In a longitudinal study data has been collected on the same observation over time. Since the repeated measures taken on the same observations are not independent from each other this is a type of multilevel data and special care needs to be taken during the analysis to account for this non-independence. These types of data […]
Panel data, also known as longitudinal data, consists of multiple observations of the same subject over time. Many disciplines will analyze such data using a random intercept or random coefficients model. In this workshop, another way of modeling such data will be discussed: using fixed effects. Fixed effects methods can be used in longitudinal studies […]
Missing data are very common in all types of data sets, but most statistical procedures assume that all data are observed. The result is that most standard statistical software packages automatically drop from the analysis all observations with any missing data. This approach can lead to very low sample sizes and biased results. This two-part […]
Time series analysis concerns with methods for visualization and statistical analysis of data collected sequentially over time. This workshop is designed to introduce researchers to basics of time series data exploration and modeling. After attending the workshop, you should be able to use standard R packages to: perform exploratory data analysis with time series;- correct […]
Fractional factorial designs are often used for screening a large number of factors potentially related to a continuous response variable of interest. Attendees will learn about: Design generators, alias structure and design resolution Plackett-Burman designs Central composite designs Box-Behnken designs The designs and methodology will be illustrated using examples and the statistical software packages, JMP […]
Partial least squares (PLS) is a data reduction method that can be useful when dealing with multicollinearity in multiple linear regression. PLS is often used in predictive analyses and discriminant analyses. The PLS algorithm creates new uncorrelated variables from old variables, but in a different way than PCA. PLS regression is considered one of the […]
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. IRT models are used to analyze tools like tests, quizzes, surveys, or any other tool/instrument which is trying to measure an underlying trait.In this workshop, we will […]
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 […]
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 […]
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 […]