ABSTRACT
Translated from German, The Handbook of Qualitative and Quantitative Content Analysis is a comprehensive handbook which offers an application-orientated introduction to qualitative and quantitative content analysis methods.
The book provides explanations for beginners from bachelor level onwards on how to select an appropriate qualitative or quantitative content analysis method and how to use the chosen method(s) depending on research interest and amount of data. Part 1 defines the basics of qualitative and quantitative content analysis and empirical research, including research quality conventions and how to do interpretation; Part 2 is a practical guide to classical qualitative content analysis and semi-automated quantitative content analysis; and Part 3 introduces Python alongside automated techniques such as correspondence analysis, semantic network analysis, sentiment analysis, and topic modelling using generative and deep learning algorithms. Each of these sections are enriched with extensive examples and cover a range of software applications, including AntConc, MAXQDA, Python, and VosViewer.
This is the ideal resource for anyone interested in content analysis research methods across the social sciences, humanities, and data sciences.
The Open Access version of this book, available at https://www.taylorfrancis.com, has been made available under a Creative Commons [Attribution-Non Commercial-No Derivatives (CC BY-NC-ND)] 4.0 license.
TABLE OF CONTENTS
part 1|123 pages
Basics of qualitative and quantitative content analysis and empirical research
chapter 2|20 pages
Definitions of qualitative and quantitative content analysis, and inductive and deductive research approaches
chapter 3|20 pages
Know your data
part 2|155 pages
Practical guide to classical qualitative content analysis and semi-automated quantitative content analysis
chapter 10|27 pages
Artificial intelligence and large language model-powered chatbots to support qualitative content analysis
part 3|294 pages
Practical guide to automated big data content analysis
