Qualitative market research focuses on data trustworthiness rather than focusing on the data, which would be defined as quantitative research. Qualitative research is important because it measures things that numbers might not be able to define, qualitative methods sometimes identify trends before they show up in the quantitative data.
Data trustworthiness has four key components: credibility, transferability, dependability, and confirmability.
Triangulation and member checks help establish credibility and contribute to trustworthiness. Other factors include prolonged engagement with and persistent observations of research subjects.
Triangulation asks the same research questions of different study participants and collects data from different sources through different methods to answer the same questions. Member checks occur when researchers asks participants to review the data collected by interviewers and the researchers' interpretations of that data. Participants generally appreciate the member check process because it gives them a chance to verify their statements and fill in any gaps from earlier interviews. Trust is an important aspect of the member check process.
Transferability generalizes study findings and attempts to apply them to other situations and contexts. Researchers cannot prove definitively that outcomes based on the interpretation of the data are transferable, but they can establish that it is likely.
Purposive sampling, a form of nonprobability sampling, is used to maximize specific data relative to the context in which it was collected. This differs from the aggregate information that would be the outcome in quantitative research. Purposive sampling considers the sample subjects' characteristics, which are directly related to the research questions.
Many qualitative researchers believe that if credibility has been demonstrated, it is not necessary to also and separately demonstrate dependability. However, if a researcher permits parsing of the terms, then credibility seems more related to validity, and dependability seems more related to reliability.
Sometimes data validity is assessed through the use of a data audit. A data audit can be conducted if the data set is both rich-thick so that an auditor can determine if the research situation applies to their circumstances. Without sufficient details and contextual information, this is not possible. Regardless, it is important to remember that the aim is not to generalize beyond the sample.
Qualitative research can be conducted to replicate earlier work, and when that is the goal, it is important for the data categories to be made internally consistent. Authors Yvonna S. Lincoln and Egon G. Guba stated in their 1985 book "Naturalistic Inquiry" that researchers must devise rules that describe category properties and that can, ultimately, be used to justify the inclusion of each data bit that remains assigned to the category as well as to provide a basis for later tests of replicability.
It's important for other researchers to be able to replicate the results to show that those results are a product of independent research methods and not of conscious or unconscious bias.
Dye, J.G, Schatz, I. M., Rosenberg, B. A., and Coleman, S. T. (2000, January). Constant comparison method: A kaleidoscope of data. The Qualitative Report, 4(1/2).
Glaser, B., and Strauss, A. (1967). The discovery of grounded theory: Strategies for qualitative research. Chicago, IL: Aldine.
Lincoln, Y. S., and Guba, E. G. (1985). Naturalistic inquiry. Newbury Park, CA: Sage.