data issues

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

Have you ever compared the list of assumptions for linear regression across two sources? Whether they’re textbooks, lecture notes, 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.

December 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.

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?

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.

Eight Data Analysis Skills Every Analyst Needs

by Karen Grace-Martin 1 Comment

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.

Member Training: Multiple Imputation for Missing Data

by Jeff Meyer

There are a number of simplistic methods available for tackling the problem of missing data. Unfortunately there is a very high likelihood that each of these simplistic methods introduces bias into our model results.

Multiple imputation is considered to be the superior method of working with missing data. It eliminates the bias introduced by the simplistic methods in many missing data situations.
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