RSCH FPX 7864 Assessment 3 ANOVA Application and Interpretation

RSCH FPX 7864 Assessment 3 ANOVA Application and Interpretation

Student Name

Capella University

RSCH-FPX 7864 Quantitative Design and Analysis

Prof. Name

Date

The Data Analysis and Application

Introduction

The independent t-test examines the relationship between gender (independent variable) and GPA (dependent variable). Gender is categorical, while GPA is continuous. The research question explores the role of gender in GPA levels, with the null hypothesis stating that gender does not impact GPA levels, and the alternative hypothesis suggesting that gender does not affect GPA. The assumptions for the test are presented in Table 1.

Table 1. Independent Samples Test

Levene’s Test for Equality of Variances

Levene’s test evaluates homogeneity of variances between GPA (dependent variable) and Gender (independent variable). A significance level less than .05 indicates significantly different variances. In this study, the data in Table 1 shows a significance level of .758, suggesting no significant difference between the two variables. Equal variances are assumed, indicating homogeneity.

Results and Interpretation

Table 2. Group Statistics

Group statistics in Table 2 display 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. Although equal variances are assumed, the assumption of homogeneity is not met. The Levene’s test significance value of .048 indicates slightly different variances. However, as .048 < .05, the null hypothesis is not rejected, and the variability of groups is not significantly different.

Statistical Conclusions

This study investigates 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 explore support networks for achieving higher GPAs.

Application

Effective HR leaders can use trend analysis tools, such as time and attendance, to identify factors impacting staff outcomes. For example, staffing initiatives and safety training effectiveness can be assessed. Continuous analysis helps identify factors influencing workplace activities and employee outcomes.

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

RSCH FPX 7864 Assessment 3 ANOVA Application and Interpretation