Population surveys are often implemented using a complex design which can include features such as stratification, clustering, multi-stage sampling, and weighting. The statistical analysis of such a survey will yield incorrect results if the design is not properly taken into consideration in the analysis. In this hands-on workshop, we will demonstrate how to analyze complex […]
The aim of a cluster analysis is to group a set of objects in such a way that members of the same group (or cluster) are more similar to each other than to those in other groups. This is an unsupervised machine learning method, also known as data segmentation or class discovery. This workshop will […]
In some studies, a researcher wishes to establish that two treatments are not different from one another. Commonly, one treatment is the “gold standard” and another is an alternate treatment that is cheaper, safer or less invasive. It is not enough to conduct an experiment that fails to reject the null hypothesis that the two […]
There are many situations in which we may be interested in doing “multiple comparisons.” For example, after an ANOVA, we may want to compare individual groups to one another (post-hoc pairwise comparisons), or we may be running a variety of models within the same experiment. When making these comparisons, however, we increase the chance of […]
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 […]