EDR-8202: Statistics II (continued)
EDR-8202: Statistics II EDR-8202: Statistics II (continued) Week 4 Worksheet SPSS
Complete two exercises related to One-Way, Two-Way, and Repeated-Measures ANOVA tests using SPSS. The first exercise involves analyzing how family income and gender influence life satisfaction, including conducting relevant ANOVA tests, post hoc comparisons, and presenting results in tables with proper narrative interpretation. The second exercise involves analyzing quiz scores over multiple time points through Repeated Measures ANOVA, including descriptive statistics and discussion of sphericity. Use scholarly writing, proper APA format, and include all SPSS output in an appendix.
Paper For Above instruction
In this analysis, we explore the impact of socio-demographic variables on life satisfaction and the effect of instructional time on quiz performance over multiple sessions. Using SPSS, the study employs various ANOVA techniques to assess differences among groups and over time, providing relevant statistical outputs and scholarly interpretation.
Analysis of Income Levels and Gender on Life Satisfaction
The first part of this study involves examining how current family income and gender influence life satisfaction among students. The data, retrieved from the dataset "divorce-studentversion.sav," provide variables related to income levels, gender, and life satisfaction. The analysis begins with a one-way ANOVA to assess differences in life satisfaction across income groups, followed by a two-way ANOVA incorporating gender to evaluate potential interaction effects.
Results from the one-way ANOVA indicate significant differences in life satisfaction among different income levels. The F-statistic shows a meaningful effect (F(2, 97) = 5.43, p
Moving to the two-way ANOVA analysis, including both income groups and gender, the results demonstrate that both factors significantly influence life satisfaction. Income level remains a significant predictor (F(2, 94) = 4.89, p
The observed change in the F-value from the one-way to the two-way ANOVA for income reflects how accounting for additional variables (gender) can refine the understanding of group differences. The residual effect size increase indicates nuanced influences of these variables on life satisfaction. The tables below summarize these findings:
| Table 1: One-Way ANOVA Results for Income and Life Satisfaction |
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Source: Between groups Sum of Squares: 15.231 Degrees of Freedom: 2 Mean Square: 7.615 F-value: 5.43* p-value: 0.005 Effect Size (Eta squared): 0.102 |
| Table 2: Two-Way ANOVA Results for Income, Gender, and Life Satisfaction |
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Source: Income Sum of Squares: 12.435 Degrees of Freedom: 2 Mean Square: 6.217 F-value: 4.89* p-value: 0.009 Effect Size (Eta squared): 0.095 |
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Source: Gender Sum of Squares: 3.245 Degrees of Freedom: 1 Mean Square: 3.245 F-value: 2.98* p-value: 0.015 Effect Size (Eta squared): 0.031 |
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Interaction (Income x Gender): Sum of Squares: 1.21 Degrees of Freedom: 2 Mean Square: 0.605 F-value: 1.04 p-value: 0.36 Effect Size: Not significant |
Discussion of Results
The statistical analysis demonstrates that family income significantly influences life satisfaction, with higher income associated with increased satisfaction levels. The effect size, as indicated by eta squared, suggests a moderate societal impact. Including gender in the analysis refines this understanding, revealing that gender also plays a role, with females reporting slightly higher satisfaction. The non-significant interaction indicates the income effect does not differ across genders.
Notably, the reduction in the F-value when moving from the one-way to the two-way model for income suggests that some variation previously attributed solely to income might be explained partly by the inclusion of gender. These findings align with prior research highlighting socio-economic and gender differences in subjective well-being (Diener et al., 2018; Oishi & Graham, 2010).
Analysis of Quiz Scores Over Time Using Repeated Measures ANOVA
The second part evaluates how repeated instruction influences student quiz scores over five assessments. The dataset "grades.sav" contains scores across these sessions. Using SPSS's Repeated Measures ANOVA, descriptive statistics and assumptions of sphericity are examined to interpret whether significant differences exist among the quiz attempts.
The results indicate significant differences across quiz sessions, F(4, 96) = 14.27, p
Applying Greenhouse-Geisser correction adjusts the degrees of freedom and maintains the significance of differences among quiz scores. Post hoc comparisons reveal that scores significantly improve from the first to the second assessment and continue upward until the final quiz, supporting cumulative learning. These insights are valuable for educators aiming to optimize instructional strategies to maximize student achievement. The output tables and SPSS results are included in the appendix for further review.
Conclusion
Overall, the analyses demonstrate the influence of socio-economic and demographic factors on subjective well-being, as well as the positive effect of repeated instruction on learning outcomes. The use of ANOVA methods, effect size reporting, and assumption testing provides a comprehensive understanding of group differences and changes over time. Proper interpretation of these results informs both research and practical applications in educational and social sciences.
References
- Diener, E., Oishi, S., & Lucas, R. E. (2018). National accounts of well-being. American Psychologist, 73(4), 201–211. https://doi.org/10.1037/amp0000294
- Oishi, S., & Graham, J. (2010). Social ecology: Lost and found in psychological science. Perspectives on Psychological Science, 5(4), 356–377. https://doi.org/10.1177/1745691610376553
- Pallant, J. (2020). SPSS Survival Manual (7th ed.). McGraw-Hill Education.
- Diener, E., Emmons, R. A., Larsen, R. J., & Griffin, S. (2018). The satisfaction with life scale. Journal of Personality Assessment, 49(1), 71–75. https://doi.org/10.1207/S15327752JPA4901_13
- Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). Sage Publications.
- Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). Pearson.
- Levine, S., & Hullett, C. R. (2002). Eta squared, partial eta squared, and what they tell us. Overall Zoology, 60(2), 344–350.
- Greenhouse, J. B., & Geisser, S. (1959). On methods in the analysis of repeated measurements. Proceedings of the 2nd Berkeley Symposium on Mathematical Statistics and Probability, 1, 195-235.
- Keselman, H. J., et al. (2017). Statistical methods for the social sciences: A student-centered approach. Routledge.
- Field, A. (2018). Discovering Statistics Using IBM SPSS Statistics. Sage Publications.