DePoy and Gitlin consider big data studies as part of the the:
A mixed methods tradition
B experimental-type tradition
C a tradition all of its own
D naturalistic tradition
The correct answer and explanation is:
The correct answer is A) mixed methods tradition.
DePoy and Gitlin conceptualize big data studies as part of the mixed methods tradition, which combines both qualitative and quantitative research approaches. Big data research typically incorporates large-scale datasets that are analyzed quantitatively. However, it also acknowledges the importance of qualitative insights to understand the context behind the numbers. By integrating diverse types of data—such as structured numerical data and unstructured qualitative data—researchers can gain more comprehensive insights into complex phenomena.
Mixed methods research allows for triangulation, which means that researchers use multiple data sources and analysis techniques to address the same research question. This approach strengthens the validity of findings because it combines the strengths of both qualitative and quantitative data. For example, while quantitative data from big datasets can highlight patterns or trends, qualitative insights from interviews, observations, or case studies provide a deeper understanding of the underlying causes or meanings behind those patterns.
Big data studies often involve sophisticated data mining, statistical analysis, and machine learning techniques to uncover insights from vast and complex datasets. However, the interpretation of those insights can be enriched by integrating qualitative perspectives, such as interviews or field observations, which provide a more nuanced understanding of the results.
In summary, big data research fits within the mixed methods tradition because it blends the rigor and generalizability of quantitative methods with the depth and contextual relevance of qualitative approaches. This hybrid approach allows for a fuller exploration of research questions, especially in fields like healthcare, education, and social sciences, where both numerical data and human experiences are essential for a complete understanding.