mrssharonkilgore
Respond to colleagues’ QUESTION in the following way: Answer…
Respond to colleagues’ QUESTION in the following way:
Answer question(s) posed by your colleague(s) for further discussion
Colleague’s response question
Hi Sharon, excellent post! A t-test’s findings assess the significance of the mean difference to ascertain whether the outcomes are random. The t-test is also a parametric analysis tool because it needs the means and standard deviations of a set of data to be calculated. Out of all the T-test procedures, what test would be most beneficial to your study?
Please note that for each response you must include a minimum of one appropriately cited scholarly reference.
DISCUSSION POST
Describe the research example related to your doctoral research proposal.
In the context of a quantitative doctoral business research proposal on strategies for acquiring financing for women-owned businesses, the use of t-tests will be used to analyze and compare data related to my research topic. The main goal of this research is to identify effective strategies that can help women of small businesses secure financing for their businesses and to compare different approaches to understand their impact on financing outcomes.
Describe a hypothetical example appropriate for each t-test, ensuring that the variables are appropriately identified.
The one-sample t-test is used to determine whether the mean of a sample significantly differs from a known or hypothesized population mean. In the context of this research, a one-sample t-test could be employed to compare the average financing success rate of women-owned small businesses to a population mean, which might be the average financing success rate for all businesses (regardless of gender) in the region or industry of interest (Sharma & Singh, 2019). The null hypothesis (H0) would state that there is no significant difference between the mean financing success rate of women-owned businesses and the population mean, while the alternative hypothesis (Ha) would indicate that there is a significant difference. Example: H0: The mean financing success rate for women-owned small businesses is equal to the population mean financing success rate for all businesses. Ha: The mean financing success rate for women-owned businesses is significantly different from the population mean financing success rate for all businesses (Green & Salkind, 2017).
The paired-samples t-test, also known as the dependent-samples t-test, is used when we have two sets of related or paired data points. In the context of this research, a paired-samples t-test could be applied to examine the effectiveness of specific financing strategies before and after their implementation (Green & Salkind, 2017). By collecting data on financing success rates for women-owned small businesses both before and after implementing a particular strategy, researchers can assess whether the strategy led to a significant improvement in financing outcomes. Example: The financing success rates of a sample of women-owned small businesses are measured before and after participating in a financing education program. The paired-samples t-test would determine whether there is a significant difference in the mean financing success rate before and after the program (Green & Salkind, 2017).
The independent-samples t-test is used to compare the means of two separate and unrelated groups. In the context of this research, an independent-samples t-test could be employed to compare the financing success rates between women-owned businesses that have used a specific strategy and those that have not (Green & Salkind, 2017). This analysis can help researchers understand if certain strategies are associated with better financing outcomes compared to businesses that did not employ them. Example: A group of women-owned small businesses that used government-backed financing programs is compared to a control group of women-owned small businesses that did not use such programs. The independent-samples t-test would assess whether there is a statistically significant difference in the mean financing success rates between the two groups (Green & Salkind, 2017).
The t-tests provide valuable statistical tools for quantitative doctoral business research. The choice between one-sample, paired-samples, and independent-samples t-tests depends on the research design and the specific hypotheses being tested (Green & Salkind, 2017). In the context of exploring strategies for acquiring financing for women-owned businesses, these t-tests will enable researchers to draw meaningful conclusions about the effectiveness of various financing approaches, contributing to the knowledge and advancement of supporting women entrepreneurs in their business endeavors.
Analyze the assumptions associated with the independent-samples t-tests and the implications when assumptions are violated.
Lumley et al. (2002) state that the major assumption of the independent samples t-test is that the test variables are regularly distributed. Even when this assumption is broken, the t-test produces a credible and accurate p-value. The variance of the normally distributed test variables for both populations is assumed to be the same. If the assumption is broken, then the p-value cannot be relied upon. The subsequent assumption is that the sample values and the test score are independently and randomly selected. In cases where this assumption is violated, the p-value cannot be relied upon.
Explain the options researchers have when assumptions are violated.
When all of the independent sample t-test assumptions hold true, parametric procedures are the most powerful analysis to consider, as pointed out by Green and Salkind (2017). If these conditions are not satisfied, the researcher may turn to nonparametric analysis as a viable option. Researchers can still use the calculated p-value if the normalcy distribution assumption is violated, however nonparametric analysis may be preferable in some cases.
References
Coleman, S., & Robb, A. (2020). Access to Capital and Gender Inequality: Implications for Women Entrepreneurs. Journal of Business Venturing, 35(4), 1-18.
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
