Full 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 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 along 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.
Contents
Part 1: Basics of Qualitative and Quantitative Content Analysis and Empirical Research 1. Introduction 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
