Back To CourseSenior Professional in Human Resources - International (SPHRi): Exam Prep & Study Guide
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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.
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 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:
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.
Whether data are quantitative or qualitative, they can be used together to give a statistical picture of a workforce. One important statistic is frequency, which is the number of times something occurs.
Say we use two sets of data, the number of overtime hours worked, which is quantitative, and what our employees find to be the most effective morale boosters, which is qualitative. We get our quantitative data from our workforce analytics software, and we get our qualitative data from surveying our employees.
Our quantitative data can be graphed as follows:
We can see the frequency of workers' ages ranging for 20 to 70 years old.
Our qualitative data can be graphed as follows:
In this graph we see the frequency of morale boosters that work, including money, vacation, teamwork, and parking spaces.
Together, our quantitative and qualitative data show us that the majority of our workers are between 31 and 50, and the majority of our workers find vacation to be the key motivator in improving their morale. We can assume that individuals in this age range want to spend more time with their families, so it might be good for us to invest in more family-based activities. We could also assume that increasing vacation time and salaries will improve morale. We may even take a leap and guess that these increases would also improve the retention rates of 31- to 50-year-olds.
Quantitative data are numbers, and they can be measured to produce quantitative statistics. In contrast,qualitative data involves actions and behaviors that are observed. Quantitative and qualitative data are important to HR managers, because they provide a picture based on numbers, as well as the feelings of workers. When we change something in the workplace, how will employees react? Can we find ways to make improvements? Are there investments that are a waste of time? A manager uses data to maximize the business' human resources, and to do this, both qualitative and quantitative data are used to show the big picture of what we can do differently, better, and most efficiently.
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