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 […]
When data is measured at multiple spatial locations, it often follows that measurements that are close together are more similar than measurements that are further apart. This puts us at risk of violating the linear model assumption of independent observations. Researchers can try to detect violation of independence via visual inspection of maps of residuals […]
A latent growth curve model is a form of a structural equation model (SEM). Latent growth curves provide a flexible framework for modeling longitudinal data. Within this approach, it is possible to model both linear and non-linear growth curves with unique covariance structures. This methodology also allows for multiple growth curves to be modeled simultaneously, […]
Longitudinal data consists of repeated measurements of the same individuals or experimental units over time. Analysis of longitudinal data can be challenging because repeated observations from the same subject are often temporally autocorrelated, violating the independence assumption of standard regression models. To deal with this issue, regression models can be extended to include covariance structures […]
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A well-designed experiment, study, or trial is vital to draw (causal) conclusions from data that are valid and efficient. This workshop is intended to provide a guided exploration of different design choices, corresponding appropriate statistical models, and their consequences and trade-offs to help you plan a stronger study. Topics covered will include: Validity and types […]