Rigor in qualitative and quantitative research

Rigor in Social Science Research

 

What is Scientific Rigor in Qualitative Research – a definition

Standards of rigor, and how to enhance them in the social sciences.

First and foremost, it is necessary to avoid confusing research rigor with concepts such as measurement precision, quantification, and generalizability. These concepts are choices that must be made by each investigator in determining how to best meet his or her research objectives and are not something that should be inherently desired in-and-of-itself. Researchers need to be cautious about making claims that some data collection or data analysis techniques are “more” rigorous than others.

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Caution is also advisable in regards to claiming fixed “standards” for specific methodological techniques. Methodological techniques share a common set of core properties but include a wide range of variations and nuances. The power of a given research technique lies in its ability to be adapted to multiple research situations. Truncating the variability around a technique will ultimately make the tool less useful.

This also includes attempts to link specific techniques to specific research goals: As tools, methods merely are a means to an end. It is surprising how such means can be adapted to serve many different goals. For example, one can easily imagine scenarios where paired comparisons could be used to explore, describe, compare, or test hypotheses.

Yet, researchers ought to stop associating standards and rigor only with confirmatory and hypothesis-driven research. There is no reason why standards of rigor cannot be set for exploratory and descriptive research as well. While some of the criteria may vary based on specific research objectives, some of the criteria will cut across all types of research.

Rigorous (“trustworthy”) research is research that applies the appropriate research tools to meet the stated objectives of the investigation. For example, to determine if an exploratory investigation was rigorous, the investigator would need to answer a series of methodological questions: Do the data collection tools produce information that is appropriate for the level of precision required in the analysis? Do the tools maximize the chance of identifying the full range of phenomenon of interest? To what degree are the collection techniques likely to generate the appropriate level of detail needed for addressing the research question(s)? To what degree do the tools maximize the chance of producing data with discernible patterns? Once the data are collected, to what degree are the analytic techniques likely to ensure the discovery of the full range of relevant and salient themes and topics? To what degree do the analytic strategies maximize the potential for finding relationships among themes and topics? What checks are in place to ensure that the discovery of patterns and models is not superfluous? Finally, what standards of evidence are required to ensure readers that results are supported by the data?

The clear challenge is to identify what questions are most important for establishing research rigor (“trustworthiness”) and to provide examples of how such questions could be answered for those using qualitative data. Clearly, rigorous research must be both transparent and explicit; in other words, researchers need to be able to describe to their colleagues and their audiences what they did (or plan to do) in clear, simple language. Much of the confusion that surrounds qualitative data collection and analysis techniques comes from practitioners who shroud their behaviors in mystery and jargon. For example, clearly describing how themes are identified, how codebooks are built and applied, how models were induced helps to bring more rigor to qualitative research.

Researchers also must become more familiar with the broad range of methodological techniques available. Social science has become methodologically parochial: Content analysts, grounded theorists, semantic network analysts, and analytic inductionists rarely talk to each other. Cross-fertilization across methodological traditions, especially those that are dominated by a single discipline, is rare enough indeed. Even more worrisome is the growing tendency for researchers to attack all problems with the same type of methodological tool.

Given the multiple challenges outlined above, the introduction of methodologically neutral and highly flexible qualitative analysis software like ATLAS.ti must be considered as extremely helpful indeed. It is highly apt to both support interdisciplinary cross-pollenation and to bring about a great deal of trust in the presented results. By allowing the researcher to combine both the source material and his/her findings in a structured, interactive platform, while producing both quantifiable reports and intuitive visual renderings of their results, ATLAS.ti adds a hither to unknown level of trustworthiness to qualitative research. Moreover, it permits the researcher to apply multiple approaches to his/her subject, to collaborate across philosophical boundaries, and thus to significantly enhance the level of rigor in qualitative research.