Case Study 4 - BU618 Pt 1
Problem Statement
Diversity and Inclusion (D&I) reports are commonly produced by Human Resources (HR) or a Management Information (MI) function in collaboration with HR. These reports typically provide descriptive statistics on various diversity metrics within organizations. However, there is a critical question regarding the extent to which descriptive reports can offer meaningful insights into D&I issues. The analysis presented in these reports can be significantly enhanced by incorporating predictive statistical methods, which can increase the rigor and relevance of the findings for corporate strategy formulation.
In this context, we examine diversity at SlidesRUs, which employs 1,493 individuals. The gender distribution appears balanced at first glance, with 746 females, 745 males, and two individuals who did not declare their gender. However, a deeper analysis reveals significant disparities in gender representation across different job grades within the organization. The expectation of an equal proportion of men and women in each of the eight job grades is not met. Instead, there are noticeable gender imbalances, particularly with a higher concentration of women in lower job grades and men in higher job grades. This discrepancy suggests potential issues related to glass ceilings, discrimination, or unconscious bias, which necessitates a more rigorous statistical examination to inform corporate strategies effectively. A full analysis is provided below.
Data Description
The dataset used in this analysis is derived from a diversity case study within the management consulting firm SlidesRUs. The dataset, available in the files Chapter4Diversity1.xls and Chapter4Diversity1.sav, comprises data on 1,493 employees. Among these employees, 746 are female, 745 are male, and 2 individuals did not declare their gender.
The relevant variables in this dataset include:
• Gender: Categorical variable indicating the gender of the employee (female, male, or undeclared).
• Job Grade: Categorical variable representing the employee’s job grade, ranging from Administra- tor/Assistant to Managing Consultant, with a total of eight different job grades.
• Employee ID: Unique identifier for each employee.
The data collection method is not explicitly stated, but it is assumed to be extracted from the company’s HR records, ensuring a comprehensive representation of the current employee distribution.
Data trimming and processing steps included:
• Validation of Gender Data: Ensuring the accuracy of gender information by cross-referencing with HR records.
• Factorization of Job Grades and Gender: Standardizing job titles into the eight defined job levels, and Gender into two defined levels, to maintain consistency in analysis.
• Handling Missing Data: Employees with undeclared gender were noted but not excluded from the analysis to preserve the integrity of the dataset.
The choice of this dataset is justified by its comprehensive coverage of the entire employee population within SlidesRUs, allowing for a detailed examination of gender distribution across different job grades. This level of detail is essential for identifying potential issues related to glass ceilings, discrimination, or unconscious bias, thereby providing valuable insights for improving corporate D&I strategies.
A visual breakdown displaying the number of employees by job grade and gender is provided below:
Results, Interpretation and Conclusion
To assess the gender distribution across different job grades within SlidesRUs, a Chi-Square analysis was performed. This statistical test was chosen to evaluate whether there is a significant association between employee gender and job grade. The dataset comprises eight job grade levels and two gender categories (male and female). To quantify the strength of the association, Cramér’s V was calculated as an effect size measure.
The Chi-Square analysis revealed a highly significant association between job grade and employee gender (X2 = 164.7, df = 7, p < 2.2e-16). The effect size, measured by Cramér’s V, was calculated to be approximately 0.333, suggesting a medium to large association. These results indicate that gender distribution across job grades within SlidesRUs is not random and points towards potential systemic issues such as glass ceilings, discrimination, or unconscious bias.
The significant Chi-Square test result and substantial effect size suggest that there are notable disparities in gender representation across different job grades. Specifically, women are more concentrated in lower job grades, while men dominate higher job grades. This uneven distribution highlights the need for targeted interventions to address potential biases and promote a more equitable workplace. By understanding these patterns, SlidesRUs can develop strategies to mitigate these disparities, fostering a more inclusive and diverse organizational culture.