Credibility of Research Sampling Methods

It is expected for there to be potential weaknesses and innate bias within any sampling method, for a researcher can never totally account for environmental factors that might somehow influence process or results.  Margin of error is purposely calculated in studies so as to account for any outlying circumstances, which provides credibility for many situations we encounter as educators, such as assessment scoring or demographic surveys.  Different instances of research might call for different types of sampling (random, purposive, stratified random, etc) and the nature of the procedure itself can decrease potential weaknesses one might encounter.  An important component to consider in this process is to “identify, as specifically as possible, the sampling procedure rationale for using the procedure” (McMillan, 2007, p. 95) as well as detailed characteristics of the sample.  This data and process can then be evaluated by readers or interpreters, allowing them to be “conscious of how the sampling procedures might have affected the results, and how the characteristics of the subjects affect the usefulness and the generalizability of the results” (McMillan, 2007, p. 109).  Understanding the pros and cons of the procedure allows for a more discerning assessment of the credibility.


Mladenka (2012) explains that true credibility lies in the significance of the independent variables one includes in the research procedure.  It is these variables that serve to differentiate between bias or random chance (both lacking credibility or reproducibility) and worthy findings.  This viewpoint happens to be echoed in a book that my PLC read last Spring.  In ‘Whistling Vivaldi’ by Claude M. Steele (2010), the author describes a series of research experiments that explore the effect of stereotype threat on student performance in various situations.  One of the challenges presented is obtaining a truly random sampling of students for a control group who are not potentially affected by a type of internalized threat which could skew the results.  For example, in testing the affect of math stereotyping on females, they had to account concurrently for Asian stereotyping or African American stereotyping to lend credence to their findings.  In testing students through random sampling at an elite university, they had to account for the fact that everyone belonged to a particular intellectual group and consider how that would skew results over the entire population sample.  I believe the takeaway here is to consider variables from all angles, in the same way one might consider counterclaims as part of a persuasive essay, in order to account for issues that might arise over the course of researching.  By showing these prior considerations, it ultimately strengthens both the experiment and the findings.


McMillan, J. H. (2007). Educational Research: Fundamentals for the Consumer 6th ed. Pearson.


Mladenka, D. (2012).  Credible research made easy: A step by step path to formulating testable hypothesis.  IUniverse.


Steele, C. (2010).  Whistling Vivaldi: And other clues to how stereotypes affect us.  New York: W. W. Norton & Company.

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