The case refers to a study of how people in Britain perceived the leaders of the three main political parties in 2010. The data consisted of the transcripts from 14 focus groups. The data were analysed using the grounded theory method. This method required reading and categorising the data multiple times to uncover different layers of patterns and codes. Each process of categorisation yielded categories and connections that were more substantive and analytical than the previous iteration. Discourse analysis was used to complement the grounded theory method and get the most detail from the data.
1: Why was the grounded theory method useful for analysing the data in this study?
2: What are the limitations of the grounded theory method?
The aim of this case is to illustrate how thematic analysis can be used to analyse qualitative data gathered using a ‘minigroup’, which is a small focus group. The analysis of the data generated by the minigroup showed that consulting on research methodology can help researchers anticipate how future participants may experience taking part in a planned study. The case explains and illustrates key concepts used in thematic analysis and demonstrates a six-phase approach as set out by Braun and Clarke. Finally, it presents the non-linear nature of thematic analysis and offers suggestions for the effective use of this way of analysing qualitative data.
1: Why is it useful to use thematic maps and tables in thematic analysis?
2: Why is it important to ‘immerse’ yourself in your data?
This case study introduces the reader to the basic concept of regression analysis by using research on solutions to gun violence as an example. Why regression is used is explained conceptually. How to understand some of the most important numbers generated by a regression analysis, including p values, regression coefficients, R-squared values, and interaction terms is discussed. This case study is intended for college students with no background in statistics. Regression is explained conceptually rather than mathematically, and the use of jargon or technical language is minimal.
1: Why is regression analysis used in this study?
2: What are the limitations of regression analysis?