Missing data causes a lot of problems in data analysis. Unfortunately, some of the “solutions” for missing data cause more problems than they solve.
Stage 2
Member Training: Missing Data
December 1st, 2020 by TAF Support
Member Training: Preparing to Use (and Interpret) a Linear Regression Model
November 1st, 2020 by TAF Support
You think a linear regression might be an appropriate statistical analysis for your data, but you’re not entirely sure. What should you check before running your model to find out?
Member Training: Using Open Data in Research: Opportunities and Challenges
October 1st, 2020 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?
Same Statistical Models, Different (and Confusing) Output Terms
January 7th, 2020 by Jeff Meyer
Learning how to analyze data can be frustrating at times. Why do statistical software companies have to add to our confusion?
I do not have a good answer to that question. What I will do is show examples. In upcoming blog posts, I will explain what each output means and how they are used in a model.
We will focus on ANOVA and linear regression models using SPSS and Stata software. As you will see, the biggest differences are not across software, but across procedures in the same software.
Member Training: The Anatomy of an ANOVA Table
December 31st, 2019 by Jeff Meyer
Our analysis of linear regression focuses on parameter estimates, z-scores, p-values and confidence levels. Rarely in regression do we see a discussion of the estimates and F statistics given in the ANOVA table above the coefficients and p-values.
And yet, they tell you a lot about your model and your data. Understanding the parts of the table and what they tell you is important for anyone running any regression or ANOVA model.
Member Training: How to Avoid Common Graphical Mistakes
December 1st, 2019 by guest contributer
Good graphs are extremely powerful tools for communicating quantitative information clearly and accurately.
Unfortunately, many of the graphs we see today confuse, mislead, or deceive the reader.
These poor graphs result from two key limitations. One is a graph designer who isn’t familiar with the principles of effective graphs. The other is software with a poor choice of default settings.