Qualitative Data Analysis: A Methods SourcebookMiles, Huberman, and Salda?a's Qualitative Data Analysis: A Methods Sourcebook is theauthoritative text for analyzing and displaying qualitative research data. The Fourth Editionmaintains the analytic rigor of previous editions while showcasing a variety of new visual displaymodels for qualitative inquiry. Graphics are added to the now-classic matrix and networkillustrations of the original co-authors. Five chapters have been substantially revised, and theappendix's annotated bibliography includes new titles in research methods. Graduate studentsand established scholars from all disciplines will find this resource an innovative compendium ofideas for the representation and presentation of qualitative data. As the authors demonstrate,when researchers "think display," their analyses of social life capture the complex and vividprocesses of the people and institutions studied.
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Pragmatic approaches to qualitative analysis are likely valuable for IS researchers yet have not received enough attention in the IS literature to support researchers in using them confidently. By pragmatic approaches, we mean strategic combining and borrowing from established qualitative approaches to meet the needs of a given IS study, often with guidance from an IS framework and with clear research and practice change goals. Pragmatic approaches are not new, but they receive less attention in qualitative research overall and are not always clearly explicated in the literature [9]. Part of the challenge in using pragmatic approaches is the lack of guidance on how to mix and match components of established approaches in a coherent, credible manner.
An illustration of the utility of grounded theory procedures comes from a study that explored how implementing organizations can influence local context to support the scale-up of mental health interventions in middle-income countries [35]. Using a multiple case study design, the study team used an analytic approach based on grounded theory to analyze data from 159 semi-structured interviews across five case sites. They utilized line-by-line open coding, constant comparison, and exploration of connections between themes in the process of developing an overarching theoretical framework. To increase rigor, they employed triangulation by data source and type and member reflections. Their team-based plan included multiple coders who negotiated conflicts and refined the thematic framework jointly. The output of the analysis was a model of processes by which entrepreneurial organizations could marshal and create resources to support the delivery of mental health interventions in limited-resource settings. By taking a divergent perspective (grounded in social entrepreneurship, in this case), the study output provided a basis for further inquiry into the design and scale-up of mental health interventions in middle-income countries.
Framework analysis comes from the policy sphere and tends to have a practical orientation; this applied nature typically includes a more structured and deductive approach. The history, philosophical assumptions, and core processes are richly described by Ritchie and Spencer [36]. Framework analysis entails several features common to many qualitative analytic approaches, including defining concepts, creating typologies, and identifying patterns and relationships, but does so in a more predefined and structured way [37, 38]. For example, the research team can create codes based on a framework selected in advance and can also include open-ended inquiry to capture additional insights. This analytic approach is well-suited to multi-disciplinary teams whose members have varying levels of experience with qualitative research [37]. It may require fewer staff resources and less time than some other approaches.
The framework analysis process includes five key steps. Step 1 is familiarization: Team members immerse themselves in the data, e.g., reading, taking notes, and listening to audio. Step 2 is identifying a coding framework: The research team develops a coding scheme, typically using an iterative process primarily driven by deductive coding (e.g., based on the IS framework). Step 3 is indexing: The team applies the coding structure to the entire data set. Step 4 is charting: The team rearranges the coded data and compares patterns between and within cases. Step 5 is mapping and interpretation: The team looks at the range and nature of relationships across and between codes [36, 39, 40]. The team can use tables and diagrams to systematically synthesize and display the data based on predetermined concepts, frameworks, or areas of interest. While more structured than other approaches, framework analysis still presents a flexible design that combines well with other analytic approaches to achieve study objectives [37]. The case example given in section 3 offers a detailed application of a modified framework analytic approach.
A useful example comes from a study that sought to understand resistance to using evidence-based guidelines from the perspective of physicians focused on providing clinical care [45]. The analysis drew on data collected from interviews of 11 physicians selected for their expertise and diversity across a set of sociodemographic characteristics. In the first phase of the analysis, the team analyzed the full-length interviews and identified key themes and the relationships between them. Particular attention was paid to implicit and explicit meanings, repeated ideas or phrases, and metaphor choices. Two authors conducted the analyses separately and then compared them to reach a consensus. In the second phase of the analysis, the team considered the group of 11 interviews as a set. Using an inductive perspective, the team identified superordinate (or high-level) themes that addressed the full dataset. The final phase of the analysis was to identify a single superordinate theme that would serve as the core description of clinical practice. The team engaged other colleagues from diverse backgrounds to support reflection and refinement of the analysis. The analysis yielded a theoretical model that focused on a core concept (clinical practice as engagement), broken out into five constituent parts addressing how clinicians experience their practice, separate from following external guidelines.
Building on the discussion of pragmatic combination of approaches for a given study, we turn now to the question of ensuring and communicating rigor so that consumers of the scientific products will feel confident assessing, interpreting, and engaging with the findings [46]. This is of particular importance for IS given that the field tends to emphasize quantitative methods and there may be perceptions that qualitative research (and particularly research that must be completed more quickly) is less rigorous. To address those field-specific concerns and ensure pragmatic approaches are understood and valued, IS researchers must ensure and communicate the rigor of their approach. Given journal constraints, authors may consider using supplementary files to offer rich details to describe the study context and details of coding and analysis procedures (see for example, Aveling et al. [47]). We build on the work of Mays and Pope [38], Tracy [8], and others [48,49,50,51,52] to offer a shortlist of considerations for IS researchers to ensure pragmatic analysis is conducted with rigor and its quality and credibility are communicated (Table 1). We also recommend these articles as valuable resources for further reading.
The qualitative portion of the project had two primary goals. The research goal was to identify improvements to the design and delivery of capacity-building interventions for CBOs and FBOs working with underserved populations. The practice-related goal was to identify local training needs and refine an existing EBI capacity-building curriculum. We drew on the EPIS Framework [15] to support our exploration of multi-level factors that drive EBI implementation in social service settings. We conducted four focus group discussions with intended capacity-building recipients (n = 27) and key informant interviews with community leaders (n = 15). Given (1) the applied nature of the research and practice goals, (2) our reliance on an existing IS framework, (3) limited staff resources, and (4) a need to analyze data rapidly to support intervention refinement, we chose a modified framework analysis approach. Modifications included incorporating aspects of grounded theory, including open coding, to increase the emphasis on inductive perspectives. The team also modified the charting procedures, replacing tabular summaries with narrative summaries of coded data.
We encourage IS researchers to explore the diversity and flexibility of qualitative analytic approaches and combine them pragmatically to best meet their needs. We recognize that some approaches to analysis are tied to particular methodological orientations and others are not, but a pragmatic approach can offer the opportunity to combine analytic strategies and procedures. To do this successfully, it is essential for the research team to ensure fit, preserve quality, and rigor, and provide transparent explanations connecting the analytic approach and findings so that others can assess and build on the research. We believe pragmatic approaches offer an important opportunity to make strategic analytic decisions, such as identifying an appropriate balance of insider and outsider perspectives, to extend current IS frameworks and models. Given the urgency to increase the utilization and utility of EBIs in practice settings, we see a natural fit with the pragmatist prompt to judge our research efforts based on whether or not the knowledge obtained serves our purposes [63]. In that spirit, the use of pragmatic approaches can support high-quality, efficient, practice-focused research, which can broaden the scope and ultimate impact of IS research. 2ff7e9595c
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