Data will be examined utilizing statistical software, such as SPSS, to conduct a linear regression analysis. To evaluate the proposed relationships between self-leadership and employee engagement, the quantitative results for both the assumptions and hypothesis testing related to linear regression will be analyzed. Initially, seven assumptions pertinent to linear regression must be satisfied, including the presence of one independent variable, one dependent variable, linearity, independence of observations, absence of outliers, homoscedasticity, and normality. Subsequently, a linear regression analysis will be performed to assess the specific relationships between the independent variable (self-leadership) and the dependent variable (employee engagement). Preliminary analyses will encompass descriptive statistics to summarize the demographic characteristics of the sample. To test the research hypotheses, Pearson's correlation coefficient will be employed to explore the relationship between self-leadership and employee engagement. A significance level of p < 0.05 will be adopted to determine whether the findings substantiate the hypotheses. Further analyses may include multiple regression to investigate the predictive capacity of self-leadership on overall employee engagement, while controlling for demographic variables