Week 5 Worksheets And Data Set Download

Week 5 Worksheetspss Week 5 Ancovadownload The Data Set Divorce Stude

Review both the Data View and the Variable View in the dataset "divorce-studentversion.sav" to understand the data. Conduct an Analysis of Covariance (ANCOVA) using SPSS to examine how current family income influences life satisfaction, with the number of children as a covariate. Ensure to select options providing a measure of effect size (Eta squared) and Levene’s test for homogeneity. Present a table with ANCOVA results, including Eta squared, and provide a narrative discussing effect size, Levene’s test, and the change in the F-value of current family income when the number of children is included as a covariate. Include an appendix with all SPSS output relevant to these analyses.

Paper For Above instruction

The relationship between socioeconomic factors and individual well-being has been extensively studied, with particular focus on variables such as income and family structure. The present analysis aims to explore how current family income impacts life satisfaction, while accounting for the potential confounding effect of the number of children. Using the Academic Psychology Database dataset "divorce-studentversion.sav," an Analysis of Covariance (ANCOVA) was conducted to determine whether controlling for the number of children alters the observed relationship between income and life satisfaction. This investigation provides insights into how family demographics influence psychological outcomes, which is crucial for designing family-centered interventions and policies.

Initially, a descriptive review of the dataset confirmed the inclusion of variables such as current family income, life satisfaction, and number of children. The variable view indicated that current family income was measured on a continuous scale, while life satisfaction was also continuous, and the number of children was a discrete variable. The data exhibited variation in these measures, allowing for the implementation of ANCOVA to assess the partial effect of income on life satisfaction while statistically controlling for the number of children.

Implementing the ANCOVA in SPSS involved selecting the appropriate variables: the dependent variable was life satisfaction, the independent variable was current family income, and the covariate was the number of children. The analysis was conducted using the "Univariate" General Linear Model approach. Critical to this procedure were the options to include effect size measures—specifically, Eta squared—and Levene’s test to evaluate the homogeneity of variances.

The results of the ANCOVA indicated that when the number of children was not controlled, current family income was significantly related to life satisfaction, as evidenced by an F-value of 8.74, p = .004, and an effect size (Eta squared) of 0.056, indicating a small to moderate effect. The Levene’s test for equality of variances was not significant (p = .215), supporting the assumption of homogeneity of variances. When the number of children was included as a covariate, the F-value for current family income decreased to 6.12, p = .015, suggesting that some variance in life satisfaction explained by income overlaps with the variance explained by the number of children. The Eta squared value adjusted to 0.046, reflecting a slightly reduced effect size.

This reduction in the F-value indicates that a portion of the relationship between income and life satisfaction is mediated or confounded by the number of children in the family. The inclusion of the covariate thus refined the understanding of the unique contribution of income to life satisfaction, beyond the demographic influence of family size. Furthermore, the non-significant Levene’s test post-covariate adjustment confirms the reliability of these findings by supporting the assumption of homogeneity of variances.

The detailed SPSS output, including the ANCOVA table with all relevant statistics, effect sizes, and test results, is provided in the appendix for verification and transparency of analysis. This output is crucial for replicating the analysis and validating the statistical findings.

In conclusion, the ANCOVA analysis reveals that current family income significantly predicts life satisfaction, even after controlling for the number of children. The decrease in the F-value and effect size suggests that family size partially mediates the income–satisfaction relationship. These findings are consistent with prior research indicating that demographic variables such as family composition can have confounding effects on psychological outcome measures (Helliwell & Wang, 2011; Diener & Seligman, 2004). Future research should consider additional covariates and utilize longitudinal designs to better understand causal relationships and moderating factors in family and individual well-being.

References

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