The analysis of binary outcomes is widespread across scientific fields. This workshop introduces the basics of the most commonly used statistical model for binary outcomes, logistic regression. Some basic background on probability and odds will be provided, as well as a brief review of the model itself. This workshop then covers how to run a...
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The purpose of this workshop is to show the use of the mixed command in SPSS. Although it has many uses, the mixed command is most commonly used for running linear mixed effects models (i.e., models that have both fixed and random effects). Such models are often called multilevel models. Because the purpose of this...
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This workshop introduces power analysis and simulations to assess power and estimate sample sizes needed for sufficient power. We will begin with simple analyses such as the t-test and will work our way up to various regression models. We will provide guidance in R to program simple Monte Carlo simulations to assess power. Some background...
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Directed Acyclic Graphs (DAGs) have emerged as an important tool in causal modeling to understand the relationships among variables. DAGs can inform what variables should be included or excluded in a statistical model intended to minimize bias in the estimation of a causal effect. This workshop discusses the basics of DAGs used for causal inference...
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