Discussion: Data Assumptions and Parametric Statistical Tests

The accuracy of parametric statistical tests is largely based on the data distribution of the collected data. Parametric tests are based on distribution assumptions, such as normality, linearity, equality of variances, etc. These assumptions and others vary based on the statistical test; therefore, it is critical for quantitative researchers to evaluate the assumptions pertaining to their statistical analyses and identify actions taken if assumptions are grossly violated.

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To prepare for this Discussion, review the Lumley et al. (2002) article, as well as Lessons 19–21 and 24 in the Green and Salkind (2017) text. Use the Walden Library databases to identify a research example using your doctoral research proposal and consider the role and importance of the assumptions underlying each parametric test.

Post a comparison of one-sample, paired-samples, and independent-samples t-tests within the context of quantitative doctoral business research. In your comparison, do the following:

Describe the research example related to your doctoral research proposal.
Describe a hypothetical example appropriate for each t-test, ensuring that the variables are appropriately identified.
Analyze the assumptions associated with the independent-samples t-tests and the implications when assumptions are violated.
Explain options researchers have when assumptions are violated.

Be sure to support your work with a minimum of two specific citations from this week’s Learning Resources and at least one additional scholarly source.

Resources

Green, S. B., & Salkind, N. J. (2017). Using SPSS for Windows and Macintosh: Analyzing and understanding data (8th ed.). Upper Saddle River, NJ: Pearson.

Unit 5, “Creating Variables and Computing Descriptive Statistics”
- Lesson 19, “Creating Variables” (pp. 88–98)
- Lesson 20, “Univariate Descriptive Statistics for Qualitative Variables” (pp. 99–103)
- Lesson 21, “Univariate Descriptive Statistics for Quantitative Variables” (pp. 104–115)
Unit 6, “t Test Procedures”
- Lesson 24, “Independent-Samples t Test” (pp. 125–129)

Saunders, M. N. K., Lewis, P., & Thornhill, A. (2015). Research methods for business students (7th ed.). Essex, England: Pearson Education Unlimited.

Chapter 12, “Analysing Quantitative Data”

Lumley, T., Diehr, P., Emerson, S., & Chen, L. (2002). The importance of the normality assumption in large public health data sets, Annual Review of Public Health, 23(1), 151–170. doi:10.1146.annurev.publheath.23.100901.140546

Note: You will access this article from the Walden Library databases.

Paul, H., & Garg, P. (2014). Organizational commitment of frontline sales professionals in India: Role of resilience. International Journal of Business Insights and Information, 7(2), 12–18. Retrieved from http://www.ijbit.org/home

Note: This article contains several statistical analyses, to include the independent-samples t-test. You will access this article from the Walden Library databases.