Reference no: EM131549548

Question: module 5 dq 1 argument Rachel

The Pearson correlation coefficient measures the strength of a linear relationship between bivariate data (Pressman, 2016). Therefore, if the Pearson correlation coefficient does not reach statistical significance we can conclude that the bivariate data does not have a linear relationship (for example, the data could exhibit a parabolic, logistic, or exponential relationship instead of a linear relationship), or that the data does not have any mathematical relationship or pattern. In general, when working with bivariate data best practices are to create a scatterplot of each bivariate pair before calculating correlation and performing a hypothesis test so that the researcher can obtain a subjective measure of whether a linear, nonlinear, or no apparent relationship (i.e. pattern) is observed within the data. Using the information gleaned from the scatterplot, the researcher can determine whether to calculate a Pearson correlation coefficient and subsequent test for significance, and then the researcher can offer an appropriate conclusion.

Although the sample size is considered to be quite large, the result for this study cannot be generalized for all graduate students in electrical engineering programs across the United States. The sample was taken from a highly prestigious school in which high GRE scores are most likely the norm as well as high GPAs. Ivy League schools are not only known for attracting the best and brightest students from the United States, they also attract highly qualified international students. Furthermore, Gasman, Hirschfeld, and Vultaggio (2008), reveal that African American students are underrepresented in the graduate student population at Ivy League schools, providing yet another indicator that the sample obtained most likely does not represent the population of all graduate students in electrical engineering programs across the United States. Thus, the results cannot be generalized to all graduate students in electrical engineering programs across the United States. However, the results might be generalizable to other Ivy League graduate students in electrical engineering programs across the United States.