This workshop is being offered jointly with the Cornell University Library’s Data Den, a workshop series from our colleagues in Research Data and Open Scholarship (RDOS). Both an introduction to Julia and an introduction to statistics using Julia will be offered. Neither workshop requires attending the other, but will provide context for different uses of […]
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 marginaleffects and emmeans packages in R to explore the results of linear and generalized linear models. When models include many categorical predictors or interaction terms, the reported estimates of the model coefficients can be difficult to interpret, but these tools can provide insights for interpreting the model. […]
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 […]
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 […]
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 […]
Choice models can be used to describe how any individual makes a choice between a discrete set of choice alternatives. These methods are widely used in many fields including social sciences, economics, marketing, transportation, health, and animal behavior. This workshop will consist of a detailed description of the multinomial logit model, and the related conditional […]
Penalized regression 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 […]