An HR consultant conducted a study of determinants of beginning salary of employees at a local parastatal. The results of the study are presented below.
a. Predictors: (Constant), gender of employees, age of employee (yrs), job category, education level in years, work experience in months b. Dependent Variable: beginning salary
a. Dependent Variable: beginning salary
(a) State the coefficient of determination and interpret its value. [3]
(b) Describe the impact of each factor on beginning salaries. [5]
(c) Which factor has the greatest influence on beginning salaries of employees at this parastatal? Justify your answer. [3]
(d) Conduct an F-test for the overall significance of the regression model. [5]
You will need data to work this study.
Whew, I wondered why I could not make any headway here.
To find the answers to these questions, you will need to perform a regression analysis using statistical software or tools like Excel or SPSS. Here are the steps to follow:
1. Data Preparation: Ensure that you have a dataset containing all the variables mentioned in the study, including the dependent variable (beginning salary) and the predictors (gender, age, job category, education level, work experience).
2. Regression Analysis: Perform a multiple regression analysis using the dependent variable (beginning salary) and the predictors (gender, age, job category, education level, work experience) as inputs.
(a) Coefficient of Determination (R-squared): The coefficient of determination represents the proportion of the variance in the dependent variable (beginning salary) that can be explained by the independent variables (predictors). The value ranges from 0 to 1. Interpretation: A higher R-squared value indicates a stronger relationship between the predictors and the dependent variable, suggesting that the predictors explain a larger proportion of the variation in beginning salaries.
(b) Impact of Each Factor: To determine the impact of each factor, you need to examine the regression coefficients or beta values associated with each predictor. These coefficients represent the change in the dependent variable for a one-unit change in the corresponding predictor while holding all other predictors constant. Positive coefficients indicate a positive impact on beginning salaries, while negative coefficients indicate a negative impact.
(c) Greatest Influence: To determine which factor has the greatest influence on beginning salaries, you should compare the magnitudes of the standardized coefficients or beta values associated with each predictor. The predictor with the highest absolute beta value has the greatest influence on beginning salaries.
(d) F-Test for Overall Significance: The F-test is used to test the overall significance of the regression model. It assesses whether the predictors, as a group, significantly contribute to predicting the dependent variable (beginning salary). The F-test generates an F-statistic and a p-value. If the p-value is less than the desired significance level (e.g., 0.05), you can conclude that the regression model is statistically significant.
To perform these calculations, it is recommended to use statistical software or tools that can automatically generate regression results, such as Excel's Data Analysis ToolPak or dedicated statistical software like SPSS or R.