T Tests in Quantitative Research Essay

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T-tests in Quantitative Doctoral Business Research

Quantitative research is one of the methodologies that is commonly used in doctoral business research. The use of this approach is attributable to the availability of more data that requires analysis to help generate competitive advantage in the business field. The use of quantitative research entails conducting statistical analysis, which involves the use of different methods such as t-tests and ANOVA. T-test is used in hypothesis testing in quantitative studies to determine whether variations between the averages of two groups is unlikely to have emerged because of a random chance in selection of a sample. In essence, t-tests help to compare whether two groups have varying average values. In light of the role and significance of the assumptions underlying each parametric test, this paper provides a comparison of one-sample, paired-samples, and independent-sample t-tests within the context of quantitative doctoral business research. The comparison is based on a qualitative research proposal.

One-sample, Paired-Samples, and Independent-Samples T-tests

As previously indicated, t-tests are used in quantitative research evaluate whether two groups have varying average values. In this regard, t-tests help to compare two means to evaluate whether they come from the same population. One of the underlying assumptions in t-tests is that both groups have relatively equal variances and are normally distributed. However, when a two-sample t-test is conducted, it is presumed that two groups have relatively equal variances, while the other does not (Lumley et al., 2002).

One-sample t-test is used to compare the average value of one group to a single number or to compare a sample mean to an already identified population mean. The comparison is geared towards determining whether the variation between the two means occurred by chance only or is statistically significant.

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In quantitative doctoral business research, one-sample t-tests comprises two types of hypotheses i.e. null hypothesis and alternative hypothesis. While the alternative hypothesis assumes the existence of some variations between the actual mean and the comparison value, the null hypothesis presumes that no variation exists. On the contrary, paired-sample t-tests are used to compare two sample means from diverse populations whose members have been paired or matched. Additionally, this t-test is used to compare two sample means from one population on the same variable, but at two different time periods like a pre-test and post-test (Empirical Reasoning Center, 2018). In quantitative doctoral business research, paired-sample t-tests are used when an observation is one group is matched with a correlated observation in another group. Independent-sample t-tests are used to compare two sample means from diverse populations on the same variable. Unlike paired-sample t-tests, independent-sample t-tests do not match members or attributes from the different populations.

Qualitative Research Proposal

An example of a qualitative research proposal that would help in comparison of the above t-tests is the research proposal on international business knowledge transfer and execution within multinational corporations in China (“Example Research Proposal”, n.d.). The research proposal question is how does Chinese multinational corporations (MNCs) implement knowledge….....

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References

Empirical Reasoning Center. (2018). Hypothesis Testing: T-Tests. Retrieved from Barnard College website: https://erc.barnard.edu/spss/t_tests

“Example Research Proposal.” (n.d.). Business School. Retrieved from University of Edinburgh website: https://www.business-school.ed.ac.uk/__data/assets/pdf_file/0020/54821/Example-Research-Proposal.pdf

Laerd Statistics. (2018). Independent T-Tests Using SPSS Statistics. Retrieved September 17, 2018, from https://statistics.laerd.com/spss-tutorials/independent-t-test-using-spss-statistics.php

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

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