mrssharonkilgore
#2 Respond to colleagues’ postings in the following way: Address…
#2 Respond to colleagues’ postings in the following way:
Address the content of each colleague’s analysis and evaluation of the topic and of the integration of the relevant resources
Please note that for each response you must include a minimum of one appropriately cited scholarly reference.
Colleague’s POSTING
Describe the research example related to your doctoral research proposal.
In my doctoral research, I’m investigating the strategies that supply chain managers can utilize to minimize the impact on business operations and profitability during a crisis. One of the challenges is the inadequacy of information relating to digital technologies successfully enhancing the supply chain resilience during a crisis. In this case, the independent sample t-test would help assess if the profitability of multinationals in Detroit differs based on their system automation level. Here, the dependent variable is the profitability, while the dependent variable is the system automation level, categorized as fully automated, partially automated, and no automation. The test would help to understand whether the level of automation influences the company’s profitability.
Describe a hypothetical example appropriate for each t-test, ensuring that the variables are appropriately identified.
The one-sample t-test compares the mean value of a given sample variable to a known value (Green & Salkind, 2017). For example, assuming the cutoff point for all math classes in a college is 70. A math teacher may want to know if the students’ performance last semester met this goal by comparing their average performance to the 70-pass mark. The paired sample t-test compares the difference between two variables for one group of participants, where variables are separated by time (Green & Salkind, 2017). It means the samples are tested twice over the designated period (Ross & Willson, 2018). For example, assuming a manager intends to know the effectiveness of a certain training program on the employee’s performance. Using the paired t-test, the manager may measure the average employee’s performance before, and after the program, they compare the results. Lastly, the independent t-test measures the differences between two unrelated and unequal groups (Green & Salkind, 2017). For example, an economist intends to understand if the household annual income differs based on ethnicity. They can collect data samples of the annual incomes of different people based on ethnicity, categorized as, Hispanics, African Americans, Caucasians, Native Americans, Asian Americans, and others.
Analyze the assumptions associated with the independent-samples t-tests and the implications when assumptions are violated.
According to Green and Salkind (2017); Lumley et al. (2002), the major assumption of independent samples t-test is that the test variables are normally distributed. The t-test yields an accurate and credible p-value even when this assumption is violated. Another assumption is that the variance for the normally distributed test variables for both populations is equal. If the assumption is violated, then the p-value should not be trusted. The next assumption is that the sample values and the test score are randomly selected and independent. In this assumption is violated, the p-value is not reliable (Green & Salkind, 2017).
Explain options researchers have when assumptions are violated.
Green and Salkind (2017) noted that the parametric procedures are the most powerful analysis to consider when all the independent sample t-test assumptions are met. However, if they are not met, the researcher may consider the alternatives, nonparametric analysis. If the normality distribution assumption is not met, the researcher can still rely on the p-value generated, but in some circumstances, the nonparametric analysis may be the best option.
References
Green, S. B., & Salkind, N. J. (2017). Using SPSS for Windows and Macintosh: Analyzing and understanding data (8th ed.). Upper Saddle River, NJ: Pearson.
Lumley, T., Diehr, P., Emerson, S., & Chen, L. (2002). The importance of the normality assumption in large public health data setsLinks to an external site.Links to an external site., Annual Review of Public Health, 23(1), 151-170. doi:10.1146.annurev.publheath.23.100901.140546
Ross, A., & Willson, V. L. (2018). Basic and Advanced Statistical Tests: Writing Results Sections and Creating Tables and Figures. Germany: SensePublishers
