This workshop will provide a hands-on overview of the Bayesian approach to statistical inference. Topics will include the foundations of Bayesian modeling, choice of prior distribution, Monte Carlo simulation, and practical implementation. Examples will be done using R, using the Stan language (https://mc-stan.org). This will be done using the cmdstanr R package (R Interface to […]
A receiver operating characteristic (ROC) curve is a graph that shows the diagnostic capability of a continuous variable at various thresholds predicting a binary outcome. This graph shows the relationship between the true positive rate to the false positive rate at different threshold values of the predictor variable. Different algorithms for defining the optimal threshold […]
Epidemiology is the study of the distribution and determinants of the presence of disease in populations. The goal of epidemiological studies is often to quantify the relationship between potential risk factors or exposures and disease. To accomplish this goal, there are several commonly used study designs including cross-sectional, cohort, and case-control. The study design dictates […]
This workshop will cover the basics of fitting non-linear models. Topics covered include choosing an appropriate non-linear function, choosing appropriate starting values, evaluation model assumptions, evaluating model fits, and prediction. We will also cover ways to compare non-linear fits by group ( i.e. by includingcovariates in a model), as well as touch on covering non-linear […]
This workshop is intended for people who feel comfortable with the material covered in our Introductory Statistics Using R workshop. The dplyr and tidyr packages are versatile tools for efficiently manipulating datasets in the free statistical software package R. These packages employ a constrained number of “verbs” which are functions that correspond to the most […]
Structural regression models allow researchers to do path analysis on latent variables. At its core, structural regression models have a measurement model, which is estimated using confirmatory factor analysis, and a structural part, which consists of hypotheses about the direct and indirect effects among observed or latent variables. Researchers often conflate the terms structural equation […]
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, […]