Multimodel inference (MMI) is a model selection framework that has recently gained some popularity as an alternative to null hypothesis significance testing. This type of inference favors stepwise approaches (forward and backwards model selection) to determine a single best “final” model. This workshop gives an overview of the MMI framework, in which researchers generate a […]
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 […]
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, […]
Ordination is a statistical technique that allows us to graphically summarize complex relationships. Also considered “dimension reduction,” these techniques offer a way to simplify many correlated variables (“dimensions”) into two or three, axes, which are must easier to interpret. In two dimensions we can then map our data points on a figure, and this often […]
Response surface methodology is a related design topic in which the goal is to determine settings for optimizing the expected response. The designs and methodology will be illustrated using examples from the rsm library in R.
Factor Analysis is a widely used multivariate technique. It is a data reduction technique that examines the underlying relations that exist among a set of variables. In doing so it assumes that a small number of unobserved variables, called factors, are responsible for the correlation among a large number of observed variables. This workshop is […]
One of the first steps in planning a new study is determining an appropriate sample size. A sample size should be large enough to have a high probability of detecting an effect of a treatment but small enough that it is within the confines of the study?s budget and minimizes the potential risks to human […]
Meta-analysis is a statistical procedure for combining the results from multiple studies in an effort to increase power (over individual studies), improve estimates of effect sizes, or to resolve uncertainty when research disagrees. In this workshop, we will provide an introduction to the theory and statistical methods behind meta-analysis. Topics will include: Extraction of data […]
This workshop is designed to teach researchers how to get started analyzing data.
This workshop will focus on the basic analyses, procedures, and best practices that any researcher should consider when faced with a new dataset.