HR Metrics: Qualitative & Quantitative Data

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  • 0:03 Overview of HR Metrics & Data
  • 0:40 Quantitative Data
  • 1:43 Qualitative Data
  • 3:16 Example: Producing Statistics
  • 4:44 Lesson Summary
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Lesson Transcript
Instructor: Beth Loy

Dr. Loy has a Ph.D. in Resource Economics; master's degrees in economics, human resources, and safety; and has taught masters and doctorate level courses in statistics, research methods, economics, and management.

This lesson explains how quantitative and qualitative data can be used in HR to maximize potential. Used together, these data help improve retention, recruiting, and other key elements necessary for success, such as productivity and morale.

Overview of HR Metrics & Data

Workforce analysis is used in business to measure the inner workings of companies. Often, decisions are made based on how much or how little profit a company makes. However, HR data are vital resources in making management decisions and can provide a wider picture of why, how, and when employees do certain things.

Data are pieces of information used to analyze something. Two types of data used in workforce analysis are qualitative and quantitative data. Although there are several differences, the most apparent difference is that quantitative data involves numbers.

Quantitative Data

Quantitative data are numbers, and they can be measured to produce quantitative statistics. HR examples of quantitative data are the retention rate, salary, hours of overtime worked, number of professional development hours taken, and age. Many of these numbers are easily collected in workforce analytics software and can be analyzed with statistics. The term statistics refers to a set of mathematical procedures that are used for organizing, summarizing, interpreting, and reporting information.

Gathering quantitative data is fairly straightforward if the data are already being collected. One common type of quantitative data that most of us are familiar with is demographic data, which includes age, gender, race, and education.

Combined with other data, it can tell us:

  • At what age employees are retiring.
  • Whether women are advancing as quickly as men.
  • If enough minorities are being recruited.
  • If education is a factor in production.

Qualitative Data

Qualitative data, on the other hand, involve actions and behaviors that are observed, not measured. There are no numbers produced. This means that the data are subjective. What data are recorded is up to the interpretation of the person who is recording it. Examples include why an employee stays or leaves a company, how a supervisor manages, whether a retirement benefit is worth taking, how comfortable an office setting is, and what value teamwork is to success.

These data can be gathered from surveys, interviews, discussions, case-study analyses, and observations. Given the technology available, qualitative data can be gathered from more than just telephone and mail surveys. Skype, instant messaging, email, Twitter, LinkedIn, and Facebook can all be used to collect qualitative data.

The benefits to using qualitative data are that it produces very detailed information. This means qualitative data are vital to HR decisions, because it can provide the reasons to the how, why, what, where, and when. How can morale be sustained through a reduction in force? Why do employees leave the company? What produces high morale? Where do employees go for training? When do employees feel most supported? The drawbacks to using qualitative data are that the data are inherently biased, and it is very difficult to prevent this either from the researcher gathering the data, or the subject providing the data.

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