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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Member Training: Introduction to Binary Logistic Regression
December 3rd, 2024 by TAF Support
Member Training: Introduction to Item Response Theory and Rasch Models
November 1st, 2024 by TAF Support
How do you know if the items of a test are hard or easy; fair or biased; accurate at measuring ability or not?
Item Response Theory (IRT).
In this training, you will see, with real life examples, how IRT answers these questions to assess a test.
Member Training: Types of Longitudinal, Repeated Measures, and Time Series
October 2nd, 2024 by TAF Support
How 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: Introduction to Structural Equation Modeling
June 1st, 2024 by guest contributer
Structural Equation Modeling (SEM) is a popular method to test hypothetical relationships between constructs in the social sciences. These constructs may be unobserved (a.k.a., “latent”) or observed (a.k.a., “manifest”).
In this training, you will learn the different types of SEM: confirmatory factor analysis, path analysis for manifest and latent variables, and latent growth modeling (i.e., the application of SEM on longitudinal data).
We’ll discuss the different terminology, the commonly used symbols, and the different ways a model can be specified, as well as how to present results and evaluate the fit of the models.
This training will be at a very basic conceptual level; however, it is assumed that participants have an understanding of multiple regression, interpretation of statistical tests, and methods of data screening.
Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.
Member Training: Interactions in Poisson and Logistic Regression – Part 2
December 1st, 2023 by Kim Love
Interactions in statistical models are never especially easy to interpret. Throw in non-normal outcome variables and non-linear prediction functions and they become even more difficult to understand. (more…)
What is a Randomized Complete Block Design?
July 24th, 2023 by Kim Love
Designing experiments would always be simple if we could just randomly assign subjects to different treatment conditions with no other restrictions. Unfortunately, that doesn’t always work.
For example, there are many experimental situations where the subjects aren’t independent of each other. The subjects that are related to each other are combined into clusters called “blocks.” It can happen due to practicalities of running an experiment efficiently or you can intentionally plan it as a way to reduce random variance.
In either case, this is a randomized complete block design. It’s a great design to become familiar with because it will greatly expand your ability to create and analyze experiments.
How It Works
When you have subjects that share characteristics with one another, it can sometimes be difficult to isolate those characteristics directly. This makes it hard to record them as additional variables. By identifying the subjects that are similar, you can still capture how those characteristics affect the outcome. Subjects that are similar are grouped into “blocks.”
From there, you can make treatment assignments so that you put subjects from the same block into different treatment groups.
Why different treatment groups? Suppose subjects from the same block were assigned to the same treatment group. (more…)