Grades on Canvas
As I mentioned before, I’m mostly a qualitative researcher. I have reviewed numerous quantitative studies and can tell you some of the pitfalls of this discipline from that perspective, however.
The “So What” Factor
It is much easier, in my view, to justify a quantitative research design. As long as your literature review is solid, a hypothesis will naturally arise. As long as this hypothesis isn’t too outlandish, it is probably testable via a survey.
When reviewing a lot of quantitative research, however, I’m left thinking “so what?” I’m left wondering what was really tested. I don’t believe quantitative research can tell us much about human communication, for instance, and I certainly don’t think it can predict how humans will behave in future situations.
In quantitative research, statistics help researchers to prove cause and effect relationships, as opposed to things being up to chance. Descriptive statistics allow researchers to describe data, such as when using mean, median, and mode to describe frequency distributions. Inferential statistics allow a researcher to conclude that relationships exist among variables by using chi-square, t-tests, and f-tests. Quantitative research studies must have validity and reliability. Validity in quantitative research refers to whether or not the experiment actually measures what it claims to be able to measure, and experimental studies need to
have both internal and external validity. Internal validity means that any change in the dependent variable is due to the independent variable, while external validity means that the study’s results can be generalized to outside situations beyond the experimental setting. Reliability refers to whether or not the experiment accurately measures a single element of human ability.
Quantitative research’s main limitation is that it takes place in a highly structured and isolated setting – “unnatural conditions” – and while this allows researchers to draw strong conclusions, this setting makes for a situation that is not always accurate to the way people actually act in the “real world.” – Margaret (section 601)
Research Should Solve Problems
My main critique of quantitative research, however, is a critique of all research: I firmly believe all research should help solve real-world problems. Academics are frequently guilty of doing research for research’s sake. This has a lot to do with the mechanism by which our research is published: peer review. There is no real assessment mechanism for why research was conducted, or for whether or not it had any outcomes whatsoever. The only thing peer review tells you is: does the research meet the norms of the field.
This is a huge problem in a field that is supposed to be linked to a profession that happens outside academia, however. If our research isn’t assessed based on its impacts beyond academia, then TPC becomes simply another academic discipline that only speaks to internal audiences.
Quantitative descriptive research narrows the scope of ethnographies (qualitative studies) to include only the most important variables, quantifies them and then looks for patterns and cause and effect relationships. It’s different from qualitative research in that its focus is more detailed and specific than a broad, overall description; it includes a statistical analysis of isolated variables. Unlike experimental quantitative research, it does not use a control group and there are no treatments applied. (p. 82)
Quantitative descriptive research could be considered a “sequel” to a qualitative study – the qualitative study extracts a broad, highly descriptive narrative of a situation, and then using that description the researcher determines the most critical unknowns within and designs a quantitative descriptive study to extract and analyze the unknowns independently. – Susan (Section 602)