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Data Analysis Plan: Comparing Final Scores Based on Review Session Attendance, Thesis of Accounting

Western Governors University (WGU)Accounting

A data analysis plan for comparing final scores of students who attended a review session versus those who did not. It outlines the research question, hypotheses, and statistical assumptions. The document then details the results of an independent samples t-test, demonstrating the lack of a significant difference in final scores between the two groups. It concludes with a discussion of the limitations of the study and potential applications of the findings in the field of psychology, particularly applied behavior analysis.

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Data Analysis and Application Capella University Data Analysis Plan The main objective of conducting a t-test is to compare the means between two samples. In this analysis, we will compare the means of those who attended the review or did not attend the review and final scores. Review is a categorical variable that is a dichotomous, nominal variable because it has two groups, yes and no. Final is a continuous variable that is used to determine the group mean. The research question for the variables would be, is there a difference in final scores between attending the review sessions or not attending the review sessions? The null hypothesis is that there is no significant mean difference in final scores from attending the review session or not attending the review session. The alternate hypothesis is

that there is a significant mean difference between final scores from attending the review session or not attending the review session. Testing Assumptions

Assumption Checks

Test of Equality of Variances (Brown-Forsythe)

F df 1 df 2 p

final 0.794 1 103 0.

Levene’s test shows p = 0.375 which means that homogeneity of variance assumption is

violated. The homogeneity of variance assumption is violated if p < 0.5 (Warner, 2020). Because

the homogeneity assumption is violated, we must run the “Welch” version of the independent

samples t-test (Warner, 2020).

those who did not. In conclusion of the t-test we were able to find that there was no significant mean difference between the two groups that attended or did not attend the review. Some limitations of the statistical test are that we only were able to measure the difference in the sample sets once, meaning other tests that were conducted may have been affected by attending or not attending the review. Another limitation is that there is no control group to narrow down what is available to analyze and to help in the avoidance of Type I errors (“ 7864 Course Study Guide, n.d.). Application Utilizing independent t-tests to study two sample populations is extremely useful to the psychology field, and in particular the applied behavior analysis field. Looking at variables such as gender can be studied to see the differences in experiences. Studying independent t-tests could also be used to identify differences in experiences between ethnicities and socioeconomic. Professionals in these fields can use the data gathered to better serve those under their care by understanding and accounting for these differences. Better understanding and having more data can lead to further studies.

References “ 7864 Course Study Guide” In Capella University. https://courseroom.capella.edu/courses/ 18313 /files/ 3115027 ?wrap= 1

Data Analysis Plan: Comparing Final Scores Based on Review Session Attendance, Thesis of Accounting

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Partial preview of the text

Download Data Analysis Plan: Comparing Final Scores Based on Review Session Attendance and more Thesis Accounting in PDF only on Docsity!

Assumption Checks

Test of Equality of Variances (Brown-Forsythe)

F df 1 df 2 p

final 0.794 1 103 0.

Levene’s test shows p = 0.375 which means that homogeneity of variance assumption is

violated. The homogeneity of variance assumption is violated if p < 0.5 (Warner, 2020). Because

the homogeneity assumption is violated, we must run the “Welch” version of the independent

samples t-test (Warner, 2020).

Warner, R. M. (2020). Applied Statistics I (3rd^ ed.). SAGE Publications, In. (US).

https://capella.vitalsource.com/books/ 9781506352817