Factor Analysis is a widely used multivariate technique. It is a data reduction technique that examines the underlying relations that exist among a set of variables. In doing so it assumes that a small number of unobserved variables, called factors, are responsible for the correlation among a large number of observed variables. This workshop is […]
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Meta-analysis is a statistical procedure for combining the results from multiple studies in an effort to increase power (over individual studies), improve estimates of effect sizes, or to resolve uncertainty when research disagrees. In this workshop, we will provide an introduction to the theory and statistical methods behind meta-analysis. Topics will include: Extraction of data […]
This workshop is designed to teach researchers how to get started analyzing data.
This workshop will focus on the basic analyses, procedures, and best practices that any researcher should consider when faced with a new dataset.
Ordinal logistic regression is an extension of binary logistic regression. This statistical method can be implemented when modeling a dependent variable that is an ordinal (categorical) variable. This workshop will discuss the assumptions behind the ordinal logistic model, the interpretation of the coefficients from the model, post hoc tests.
This workshop will teach participants how to make sense of their output from linear statistical models, with an emphasis on the meaning of the model parameters. The workshop is intended for participants who have at least one semester of statistics and some previous experience with linear regression or ANOVA. This is a review and is […]
The Lasso is a modern statistical method that has gained much attention over the last decade as researchers in many fields are able to measure far more variables than ever before. Linear regression suffers in two important ways as the number of predictors becomes large: First, overfitting may occur, meaning that the fitted model does […]