Graduate Quantitative Methods Center

Supporting Graduate Student Academic and Professional Success

Previous Workshop Resources

Don’t worry if you miss one of our workshops. We record our workshops and post them here for you to watch later. 

Previous Workshop Resources

Don’t worry if you miss one of our workshops. We record our workshops and post the most recent ones here for you to watch later. Please note that UCR login credentials are required to access these workshop recordings. 

  • LaTeX Series
    • Advanced Formatting in LaTeX (Winter 2018) (Slides)
    • How to Use the UCR LaTeX Dissertation Template (Winter 2019, Fall 2020) 
    • Introduction to Latex (Winter 2020, Fall 2020) 
    • Introduction to LaTeX (Fall 2021) (Video)
    • Introduction to LaTeX (Summer 2022) (Video)
    • Introduction to LaTeX (Fall 2022) 
    • Introduction to LaTeX (Winter 2023) (Video)
    • Introduction to LaTeX (Spring 2024) (Video)
    • LaTeX Basics (Fall 2017) (Handout, Resources)
    • LaTeX Dissertation Formatting (Spring 2018) (Slides)
    • LaTeX Fundamentals (Fall 2015) (Slides)
    • Writing Articles in LaTeX (Spring 2018) 
  • Programming and Databases
    • Introduction to Programming (Fall 2019) 
    • Java
      • JAVA for Beginners (Spring 2018) 
      • Java Fundamentals (Fall 2015) (Slides)
    • Machine Learning for Beginners (Spring 2019) 
    • Machine Learning for Beginners (Summer 2022) (Video, Slides)
    • MATLAB Series
      • Data Processing in MATLAB (Winter 2018) (Slides)
      • Introduction to MATLAB (Winter 2018) (Slides)
      • MATLAB Basics (Spring 2019) 
      • Introduction to MATLAB (Fall 2022) (Video)
    • Network Analysis (Winter 2018) 
    • Python Series
      • A Primer on Neural Networks with Python (Spring 2019) 
      • Data Management in Python
        • Data Management in Python (Winter 2018) (Code)
        • Data Management in Python (Fall 2018, Winter 2019) 
        • Data Management in Python (Spring 2020) (code)
        • Data Management in Python (Winter 2023) (code)
      • Data Visualization in Python
        • Data Visualization in Python (Winter 2019, Winter 2020) 
        • Visualization in Python (Winter 2023) (Code, Video)
        • Visualization in Python (Winter 2024) ( Code, Video)
      • Data Management & Big Data Analysis (Fall 2019) 
      • Geovisualization with Python (Winter 2018)  
      • Graphing in Python (Fall 2017) 
      • Introduction to Python
        • Introduction to Python (Fall 2016) (Slides, Code)
        • Introduction to Python (Fall 2017, Fall 2018, Fall 2019) 
        • Python Fundamentals (Winter 2018) (Video)
        • Introduction to Python (Winter 2021) (Video, Slides)
        • Introduction to Python (Winter 2022) (Video)
        • Introduction to Python (Fall 2022)
        • Introduction to Python (Winter 2023) (Video, Code)
      • Introduction to Web APIs in Python:
        • Introduction to Web APIs with Python (Winter 2018) 
        • Introduction to Web APIs Using Python (Winter 2022) (Video)
      • Machine Learning in Python (Spring 2018) (Slides pt1, pt2, code)
      • Text Scraping: Twitter API with Python (Fall 2019) 

      • Web Scraping with Python (Spring 2023) (Video, Code)

    • SQL and Databases
    • SolidWorks (Spring 2019) 
  • Statistical Software Series
    • R Series
      • Time Series Regression in R (Spring 2022) (Video)
      • Advanced Data Manipulation in R (Winter 2019) 
      • Bayesian Data Analysis in R (Fall 2021) (Video)
      • Building Your First Shiny App in R (Spring 2020) 
      • Building Your First Shiny App in R (Spring 2021) (Video)
      • Data Management in R (Winter 2020, Spring 2020) 
      • Data Management in R (Spring 2021) (Video)
      • Data Manipulation in R (Fall 2017) (Code)
      • Data Manipulation in R (Fall 2019, Fall 2020)
      • Data Manipulation in R (Fall 2021) (Video, Slides)
      • Data Visualization in R with ggplot2 (Winter 2019) 
      • Data Visualization in R (Winter 2020) 
      • Data Visualization in R (Spring 2020) (Code) 
      • Data Visualization in R (Fall 2020) (slides)
      • Data Visualization in R (Winter 2022) (Video)
      • Data Visualization in R (Fall 2022)
      • Debugging Techniques in R (Winter 2020) 
      • Debugging in R (Spring 2020) 
      • Graphing in R (Fall 2017) (Code)
      • Intermediate Topics in R
        • Intermediate Topics in R (Fall 2018) (Markdown file)
        • Intermediate Topics in R (Winter 2018) (Video)
      • Introduction to R
        • Introduction to R (Fall 2017) (Code)
        • Introduction to R (Summer 2018) (Code)
        • Introduction to R (Fall 2019) (Slides)
        • Introduction to R (Winter 2020) 
        • Introduction to R (Fall 2020) (Slides)
        • Introduction to R (Winter 2021) (Video)
        • Introduction to R (Fall 2021) (Video)
        • Introduction to R (Spring 2022) (Video)
        • Introduction to R (Fall 2022)
        • Introduction to R (Winter 2023) (Slides)
        • Introduction to R (Summer 2023 at DS-Path) (Video, Code and Slides )
      • Introduction to R documents (Winter 2020) 
      • Introduction to R's Tidyverse (Spring 2020, Spring 2021) 
      • Introduction to R and the Tidyverse Package (Video)
      • Machine Learning in R
        • Machine Learning in R (Winter 2018) (Slides)
        • Machine Learning in R (Spring 2018) 
        • Machine Learning in R (Winter 2021) (Video)
        • Machine Learning in R (Spring 2022) (Video)
      • Multilevel Modeling in R (Winter 2018) (Code)
      • Statistical Analysis in R (Fall 2019) (Slides)
      • Statistical Analysis in R (Spring 2021) 
      • Regression in R (Spring 2018) (Slides, Code)
      • Regression in R (Spring 2020) (Slides, Code) 
      • Linear Models in R (Fall 2022)
    • Dedoose Series
      • Getting Started with Dedoose: Project Set-up, Data Preparation, and Coding (Fall 2022) (Video)
      • Exploring Patterns and Relationships: Advanced Tools in Dedoose (Fall 2022) (Video)
    • Stata Series
      • Bayes in Stata (Winter 2018) (Slides_1, Slides_2, Slides_3)
      • Extended Regression Models (Winter 2018) (Slides)
      • Instructional Lab (Winter 2020) (Data, code)
      • Introduction to Stata (Spring 2019-Part 1) 
      • Introduction to Bayesian Statistics Using Stata (Spring 2019-Part 2) 
      • Multilevel/Longitudinal Modeling (Spring 2019-Part 3) 
      • Structural Equation Modeling (Spring 2019-Part 4)
      • Latent Class Analysis (Winter 2018) (Slides_1, Slides_2)
      • Multilevel Modeling in Stata (Winter 2018) (Slides_1, Slides_2, Slides_3)
      • Structural Equation Modeling in Stata (Winter 2018) (Slides_1, Slides_2)
      • Data Cleaning and Management with Stata (Summer 2023) (Code, Video)
      • Stata Workshop Series (Spring 2019)
        • Bayesian Methods 
        • Introduction to Stata
        • Multilevel Modeling 
        • Structural Equation Models 
      • Stata Workshop Series (Spring 2020) 
        • Morning Session 
        • Afternoon Session 
      • Stata Workshop Series (Spring 2021)
        • Introduction to Stata and Basic Descriptive & Inferential Statistics (Video)
        • Introduction to Graphics in Stata (Video)
        • Bayesian Multilevel/Longitudinal Modeling in Stata (Video)
        • Survival Analysis in Stata (Video)
      • Stata Workshop Series (Spring 2022)
        • Introduction to Stata (Video)
        • ANOVA, Contrasts, and Pairwise Comparisons (Video)
        • Introduction to Bayesian Analysis using Stata (Video)
        • Missing Data and Multiple Imputation (Video)
      • Stata Workshop Series (Spring 2023)
        • How to Create Basic Stata Commands  (Video)
        • How to Create Basic Stata Estimation Commands  (Video)
        • Fitting Models and Interpreting Results  (Video)
      • Stata Workshop Series (Spring 2024) (Slides and Code) 
        • Introduction to Mediation Analysis (Video)
        • Panel Data and Mixed Effects Models: What's the Difference? (Video)
        • Introduction to Difference-in-Differences (Video)
        • How to Create Basic Stata Commands (Video)
    • Statistical Analysis in SPSS (Winter 2018) 
    • Introduction to SAS (Fall 2020) 
  • Statistical Topics and Methodology
    • ANOVA
      • ANOVA (Fall 2014) (Slides)
      • ANOVA (Fall 2016, Winter 2020, Spring 2020, Fall 2020)
      • ANOVA (Fall 2017) (Video, Slides)
      • ANOVA (Winter 2022) (Video, Slides)
      • ANOVA: Advanced Designs (Winter 2015, Winter 2017) 
      • Introduction to ANOVA (Winter 2019, Summer 2019) 
    • Network Analysis
      • Introduction to Network Analysis (Winter 2020) 
      • Application of Network Analysis (Spring 2020) 
      • Network Analysis in R/igraph (Winter 2023) (Video,Slides)
    • Introduction to Longitudinal Data Analysis (Spring 2021) (Video)
    • Bayesian Estimation
      • Bayesian Estimation using MCMC (Winter 2014) ( Slides and Code)
      • Bayesian Estimation using MCMC (Fall 2015) (Slides, Code)
      • Bayesian Estimation using MCMC (Fall 2017) (Slides, Example)
      • Bayesian Estimation using MCMC (Spring 2022) (Video)
      • Bayesian MCMC Using OpenBUGS (Winter 2023) (Video, Slides)
      • Introduction to Computational Bayesian Estimation (Fall 2019) (Slides and Code)
    • Categorical Data Analysis (Winter 2020) 
    • Categorical Data Analysis (Winter 2021) (Video)
    • Categorical Data Analysis (Winter 2022) (Video)
    • Categorical Data Analysis (Fall 2022) (Video)
    • Categorical Data Analysis (Fall 2023) (Video)
    • Causal Inference
      • An Overview of Causal Inference (Fall 2017) (Slides)
      • An Overview of Causal Inference (Winter 2018) 
      • Basic Designs for Causal Inference (Spring 2019) 
      • Computational Bayesian Inference (Spring 2019) 
      • Introduction to Statistical Inference (Summer 2018) 
      • Causal Inference (Fall 2020) 
      • Causal Inference: What's Trending in Difference-in-Differences (Spring 2023) (Slides, Video)
    • Data Reduction Techniques (Spring 2014) (Slides)
    • Effect Size and Contrast Analysis (Winter 2017) 
    • Experimental Design
      • Experimental Design (Winter 2018, Spring 2019) 
      • Experimental Design (Spring 2021) (Video)
      • Experimental Design (Winter 2022) (Video)
    • General Linear Model
      • Introduction to the General Linear Model (Spring 2017) (Slides)
      • Introduction to the General Linear Model (Fall 2017) 
      • Introduction to Generalized Linear Models (Spring 2015) (Slides and Code)
      • Introduction to General Linear Models (Spring 2019) 
      • Introduction to Generalized Linear Models (Spring 2020)
      • Introduction to Generalized Linear Models (Winter 2021) (Video)
      • Generalized Linear Models (Spring 2022) (Video)
    • How to handle missing data (Winter 2020) 
    • How to handle missing data (Summer 2022) (Video, Slides)
    • Hypothesis Testing (Fall 2019) (Slides)
    • Hypothesis Testing (Winter 2020)
    • Hypothesis Testing (Fall 2020) 
    • Hypothesis Testing and Statistical Power (Spring 2021) (Video)
    • Hypothesis Testing (Fall 2021) (Video)
    • Choosing the Right Test for Your Question and Data (Spring 2024) (Video)
    • Improving the Credibility of Your Research (Fall 2017) (Slides)
    • Instrumental Variables in Rubin Causal Model (Spring 2014)
    • Law of Large Numbers and Central Limit Theorem (Fall 2023) (Slides and Code, Video)
    • Multilevel Modeling
      • Multilevel Modeling in R (Winter 2018) 
      • Introduction to Multilevel Modeling (Fall 2018)
      • Introduction to Multilevel Modeling (Spring 2020) 
      • Introduction to Multilevel Modeling (Spring 2021) (Video)
    • Multilevel/Mixed Models (Fall 2022)
    • Introduction to Survival Analysis (Winter 2015) (Slides_1, Slides_2)
    • Introduction to Survival Analysis (Fall 2020) 
    • Machine Learning
      • Machine Learning in Python (Spring 2018) (Slides pt1, pt2, code)
      • Machine Learning in R (Winter 2018)  Slides)
      • Machine Learning in R (Spring 2022) (Video)
      • Introduction to Machine Learning (Fall 2022)
      • Introduction to Machine Learning (Winter 2023) (Slides)
    • Maximum Likelihood and Method of Moments Estimation (Spring 2015) (Slides)
    • Maximum Likelihood Estimation and Methods of Moments Estimation (Winter 2021) (Video)
    • Mixture Models and Their Applications (Spring 2015) (Lecture notes)
    • Mixture Models (Spring 2021) (Video)
    • Multidimensional Data Analysis (Fall 2019) (Slides)
    • Multidimensional Data Analysis (Winter 2021) (Video)
    • Multidimensional Data Analysis (Winter 2022) (Video)
    • Multidimensional Data Analysis (Spring 2023) (Video, Code)
    • Non-parametric Tests (Winter 2015) (Slides)
    • Non-parametric Testing Procedures (Spring 2020) 
    • Nonparametric Testing Procedures (Spring 2022) (Video)
    • Power and Sample Size Calculation
      • Power and Sample Size Calculation (Fall 2015) (Slides)
      • Power and Sample Size Calculation (Fall 2016) (Slides)
      • Power in Clustered Designs (Fall 2017) ( Slides)
    • Probability (Fall 2019) (slides)
    • Probability Theory (Spring 2021) (Video, slides)
    • Regression 
      • A New Look at Regression Analysis (Winter 2017) 
      • Introduction to Linear Regression (Summer 2019) 
      • Introduction to Panel Regression (Spring 2015) ( Slides and Code, datasets)
      • Regression in R (Spring 2018) (Slides, Code, )
      • Regression and Correlation (Fall 2019) (Slides)
      • Regression (Winter 2020) 
      • Correlation and Regression (Fall 2020) 
      • Linear Regression (Spring 2022) (Video)
      • Introduction to Linear Regression (Spring 2023) (Video)
    • Simulation & Cross-Validation (Spring 2023) (Video, Code)
    • Statistical Inference
      • Introduction to Statistical Inference (Winter 2015) (Slides)
      • Introduction to Statistical Inference (Fall 2015) (Slides)
      • Introduction to Statistical Inference (Summer 2018) (Slides)
    • Structural Equation Modeling 
      • Structural Equation Modeling  (Spring 2016) (R code and Data, Slides)
      • Structural Equation Modeling  (Winter 2019) 
    • Survey Research
      • Survey and Sampling Methods (Winter 2014) (Slides)
      • Survey and Sampling Methods (Fall 2019) 
      • Survey Research Design (Spring 2018) 
      • Survey Research with Qualtrics (Spring 2019)
    • Text Analysis in the Social Sciences (Presented by Molly Roberts of UCSD) (Winter 2018) (R Code, Datasets)
    • Unleash the Power of Natural Language Processing  (Slides)
    • What Population Does Your Sample Represent? (Spring 2015) (Slides)
    •  Making Sense of Principal Component Analysis and Eigenvalues (Winter 2024) (Slides, Video)
  • Other Topics
    • Introduction to GitHub (Winter 2022) (Video)
    • Applied Quantitative Transparency and Reproducibility Methods (Spring 2015) 
    • Data Analysis in GraphPad Prism (Winter 2019) 
    • Data Management in Excel (Fall 2018) 
    • Decoding ChatGPT (Spring 2023) (Video)
    • Geoprocessing and Geodatabase Design (Winter 2017) 
    • Getting Started with Open Science Framework (Fall 2014) 
    • Introduction to Programming (Fall 2017)
    • Mathematical Tools In Excel (Fall 2015) ( Slides and sheets)
    • Make a Personal Website with GitHub (Summer 2023) (Video)
    • Make a Personal Website with GitHub (Spring 2024) (Video)
    • Network Analysis (Winter 2018) (Slides 1, 2, 3, 4, 5, 6)
    • Can We Predict Breakoff and Intervene Appropriately in Web Surveys? (Spring 2021) (Video)
    • Research Credibility
      • Improving the Credibility of Your Research (1) (Fall 2017) 
      • Improving the Credibility of Your Research (2) (Fall 2017) 
      • Research Credibility (Fall 2019) 
    • Introduction to Artificial Intelligence (Spring 2023) (Slides, Video)
    • Introduction to Data Science (Winter 2024) (Video)
    • Introduction to ImageJ (Winter 2022) (Video)
    • Introduction to ImageJ (Fall 2022) (Video)
    • Introduction to Bioinformatics (Winter 2022) (Video)
    • Bringing Satellite Data Down to Earth (Spring 2023)  (Video)
    • Introduction to GIS with Python (Spring 2023) (Video , Code)