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Center for Statistical Computing

Statistical Workshops

Center for Statistical Computing (CSC) welcomes all graduate students, staffs and faculty members to join our statistical workshops in ZOOM.  Our workshops include using statistical software such as SPSS, SAS, Stata, R and WinBUGS and topics in applied statistics. The basic series of statistical workshop descriptions, schedules and registration procedure are listed as follows:

Spring 2023 Statistical Workshop Schedule

  • SPSS 1

This is a hands-on workshop designed to enable attendees to perform useful data analysis using SPSS for Windows. Topics include: entering and reading data, documenting variable and value labels, examining frequency and crosstab tables for individual and group data, recoding variables, conducting independent sample t-tests, and simple linear regression.

  • SPSS 2

This workshop covers additional data management and statistical procedures, including selection of cases, combining cases from two files, and linking files together with different information. Statistical procedures include the chi-square test, one-way ANOVA, repeated measurement analysis, non-parametric statistics, multiple regression, and logistic regression.

  • Introduction to R for Statistical Analysis

This workshop focuses on performing basic analyses such as descriptive statistics, frequency distributions, chi-square tests, independent sample t-tests, one-way ANOVA, and linear and logistic regressions. Topics also include: downloading and installing R packages, reading and writing data files, and creating R graphs. R is free, open-source, and supported by a strong user community. 

  • Stata 1

This workshop is an introduction to Stata that covers both graphic user interface and intuitive command syntax approaches. It aims to teach basic Stata operations in a quick and accurate way. Topics include: browsing data, data management, descriptive statistics, independent samples t-test, and simple linear regression models.

  • Stata 2

This workshop covers additional data management topics such as data transformation, recoding variables, and constructing new variables. It also covers the use of log files, do files, and additional statistical procedures such as the chi-square test, one-way ANOVA, simple and multiple linear regression, and regression diagnostics.

  • Statistical Analysis Using Excel

The workshop teaches tips for improving efficiency while using Excel for data analysis. Topics include: entering data, data organization and descriptive statistics, examining frequencies and crosstab tables, conducting independent and paired sample t-tests, correlation analysis, and simple linear regression. 

  • SAS 1

This workshop is an introduction to the SAS system that concentrates on the SAS DATA STEP with emphasis on data input, manipulation, output, and summary. Topics include: creating SAS working data sets and data files, importing data from SPSS and Excel files, formatting variable and value labels, and conducting simple statistical procedures such as PROC FREQ and PROC MEANS.

  • SAS 2

This workshop covers the analysis of designed experiments with PROC ANOVA and PROC GLM, and linear and non-linear regression techniques with PROC REG and PROC GENMOD. Topics include: one-way and two-way analysis of variance, simple and multiple linear regression, regression diagnostics, and logistic regression.

  • Introduction to RStudio

This workshop introduces RStudio, a user-friendly integrated development environment for the R language. Important R concepts such as elementary data structures, atomicity, plotting using ggplot2, functions, vectorization, 3D regression plotting, and logistic regression are covered.

  • COVID-19 Data Analysis Using R  

We will download COVID-19 data for states and Massachusetts from the Center for Systems Science and Engineering of Johns Hopkins University and the Department of Public Health (DPH) Massachusetts. We will use time series and spatial regression models to analyze the COVID-19 data with R packages forecast, tseries, spdep, maptools, and ggplots. This workshop will also demonstrate how to use R to generate reports for COVID data.  

  • Structural Equation Modeling I (AMOS & Mplus)

This workshop introduces structural equation modeling (SEM) techniques. SEM is used to test ‘complex’ relationships between observed (measured) and unobserved (latent) variables. Topics include: fundamentals underlying SEM, SEM notation, path diagrams, data preparation, observed variables, model specification, parameter estimation, and assessment of model fit. AMOS and Mplus are used to demonstrate examples;

  • Structural Equation Modeling II (AMOS & Mplus)

The second SEM workshop covers measurement error, latent variables, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), developing structural equation models with estimation, and model testing. Also, the SEM for longitudinal data using latent growth models will be introduced in this workshop. Latent growth modeling is used to model individual change and can be used to test treatment effects and time dependent covariates. Mplus syntax and output are used to demonstrate model structures, parameter estimation, and model modification;

  • Introduction to HLM (Mixed Models) using SPSS

This workshop introduces the basic principles of multilevel/hierarchical linear models. Topics include: the need 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.

  • Sample Size Estimation and Power Calculation using SAS

This workshop covers sample size determinations and power estimation for various statistical comparisons and tests using the PROC POWER procedure in SAS.

  • Missing Data Analysis using SAS & Stata

This workshop covers the mechanisms of missing data, analysis of non-random selection bias, and methods of single and multiple imputation (MI) using SAS[NC1]  and Stata. Missing data are common across all types of data sets. Most statistical software packages automatically eliminate entire cases with missing data from analysis, potentially leading to low sample sizes and biased results.

  • Introduction to Statistical Learning using R 

This is an introductory workshop in statistical learning focusing on the important elements of modern data analysis such as regression and classification methods. Topics include: linear and logistic regression, linear discriminant analysis, cross-validation, principal components, and clustering. R is used for data analysis examples. 

  • Spatial Regression Using R

This workshop introduces spatial regression using tools such as R’s maptools and spdep packages. Topics include: spatial data visualization in R, spatial autocorrelation, statistics for spatial dependence, spatial weights, and spatial regression models.

  • Intro to Time Series Analysis using R

This workshop emphasizes the practical aspects of time series analysis. Methods are hierarchically introduced, starting with terminology and exploratory graphics, moving to descriptive statistics, and ending with practtical modeling procedures including how to choose an appropriate time series forecasting method, fit a model, evaluate its performance, and use it for forecasting. It focuses on the most popular business forecasting methods: regression models, smoothing methods including Moving Average (MA) and Exponential Smoothing, and Autoregressive (AR) models. Practical implementation in R is illustrated at each stage of the workshop.

  • Introduction to SAGE Campus

SAGE Campus is a learning platform with designed online courses for skills and research methods. These courses are fully self-paced, packed with an engaging mix of video, interactives, and formative assessments. This workshop focuses on an overview of SAGE Campus courses and helping students to set up an account to enroll in SAGE courses. One of SAGE online course Introduction to R will be used as an example.