*RESEARCH DATA SERVICES (RDS) @ Georgia State University Library: WORKSHOPs ~ Data Analysis Tools ~ Quantitative (Stata, SPSS, SAS, R, Python) & Qualitative (NVivo)

Workshop Topics:

• Using RStudio to work with R
• R syntax, commands, functions, and packages
• Opening, viewing, and exploring data
• Generating basic descriptive statistics from data

R 2: Tidyverse and Manipulating Data

Workshop Topics:

• Introduction to Tidyverse packages (emphasis on dplyr)
• Transforming and generating variables
• Handling data with missing values
• “Piping” data and data processes

R 3: Data Visualization and Mapping

Workshop Topics:

• Creating statistical plots using ggplot2
• Customizing plot colors, themes, labels, etc…
• Working with GIS data to create maps
• Modifying maps with overlays, custom aesthetics, and additional data

R 4: Statistical Modelling

Workshop Topics:

• Basic analysis, descriptive statistics, t-tests
• Creating linear models (multiple linear regression & logistic regression)
• Evaluating linear models and generating predictions
• Creating simple machine learning models (Time permitting)

This video-only recorded workshop provides a short, high-level overview of Google Colab and how it relates to the other Python workshops. There is no live version of this workshop; it is solely available as a recorded version on our recorded workshops page linked above.

Workshop Topics:

• Brief overview of Google Colab
• Uploading, managing, and saving data in Google Colab environment

Python & Data 1: Getting Started with Python

Workshop Topics:

• Using Google Colab and Jupyter to work with Python
• Python syntax, commands, functions, and packages/modules
• Opening, viewing, and exploring data
• Generating basic descriptive statistics from data

Python & Data 2: Manipulating & Transforming Data

Workshop Topics:

• Selecting, sub-setting, and manipulating data
• Transforming and generating variables
• Handling data with missing values
• Generating crosstabs / contingency tables

Python & Data 3: Visualizing Data & Creating Models

Workshop Topics:

• Plotting and visualizing data using Matplotlib and Seaborn
• Defining statistical models using both formulas and matrices
• Fitting and inspecting statistical models (e.g. anova, linear regression)

Fundamentals of supervised learning in Python; applying a rudimentary ML model using univariate linear regression (i.e., one feature).

Workshop Topics:

• Overview: “What is Machine Learning?”
• Univariate Linear Regression Model
• Mean-Squared Error Cost Function
• Gradient Descent Algorithm for Linear Regression

Prerequisites: Python & Data Workshop Series 0-3: https://lib.gsu.edu/rds-recordings

Python for Machine Learning (ML) 2: Multivariate Linear Regression

Workshop Topics:

• Common text and string operations
• Tokenization, transformations, and processing
• Regular Expressions
• N-grams and term frequencies

This workshop is the first of a two-part series on SPSS, a statistical software package that is widely used by scientists throughout the social sciences for analysis of quantitative data.

Please note: This workshop focuses on using the point-and-click method for using SPSS; the syntax/code method is introduced briefly.

Workshop Topics

• Navigating SPSS
• Entering and importing data from different formats (such as text and Excel files)
• Defining variables (defining and labeling codes, selecting appropriate levels of measurement)
• Manipulating and transforming data (selecting cases and splitting files; recoding and computing variables)
• Running descriptive statistics
• Generating simple graphs

Prerequisites: None.

SPSS 2: Analyzing Data

This workshop is the second of a two-part series on SPSS, a statistical software package that is widely used by scientists throughout the social sciences for analysis of quantitative data.

Please note: This workshop focuses on using the point-and-click method for using SPSS; the syntax/code method is introduced briefly.

Workshop Topics

• Cross-tabulation and Chi-Square tests
• Analysis of Variance (ANOVA)
• T-tests
• Correlation analysis
• Multiple regression analysis

Prerequisites: Attendance at SPSS 1 preferred, or completion of Parts 1-6 of the Lynda.com "SPSS Statistics Essential Training" tutorial.

This is the first SAS workshop in a two-part series. This interactive workshop will introduce users to the SAS system. Applied, hands on, examples using real data will be used. NOTE: This workshop is aimed at people who do not have experience using the SAS system. Those who have used SAS in the past may find this workshop too foundational and are encouraged to attend our forthcoming advanced SAS sessions.

Workshop Topics:

• Reading data into SAS
• Conducting basic data cleaning and recoding
• Using basic SAS procedures (e.g., PROC CONTENTS, PROC PRINT, PROC FREQ) to view and understand data.
• PROC FREQ, PROC UNIVARIATE, and PROC MEANS will be demonstrated to complete basic descriptive statistics.
• An introduction to bivariate statistics in SAS.

Prerequisites: No prior experience with SAS is required. Basic understanding of univariate and bivariate statistics is helpful but not required.

SAS 2: Data Analysis

This is the second SAS workshop in a two-part series. In this interactive workshop, SAS users will go beyond the basics to develop comfort with more advanced statistical analyses using the SAS system. Applied, hands on, examples using real data will be used. NOTE: Basic knowledge of the SAS system will be helpful for those who want to participate in the applied portion of the workshop.

Workshop Topics:

• Conducting bivariate and multivariable analyses using SAS procedures like PROC FREQ, PROC TTEST, PROC ANOVA, PROC GLM, PROC LOGISTIC, and PROC REG.
• Best practices for checking statistical assumptions, selecting appropriate statistical procedures, and reporting and visualizing results will be discussed.

Prerequisites: Basic knowledge of the SAS system will be helpful for those who want to participate in the applied portion of the workshop. Basic understanding of bivariate and multivariable statistics is helpful.

This workshop is the first of a three-part series on Stata. Stata is a statistical software package. Stata is widely used by scientists throughout the social sciences for analysis of quantitative data ranging from simple descriptive analysis to complex statistical modeling.

Please note: This workshop completes all analysis using code. No previous knowledge of coding is required.

Workshop Topics:

• Opening data
• Generating variables (basic)
• Frequency distributions
• Analysis: summary statistics

Prerequisites: None.

Stata 2: Basic Data Analysis

This workshop is the second in a three-part series on Stata. Stata is a statistical software package. Stata is widely used by scientists throughout the social sciences for analysis of quantitative data ranging from simple descriptive analysis to complex statistical modeling.

Please note: This workshop completes all analysis using code. No previous knowledge of coding is required.

Workshop Topics:

• Generating variables (advanced)
• Analysis: Chi-square, ANOVA, regression
• Graphs
• Navigating help features

Prerequisites: Stata 1 or basic knowledge of Stata.

Stata 3: Advanced Data Analysis

This workshop is the third in a three-part series on Stata. Stata is a statistical software package. Stata is widely used by scientists throughout the social sciences for analysis of quantitative data ranging from simple descriptive analysis to complex statistical modeling.

Please note: This workshop completes all analysis using code. No previous knowledge of coding is required.

Workshop Topics:

• Troubleshooting code
• Generating scales
• 3, 4, and 5 way cross tabulations

Prerequisites: Stata 1 and Stata 2 or moderate knowledge of Stata.

This is the first workshop in a two-part series on NVivo qualitative data analysis software.

Workshop Topics

• Getting to know the NVivo workspace
• Exploring different types of data types/files that can be analyzed
• Basic coding of text-based files
• Using Queries and Automated features to explore and code your data
• Recording comments and ideas

Prerequisites: Basic understanding of qualitative research methods is suggested, but not required. Watching this short tutorial on coding qualitative data before attending the workshop is recommended.

NVivo 2 for Windows: Exploring Your Data

This is the second workshop in a two-part series on NVivo qualitative data analysis software.

Workshop Topics

• Creating Classifications with Attribute Values to facilitate comparative analyses
• Crosstab and Matrix Coding queries
• Data visualizations
• Sharing findings with Reports and exporting Codebooks

Prerequisites: NVivo 1

This workshop is on using NVivo qualitative data analysis software to do team work / team coding in NVivo involving 2 or more people. It will cover various strategies for tracking team members’ work in NVivo and comparing coding, including generating inter-rater reliability measures.

Workshop Topics:

• Creating and organizing NVivo project files for independent coding
• Merging / importing individual team member's project files into a master copy file.
• Comparing coding between team members, including generating inter-rater reliability measures
• Challenges of having team members working across Windows and Mac versions

Prerequisites: Attendance at minimally the NVivo 1 workshop (live or recorded) and optionally the NVivo 2 workshop (live or recorded).