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RSCH FPX 7864 Assessment 3 ANOVA Application and Interpretation

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    RSCH FPX 7864 Assessment 3 ANOVA Application and Interpretation

    Student Name

    Capella University

    RSCH-FPX 7864 Quantitative Design and Analysis

    Prof. Name


    ANOVA Application and Interpretation

    The analysis of variance (ANOVA) is employed to investigate the association between gender (independent variable) and GPA (dependent variable). Gender is a categorical variable, whereas GPA is continuous. The research inquiry aims to explore the influence of gender on GPA levels. The null hypothesis posits that gender has no impact on GPA levels, while the alternative hypothesis suggests that gender does affect GPA. The assumptions for the test are detailed in Table 1.

    Table 1

    Independent Samples Test

    Levene’s Test for Equality of Variances

    Levene’s test examines the homogeneity of variances between GPA (dependent variable) and Gender (independent variable). A significance level below .05 indicates significantly distinct variances. In this investigation, the data in Table 1 reveals a significance level of .758, suggesting no substantial difference between the two variables. Assumptions of equal variances are upheld, signifying homogeneity.

    Results and Interpretation

    Table 2

    Group Statistics

    Table 2 presents group statistics indicating the mean GPA for females (n=64) at 2.97 with a standard deviation of 0.678 and for males (n=41) at 2.69 with a standard deviation of 0.739. Despite assuming equal variances, the homogeneity assumption is not met. The significance value of .048 in the Levene’s test indicates slightly different variances. However, since .048 < .05, the null hypothesis is not rejected, and the groups’ variability is not significantly different.

    Statistical Conclusions

    This study explores GPA differences among genders using an equal variance t-test, revealing a statistically significant difference in GPA means for males and females (Males: M = 2.69, SD = 0.74; Females: M = 2.97, SD = 0.68, p = .758 > .05). Limitations include assumptions associated with the t-test and unequal sample sizes. Future studies could delve into support networks for achieving higher GPAs.


    Proficient HR leaders can utilize trend analysis tools, such as time and attendance, to identify factors influencing staff outcomes. For instance, staffing initiatives and safety training effectiveness can be evaluated. Continuous analysis aids in recognizing factors impacting workplace activities and employee outcomes.


    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., Stanford University Press, pp. 278-292.

    RSCH FPX 7864 Assessment 3 ANOVA Application and Interpretation