Population surveys are often implemented using a complex sampling design which can include features such as stratification, clustering, multi-stage sampling, and weighting. The statistical analysis of such a survey may yield biased population estimates and incorrect variance estimates if the design is not properly taken into consideration in the analysis. The purpose of this workshop […]
Population surveys are often implemented using a complex sampling design which can include features such as stratification, clustering, multi-stage sampling, and weighting. The statistical analysis of such a survey may yield biased population estimates and incorrect variance estimates if the design is not properly taken into consideration in the analysis. In this hands-on workshop, we […]
Population surveys are often implemented using a complex sampling design which can include features such as stratification, clustering, multi-stage sampling, and weighting. The statistical analysis of such a survey may yield biased population estimates and incorrect variance estimates if the design is not properly taken into consideration in the analysis. In this hands-on workshop, we […]
Population survey analyses rely on using valid survey weights. Creating and adjusting these weights is critical to ensuring that analyses of survey data accurately reflect the population and yield correct conclusions. This workshop will introduce participants to methods for creating, adjusting, and applying survey weights, including: Creating base weights for probability and nonprobability samples Modifying […]
Population surveys are often implemented using a complex design which can include features such as stratification, clustering, multi-stage sampling, and weighting. The statistical analysis of such a survey will yield incorrect results if the design is not properly taken into consideration in the analysis. In this hands-on workshop, we will demonstrate how to analyze complex […]
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 […]
In some studies, a researcher wishes to establish that two treatments are not different from one another. Commonly, one treatment is the “gold standard” and another is an alternate treatment that is cheaper, safer or less invasive. It is not enough to conduct an experiment that fails to reject the null hypothesis that the two […]
There are many situations in which we may be interested in doing “multiple comparisons.” For example, after an ANOVA, we may want to compare individual groups to one another (post-hoc pairwise comparisons), or we may be running a variety of models within the same experiment. When making these comparisons, however, we increase the chance of […]
This workshop will teach participants how to make sense of their output from linear statistical models, with an emphasis on the meaning of the model parameters. The workshop is intended for participants who have at least one semester of statistics and some previous experience with linear regression or ANOVA. This is a review and is […]
Generalized linear models are an extension of ordinary linear regression models when the response is not normally distributed. This is the case when working with count data, as counts are whole numbers, bounded by zero and often have a right skewed distribution. The workshop will first introduce an appropriate discrete distribution for count data: the […]