RSCH FPX 7864 Assessment 3 ANOVA Application and Interpretation

RSCH FPX 7864 Assessment 3 ANOVA Application and Interpretation

Name

Capella University

RSCH-FPX 7864 Quantitative Design and Analysis

Prof. Name

Date

Data Analysis and Application

In the independent T-test, the variables involved are gender, which serves as the independent variable, and GPA, which is the dependent variable. Gender is a categorical variable, while GPA is continuous. The research question guiding this analysis is: Does gender influence GPA levels? The null hypothesis posits that gender does not impact GPA levels, implying that the mean scores within the sample are not significantly different. Conversely, the alternative hypothesis suggests that gender has an effect on GPA, indicating that the mean GPA for females and males differs significantly.

Test Assumptions

Table 1. Independent Samples Test

The results from Levene’s test for the equality of variances (Levene, 1960) are examined to determine the homogeneity of variances between the dependent variable, GPA, and the independent variable, gender. The significance value obtained from Levene’s test is analyzed to check for any notable differences. A significance level below .05 would indicate that the variances are significantly different, whereas a significance level above .05 suggests that the variances are approximately equal. In this case, the reported significance level between GPA and gender is .758, which exceeds the threshold of p=.05. Therefore, equal variances are assumed, indicating that the assumption of homogeneity has been satisfied.

Results and Interpretation

Table 2. Group Statistics

RSCH FPX 7864 Assessment 3 ANOVA Application and Interpretation

The statistics concerning gender and GPA are presented in Table 2. Females (n=64) have a mean GPA of 2.97, with a standard deviation of 0.678, while males (n=41) show a mean GPA of 2.69 and a standard deviation of 0.739. Based on Levene’s test, equal variances are assumed, and the assumption of homogeneity has been satisfied. The calculated significance (2-tailed) value is 0.048. Since 0.048 is less than p=0.05, it indicates a slight difference between the variances for GPA and gender, although it fails to reject the null hypothesis. This suggests that the variability between the groups is not significantly different, indicating that male GPAs are lower when compared to female GPAs.

Statistical Conclusions

This analysis explored the GPA differences between genders (male and female). An equal variance t-test revealed a statistical disparity between the mean GPAs. The data gathered included a total of (N=105) students, consisting of both males and females. The results indicated that females had a mean GPA of M = 2.97 (SD = 0.68) while males had a mean GPA of M = 2.69 (SD = 0.74). The t-test was executed at a significance level of P=0.05, yielding F (1,103) = 0.95, p = .758 > 0.05, leading to the conclusion that gender does indeed influence GPA. However, several limitations were noted. Field (2018) points out that violations of assumptions related to the between-samples t-test may affect results. Additionally, the unequal sample sizes may skew results, with females being N=64 and males being N=41. Such disparities can contribute to higher GPAs among females. Future studies should focus on identifying effective support networks that enhance GPA achievement for students.

Application

An effective HR leader must consistently analyze trends that may affect the workplace. Such trends might include time and attendance, staff turnover, and benefits utilization, among others. Human Resources can utilize these insights to identify factors influencing employee performance and satisfaction, allowing them to assess the elements that contribute to different outcomes. For example, one could compare staffing initiatives against new hires, treated as the dependent variable, or examine the impact of injury and safety training programs.

References

Field, A. (2018). Discovering Statistics Using IBM SPSS Statistics. SAGE Publications.

Levene, H. (1960). In Contributions to Probability and Statistics: Essays in Honor of Harold Hotelling (I. Olkin et al. eds., pp. 278-292). Stanford University Press.