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Maasai Mara University *

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Statistics

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Nov 24, 2024

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Quantitative Research Report Student's Name Instructor's Name Institution Affiliation Course Code Course Title Date of Submission
Part A: ONE WAY REPEATED MEASURES ANOVA Unlike the T-Test analysis, which is typically used for assessing the mean for one group to another, an ANOVA for repeated measures is employed when we want to compare the mean scores between three or more groups on separate observations. Part one: Disorder is an illness that disturbs normal mental or physical functions of a someone or an animal. Mental, anxiety, depression, and eating disorders are among some of the examples of disorders in human beings. Disordered eating mostly arises when individuals have pressure in changing their body appearance. Some people tend to eat more to increase their weight as others forgo eating to lose weight. This is believed to have been as a result of exposure to media. Therefore, this section is meant to shade more light on the predictor of the eating disorder. We will do the statistical analysis as per the hypotheses below: H0 : all body dissatisfaction assessment on the three occasion were equal (baseline (T1) = post- intervention (T2) = 4-week follow-up (T3). There are no differences between predictor variable on the dependent variable. H1 : at least one related group mean is different; they are not all equal. There are differences between dependent and independent variable. Specification, Testing, And Outcome of Relevant Assumptions It is crucial to confirm that we agree with the necessary assumption for the ANOVA test before moving on with the ANOVA for repeated measures analysis:
Assumption 1 (Independence) : Since each observation needs to be independent, there needs to be a single participant in every group for the study. There is no method for the subject to influence members of other groups, and each participant is limited to membership in one. Assumption 2 (Sphericity) : There must be an equal variance between the groups. Assumption 3 (Normality ): Data from the population under study must have a multivariate normal distribution Check your assumptions: Is the dependent variable at least scale (ratio or interval)? Yes, we are using interval data style. Are there any outliers in the sample? In examining if any data might be considered an outlier, we can use the descriptive options we learned previously. Part Two Reporting Results: There was a decrease in levels of body dissatisfaction Time 1 (M = 25.91, S.D = 3.817), to Time 2 (M = 23.53, SD = 3.104), and Time 3 (M = 23.15, SD = 2.913).
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The Multivariate Test is conducted using four different test statistics (Pillai’s Trace, Wilks’ Lambda, Hotelling’s Trace, and Roy’s Largest Root) which provide basically the same information. The most important metric here is the P-value (Sig. column) with values across all statistical tests for depression p – value of 0.000 therefore statistically significant since our p- value is less than 0.05. Mauchly’s Test of Sphericity test determines if the variances between all differences between all possible pairs of groups are equal (whether the sphericity assumption is violated or not). The Mauchly’s Test has an α level of 0.05 and to meet the sphericity assumption we need to have a value greater than that. In our case, the calculated P-value is 0.000 therefore we are not meeting the sphericity assumption .
As with the previous tests, the Sphericity Assumed has an α level of 0.05, and a P-value <0.05 shows statistical significance. There is an α = 0.05 in the within-subjects contrast tests. As indicated by the above table, disordered eating is statistically significant in our case study, with a P-value <0.05.
Estimated marginal means If any statistical differences exist between the pairs—in our example, the three research periods—the Pairwise Comparison test finds them. As you can see, each pair has statistical significance (P-value <0.05).
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Part B: STANDARD MULTIPLE REGRESSION Research scenario: Depression is on the rise! As budding psychologists, you are interested in determining what the predictive factors of depression are. Part one: Depression is a state of being unsettled mind functioning. Depression is on the rise worldwide. This section we determine the whether the four mentioned predicting factors (problems at work place, gender difference, age, and marital status) actually have got an influence on the rise of depression. We will base our test under the following hypothesis: H0 : Work related problems, gender differences, marital conflict, and age are the main factors that contribute majorly to depression. H1 : Work related problems, gender differences, marital conflict, and age are not the main factors that have contributed to depression. Specification, testing, and outcome of relevant assumptions One of the most commonly misinterpreted statistical concepts is the normalcy requirement for multiple regression. To use multiple regression models, the following five fundamental assumptions must hold true: The five criteria are as follows: independent variable independence, normalcy, error independence, linearity, and homoskedasticity.
Assumptions Checked as Part of The Analysis Part Two
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