Data Analysis

Data analysis is the process of collecting, modeling, and analyzing data to extract insights that support decision-making. To help you understand the potential of analysis, the interpretation, and how you can use it to enhance your research practices, we will answer a host of analytical questions. Not only will we explore data analysis methods and techniques, but we’ll also look at different types of data analysis examples while demonstrating how to perform analysis in the real world to help you build skills.

IBM SPSS

IBM SPSS (Statistical Product and Service Solution) is a flexible statistical analysis program running on Windows and Mac operating systems.  The menu-driven interface makes it well suited for beginner users of statistical software programs. 

If you're faculty, staff, or student at the University of Windsor, you are eligible to obtain your own copy of it at discounted prices from our university.

A network version of IBM SPSS is currently available at no cost to faculty, staff, and student.

The latest version is 28.0.1. Instructions for downloading and installing IBM SPSS can be found here.

Stata

Stata offers a mix of entry level and advanced tools for data management, graphical and statistical procedures using command-based instructions.

Stata is available for purchase at reduced rates to students. Orders should be placed directly with StataCorp. Please see student single-user page at the StataCorp web site. Your order will be shipped to you directly.

SAS

SAS is a comprehensive statistical software package that runs on Windows, Mac and UNIX systems. Although the program is generally well suited to power users, it has a point-and-click interface which makes some features more accessible to new users. 

It is generally required to bring your computer in IT Service to have the installation done. 

R

R is a freely available open source software environment for statistical analysis. 

When installing, you can simply accept the default settings.
   
  Windows Users
         
          To Install R:

1. Open an internet browser and go to www.r-project.org.
2. Click the "download R" link in the middle of the page under "Getting Started."
3. Select a CRAN location (a mirror site) and click the corresponding link. 
4. Click on the "Download R for Windows" link at the top of the page. 
5. Click on the "install R for the first time" link at the top of the page.
6. Click "Download R for Windows" and save the executable file somewhere on your computer.  Run the .exe file and follow the installation instructions. 
7. Now that R is installed, you need to download and install RStudio. 

        To Install RStudio:

1. Go to www.rstudio.com and click on the "Download RStudio" button.
2. Click on "Download RStudio Desktop".
3. Click on the version recommended for your system, or the latest Windows version, and save the executable file.  Run the .exe file and follow the installation instructions.

   Mac Users
       
        To Install R:

1. Open an internet browser and go to www.r-project.org.
2. Click the "download R" link in the middle of the page under "Getting Started."
3. Select a CRAN location (a mirror site) and click the corresponding link.
4. Click on the "Download R for (Mac) OS X" link at the top of the page.
5. Click on the file containing the latest version of R under "Files."
6. Save the .pkg file, double-click it to open, and follow the installation instructions.
7. Now that R is installed, you need to download and install RStudio.

       To Install RStudio:

1. Go to www.rstudio.com and click on the "Download RStudio" button.
2. Click on "Download RStudio Desktop".
3. Click on the version recommended for your system, or the latest Mac version, save the .dmg file on your computer, double-click it to open, and then drag and drop it to your applications folder.

Are there workstations on campus where I can run it?

R, SPSS, SAS: All library workstations.
STATA: Remote Access Available.
 

NVivo

NVivo software is designed to help researchers organize, code, and analyze qualitative and mixed methods research data.

The statistical analysis methods should be chosen based on the number and nature of dependent variables (sometimes referred to as outcome variables), the number and nature of your independent variables (sometimes referred to as predict variables) and the purpose of your research. The detailed information can be found here. 

UCLA's introductory guide to data analysis Examples using different statistical software:
https://stats.idre.ucla.edu/other/dae/

You can find some upcoming free webinars here.