Binary logistic regression is one of the most useful regression models. It allows you to predict, classify, or understand explanatory relationships between a set of predictors and a binary outcome.
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Research Question
Member Training: Introduction to Binary Logistic Regression
December 3rd, 2024 by TAF SupportMember Training: Types of Longitudinal, Repeated Measures, and Time Series
October 2nd, 2024 by TAF SupportHow do you know when to use a time series and when to use a linear mixed model for longitudinal data?
What’s the difference between repeated measures data and longitudinal?
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Member Training: Operationalizing Research Questions
July 31st, 2023 by TAF Support
One of the hardest steps in any project is learning to ask the right research question!
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The Right Analysis or the Best Analysis? What to Do When You Can’t Run the Ideal Analysis
March 9th, 2020 by Karen Grace-MartinOne 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: in many data situations there isn’t one right analysis.
Eight Data Analysis Skills Every Analyst Needs
October 24th, 2019 by Karen Grace-MartinIt’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: Model Building Approaches
January 1st, 2019 by TAF SupportThere is a bit of art and experience to model building. You need to build a model to answer your research question but how do you build a statistical model when there are no instructions in the box?
Should you start with all your predictors or look at each one separately? Do you always take out non-significant variables and do you always leave in significant ones?