1st Edition
The Handbook of Qualitative and Quantitative Content Analysis Introduction to Classical, Digital, AI-supported, and Automated Data Analysis
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
Biography
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.
