Statistics and Research Workshop Series
View Recordings of Previous Sessions
| Date/Time | Session Title | Description | Links |
|---|---|---|---|
| May 19, 2026 9:00 - 11:00 | Introduction to SPSS | SPSS is a menu-driven statistical package for the social sciences. This workshop is for people just getting started with SPSS. It will cover SPSS from the very beginning, including: (1) introduction of the SPSS interface and data layout; (2) types of measurement; (3) how to enter data in SPSS and import external data (including Excel data and CSV data) into SPSS; (4) variable coding; (5) how to code, recode, and compute variables; (6) how to merge two data files. Facilitated by Hui Bian | Register for Introduction to SPSS Join Introduction to SPSS |
| May 19, 2026 1:00 - 3:00 | Introduction to JMP | JMP is a point-and-click based program for statistical analysis developed by SAS. The purpose of this workshop is to provide a hands-on demonstration of the software. Topics will include data manipulation, graphical and numerical data description, and preliminary statistical analysis such as correlation, regression, t-test, ANOVA and Chi-square test. Facilitated by Xiangming Fang | Register for Introduction to JMP Join Introduction to JMP |
| May 20, 2026 9:00 - 11:00 | Qualtrics | Qualtrics is an online survey tool available to faculty, staff, and students. This workshop will guide you through the process of building a survey project. We will learn how to create new items, customize response options, add display and skip logic, and distribute a survey. Facilitated by Courtney Baker | Register for Qualtrics Join Qualtrics |
| May 20, 2026 1:00 - 3:00 | Introduction to R/RStudio | This workshop will cover the basic of R knowledges, data reading, data processing, descriptive analysis, simple linear regression and multiple linear regression, plot figures using ggplot, and R Markdown. Facilitated by Franklin Zhou | Register for Introduction to R/RStudio Join Introduction to R/RStudio |
| May 21, 2026 9:00 - 11:00 | Python for Data Analysis | Python is a free, open-source language used for statistical computing and data analysis. It has a vast library of packages for complex research, and it is now a standard tool for statisticians and researchers across many fields. This workshop covers the basics of Python programming, data management using Pandas and Numpy, data visualization with Matplotlib, and common regression analysis with Statsmodels. Facilitated by Arthur Liu | Register for Python for Data Analysis Join Python for Data Analysis |
| May 21, 2026 1:00 - 3:00 | Building a Dashboard Using Power BI | This workshop will cover the following topics: introduction to Power BI, getting data, transform data, modeling data, build visuals, publish and share dashboards. Facilitated by Franklin Zhou | Register for Building a Dashboard Using Power BI Join Building a Dashboard Using Power BI |
| May 26, 2026 9:00 - 12:00 | Descriptive Statistics | This workshop introduces the fundamental principles of descriptive statistics with a focus on practical implementation in R. Participants will learn how to summarize and explore data using key measures of central tendency (mean, median, and mode), variability, and graphical techniques such as histograms and boxplots. The session emphasizes understanding the structure and distribution of real-world data, identifying patterns, and detecting potential outliers. Building on the traditional workflow of “getting to know your data” , the workshop replaces point-and-click software with reproducible R-based analysis. Attendees will gain hands-on experience in importing datasets, generating summary statistics, and creating visualizations using basic R functions and tidy workflows. By the end of the session, participants will be able to interpret descriptive outputs and communicate findings effectively in a public health or research context. This workshop is designed for beginners and serves as a foundation for subsequent statistical inference and modeling. Facilitated by Zikun Yang | Register for Descriptive Statistics Join Descriptive Statistics |
| May 26, 2026 1:00 - 4:00 | Inferential Statistics | This workshop is designed for individuals with little to no background in math or statistics. Its goal is to introduce you to the fundamental concepts of statistical inference and explore commonly used methods, with a focus on when and how to apply them effectively. Facilitated by Courtney Baker | Register for Inferential Statistics Join Inferential Statistics |
| May 27, 2026 9:00 - 12:00 | Logistic Regression | This workshop serves as a basic introduction to logistic regression. Logistic regression is used when the outcome variable in a regression model is binary, e.g., yes/no. We will review the basics of the underlying logistic regression models and focus on fitting models and interpreting results using common statistical software such as R and SPSS. Facilitated by Alex Schoemann | Register for Logistic Regression Join Logistic Regression |
| May 27, 2026 1:00 - 4:00 | T Test and ANOVA | This workshop will provide an overview of the theoretical framework and assumptions of one-sample t-test, matched pairs t-test, independent samples t-test, and one-way and two-way ANOVA. IBM SPSS will be used to demonstrate the implementation of these tests and the assessment of the assumptions. Facilitated by Xiangming Fang | Register for T Test and ANOVA Join T Test and ANOVA |
| May 28, 2026 9:00 - 12:00 | Time Series Analysis in Stata | This three-hour workshop introduces participants to time series analysis using Stata, one of the most widely used statistical software packages in economics, finance, public health, and the social sciences. Participants will develop hands-on competency with Stata commands for organizing, visualizing, testing, and modeling time-ordered data, skills that are directly applicable to both applied research and dissertation work. The workshop is structured to be accessible to participants with no prior Stata experience while simultaneously offering analytical depth useful to intermediate users. By the end of the session, participants will be able to independently conduct a complete time series analysis pipeline: from data import and declaration through model estimation, diagnostic testing, and forecasting. No prior Stata experience required. Software: Stata (any version 14 or later). A 30-day trial is available on Strata's Website. Facilitated by Thanh Ngo | Register for Time Series Analysis in Stata Join Time Series Analysis in Stata |
| May 28, 2026 1:00 - 4:00 | Linear Regression | This workshop will provide an introduction to simple and multiple linear regression models. Topics include model assumptions, parameter estimation and interpretations, model selection, model diagnosis (e.g., tolerance and variance inflation factor), and ways to accommodate assumption violations. SPSS will be used for demonstration. Facilitated by Whitney Moore | Register for Linear Regression Join Linear Regression |
| June 2, 2026 9:00 - 11:00 | Research Design Fundamentals | How do you choose the right path for your research project? Whether you’re preparing a thesis, a grant proposal, or a research project, selecting the appropriate research design is the critical first step to ensuring your results valid, reliable, and meaningful. In this session, we will explore some fundamental approaches: experimental design including randomized controlled trials (RCTs), quasi-experimental design, survey research, qualitative research design, and mixed design. Facilitated by Hui Bian | Register for Research Design Fundamentals Join Research Design Fundamentals |
| June 2, 2026 1:00 - 3:00 | Factor Analysis | Many of the constructs we study cannot be directly observed, instead these latent variables must be inferred from other measured variables through factor analysis. In this workshop, we will discuss methods to assess and estimate latent variables. We will discuss different models for estimating latent variables including exploratory factor analysis (EFA), and confirmatory factor analysis (CFA). Examples will be in R (EFA and CFA) and SPSS (EFA). Facilitated by Alex Schoemann | Register for Factor Analysis Join Factor Analysis |
| June 3, 2026 9:00 - 11:00 | Survival Analysis | Survival analysis is time-to-event analysis. It is a statistical method used to analyze and model the time until an event of interest occurs. Survival analysis is particularly useful when studying time related events such as death, disease recurrence, graduation from college, etc. In this workshop, Kaplan-Meier and Cox proportional hazards regression will be introduced. Facilitated by William Irish | Register for Survival Analysis Join Survival Analysis |
| June 3, 2026 1:00 - 3:00 | Handling Missing Data with R | This workshop will focus on using R to address the problem of missing data. It includes identifying missing data, visualizing missing data, applying hands-on imputation methods, and pooling results. Facilitated by Franklin Zhou | Register for Handling Missing Data with R Join Handling Missing Data with R |
| June 4, 2026 9:00 - 11:00 | Nonparametric Tests | This workshop will explore when to use nonparametric tests and the differences between parametric and nonparametric tests. We will conduct some data analyses to test two or more samples, including the Wilcoxon signed-rank test, the Mann-Whitney U test, and the Kruskal-Wallis H test. The presentations will use SPSS and R software. Facilitated by Hui Bian | Register for Nonparametric Tests Join Nonparametric Tests |
| June 4, 2026 1:00 - 3:00 | Latent Class Analysis Intro to Mixture Modeling | This workshop will provide an introduction to latent mixture modeling steps using latent class analysis (LCA) as the example analysis type. Distinctions between mixture modeling types (e.g., research questions answered, variable types) will be covered before going through the steps for completing an LCA model, which uses categorical variables for indicators. The steps for model convergence, class enumeration, and how to bring in covariates will be covered. Mplus syntax will be provided for the example model (the demo version of Mplus will be sufficient for this workshop). Facilitated by Whitney Moore | Register for Latent Class Analysis Intro to Mixture Modeling Join Latent Class Analysis |
Individuals requesting accommodation under the Americans with Disabilities Act (ADA) should contact ECU’s ADA coordinator at least 48 hours prior to the event at 252-737-1018 or ada-coordinator@ecu.edu.
Link to archived Statistics and Research Workshops
Note: Research/statistics consulting is available at regular hours (8am-5pm, M-F). All workshops and consultations are open to graduate students as well.