Using Clustering as a Tool:Mixed Methods in Qualitative Data Analysis

In this article I discuss cluster analysis as an exploratory tool to support the identification of associations within qualitative data. While not appropriate for all qualitative projects, cluster analysis can be particularly helpful in identifying patterns where numerous cases are studied. I use as illustration a research project on Latino grievances to offer a detailed explanation of the main steps in cluster analysis, providing specific considerations for its use with qualitative data. I specifically describe the issues of data transformation, the choice of clustering methods and similarity measures, the identification of a cluster solution, and the interpretation of the data in a qualitative context.Keywords: Cluster Analysis, Qualitative Analysis, Data Exploration, Mixed MethodsTools and Techniques for the Analysis of Qualitative DataQualitative research produces a great volume of data that can be challenging, and at times overwhelming, to analyze. While literature on qualitative data collection methods and theoretical approaches for their analysis is increasingly rich, a gap still remains on works that offer detailed accounts on data analysis techniques (Attride-Stirling, 2001; Fielding, 2005). This gap is maintained in part by the recognition that different theoretical approaches demand different analytic approaches to qualitative research, and also by the still existent guarded acceptance (and in some cases rejection) of tools, particularly computer-based ones or those traditionally used in quantitative approaches, that although providing an appearance of clarity can misconstrue qualitative research findings (Gilbert, Jackson, & di Gregorio, 2014; James, 2013).However, quantitative analytical tools can be helpful in qualitative analysis, as long as there is clarity on what can and cannot be done with them. Literature that details the use of specific mixed techniques in qualitative analysis is important to avoid misusing them, or misrepresenting their findings. Additionally, clear explanations provide transparency to qualitative data analysis processes, which are sometimes regarded as obscure and lacking trustworthiness by scholars and practitioners not fully familiar with qualitative methodologies (Barusch, Gringeri, & George, 2011; Miller & Crabtree, 1994).Cluster Analysis in Qualitative ResearchIn this article I discuss cluster analysis as an exploratory tool to support the identification of associations within qualitative data. While not appropriate for all qualitative projects, cluster analysis can be helpful in identifying patterns where numerous cases are studied. As the number of elements and facets considered for each case increase, so does the complexity of finding associations between them. When this happens, even the analytic tools provided for this purpose by commonly used software tools for qualitative analysis (e.g., coding co-occurrences, matrixes or Boolean searches) can fall short, with the related risk of reaching conclusions that overly reflect the researcher's preconceptions. Some qualitative analysis software (NVivo, QDA Miner, ANTRHOPAC) have included cluster analysis tools, mainly as text-mining tools (Silver & Lewins, 2014). However, its use remains limited or underreported, perhaps due to their limitations in the number of items allowed (ANTHROPAC), the techniques and measures available (NVivo), or the array of data used to determine the formation of clusters.The use of cluster analysis with qualitative data has already been discussed and documented (Guest & McLellan, 2003; MacQueen et al., 2001), but remains underused. It is possible that this is partially due to an obscurity regarding how to apply this tool to qualitative data, and a need for detailed but accessible explanations of the basic considerations and steps required from qualitative researchers interested in clustering as a tool. Through the detailed presentation of a case study using content cluster analysis in a qualitative project, I explain in this article the main elements of this technique as it applies to qualitative data, including the steps and considerations required to perform clustering and interpret its results. …