Comparing Groups

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Comparing Groups
Read the essay below/attached and provide additional input and outside scholarly resources separately. Also build upon the below essays or 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. Must be substantive, using at least 3 peer-reviewed source citations in APA format in the past 5 years. Must further the discussion with a minimum of 300 words. Any sources cited must have been published within the last five years.

Text readings:
Morgan, G. A., Barrett, K. C., Leech, N. L., Gloeckner, G. W. (20190715). IBM SPSS for Introductory Statistics, 6th Edition. (Chapter 9-10)

Essay 1
Comparing Groups
Comparing Groups
D.8.9.6.a. In Output 9.6: Describe the F, df, and p Values for Each Dependent Variable as You Would in an Article.
F(2, 70) = 4.09, p = .021, for grades in h.s.
F(2, 70) = .76, p = .47, for visualization test
F(2, 70) = 7.88, p = .00, for math achievement test
The 2, 70 are the degrees of freedom (df) for the between groups “effect” and the within groups “error”, respectively (Morgan et al., 2019). F tables also usually include the mean squares, which indicate the amount of variance for that “effect” divided by the degrees of freedom for that “effect” (Morgan et al., 2019).
D.8.9.6.b. Describe the Results in Nontechnical Terms for Visualization and Grades. Use the Group Means in Your Description.
Grades in h.s. (p = .220) and visualization test (p = .153) in the Levene’s test are not significant and the assumption is not violated (Morgan et al., 2019).
D.8.9.7. In Outputs 9.7 a and b, What Pairs of Means Were Significantly Different?
In Output 9.7.a., by examining the two subset boxes, researchers can see that the low education group (M = 5.34) is different from the high education group (M = 6.53) because both means do not appear in the same subset (Morgan et al., 2019). In Output 9.7.b., students whose fathers had a B.S. degree were significantly different on math achievement from student’s fathers that had a high school degree or less (p = .008) (Morgan et al., 2019).
D.8.9.8. In Output 9.8, Interpret the Meaning of the Sig. Values for Math Achievement and Competence. What Would You Conclude, Based on This Information, About Differences Between Groups on Each of These Variables?
In Output 9.8., p (Asymp. Sig.) value for math achievement is .001, which is the same as it was in Output 9.6 using the one-way ANOVA because Kruskal-Wallis (K-W) and ANOVA have similar power to sense a variance (Morgan et al., 2019). There is no statistically significant difference among the father’s education groups on the competence scale(p = .999) (Morgan et al., 2019). Because there are no post hoc tests built into the K-W test, although all samples should be known, researchers cannot tell which of the pairs of father’s education means are different on math achievement(Morgan et al., 2019; Guo et al., 2013).
D.8.9.9. Compare Outputs 9.6 and 9.8 With Regard to Math Achievement. What are the Most Important Differences and Similarities?
In Output 9.6., for the Test of Homogeneity of Variances Table, math achievement (p = .049), the Levene’s test is significant and thus the assumption of equal variances is violated (Morgan et al., 2019). In Output 9.8., the Asymp. Sig for math achievement is p = .001, which means is the same as in Output 9.6. using the one-way ANOVA (Morgan et al., 2019),
D.8.9.10.
D.8.9.10.a. In Output 9.9: Is the Interaction Significant?
F(1, 71) = .337, p = .563
In Output 9.9., the interaction is not statistically significant however, if the interaction was statistically significant, researchers would need to be cautious about the interpretation of the main effects because the interpretation could be misleading (Morgan et al., 2019).
D.8.9.10.b. Examine the Profile Plot of the Cell Means That Illustrates the Interaction. Describe it in Words.
The profile plots of cell means helps researchers to visualize the nature of a significant interaction when one exists (Morgan et al., 2019). In Output 9.9., the profile plots are approximately parallel to one another for academic track in both fast track and regular track.
D.8.9.10.c. Is the Main Effect of Academic Track Significant? Interpret the eta Squared.
The main effect of academic track is statistically significant (p <.001), and because the interaction is not statistically significant, the “effect” of the math grades on math achievement is about the same for bot academic tracks (Morgan et al., 2019). In Output 9.9., eta (not squared) for math grades is about .41 and thus a medium to large effect (Morgan et al., 2019). Eta for academic track is about .40, also a medium to large effect (Morgan et al., 2019). The overall adjusted R is about .46, a large effect (Morgan et al., 2019). D.8.9.10.d. How About the “Effect” of Math Grades? In Output 9.9., the “effect” of math grades on math achievement is about the same for both academic tracks (Morgan et al., 2019). If the interaction were statistically significant, researchers could then say that the “effect” of math grades depended on which academic track on was considering (Morgan et al., 2019). D.8.9.10.e. Why Did We Put the Word Effect in Quotes? The word “effect” in the title of the table can be misleading because the study was not a randomized experiment (Morgan et al., 2019). Thus, researchers cannot say in reports that the differences in the dependent variable were caused by or were the effect of the independent variable (Morgan et al., 2019). D.8.9.10.f. Under What Conditions Would Focusing on the Main Effects be Misleading? Under the conditions that interactions are statistically significant in the ANOVA table called Tests of Between-Subject Effects, focusing on the main effects may be misleading. References Guo, S., Zhong, S., & Zhang, A. (2013). Privacy-preserving Kruskal–Wallis test. Computer Methods and Programs in Biomedicine, 112(1), 135-145. https://doi.org/10.1016/ j.cmpb.2013.05.023 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