# Search CSCU

## Introduction to PERMANOVA

Workshop

PERMANOVA, (permutational multivariate ANOVA), is a non-parametric alternative to MANOVA, or multivariate ANOVA test. It is appropriate with multiple sets of variables that do not meet the assumptions of MANOVA, namely multivariate normality. It is often used with data that is highly skewed, zero-inflated, ordinal, or qualitative, such as ecological community data, microbiome data, or […]

## Introduction to Item Response Theory (IRT)

Workshop

Item response theory (IRT) consists of a family of mathematical models which can be used to estimate and evaluate the relationship between observed variables and a latent trait. Different IRT models exist for different types of observed variables. In this workshop, we will mainly focus on IRT models for binary observed variables. These types of […]

## Data Visualization

Workshop

Data Visualization is one of the most important ways to communicate your research findings. This workshop will cover guidelines and best practices for creating graphs and figures that accurately and effectively convey the results of your research and the message you want to tell. We will introduce a number of resources available to get ideas […]

## Canonical Correlation Analysis

Workshop

The goal of a canonical correlation analysis (CCA) is to find the linear combinations in two sets of variables that maximize the correlations. By analyzing the variance-covariance structure of these two sets of variables, uncorrelated pairs of linear combinations from the two sets of variables (called canonical variables) are created and their correlations (called canonical […]

## An Introduction to Writing Loops in R for Efficient Statistical Analyses

Workshop

Loops can be used in a variety of ways to tackle statistical analyses that would be overwhelming otherwise. For instance, loops can be used to run a particular model or produce a series of figures over a sequence of variables. In this workshop, we will cover several examples in which loops are utilized to make […]

## Nonparametric Statistical Methods

Workshop

Nonparametric statistical methods or distribution-free tests are statistical procedures that do not rely on assumptions about the underlying distribution of the data. These methods are useful when the assumptions of parametric tests are not met or when little is known about the population parameter of the variable of interest in the population. They are particularly […]

## Multiple Linear Regression with Spatial Data

Workshop

Multiple linear regression is a time-tested and powerful tool for inferring relationships in multivariate data. However, when the observations are associated with spatial locations, the independence assumption is often not appropriate. This workshop will motivate the use of models with spatially correlated residuals and provide hands on experience fitting and interpreting models in the R […]

## Propensity Score Analysis

Workshop

Randomized controlled trials are considered to be the gold standard in research design due to removal of confounding through randomization. However, in many instances true experimental designs are not always practical, or even ethical. Propensity score analysis methods can reduce bias in estimating treatment effect that is introduced due to non-random assignment of experimental units […]

## Getting started with data analysis using SAS

Workshop

This workshop is designed for people who need to analyze their data using SAS. We will focus on the workflow, best practices, and procedures that any researcher should consider when faced with a new dataset and illustrate them in a hands-on fashion using SAS. Researchers should have access to a copy of SAS if they […]

## Introduction to Multinomial Logistic Regression

Workshop

Multinomial logistic regression is an extension of binary logistic regression. This statistical method can be implemented when modeling a dependent variable that is a categorical variable with more than two levels. This workshop will cover the mathematics of the multinomial logistic regression model, the interpretation of coefficients, model fit, and post-hoc tests.