Data Analysis Resources
Statistics & Data Analysis
Brushing Up on Statistics:
To effectively use your chosen statistical software, you may need to brush up on the fundamentals of statistics. How do you know what tests to run? How do you decide your levels of measurement? What kind of data do you have? Thankfully, the internet has thousands of resources from books and articles to blogs, forums, and videos to help get you up to speed. Whether you’re a beginner or an expert, love equations or not, you can find the statistical help you need.
Here are a few articles and links that get right to the point:
Social Science Statistics: Social Science Statistics is a delightful way to get your feet wet as a beginner as you ponder which statistical tests to use in your research. They have SPSS based tutorials, offer quizzes, and even provide a wizard to help you decide what tests to use. Even better, they provide super simple calculators. Enter a few values by hand and run the test to see how it works. (Visit Social Science Statistics.)
Talk Stats Forum: Just as it says – a place to talk about all things statistics and statistical, beginner to advanced. Free to use. Can register and post questions as a dissertation student. Fun to browse. (Visit Talk Stats Forum.)
Statistics How To: Excellent online statistics handbook. Extensive resources.
29 Statistical Concepts Explained in Simple English - Part 1: A series of blog posts by scientist and mathematician, Vincent Granville, this content is exceptionally well written for non-mathematicians. Granville does an excellent job of simplifying statistical tests and explaining what they do. There are many more than twenty-nine concepts. To begin, go to the home page and scroll down to Part 1. (Visit Granville's Blog Post.)
Note: Granville's blogs on this subject are posted in 12 parts. To find them, click here to access the Data Science Central Search Engine then click on the Part.
Choosing a Statistical Test
Parab & Bhalerao (2010) have written a straightforward article that covers levels of measurement, P values, hypothesis testing, and selecting the correct test. An excellent review.
Parab, S., & Bhalerao, S. (2010). Choosing statistical test. International Journal of Ayurveda Research, 1(3), 187–191. https://doi.org/10.4103/0974-7788.72494
NYU Libraries: Offers wonderful statistics guides. Visit NYU Libraries Statistics Guide.)
This includes a detailed interactive table that helps you decide what statistical tests to use. Once you've decided, it then links you to other sites that provide examples of how to run the test in common statistical packages. An encyclopedia of knowledge in one clickable chart, this site is a must-visit. To access the table, pull down the menu under the statistical guidance tab and start with the tests for one dependent variable. (Visit NYU Interactive Chart.)
Cancer Research And Biostatistics (CRAB): A collaboration between CRAB and the SWOG Cancer Research Network. The website offers free to use statistical tools for high-level analyses. (Visit Cancer Research And Biostatistics.)
WISE: Web interface for statistics education. WISE offers interactive tutorials and videos to provide you with resources to better understand statistics. Free to use. Be sure to access their extensive links to other statistical websites. (Visit WISE.)
Data and Statistics About the U.S. Statistics and data from the government. A good place to start when looking for demographic or census statistics for your study.
Data Analysis Applications
Does the prospect of analyzing your data feel daunting? It might be an unfamiliar process. Yet, like every step of this dissertation journey, you’ll learn the basics quickly. Your first step is to establish a working familiarity with a statistical software program. Statistical software programs allow you to analyze your data, as hand-computed statistics are a thing of the past. If your data is quantitative or mixed research, you’ll need software to help sort and code the copious narratives you will collect. Below are some available and affordable statistical software options for both quantitative and qualitative analysis.
Jamovi: Free and open-source intuitive statistical application that uses R, much like a spreadsheet. Jamovi is designed more for beginners than advanced R users. It aims to avoid R jargon and allows developers to create plug-ins to further assist the user. Creators designed Jamovi as a more intuitive way to enter and visualize data, including charts, graphics, and diagrams. It imports and exports data in a wide variety of formats and integrates with R so you can analyze in R and display it in Jamovi. Provides an active forum for discussion and questions. See the Jamie library for Jamovi plug-ins or “apps” created by users to further assist the user. (Visit Jamovi.)
JASP: JASP (Jeffreys’s Amazing Statistics Program) is an open-source project supported by the University of Amsterdam. JASP is also free and uses a GUI similar to SPSS. Extensive support includes a free data library, textbook, manuals, and video tutorials. Supports Linux, (Mac) OS X, and Windows. (Visit JASP.)
Check out the free downloadable textbook for JASP here: https://learnstatswithjasp.com/
Download the latest version here: https://jasp-stats.org/download/.
SPSS (IBM): SPSS is an established favorite for researchers conducting social and psychometric studies. Primarily utilizing a point-and-click guided user interface (GUI), it’s excellent for analyzing basic psychometric data. For advanced work, you can use its SPSS syntax language to provide direct instructions. The drawback of SPSS is its accessibility and cost. While affordable subscriptions are available for actively enrolled students with a university (*.edu) email address, the cost for your subscription later as an independent consumer is high. (Visit SPSS (IBM).)
Be sure to read the license terms which require you to be actively enrolled in an educational institution. In general, the Grad Pack is only available to those involved in obtaining a degree or participating in an educational program. Research must not be commercial. Supports (Mac) OS X and Windows.
PSPP (GNU): PSPP is the freeware version of SPSS. It works like SPSS yet in a more limited way. PSPP follows a very similar point-and-click menu and submenu layout, using a guided user interface. Like SPSS, PSPP offers a syntax version you can use to provide direct commands. PSPP can read SPSS data files and syntax files. PSPP offers limited graphics. If you’re familiar with SPSS and need a freeware version for your dissertation analysis, PSPP will do what you need. PSPP is free. Read about the free software movement as explained by the PSPP creators at GNU.
Download the most recent version, 1.4.1, here: http://www.gnu.org/software/pspp/get.html. Supports GNU Linux, (Mac) OS X, and Windows.
R: R is a powerful freeware program language well established in the research community. It is used by programmers, statisticians, and researchers to write statistical data analysis programs. Think of R as a stand-alone programming language with command prompts.
The R download is found at Comprehensive R Archive Network (CRAN) cloud-based mirror sites (https://cran.r-project.org/mirrors.html). The R Project CRAN site is located here: https://cloud.r-project.org/. Supports GNU Linux, (Mac) OS X, and Windows. The download page includes links to manuals. Support for R is provided by the R Foundation.
While PSPP is GUI-driven, R’s command line approach can be harder to learn. As a beginner, you might want to add a GUI interface program to sit on top of R and offer directions. R Commander (Rcmdr) is an established GUI helper.
RStudio Desktop: RStudio Desktop is an open-source edition of RStudio.
Think of RStudio Desktop as a toolbox application for developing scripts for R. It includes an R editor and code execution platform. Download here: https://rstudio.com/products/rstudio/download/. Supports Linux, (Mac) OS X, and Windows. https://rstudio.com/products/rstudio/#rstudio-desktop
Taguette: For qualitative data analysis, Taguette offers a free and open-source tool. With Taguette, you import qualitative data via docs, text files, RTF, HTML, and others. Highlight and group your text. Highlight words and sections and tag them with your codes. You can export your codebook, highlighted documents, and results. (Visit Taguette.)
RDQA: RDQA (R Qualitative Data Analysis package) is a GUI-based interface designed to integrate with R. It allows you to create a software analysis system using both R and RDQA to analyze both qualitative and quantitative data. You can use R to conduct statistical analysis and data manipulation on the qualitative codes you generate in RQDA. RDQA only accepts plain text data. Allows coding, memos, search files by keyword, manipulation of codes, and inter-code relationships. Several links to videos and pdf file support are provided by users. (Visit RDQA.)