practices for Organizing your Data Analysis
  • Three Habits in Data Analysis That Feel Efficient, Yet are Not
  • Best Practices for Data Preparation

Member Training: Missing Data

by TAF Support

Missing data causes a lot of problems in data analysis. Unfortunately, some of the “solutions” for missing data cause more problems than they solve.

[Read more…] about Member Training: Missing Data

Tagged With: data issues, listwise deletion, mean imputation, Missing Data, Multiple Imputation

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Member Training: Using Open Data in Research: Opportunities and Challenges

by TAF Support

Open data, particularly government open data is a rich source of information that can be helpful to researchers in almost every field, but what is open data? How do we find what we’re looking for? What are some of the challenges with using data directly from city, county, state, and federal government agencies?

[Read more…] about Member Training: Using Open Data in Research: Opportunities and Challenges

Tagged With: data issues, open data

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What to Do When You Can’t Run the Ideal Analysis 

by Karen Grace-Martin Leave a Comment

One activity in data analysis that can seem impossible is the quest to find the right analysis.

I applaud the conscientiousness and integrity that underlies this quest. The problem is in many data situations there isn’t one right analysis.

[Read more…] about What to Do When You Can’t Run the Ideal Analysis 

Tagged With: choosing statistical analysis, communicate results, data analysis plan, data issues, Research Question, Study design

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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
  • What to Do When You Can’t Run the Ideal Analysis 
  • Best Practices for Data Preparation
  • Member Training: Data Cleaning

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