Statistical Workshops
The Center for Statistical Computing (CSC) welcomes all graduate students, staff, and faculty to participate in our statistics workshops. These sessions are held on Zoom and/or in Lab C (and the GOLD Lab) on the upper level (UL) of Healey Library. Our workshops cover using statistical software such as SPSS, SAS, Stata, Excel, R, RStudio, and Python, along with a variety of statistical procedures. We also provide topics in applied statistics encompassing recently developed statistical methods, utilizing tools such as SPSS, SAS, Stata, R, AMOS, Mplus, and WinBUGS. Descriptions for each workshop are provided below:
Fall 2026 Statistics Workshop Schedule
Basic Statistical Workshops Schedule and Description (Fall 2026)
Applied Statistical Workshops Schedule and Description (Fall 2026)
Statistics Workshop Descriptions
Introduction to Stata: This hands-on workshop provides a practical introduction to Stata using both its graphical user interface and command syntax. Participants will learn the fundamentals of working with Stata, including importing and browsing data, managing and recoding variables, generating descriptive statistics, conducting independent-samples t-tests and chi-square tests, and fitting linear regression models.
This hands-on workshop introduces participants to data management, statistical analysis, and visualization using R, a free, open-source software environment supported by an active user community. Topics include installing and loading R packages, importing and exporting data files, generating descriptive statistics and frequency distributions, creating graphs, and conducting chi-square tests, independent-samples t-tests, one-way ANOVA, and linear and logistic regression analyses.
This hands-on workshop introduces participants to data management and statistical analysis using SPSS for Windows. Topics include entering and importing data; creating an SPSS data file with variable names, variable labels, value labels, and missing-value definitions; generating frequency tables and crosstabulations; recoding variables; conducting independent-samples t-tests; and performing simple linear regression.
This hands-on workshop introduces practical tools and techniques for efficiently managing and analyzing data in Excel. Topics include entering and organizing data, generating descriptive statistics, examining frequency tables and crosstabulations, conducting independent-sample and paired-sample t-tests, performing correlation analysis, and fitting simple linear regression models.
This workshop begins with a brief review of the data management and statistical analysis skills introduced in SPSS 1 and then covers more advanced SPSS procedures. Data management topics include selecting cases, combining cases from multiple files, and merging files containing different variables. Statistical topics include chi-square tests, one-way ANOVA, repeated-measures analysis, nonparametric tests, multiple regression, and logistic regression.
This hands-on workshop introduces participants to SAS for data management and statistical analysis. Topics include navigating the SAS environment, importing and preparing data sets, managing variables, generating descriptive statistics and tables, and conducting basic statistical analyses, including t-tests, chi-square tests, correlation analysis, and linear regression models. The workshop will also introduce methods for examining statistical assumptions and evaluating regression model fit.
This beginner-level, hands-on workshop introduces participants to Python programming and its applications in statistical analysis. No prior Python experience is required. Using practical examples from social sciences, the workshop covers the Python environment, basic programming concepts, common data types, importing and managing data, and generating descriptive statistics. Statistical topics include t-tests, Chi-square tests, analysis of variance (ANOVA) and linear regression.
This hands-on workshop introduces participants to statistical analysis and visualization using RStudio, a user-friendly integrated development environment for the R programming language. Topics include navigating the RStudio environment, installing and loading R packages, importing and exporting data, working with basic data structures, generating descriptive statistics, creating graphs with ggplot2, and conducting linear and logistic regression analyses.
This hands-on workshop introduces participants to using ChatGPT as a tool to support statistical analysis. Topics include formulating statistical questions, selecting appropriate methods, generating and reviewing programming code, and interpreting and presenting results. Practical examples include t-tests, one-way ANOVA, Chi-square tests, and linear regression. The workshop also emphasizes verifying ChatGPT-generated results and using appropriate practices to protect confidential or sensitive data.
This workshop provides an overview of the fundamental principles of multilevel/hierarchical linear models. Topics include the necessity for appropriate methods to model dependencies (e.g., clustering of students within schools), formulating and interpreting two-level multilevel models and their relevant parameters, and using SPSS to estimate model parameters
This workshop introduces methods for determining required sample sizes, estimating statistical power, and selecting or calculating appropriate effect sizes for a variety of statistical tests and study designs. Participants will learn how sample size, effect size, significance level, and statistical power are related, and how to conduct these calculations using the PROC POWER procedure in SAS and G*Power.
This is an introductory workshop in statistical learning focusing on the important elements of modern data analysis such as regression and classification methods. Topics covered include linear and logistic regression, linear discriminant analysis, cross-validation, principal components, and clustering. Data analysis examples in this workshop are demonstrated using R.
This workshop covers the mechanisms of missing data, analysis of non-random selection bias, and methods of single and multiple imputation (MI) using SAS and Stata. Missing data is a common issue in various datasets. Most statistical software packages automatically eliminate entire cases with missing data from analysis, potentially leading to low sample sizes and biased results.
This workshop is designed to introduce the fundamental principles of classification techniques in data science, with a particular emphasis on Logistic Regression and Random Forest using R programming language. Participants will develop a conceptual understanding of both models, gain hands on experience implementing them in R, and examine their respective strengths and limitations through real-world examples. The session aims to equip students with the analytical skills needed to critically evaluate and apply classification methods in research and data analysis across a range of academic disciplines.
This workshop introduces techniques for structural equation modeling (SEM). SEM is employed to test complex relationships between observed (measured) and unobserved (latent) variables. Topics covered include fundamentals underlying SEM, SEM notation, path diagrams, data preparation, mediation analysis, path analysis, parameter estimation, and assessment of model fit. AMOS and R are used to demonstrate examples.
The second SEM workshop delves into advanced topics including measurement error, latent variables analysis, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), development of structural equation models with estimation, and model testing. Additionally, this workshop introduces latent growth models for longitudinal data. An R program and AMOS are utilized to demonstrate model structures, parameter estimation, and model modification.
This workshop introduces statistical methods for survival (event history) analysis, with a focus on studies in which the outcome is a time-to-event variable. Topics include estimating and comparing survival times using life-table and Kaplan–Meier methods and modeling the effects of risk factors and other covariates on survival using the Cox proportional hazards regression model. Participants will learn how to interpret survival curves, hazard ratios, and model results. IBM SPSS Statistics will be used for data analysis.
This workshop introduces event-study regression as a causal inference method for examining how the effect of a specific event or treatment changes over time. Within a difference-in-differences (DiD) framework, changes in outcomes before and after the event are compared between treated and control groups. Topics include data preparation, traditional and staggered-adoption DiD models, dynamic treatment effects, assessment of pre-treatment trends, and graphical presentation of event-study estimates. The workshop will use the R packages fixest, plm, and did.