ANALYSIS OF VARIANCE (ANOVA)

(Lauren Blondeau, Department of Statistics and Data Sciences) 

The purpose of this course is to familiarize participants with the use and interpretation of the In this course, participants will learn the theory, use, and application of ANOVA) statistical test. ANOVA is used to analyze group differences on numeric response variables; it has applications across a wide variety of domains including science and business.

APPLIED HIERARCHICAL LINEAR MODELING

(Catherine Cubbin, School of Social Work)

This applied, hands-on course provides an introduction to the basic concepts and applications of hierarchical linear models. The course will cover applications in social science research (e.g. neighborhood effects research, school effect research), growth curve modeling (e.g., repeated measures on individuals), as well as introduce models for dichotomous outcomes.

INTRODUCTION TO CAUSAL INFERENCE

(Nathaniel Raley Woodard, Department of Statistics and Data Sciences)

This course covers contemporary statistical approaches to questions about causality. It introduces an important framework for thinking about cause-and-effect (the potential outcomes framework) both in the context of randomized experiments and in observational studies. Techniques covered in the course include blocking/stratification, instrumental variables estimation, matching methods (including propensity scores), and regression-discontinuity designs.

MISSING DATA ANALYSIS USING MPLUS

(Keenan Pituch, Educational Psychology)

This workshop covers the problem of missing data that is common to social science research. Topics include patterns and mechanisms of missing data as well as conventional and modern missing data treatments, focusing particularly on the use of maximum likelihood and multiple imputation. Missing data treatments will be applied to various statistical models, such as multiple regression and factor analysis. Workshop participants will learn when a given missing data treatment is suitable and how such methods can be implemented using Mplus software.

STATISTICS FOR THE DISSERTATION

(Sarah Collins, Department of Statistics and Data Sciences)

A comprehensive review of common statistical techniques for PhD students in non-mathematically leaning fields. We will cover methods that may be useful as they design their dissertations such as t-tests, linear and multiple regression, various correlation equations (Pearson, Spearman, point-biserial), logistic regression, ANOVA, and ways to apply these in combination with qualitative research. An emphasis will be place on learning how to interpret the terms associated with these methods.

THE POWER AND PLEASURE OF PROBABILITY

(Joel Nibert, Department of Mathematics)

Participants will learn fundamental rules for computing probabilities, including the explanations behind some famous paradoxical puzzles, gain insight into statistical practice (including the frequentist vs. Bayesian debate) through a deeper understanding of connections with probability theory, dispel misconceptions and cognitive biases surrounding randomness, and explore simulation as a tool for problem solving and as a means to understand limit theorems.

 

 

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