John Creswell uses the analogy of lowering the readers into the figurative well rather than simply throwing the reader into the well head first, but in chapter eight, Creswell seems to dangle the reader by one leg over the well for the entire chapter. In this chapter, he discusses quantitative methods. Creswell begins with the different survey designs that can be implemented and their importance. Then he goes on to distinguish between validity and reliability respectively in correlation to quantitative research. It is important to comment on the three characteristics of both, validity and reliability, because in qualitative methods, these components differ, which I will mention later in this blog. As for the quantitative definitions for validity and reliability, validity focuses on content; predictions; and does the items give way to helping build results based on the hypotheses. Reliability, in a quantitative sense, is determined by consistency across constructs; stability over time; consistent errors (149-150). After describing the importance of validity and reliability, Creswell moves on to point out the statistical analysis. Although her does give a very well organized chart of the common statistical tests that are used, this is where he begins to dangle his reader over the well by the leg. He could have mentioned that at the introductory level, which is what this book is primarily aimed at, mostly deals with the Chi-square or the T-test. These tests can analyze between variables and between populations. Also, neither in Creswell’s table 8.3 nor in his narrative did he mention that you can use those test if you have more than one variable except for the Spearman Rank-Order Correlation Test. For example, the experiment that I mentioned in class with testing three different light sources where the fluorescent light was the independent variable and the LED light and the fire were the dependent variables and I used a T-test for that experiment. Another thing that is interesting is when Creswell talks about the bell shape curve. For an author who stresses the use of the proper language, he leaves out important, basic key terms that benefit quantitative method and introductory statistics. In the case of the bell shaped curve, he does not mention that the graph can be skewed based on the data. This is similar to how a personal bias could sway the qualitative data. When the bell shaped curve is skewed it can either be skewed left or right. These would then be considered directional. The rest of this chapter goes on to further explain how to analyze and interpret quantitative methods.
Chapter nine reflects on the qualitative methods. Creswell discusses the same aspects as chapter eight, but in a qualitative setting. Some big differences are that researchers are the instrument in which the data is being collected in qualitative research whereas quantitative methods it could be something like a survey, and the interpretation of the data is filtered through a “theoretical lens” (176). To further elaborate on collecting data, qualitative methods consists of observing; audio-visual; documenting; as well as interviews (181). As I had mentioned earlier, there is also a difference in the qualitative definitions for validity and reliability. For this method, validity only seems to be interested in if there were results. For reliability, their concern is somewhat similar to the quantitative in that part of the approach it “is consistent across different researchers and different projects” (qtd in Creswell 190). Creswell redeems himself in chapter nine and hoists the reader out of the well and help them on their way to qualitative alley.
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