In the scientific method, the type of methodology and data-gathering needed depends on the type of data required, and ultimately, the problem presented. In every situation this varies, and there is the best methodology for every type of problem in scientific experimentations.
It is reported, however, that in the study of gender stereotypes, experimentations result into flawed interpretations, with limitations and data variables not clearly defined, that ultimately lead to distortions to the point of fallacy in these stereotypes. (Caplan, 1994, p20)
Ex Facto methodology is the technique getting a sample population that will represent the entire population to be examined. The tests will be applied to this population and the result is assumed to apply to at least the majority of the entire population. The issue with gender stereotypes is that the population is too varied and there are many common traits in the two groups. Men and women come from different age, race, color, upbringing, culture, etc., that a sample population that will best represent everyone will be a very tricky selection. (Christensen, 2001)
Quantitative methodology mostly needs numerical data to arrive to its conclusions. Caplan (1994, pp 20-28) almost devoted an entire chapter to enumerate the possible pitfalls of this method, which seems to be most scientific in its approach among the methodologies. Simply put, limitations in the study must be clarified when performing the experiment or study. With this in mind, it must be acknowledge that the data gathered may not be as conclusive as desired. A control group may assist in unforeseen variables that may interact with the test group or population. (Christensen, 2001)
Qualitative methodology is more descriptive than empirical. It is more inquisitive and exploratory. It does not try to give a general conclusion about a population, but rather explore its traits. This methodology has the potential to give the most accurate data regarding a subject but is also prone to grave error as it is heavily subject to personal bias. (Christensen, 2001)
For example, a research problem goes, “Is gender identity biologically determined?” First of all, the terms must be defined clearly. Should gender identity be equal to sex of a person, or should it connote the social issue of a third and fourth (and so on gender)?
If the first, then the answer to the problem is a simple yes, as sex of a person, by definition, is determined biologically. If the answer is this simple, then why is there a problem in the first place? The research can look into cases wherein the sex is unable to be determined biologically, such as the rare cases of hermaphrodites.
The second question is mostly likely the challenge of the research problem. The research needs to zone in on what kind of results it requires. Does it want to prove or disprove the existence of a third and fourth gender, or does it want to know if these supernumerary genders can be determined biologically? These entail complex studies that could be very well applied with meta-analysis (Caplan, 1994, p27) which combines the methodologies to come up with more reliable results. Some studies, wherein members of the s-called third and fourth gender are found to have traumatic experiences prior to their gender shift, supports the hypothesis that these extra genders are socially induced, can be reversed, and therefore not innate to humanity. On the other hand, studies that show members of these excess genders who are aware of their difference since childhood support the hypothesis that these third and fourth gender identities may be, in fact, biologica
Caplan (1994, p29) advocates more direct objective, more data gathering, clearer limitations so that the study of gender stereotypes will be more accurate and useful to our discipline of seeking the truth.
Reference
Caplan, J.. (1994). Thinking Critically About Reasearch on Gender. NY: HarperCollins College Publishers
Christensen, L.B. (2001). Experimental Methodology (8th Ed). Boston, MA: Allyn and Bacon. Retrieved from: http://www.papermasters.com/quantitative_research.html
