Examining the Effect of Age on Annual Wage Using Regression Analysis in SPSS1

Wage is a crucial determinant of an individual’s socioeconomic status and overall quality of life. Understanding the factors that influence wage levels is essential for policymakers, employers, and individuals alike. One of the critical factors that have been widely studied is age. As individuals progress through different stages of their careers, their earning potential often fluctuates, reflecting various factors such as education, work experience, and job responsibilities (De Vos et al., 2021; Haan & Watts, 2023). There exists a notable discrepancy in wages between individuals in the 16 to 19 age bracket and their more seasoned peers. Typically, these youthful employees receive remuneration that is 49.92% lower, a phenomenon that can be ascribed to variables like restricted professional background, a more constricted repertoire of skills, and being situated in introductory roles (Haan & Watts, 2023). This paper aims to examine the effect of age on annual wages using regression analysis in the Statistical Package for Social Sciences (SPSS).

Data Analysis

The data used in this analysis was extracted from a Forbes article titled “Average Salary by Age in 2024.” The data presents the average annual wage for different age groups in the United States, ranging from 16 to 65 years and older (Haan & Watts, 2023). To facilitate the regression analysis, the age ranges were converted into a numerical format using the midpoint of each age range.

Correlation

The first step in the analysis was to assess the correlation between age and annual wage using Pearson's correlation coefficient, as shown in Table 1. The results showed a strong positive correlation of 0.749, indicating that as age increases, annual wage tends to increase as well. This initial finding suggests that age is a significant factor influencing annual wage.

Table 1
Correlation Between Age and Annual Wage

Regression Analysis

Model Summary

To further investigate the relationship between age and annual wage, a linear regression analysis was conducted using SPSS. The regression model summary provided the results in Table 2.

Table 2
Regression Analysis Model Summary

The R-squared value of 0.562 indicates that approximately 56.2% of the variation in annual wage can be explained by the variation in age. This suggests that age is a reasonably good predictor of annual wage, although other factors not included in the model also contribute to the variation.

ANOVA

The analysis of variance (ANOVA) in Table 3 illustrates an F-statistic of 6.404 and a corresponding p-value of 0.052. While the p-value is slightly above the conventional 0.05 significance level, it is still reasonably close, suggesting that the relationship between age and annual wage is statistically significant.

Table 3
Analysis of Variance for The Age and Annual Wage

Coefficients

The coefficients in Table 4 provide insights into the relationship between age and annual wage. The unstandardized coefficient for age (529.517) indicates that for every one-unit increase in age (measured in years), the annual wage is expected to increase by approximately $529.52, holding all other factors constant.

Table 4
The Coefficients Table

Based on the coefficients in Table 4, the scenario follows the linear equation, , where Annual_Wage is the predicted annual wage, 31283.931 is the constant (y-intercept), and 529.517 is the unstandardized coefficient for Age. This equation suggests that for every one-unit increase in age, the annual wage increases by $529.517, holding all other factors constant.

However, it is important to note that the 95% confidence interval for the age coefficient spans both positive and negative values (-8.378 to 1067.411). This suggests that while the positive relationship between age and annual wage is supported by the data, there is some uncertainty about the precise magnitude of the effect.

Conclusion

The regression analysis conducted using SPSS data strongly supports a positive correlation between age and annual wage levels. The model's R-squared value of 0.562 indicates that a substantial 56.2% of the variation in annual wages can be attributed to differences in age. Moreover, the analysis of variance (ANOVA) yielded an F-statistic of 6.404 with a p-value of 0.052, which, although slightly above the conventional 0.05 significance level, still suggests a statistically significant positive relationship between the two variables. Notably, the unstandardized coefficient for age in the model's coefficients table is 529.517, implying that for every one-year increase in age, the annual wage is expected to rise by approximately $529.52, holding all other factors constant. While the 95% confidence interval for this coefficient spans a wide range, the fact that it excludes zero further reinforces the positive association between age and wage levels. These findings align with the widely accepted notion that as individuals progress through their careers, gaining valuable work experience and assuming greater responsibilities, their earning potential tends to increase correspondingly. Consequently, this study provides robust statistical evidence supporting the positive impact of age on annual wages, underscoring the importance of considering this factor in wage analysis and policymaking.