The correlation table above shows that several of the independent variables have multicollinearity. This is because the values in the table many are close to one showing a strong multicollinearity. One of the reasons for this is because the participants i

The correlation table above indicates that several independent variables exhibit multicollinearity, as many of the values are close to one, suggesting a strong correlation between them. One potential reason for this is that participants in the study may belong to overlapping groups defined by these independent variables. This overlap can lead to redundancy in variable testing, prompting me to focus on the highest correlations highlighted in the results. The correlation between Industry and Gender is particularly strong, which aligns with cultural norms where certain industries in the United States are heavily dominated by one gender. Industries such as construction, manufacturing, natural resources and mining, and trade, transportation, and utilities tend to be predominantly male, whereas sectors like education and health services, as well as leisure and hospitality, are more female-dominated. Additionally, there is a strong correlation between Industry and Job Stayer. This suggests that certain industries may have a higher proportion of Job Stayers, while others experience greater turnover. Industries less affected by economic cycles, such as trade, transportation, and utilities, as well as education and health services, may show more stability in job retention. In contrast, sectors like information, manufacturing, construction, and leisure and hospitality could be more susceptible to fluctuations in turnover rates.