By the end of this chapter, you will be able to:
These skills will help you make smart, data-driven decisions that support your organization's success in the real world.
Compiled by: John Gitau
Data collection forms the foundation of all statistical analysis in human resource management. In Kenya’s dynamic workplaces, HR professionals rely on accurate data to make informed decisions on recruitment, training, performance appraisal, and employee welfare. This chapter introduces the principles of data collection and explores the primary and secondary methods specifically relevant to HR contexts. Mastery of these concepts enables HR practitioners to gather reliable data that supports strategic workforce management and organizational development.
Data collection is the systematic process of gathering information to address specific questions or problems. In HR, this involves obtaining accurate data on employee demographics, performance metrics, satisfaction levels, and other workforce attributes. Effective data collection ensures that HR decisions are evidence-based, reducing bias and improving policy outcomes. The process must be designed to minimize errors, respect confidentiality, and comply with Kenyan labor laws and data protection regulations.
Data collection refers to the systematic gathering of facts, figures, or information relevant to a particular objective. In HR, its purpose includes identifying workforce trends, measuring employee engagement, and assessing training needs.
Data collected in HR falls into two broad categories: qualitative and quantitative. Each type serves different analytical purposes and requires distinct collection techniques.
HR data collection faces challenges that can affect data validity and reliability. Understanding these challenges helps in designing robust data collection processes.
The data collection process involves multiple stages to ensure quality and relevance.
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Create a free accountThis chapter introduced the fundamental concepts of data collection, emphasizing the importance of gathering accurate information for statistical analysis. It explored the two main methods of data collection: primary data, which is collected firsthand, and secondary data, which is obtained from existing sources. The chapter then examined sampling techniques, distinguishing between probability sampling, where every member has a known chance of selection, and non-probability sampling, which relies on subjective judgment. Various methods of data presentation were discussed, including frequency distribution tables and a range of diagrams such as bar charts, pie charts, histograms, and frequency polygons, each serving different purposes in illustrating data patterns. The chapter also covered different types of graphs used in statistics, including basic time series graphs for observing trends over time, z-charts for quality control, Lorenz curves for income distribution, and semi-log graphs for exponential data. Finally, the concept and construction of cumulative frequency curves, or ogives, were explained as a tool for understanding data accumulation and percentiles. This comprehensive overview equips students with essential skills to collect, sample, present, and interpret data effectively in practical contexts.
Example: A hotel in Nairobi has 5 employee groups with the following data:
| Group | Number of Employees | Total Salary Paid (Ksh) |
|---|---|---|
| A | 10 | 200,000 |
| B | 20 | 400,000 |
| C | 30 | 600,000 |
| D | 25 | 800,000 |
| E | 15 | 1,000,000 |
Total employees: \(10+20+30+25+15=100\)
Total salary: \(200,000+400,000+600,000+800,000+1,000,000=3,000,000\)
Calculate cumulative percentages:
| Group | Cumulative Employees | % Employees | Cumulative Salary | % Salary |
|---|---|---|---|---|
| A | 10 | 10% | 200,000 | 6.67% |
| B | 30 | 30% | 600,000 | 20% |
| C | 60 | 60% | 1,200,000 | 40% |
| D | 85 | 85% | 2,000,000 | 66.67% |
| E | 100 | 100% | 3,000,000 | 100% |
Plot cumulative % employees (x-axis) vs cumulative % salary (y-axis) to form the Lorenz curve.
Example: A manufacturing firm records recruitment costs over 5 months:
| Month | Cost (Ksh) |
|---|---|
| 1 | 50,000 |
| 2 | 60,000 |
| 3 | 80,000 |
| 4 | 100,000 |
| 5 | 130,000 |
Calculate logarithm (base 10) of each cost:
| Month | Cost (Ksh) | \(\log_{10}(\text{Cost})\) |
|---|---|---|
| 1 | 50,000 | 4.699 |
| 2 | 60,000 | 4.778 |
| 3 | 80,000 | 4.903 |
| 4 | 100,000 | 5.000 |
| 5 | 130,000 | 5.114 |
Plot months on the x-axis and \(\log_{10}(\text{Cost})\) on the y-axis (logarithmic scale). Join the points to show exponential trend.
A human resource manager collects data on the number of training sessions attended by 10 employees in a month: 2, 3, 4, 3, 5, 2, 4, 3, 5, 4. Construct a frequency distribution table for this data. (2 marks)
The following data shows the number of leave days taken by 15 employees in a year: 5, 7, 4, 6, 5, 8, 6, 7, 5, 6, 8, 7, 6, 5, 4. Draw a bar chart representing this data by leave days categories. (3 marks)
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