An interdisciplinary approach to secondary qualitative data analysis: what why and how

Abstract

Living in global data-rich societies implies the development of critical appraisal to evaluate the quality, authenticity and meaning of data that is increasingly more accessible. Data is readily available, which is an advantage; however, not all data is good data, ethically sourced and governed under the same laws and principles in every part of the world. At this time, vast amounts of data are being collected and archived worldwide, therefore the use of existing data for further analysis is increasingly more prevalent (Johnston, 2014). Data availability has increased due to the efforts of organisations to create and maintain datasets in open and accessible ways and the advances in statistical software which have facilitated greater ease of manipulation (Trinh, 2018). Secondary data analysis has the capacity to effectively use and make sense of readily available data; however, this also comes with advantages and challenges that will be explored in depth in this chapter. A case is made on the multiple benefits of engaging in secondary data analysis whilst highlighting the potential difficulties that should be given careful consideration. Secondary data analysis is an under-used methodological technique and the awareness of its benefits and how to overcome its limitations may encourage its use further (Irwin, 2013; Smith, 2008). This chapter provides general guidelines that can be useful in different fields, particularly targeted at practitioners, policy makers and researchers from different backgrounds. The chapter, however, targets all levels of expertise from the very novice to experienced users of qualitative data. As proposed and described in this chapter, secondary qualitative data analysis is defined as an innovative and creative yet rigorous and systematic research design that can respond to the fast-changing data availability across different sectors. Additionally, funders across different countries now encourage and expect researchers to consider data sharing as part of their funding proposals (Irwin, 2013). It is, therefore, a very pertinent time to explore, understand and engage in secondary data analysis.