Generalized linear models are an extension of ordinary linear regression models when the response is not normally distributed. This is the case when working with count data, as counts are whole numbers, bounded by zero and often have a right skewed distribution. The workshop will first introduce an appropriate discrete distribution for count data: the […]
While the adage “correlation does not imply causation” is true, work in the area of causal inference has pushed understanding of what kinds of causal claims can be made, especially in the context of observational data. This workshop, the first of two, will provide an overview of causal inference. Topics will include causal diagrams and […]
Learning about cause and effect is a goal of most research, but obtaining valid estimates of causal effects can be difficult, especially with observational data. This workshop will introduce a toolbox of frequently used methods for causal inference, with an emphasis on developing an understanding of how and why the methods work, when to use […]
In survival analysis, subjects are usually followed over a specified time period and the focus is on the time at which an event of interest occurs. Why not use linear regression to model the survival time as a function of a set of predictor variables? Ordinary linear regression cannot effectively handle the censoring of observations. […]
Latent profile analysis (LPA) is a statistical technique that aims to detect underlying latent groups, called profiles, from observed continuous data. LPA falls under the umbrella of a finite mixture model (FMM). The model estimates the probability of belonging to each latent profile for each participant. This should be viewed in contrast to factor analysis, […]
This workshop is designed for people who have very little experience with statistics. We strongly recommend that participants view our Basic Data and Research Skills workshop on our Cornell VOD Page beforehand. We will focus on the basic analyses, procedures and best practices that any researcher should consider when faced with a new dataset. All […]
This workshop is intended for people who have taken an introductory statistic course and feel comfortable with the material covered in our Introductory Statistics Using R workshop. This is a hands-on workshop and all methods will be demonstrated using the free statistical software package R. Topics that will be covered include: Chi-squared tests One-way and […]
This workshop will focus on the basic analyses, procedures, and best practices that any researcher should consider when faced with a new dataset.
The first step in fitting any linear models should be exploratory graphing with the raw data. In many cases, this step will provide preliminary answers to the hypotheses and will provide a general idea of the effect sizes for the predictors. Sometimes, descriptive graphs will not provide the whole picture, in particular when the response […]
This workshop is designed for people who have very little experience with statistics. We strongly recommend that participants view our Basic Data and Research Skills workshop on our Cornell VOD Page. beforehand. We will focus on the basic analyses, procedures and best practices that any researcher should consider when faced with a new dataset. All […]