T3 DQ2

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Grand Canyon University *

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832

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Statistics

Date

Jan 9, 2024

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docx

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Uploaded by BauerKingston2025

Now, imagine you are the CEO of a large hospital. You are interested in reducing turnover among nurses. You wish to find out to what extent nurses’ turnover intention is related to their overall job satisfaction, their average number of hours worked per week, and their level of professional stress. You plan a correlational study, administer an anonymous survey, and collect interval data for all four variables. What parametric and nonparametric statistical tests can you use to analyze the data? Correlational research is ideal for gathering data quickly from natural settings and helps generalize findings to real-life situations. In conjunction with correlational research, there are various reasons to use either parametric or non-parametric tests and is dependent on the specific set of assumptions that are to be tested (Pressman, 2022). Parametric analysis is used to test hypotheses about the population mean and variance and non-parametric tests are used to test the group medians. The data patterns for distribution will play a very important part in what type of statistical analysis can be performed. If normal distribution is being used, the best choice would be to use the parametric Pearson correlation (Pressman, 2022). The correlation coefficient of Pearson will be considered as a measure of linear association between random variables of bivariate X and Y (Liu et al., 2020). An additional parametric test that could be used is the analysis of variance test (ANOVA). The ANOVA test is primarily used to find statistical differences between three or more independent groups (Azizi et al., 2022). A nonparametric test may have to be considered to be used if the assumptions for a Pearson Correlation are not met. Spearman Correlation and Mann-Whitney would be a good example of non-parametric tests (Pressman, 2022). REFERENCES: Azizi, F., Ghasemi, R., & Ardalan, M. (2022). Two Common Mistakes in Applying ANOVA Test: Guide for Biological Researchers. Liu, Y., Mu, Y., Chen, K., Li, Y., & Guo, J. (2020). Daily activity feature selection in smart homes based on pearson correlation coefficient. Neural Processing Letters , 51 , 1771-1787. Pressman, M.S. (2022). Comparisons of Qualitative and Quantitative Designs. GCU doctoral research introduction to sampling, data collection, and data analysis. Grand Canyon University.

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