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.

chapter 1|18 pages

Introduction

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part 1|123 pages

Basics of qualitative and quantitative content analysis and empirical research

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chapter 3|20 pages

Know your data

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Possibilities and limitations of text, numeric, video, and pictographic data, and primary and secondary studies
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chapter 4|44 pages

Research quality conventions

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A guide for good quality empirical research
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chapter 5|37 pages

Data interpretation

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A practical guide
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part 2|155 pages

Practical guide to classical qualitative content analysis and semi-automated quantitative content analysis

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chapter 6|29 pages

Deductive qualitative content analysis

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chapter 8|30 pages

Introduction to quantitative content analysis

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chapter 9|33 pages

Deductive quantitative content analysis

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A bibliometric literature review
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part 3|294 pages

Practical guide to automated big data content analysis

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chapter 11|22 pages

Automated content analysis

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Basic concepts and useful tips prior to data collection and data analysis
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chapter 12|33 pages

Getting started with Python

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chapter 13|29 pages

Data preprocessing

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chapter 14|22 pages

Introducing and exploring a dataset statistically

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chapter 15|75 pages

Automated content analysis using relational methods

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chapter 16|43 pages

Sentiment analysis

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chapter 17|33 pages

Topic modeling with Latent Dirichlet Allocation

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chapter 18|35 pages

Topic modeling with BERTopic

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