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Non-Parametric Models by John Gregory Miller Capella University Presented in Partial Fulfillment Of the Requirements, of BMGT8034: Quantitative Research Techniques 503 Lafayette Street Pittsburg, TX, 75686 Telephone: 903-399-6100 Email: jmiller316@capellauniversity.edu Instructor: Dr. Brock Boudreau

State the research questions and associated null and alternative hypotheses. Verify assumptions associated with the non-parametric statistical test. Report correct hypothesis testing results. Identify all key limitations and draw conclusions Step A: Questions and Hypotheses Begin your project by developing a solid foundation to guide the study. 1. Develop a research question appropriate for the Mann-Whitney test. Q1 Would people accept lower offers and propose higher offers when listening to music that they like? 2. State the null and alternative hypotheses appropriate for a Mann-Whitney test. H0 – Null – The distribution of Offers Made is the same across categories of Background Music. H1 There is no significate difference in the distribution of Offers Made is the same across categories of Background Music H2 There is a significate difference in the distribution of Offers Made is the same across categories of Background Music 3. Identify the level of measurement for each variable (such as nominal, ordinal, interval, or ratio). Ordinal - When categories are ordered, the variable is known as an ordinal variable . Ordinal data tell us not only that things have occurred, but also the order in which they occurred. However, these data tell us nothing about the differences between values. 4. Using the decision tree in the back of your Field textbook, describe the selection process for the Mann-Whitney test used in this assignment. When you want to compare the distributions in two conditions and these conditions contain different entities, then you have two choices: the Mann–Whitney test (Mann & Whitney, 1947) and Wilcoxon's rank- sum test (Wilcoxon, 1945). Both tests are equivalent, and there's another Wilcoxon test, which gets extremely confusing. These tests are the non-parametric equivalent of the independent t-test, which we'll discover in Chapter 9. Two samples, To determine if the populations Independent t-test Mann-Whitney between subjects of two independent samples U test

5. Identify the significance level (alpha) for the test. While the exact significate level for this test is .074, the reported significance level is .05. Step B: Test the Assumptions While non-parametric tests are sometimes called “assumption-free tests” (Field, 2013, p. 214), we still test for normality and homogeneity of variance. In fact, the results of these tests and our inability to fix the issues are why we end up conducting non-parametric tests . 1. Identify and address any missing data, oddly coded data, and outliers. 2. Identify and test the assumptions (for example, normality and homogeneity of variance) using SPSS.

3. Create the appropriate graphs for visual analysis of the assumptions.

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