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 basic analyses, procedures and best practices that any researcher should consider when faced with a new dataset. All this will be […]
This workshop will cover how to use the emmeans package in R to explore the results of linear models. When models include many categorical predictors or interaction terms, the reported estimates of the model coefficients are difficult to interpret. Estimated marginal means are model predictions based on a set of combinations of predictor variables. This […]
There are several methods available to analyze data that consists of repeated measurements taken on subjects or experimental units over time. These methods are designed to account for between subject variability and within subject variability. In this workshop we will cover the two most commonly used methods for analyzing repeated measures data: repeated measures ANOVA […]
Using a well-maintained, reproducible data analysis strategy (or workflow) is a major productivity boost. Using a syntax-based approach permanently connects the protocol documentation to the actions performed to produce the results, reducing confusion as to what steps were taken. Figures and tables can be recreated easily, allowing for simple modification without starting from scratch. Additionally, […]
This workshop will introduce github and some of its uses in statistical contexts. Github is a powerful tool for storing code online to publicly share or to collaborate on. In this workshop, we will cover use cases for github for sharing data and/or code, and the tools for using github in a statistics context. No […]
Description coming soon.
This workshop will begin with a series of examples illustrating different settings in which discrete choice models are useful. The multinomial logit model will then be described in detail, followed by extensions to deal with heteroscedastic errors in the utility function, nested choices, and dependent choices. Analysis of various datasets will be illustrated using the […]
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 […]
Path analysis is a form of a structural equation model in which all the variables in the model are observed and all the paths of the model are estimated simultaneously. Simply put, path analysis is a series of linear regressions. Hence, the same assumptions that we have for linear regression hold for path analysis. Path […]
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 […]