The Handbook of Qualitative and Quantitative Content Analysis
Introduction to Classical, Digital, AI-supported, and Automated Data Analysis
1st Edition
Will be published approx. on 29. December 2025
Book
Hardback
614 pages
978-1-032-80313-5 (ISBN)
Description
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 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.
More details
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Postgraduate and Undergraduate Advanced
Illustrations
12 farbige Zeichnungen, 12 farbige Abbildungen, 55 s/w Zeichnungen, 24 s/w Photographien bzw. Rasterbilder, 76 s/w Tabellen, 79 s/w Abbildungen
76 Tables, black and white; 12 Line drawings, color; 55 Line drawings, black and white; 24 Halftones, black and white; 12 Illustrations, color; 79 Illustrations, black and white
Dimensions
Height: 240 mm
Width: 161 mm
Thickness: 39 mm
Weight
1118 gr
ISBN-13
978-1-032-80313-5 (9781032803135)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Christian Schneijderberg | Oliver Wieczorek | Isabel Steinhardt
The Handbook of Qualitative and Quantitative Content Analysis
Introduction to Classical, Digital, AI-supported, and Automated Data Analysis
E-Book
12/2025
1st Edition
Routledge
€0.00
Available for download
Christian Schneijderberg | Oliver Wieczorek | Isabel Steinhardt
The Handbook of Qualitative and Quantitative Content Analysis
Introduction to Classical, Digital, AI-supported, and Automated Data Analysis
E-Book
12/2025
1st Edition
Routledge
€0.00
Available for download
Persons
Christian Schneijderberg holds a PhD in sociology and is a senior researcher at the International Center for Higher Education Research at the University of Kassel, Germany.
Oliver Wieczorek holds a PhD in sociology and is a senior researcher in governance and organization at the International Center for Higher Education Research at the University of Kassel, Germany.
Isabel Steinhardt is a professor of sociology of education and head of the department of sociology at Paderborn University, Germany.
Oliver Wieczorek holds a PhD in sociology and is a senior researcher in governance and organization at the International Center for Higher Education Research at the University of Kassel, Germany.
Isabel Steinhardt is a professor of sociology of education and head of the department of sociology at Paderborn University, Germany.
Content
1. Introduction Part 1: Basics of Qualitative and Quantitative Content Analysis and Empirical Research 2. Definitions of Qualitative and Quantitative Content Analysis, and Inductive and Deductive Research Approaches 3. Know your Data: Possibilities and Limitations of Text, Numeric, Video and Pictographic Data, and Primary and Secondary Studies 4. Quality Conventions: A Guide for Good Quality Empirical Research 5. Data Interpretation: A Practical Guide; Part 2: Practical Guide to Classical Qualitative Content Analysis and Semi-automated Quantitative Content Analysis 6. Deductive Qualitative Content Analysis 7. Introduction to Inductive Qualitative Content Analysis 8. Introduction to Quantitative Content Analysis 9. Deductive Quantitative Content Analysis: A Bibliometric Literature Review 10. Artificial Intelligence and Large Language Model-powered Chatbots to Support Qualitative Content Analysis; Part 3: Practical Guide to Fully Automated Big Data Content Analysis 11. Automated Content Analysis: Basic Concepts and Useful Tips Prior to Data Collection and Data Analysis 12. Getting Started with Python 13. Data Preprocessing 14. Introducing and Exploring a Dataset Statistically 15. Automated Content Analysis using Relational Methods 16. Sentiment Analysis 17. Topic Modeling with Latent Dirichlet Allocation 18. Topic Modeling with BERTopic