ABSTRACT
This chapter examines a range of data analysis strategies employed in social research, encompassing statistical, qualitative, and computational tools. On the quantitative side, it introduces key analytical techniques such as descriptive and inferential statistics, regression modeling, etc., for analyzing numeric data, often using statistical software. On the qualitative side, it describes methods such as coding, thematic analysis, and narrative analysis, and how qualitative data can be systematically interpreted for patterns and insights. A major theme here is the expanding role of computational tools: the chapter discusses how modern software can assist in managing and analyzing data (from qualitative coding software to quantitative analysis packages and programming libraries), and how the rise of big data has led to techniques like automated text analysis, sentiment analysis, or social network analysis entering the social scientist’s toolkit. The implications of incorporating machine learning and AI-driven analytics are also considered – for example, using algorithms to detect patterns in large text corpora or to classify images – along with cautions about the “black box” problem and the need for interpretability and human oversight in computational analysis.
