Many of us love performing statistical analyses but hate writing them up in the Results section of the manuscript. We struggle with big-picture issues (What should I include? In what order?) as well as minutia (Do tables have to be double-spaced?). (more…)
Membership Webinars
Member Training: Writing Up Statistical Results: Basic Concepts and Best Practices
July 1st, 2019 by TAF Support
Member Training: A Predictive Modeling Primer: Regression and Beyond
May 31st, 2019 by guest contributer
Predicting future outcomes, the next steps in a process, or the best choice(s) from an array of possibilities are all essential needs in many fields. The predictive model is used as a decision making tool in advertising and marketing, meteorology, economics, insurance, health care, engineering, and would probably be useful in your work too! (more…)
Member Training: Multiple Imputation for Missing Data
May 6th, 2019 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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Member Training: Non-Parametric Analyses
April 1st, 2019 by guest contributer
Oops—you ran the analysis you planned to run on your data, carefully chosen to answer your research question, but your residuals aren’t normally distributed.
Maybe you’ve tried transforming the outcome variable, or playing around with the independent variables, but still no dice. That’s ok, because you can always turn to a non-parametric analysis, right?
Well, sometimes.
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Member Training: Determining Levels of Measurement: What Lies Beneath the Surface
March 4th, 2019 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.
Member Training: What’s the Best Statistical Package for You?
February 1st, 2019 by guest contributer
Choosing statistical software is part of The Fundamentals of Statistical Skill and is necessary to learning a second software (something we recommend to anyone progressing from Stage 2 to Stage 3 and beyond).
You have many choices for software to analyze your data: R, SAS, SPSS, and Stata, among others. They are all quite good, but each has its own unique strengths and weaknesses.