Association between variables
by Irimia Mihaela
2026-03-09
Scoring model
Analyzed data set: HRDataset_v14 available here
Loading the necesary packages…
- Exploratory data analysis - EDA was done in another post and available here
- Identification and detailed analysis of the studied phenomenon through the development of scoring models available here and here
- Analyzing the association between variables
- Interpretation of results and presentation of conclusions
In order to determine the factors that significantly influence an individual’s salary within a company, we selected the dataset entitled Human Resources Data Set, published on the Kaggle platform: https://www.kaggle.com/datasets/rhuebner/human-resources-data-set
This dataset was created by Dr. Carla Patalano and Dr. Rich. It was designed as an educational resource to help students learn how to perform exploratory data analysis (EDA). The dataset provides a wide range of features that enable both data visualization and the development of machine learning / predictive analytics models.
Within this dataset, we decided to explore and attempt to answer several research questions, such as:
Are there areas within the company where salary distribution is not equitable?
Is an individual’s salary influenced by any specific factors present in the dataset?
loading the dataset
The structure of the dataset
glimpse(HRDataset_v14)## Rows: 311
## Columns: 36
## $ Employee_Name <chr> "Adinolfi, Wilson K", "Ait Sidi, Karthikey…
## $ EmpID <dbl> 10026, 10084, 10196, 10088, 10069, 10002, 1…
## $ MarriedID <dbl> 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0…
## $ MaritalStatusID <dbl> 0, 1, 1, 1, 2, 0, 0, 4, 0, 2, 1, 1, 2, 0, 2…
## $ GenderID <dbl> 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1…
## $ EmpStatusID <dbl> 1, 5, 5, 1, 5, 1, 1, 1, 3, 1, 5, 5, 1, 1, 5…
## $ DeptID <dbl> 5, 3, 5, 5, 5, 5, 4, 5, 5, 3, 5, 5, 3, 5, 5…
## $ PerfScoreID <dbl> 4, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 4, 3, 3…
## $ FromDiversityJobFairID <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0…
## $ Salary <dbl> 62506, 104437, 64955, 64991, 50825, 57568, …
## $ Termd <dbl> 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1…
## $ PositionID <dbl> 19, 27, 20, 19, 19, 19, 24, 19, 19, 14, 19,…
## $ Position <chr> "Production Technician I", "Sr. DBA", "Prod…
## $ State <chr> "MA", "MA", "MA", "MA", "MA", "MA", "MA", "…
## $ Zip <chr> "01960", "02148", "01810", "01886", "02169"…
## $ DOB <chr> "07/10/83", "05/05/75", "09/19/88", "09/27/…
## $ Sex <chr> "M", "M", "F", "F", "F", "F", "F", "M", "F"…
## $ MaritalDesc <chr> "Single", "Married", "Married", "Married", …
## $ CitizenDesc <chr> "US Citizen", "US Citizen", "US Citizen", "…
## $ HispanicLatino <chr> "No", "No", "No", "No", "No", "No", "No", "…
## $ RaceDesc <chr> "White", "White", "White", "White", "White"…
## $ DateofHire <chr> "7/5/2011", "3/30/2015", "7/5/2011", "1/7/2…
## $ DateofTermination <chr> NA, "6/16/2016", "9/24/2012", NA, "9/6/2016…
## $ TermReason <chr> "N/A-StillEmployed", "career change", "hour…
## $ EmploymentStatus <chr> "Active", "Voluntarily Terminated", "Volunt…
## $ Department <chr> "Production", "IT/IS", "Production", "Produ…
## $ ManagerName <chr> "Michael Albert", "Simon Roup", "Kissy Sull…
## $ ManagerID <dbl> 22, 4, 20, 16, 39, 11, 10, 19, 12, 7, 14, 2…
## $ RecruitmentSource <chr> "LinkedIn", "Indeed", "LinkedIn", "Indeed",…
## $ PerformanceScore <chr> "Exceeds", "Fully Meets", "Fully Meets", "F…
## $ EngagementSurvey <dbl> 4.60, 4.96, 3.02, 4.84, 5.00, 5.00, 3.04, 5…
## $ EmpSatisfaction <dbl> 5, 3, 3, 5, 4, 5, 3, 4, 3, 5, 4, 3, 4, 4, 5…
## $ SpecialProjectsCount <dbl> 0, 6, 0, 0, 0, 0, 4, 0, 0, 6, 0, 0, 5, 0, 0…
## $ LastPerformanceReview_Date <chr> "1/17/2019", "2/24/2016", "5/15/2012", "1/3…
## $ DaysLateLast30 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Absences <dbl> 1, 17, 3, 15, 2, 15, 19, 19, 4, 16, 12, 15,…Based on EDA analysis we so that:
Salary exhibits non-normal behavior and strong right skewness.
Extreme values (executive-level salaries) may influence regression results.
Transformation techniques (e.g., log transformation of salary) may improve model performance.
Categorical variables such as department, position, and employment status are likely strong predictors of salary.
Gender alone may not fully explain salary variation without controlling for position and department.
## Rows: 311
## Columns: 15
## $ married_id <fct> 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0…
## $ marital_status_id <dbl> 0, 1, 1, 1, 2, 0, 0, 4, 0, 2, 1, 1, 2, 0, 2…
## $ gender_id <dbl> 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1…
## $ emp_status_id <dbl> 1, 5, 5, 1, 5, 1, 1, 1, 3, 1, 5, 5, 1, 1, 5…
## $ dept_id <dbl> 5, 3, 5, 5, 5, 5, 4, 5, 5, 3, 5, 5, 3, 5, 5…
## $ perf_score_id <dbl> 4, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 4, 3, 3…
## $ from_diversity_job_fair_id <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0…
## $ termd <dbl> 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1…
## $ position_id <dbl> 19, 27, 20, 19, 19, 19, 24, 19, 19, 14, 19,…
## $ emp_satisfaction <dbl> 5, 3, 3, 5, 4, 5, 3, 4, 3, 5, 4, 3, 4, 4, 5…
## $ special_projects_count <dbl> 0, 6, 0, 0, 0, 0, 4, 0, 0, 6, 0, 0, 5, 0, 0…
## $ days_late_last30 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ absences <dbl> 1, 17, 3, 15, 2, 15, 19, 19, 4, 16, 12, 15,…
## $ status <chr> "no_married", "married", "married", "marrie…
## $ log10_salary <dbl> 4.795922, 5.018854, 4.812613, 4.812853, 4.7…## Rows: 311
## Columns: 15
## $ married_id <dbl> 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0…
## $ marital_status_id <dbl> 0, 1, 1, 1, 2, 0, 0, 4, 0, 2, 1, 1, 2, 0, 2…
## $ gender_id <dbl> 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1…
## $ emp_status_id <dbl> 1, 5, 5, 1, 5, 1, 1, 1, 3, 1, 5, 5, 1, 1, 5…
## $ dept_id <dbl> 5, 3, 5, 5, 5, 5, 4, 5, 5, 3, 5, 5, 3, 5, 5…
## $ perf_score_id <dbl> 4, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 4, 3, 3…
## $ from_diversity_job_fair_id <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0…
## $ termd <dbl> 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1…
## $ position_id <dbl> 19, 27, 20, 19, 19, 19, 24, 19, 19, 14, 19,…
## $ emp_satisfaction <dbl> 5, 3, 3, 5, 4, 5, 3, 4, 3, 5, 4, 3, 4, 4, 5…
## $ special_projects_count <dbl> 0, 6, 0, 0, 0, 0, 4, 0, 0, 6, 0, 0, 5, 0, 0…
## $ days_late_last30 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ absences <dbl> 1, 17, 3, 15, 2, 15, 19, 19, 4, 16, 12, 15,…
## $ salary <int> 62506, 104437, 64955, 64991, 50825, 57568, …
## $ status <chr> "no_married", "married", "married", "marrie…
##
## Welch Two Sample t-test
##
## data: salary by sex
## t = -0.9956, df = 296.39, p-value = 0.3203
## alternative hypothesis: true difference in means between group female and group male is not equal to 0
## 95 percent confidence interval:
## -8461.792 2776.447
## sample estimates:
## mean in group female mean in group male
## 67786.73 70629.40
##
## Welch Two Sample t-test
##
## data: salary by status
## t = 0.45511, df = 253.63, p-value = 0.6494
## alternative hypothesis: true difference in means between group married and group no_married is not equal to 0
## 95 percent confidence interval:
## -4465.733 7150.088
## sample estimates:
## mean in group married mean in group no_married
## 69827.72 68485.54
## Df Sum Sq Mean Sq F value Pr(>F)
## race_desc 5 4051661020 810332204 1.286 0.27
## Residuals 305 192133817279 629946942
## Df Sum Sq Mean Sq F value Pr(>F)
## race_desc 5 4051661020 810332204 1.286 0.27
## Residuals 305 192133817279 629946942
To visually explore the relationships between multiple categorical variables and salary groups among employees we will use a parallel plot.
department → emp_satisfaction → employment_status → marital_desc → performance_score → position → salary_group → sex
The parallel sets plot highlights strong associations between department, position and salary group. Most employees in the Production department fall into the lowest salary category (<55000), while higher salary categories are primarily associated with IT and Software Engineering positions. Gender differences appear visually, with higher salary bands showing a greater proportion of male employees.