Qualitative Data Analysis

Approaches To Qualitative Data Analysis

Two fundamental approaches to qualitative data analysis stem from the deductive and inductive dichotomy that hinges on the relationship between data and theory, coding procedures, research focus and other underlying principles. Hennink et al. (2020, p. 270) propound that “data analysis involves the interplay between induction and deduction.” In the same vein, Bingham and Witkowsky (2022, p. 134) argue that “A data analysis process that draws on both deductive and inductive practices supports a more organized, rigorous, and analytically sound qualitative study.” Specifically, the deductive approach involves utilizing a predetermined framework or a set of existing concepts that provide a lens through which the data is interpreted (Azungah, 2018; Braun & Clarke, 2006). This theory-driven or top-down approach is particularly useful for organizing or sorting data into themes or categories that are informed by contemporary literature or theory (Bingham, 2023). The focus of deductive strategies is on substantiating, confirming, refining, or refuting existing theory based on the evidence extracted from the data. While this approach enables researchers to gain in-depth insights into the details and nuances in the data, the sole reliance on the prescribed theories or concepts may lead to the risk of overlooking other significant elements or over-interpreting the data (Kennedy & Thornberg, 2017). Conversely, the inductive approach aims to develop new concepts, theories, or patterns from the collected data. Researchers adopting this approach often spend a large amount of time scrutinizing the data repetitively to identify and assign codes out of the text they examine. Thomas (2006, p. 239) notes that “although the findings are influenced by the evaluation objectives or questions outlined by the researcher, the findings arise directly from the analysis of the raw data, not from a priori expectations or models.” In other words, this data-driven or bottom-up approach allows the data to guide the exploration and understanding of the research problems through which unanticipated findings and novel ideas emerge. As a result, it seems messier than the deductive approach since “inductive practices go beyond sorting and require the researcher to pull out what is happening in the data and allow the data to speak to them” (Bingham & Witkowsky, 2022, p. 141). Yet, its value lies in the development of in-depth understandings of the specific context or phenomenon under study as well as capturing the complexity of the data.