The student must then post 1 reply to another student’s post. The reply must summarize the student’s findings and indicate areas of agreement, disagreement, and improvement. It must be supported with scholarly citations in the latest APA format and corresponding list of references. Please reply to Marilyn. (she/her) in the first person.

Marilyn A. Campbell

Discussion Thread: Comparing Groups

BUSI 820: Quantitative Research Methods

D.8.9.6 In Output 9.6: (a) Describe the F, df, and p values for each dependent variable as you would in an article. (b) Describe the results in non-technical terms for visualization and grades. Use the group means in your description.

They are computing both kinds of ANOVA and similar nonparametric statistics for ANOVA. ANOVA’s one-way and unique factor is comparable to the father’s education levels concerning the mathematical performance variables due to variance in the groups. Variance analysis (ANOVA), the study of experimental designs, consists of F-trials of the main effects and interactions (Rouder et al., 2016). This performant test has the three fathers’ education group difference on math achievement, which tells us that the difference between the fast track and regular track is slight with 1.05 points out of 25. The test indicated that the education groups of the three fathers differed in math performance χ2 (2, N = 73) = 13.38, p = 001 (Morgan, 2019). The ANOVA chart shows a statistically significant difference in parent education groups on high school grades or student visualization scores. The homogeneity variance table presents the Levene test to verify that the differences between the three father education groups are equal. ANOVA is testing the means for the. The Levene test is negligible for grades (p=.220) and the visualization test (p=.153), and variances are breached. In general, F is important, and researchers could have used the specific test designed for situations of unequal variances (Moran et al., 2007). and al. 2019).

D.8.9.7 In Outputs 9.7 a and b, what pairs of means were significantly different?

In Entry 9.7, those who completed high school or less had a more considerable difference from the other two undifferentiated comparisons. In 9.7b, the average number of high school graduates is different. The comparison of the other two is not further. For 9.7b, the average scores of high school graduates or below and BS are more diverse than those fathers who have some colleges was significant (Morgan, 2019).

D.8.9.8 In Output 9.8, interpret the meaning of the sig. Values for math achievement and competence. Based on this information, what would you conclude about differences between groups on each of these variables?

Nonparametric analysis of Kruskal-Wallis (KW) variance is often used instead of a standard unidirectional ANOVA when data are from a suspected non-normal population (Elliott and Hynan 2011). In the case of the Mann-Whitney trial, it provides the mean ratings for the two independent variables. The Kruskal-Wallis test (K-W compares the averages for the three father education groups (Morgan (2019)). The procedure tests group differences but does not provide a specific post-hoc pair comparison (Elliott & Hynan, 2011). The test was conducted for a statistically significant difference between the father education groups in math achievement due to unequal variance in the groups; the trial indicated that the three father education groups differed on the math achievement χ2 (2, N = 73) = 13.38, p = .001 (Morgan, 2019). The post hoc Mann-Whitney test compared the three pairs, and the use of a correct Bonferroni p-value of 0.017 indicates a statistical meaning. The average rank of those whose father attended college (36.59, n = 16) was much higher than those whose father graduated from high school. The effect size of this comparison was small to medium, or not much, and the eh r effect was somewhat smaller than the corresponding test.

D.8.9.9 Compare Outputs 9.6 and 9.8 about math achievement. What are the most important differences and similarities?

When comparing the output of 9.6 and 9.8 on mathematics scores, 9.6 provides descriptive statistics on the scores of the mathematics test scores, and 9.8 provides the average ranking of each education group of the three fathers. 9.6 The output uses the analysis of variance to compare the statistical averages of the three-parent education groups. 9.8 The output is compared using the Kruskal Wallis nonparametric test. Since the Kruskal Wallis test is nonparametric, the output of 9.8 is not necessary for the variance test. Each test used did not differ in each group.

D.8.9.10 In Output 9.9: (a) Is the interaction significant? (b) Examine the profile plot of the cell means that illustrate the interaction. Describe it in words. (c) Is the main effect of the academic track significant? Interpret the eta squared. (d) How about the “effect” of math grades? (e) Why did we put the word effect in quotes? (f) Under what conditions would be focusing on the immediate effects be misleading?

In 9.9, the representation of the interaction between two independent variables shows the influence of eta2 and traces the interaction. The first table shows that 75 participants have noted in mathematics from A to B, less than 44 people, and 31 pointed out in mathematics from A to B. They are included in the analysis due to the three o variables. The two averages in the descriptive statistics table indicate the cell and the marginal mean. The results of the interaction are described in ANOVA. The main effects of the math grade from the academic track are both statistically significant. The F for mathematical scores shows little A and B in the lowest score on the mathematical performance test than those with high mathematical scores (M = 10.81 vs. 15.05) Significant Statistical Information (p < 001) (Morgan et al. 2019). The interaction is not statistically significant in the "effect" of the math grade on the math achievement test, and they are both the same on the academic tracks. If the interest were significant, it would say "effect" of the math class integration. The profile plots of the cell mean they visualized the nature of the considerable interaction, but a researcher should not be statistically noncognizant different because they can be misleading. Examine the effects of the math grades and the academic track shows that they are both significant F for the math grade means. The ANOVA test of the between-subject results is the key to the word "effect" in the misleading, and the report difference is the dependent variable that caused the impact of the independent variable. The researcher cannot report that the difference in the dependent variable causes the independent variable’s effect. If the interaction were signified, the research would need to be cautious with the interpretation of the leading products that may be misleading.
References
Elliott, A. C., & Hynan, L. S. (2010;2011;). A SAS® macro implementation of a multiple comparison post hoc test for a Kruskal–Wallis analysis. Computer Methods and Programs in Biomedicine, 102(1), 75-80. https://doi.org/10.1016/j.cmpb.2010.11.002
Morgan, G. A., Barrett, K. C., Leech, N. L., & Gloeckner, G. W. (2019). IBM SPSS for Introductory Statistics (6th Edition). Taylor & Francis. https://bookshelf.vitalsource.com/books/9781000011753

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