RSCH FPX 7864 Assessment 3 t-Test Application and Interpretation
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RSCH FPX 7864 Assessment 3 t-Test Application and Interpretation

RSCH FPX 7864 Assessment 3 t-Test Application and Interpretation

Name

Capella University

RSCH FPX 7864 Quantitative Design and Analysis

Prof. Name

Date

Data Analysis Plan

The variables used for the t-test and Levene’s test were final and review. The final variable shows the number of correct answers on the final exam, while the review variable is defined as the students who attended the review sessions, denoted by 1 = no; 2 = yes. The review is regarded as a two-level categorical variable, but the Final is treated as continuous for the purposes of this study.

The review question is, does the group of students who attended the review session (yes) and the group of students who did not (no) have significantly different mean test scores on the final exam? The Null hypothesis is that the mean test scores of the students who attended the review session and the students who did not show up for the session, do not differ significantly from one another. Finally, the alternate hypothesis is that students who attended the review session and students who did not have a significant difference in mean test scores on the final exam.

Testing Assumptions

Assumption Checks

Test of Equality of Variances (Levene’s)

Test

F

df1

df2

p

final 0.740 1 103 0.392

A t-test evaluates the assumption of equal variances between two groups using Levene’s test (Kim, 2015). The variable “final” in the output was subjected to Levene’s test to determine whether there was an equal variance between the two groups (those who attended review sessions and those who did not). There are df1=1 and degrees of freedom (df1, df2), and a test statistic (F) of 0.740. The p-value of the test is greater than 0.392, the conventional alpha threshold of 0.05. The null hypothesis, which states that the variances of the two groups are equal, is thus not rejected. Put differently, the results of the Levene test do not indicate a breach of the homogeneity of variances premise. Thus, the homogeneity of variances required by the ttest is satisfied, and the variances of the two groups are equivalent.

Results & Interpretation

Independent Samples T-Test

t

df

p

-0.410 103 0.682

Note. Student’s t-test.

Descriptives

Group

N

Mean

SD

SE

Coefficient of variation

Attended review session 55 61.545 7.356 0.992 0.120
Did not attend the review session 50 62.160 7.993 1.130 0.129

Each group’s means and standard deviations are as follows:

  • Participant group at the review session: m= 61.545, sd = 7.356
  • Did not attend the group review session: sd = 7.993, m = 62.160

To ascertain whether there was a significant difference between the means of the two groups, the independent samples t-test was employed. Results of the t-test are as follows:

  • t = -0.410
  • df = 103
  • p = 0.682

Since there is insufficient evidence, the null hypothesis cannot be rejected, as indicated by the p-value of 0.682, which is greater than the conventional alpha threshold of 0.05. Consequently, we deduce that there is no statistically significant difference in the final test scores between students who participated in review sessions and those who did not. We are unable to reject the null hypothesis based on statistical results, and the alternative hypothesis is not well-supported.

Statistical Conclusions

The purpose of this study was to determine if the final exam scores of students who attended review sessions and those who did not differed statistically significantly. An independent samples t-test was used to validate the assumption of equal variances, and the results were satisfactory. The study found that there was no statistically significant difference between the two groups’ mean final test scores. Therefore, there is no proof to support the hypothesis that attending review sessions would enhance test performance, and the null hypothesis was not proven.

RSCH FPX 7864 Assessment 3 t-Test Application and Interpretation

The independent samples t-Test can be used to examine up to two groups at once. The tTest can only be used with a single independent and dependent variable; it does not allow for multiple comparisons. Another disadvantage of the t-Test is that it may show carryover effects, which implies that instead of highlighting group differences, it might point to problems with repeated assessments. Other variables, like prior academic performance or study habits, may have had an impact on the results but were not taken into account during the study. Furthermore, there might be additional explanations for the results. For example, it’s possible that the review sessions didn’t go well or that students didn’t attend frequently enough to have an impact on their test scores.

Application

In the field of applied behavior analysis (ABA), the effectiveness of two distinct therapeutic interventions for the management of a particular behavior issue in individuals with autism or other developmental impairments can be compared using the independent samples t-test. Behavior analysts employ independent samples t-Test applications in the field of ABA for the following reasons. The designs and interventions for students’ aggressive behavior greatly depend on the results of t-tests for dependent variables.

Each participant in this specific group of students is measured twice on an outcome variable, such as a pretest-posttest design, which is the fundamental application of the t-Test in treating them. By comparing the mean changes in the dependent variable between treatment groups, I could determine whether the intervention was more effective in reducing the targeted behavior issue. Healthcare providers, psychologists, social workers, and caregivers may find it easier to select the best and most effective intervention options for their patients or loved ones with the use of this knowledge.

References

Capella University (n.d.) 7864 Course Study Guide.

RSCH FPX 7864 Assessment 3 t-Test Application and Interpretation

Kim, T. K. (2015). T test as a parametric statistic. Korean Journal of Anesthesiology, 68(6), 540. https://doi.org/10.4097/kjae.2015.68.6.540