Introduction to quantitative content analysis

This chapter presents an analysis technique that you can use to quantitatively analyze the content of 500 pages or more of text. This technique is suitable for larger interview studies and secondary analyzes of process-produced data (e.g., party platforms or Twitter). Using the example of a secondary analysis (like in Chapters 6 and 7 , the autoethnography on “How are you organizing your daily life as a student at the beginning of the online semester 2020/21?”), the sequential analysis is explained based on the AntConc software (freeware; sociolinguistics or political linguistics) and qualitative data analysis (QDA) software. Guided by the research interest and research question(s), we explain step by step how to use search words to systematically understand the text corpus and how to analyze the information found. The semi-automated indexing of the text corpus and quantitative content analysis produces an overview categorized by the search words (keywords), which is also referred to as distant reading. In contrast to in-depth qualitative content analysis, the aim of quantitative content analysis is to record the manifest content and to use this to draw findings, make interpretations, and draw conclusions. However, quantitative content analysis should be followed by in-depth, qualitative content analysis, which is explained at the end of this chapter.