Research Guides: Qualitative Data Analysis Tools: Data Analysis

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Data analysis

What is coding?

Coding is a process of categorizing and labeling your data so that patterns and themes emerge. Coding helps you identify the related content within the dataset
See:
- Skjott Linneberg, M., & Korsgaard, S. (2019). Coding qualitative data: a synthesis guiding the novice. Qualitative Research Journal, 19(3), 259–270. https://doi-org.colorado.idm.oclc.org/10.1108/QRJ-12-2018-0012
- University of Illinois' Guide to Qualitative Data Analysis
- SAGE Publications An introduction to Codes and Coding

There are many different theoretical approaches to qualitative data analysis (QDA). Here are a few of them, along with recommended readings to learn more about what these approaches are and how to implement them in your research.

1. Thematic Analysis: researcher(s) interpret data to identify themes. Codes are developed to represent the themes, allowing researchers to examine the relationships between the various codes (along with their frequency and co-occurrence), from which they can develop an in-depth thematic understanding of the data. Theories about the data are based on the data.

See:

Guest, G., MacQueen, K. M., & Namey, E. E. (2012). Applied thematic analysis. SAGE Publications.
YouTube: Coding and Thematic Analysis Explained in 5 Minutes (w/ Professor Kruikow)

2 Grounded Theory:

See: Charmaz, K., & Thornberg, R. (2021). The pursuit of quality in grounded theory. Qualitative Research in Psychology, 18(3), 305–327.

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Databases for Learning Data Methods

An introduction to data analysis in R : hands-on coding, data mining, visualization and statistics from scratch by Alfonso Zamora Saiz, Carlos Quesada González, Lluís Hurtado Gil, Diego Mondéjar Ruiz. This textbook offers an easy-to-follow, practical guide to modern data analysis using the programming language R. The chapters cover topics such as the fundamentals of programming in R, data collection and preprocessing, including web scraping, data visualization, and statistical methods, including multivariate analysis, and feature exercises at the end of each section. The text requires only basic statistics skills, as it strikes a balance between statistical and mathematical understanding and implementation in R, with a special emphasis on reproducible examples and real-world applications.
Python for data analysis : data wrangling with Pandas, NumPy, and Jupyter by Wes McKinney Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.10 and pandas 1.4, the third edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You'll learn the latest versions of pandas, NumPy, and Jupyter in the process.
Intelligent Data Analysis by Deepak Gupta (Editor); Siddhartha Bhattacharyya (Editor); Ashish Khanna (Editor); Kalpna Sagar (Editor) This book focuses on methods and tools for intelligent data analysis, aimed at narrowing the increasing gap between data gathering and data comprehension, and emphasis will also be given to solving of problems which result from automated data collection, such as analysis of computer-based patient records, data warehousing tools, intelligent alarming, effective and efficient monitoring, and so on. This book aims to describe the different approaches of Intelligent Data Analysis from a practical point of view: solving common life problems with data analysis tools.
The Subtle Sources of Sampling Bias Hiding in Your Data by Sam Ransbotham Plummeting data acquisition costs have been a big part of the surge in business analytics. We have much richer samples of data to use for insight. But more data doesn't inherently remove sampling bias; in fact, it may make it worse.
An Introduction to Data Analysis by Tiffany Bergin Covering the general process of data analysis to finding, collecting, organizing, and presenting data, this book offers a complete introduction to the fundamentals of data analysis. Using real-world case studies as illustrations, it helps readers understand theories behind and develop techniques for conducting quantitative, qualitative, and mixed methods data analysis. With an easy-to-follow organization and clear, jargon-free language, it helps readers not only become proficient data analysts, but also develop the critical thinking skills necessary to assess analyses presented by others in both academic research and the popular media. It includes advice on: - Data analysis frameworks - Validity and credibility of data - Sampling techniques   - Data management - The big data phenomenon   - Data visualisation - Effective data communication Whether you are new to data analysis or looking for a quick-reference guide to key principles of the process, this book will help you uncover nuances, complexities, patterns, and relationships among all types of data.