December 13, 2016
How does quantitative information differ from qualitative information, and how can you develop the skills to gather, analyze and interpret different types of research and data in today's marketplace? Better yet, how can you use both of these data sets to your advantage in a real job in the real world?
Quantitative information is objective and comprised of numerical, measurable data. Qualitative information is subjective and based on observation and interpretation.
Both of these types of data are vital in today's business decision-making, and the ability to work with them will help you build bridges between what you learn in the classroom and the workplace, putting your career on the fast track. Skills in working with data are essential in nearly every field, and most particularly in careers related to marketing, finance, business and the broad spectrum of jobs in the science, technology, engineering and math (STEM) fields.
When you master the skills to analyze both quantitative and qualitative data, you'll have a powerful arsenal of diverse yet related abilities to help secure advancement in your current job and be more competitive when seeking new opportunities.
Quantitative skills are objective, numerical and measurable. Quantitative data analytics rely on mathematical and statistical research methods and can be used to solve business problems or to measure long-term trends. With quantitative data analysis skills, you'll be able to understand and interpret data and findings related to budgeting, mathematics, statistical analysis, probability, software applications, operations management and other areas of business strategy and management.
Some common examples of how you might create or gather or create quantitative data include surveys, statistical compilations and accounting records.
Qualitative analysis does not focus upon numbers or numerical data, but instead concentrates on in-depth, observational research. These analytic skills are subjective and harder to accurately assess or measure. Qualitative analysis might focus on compiling and interpreting information to draw conclusions, assess critical thinking or design more effective business systems.
Some examples of qualitative research include observation in a clinical laboratory setting or in simulated role-playing situations; focus groups where people discuss an issue or product; structured or unstructured interviews; short questionnaires requiring narrative answers or even multiple choice checkboxes; literature reviews (such as written reports, media coverage, journals); and audio/video taped archives.
Source material and methods used to collect, analyze and interpret raw material may vary widely in a qualitative research study. While a structured data analysis is crucial before arriving at final conclusions and recommendations, a qualitative research study gathers information from observation and open-ended interviewing rather than relying strictly on the by-the-numbers methods commonly used to define a quantitative study.
The ability to analyze both quantitative and qualitative data will give you a competitive edge in a wide variety of careers. When you are able to offer both types of skills to an employer, you'll have an advantage since both skill sets are essential in most data related jobs today.
"Many of our STEM program degrees allow the two skill sets to intersect in a significant way, such as in game development, information technology, math, environmental and geoscience, data analytics, management information systems, cyber security and computer science" said Dr. Gwendolyn Britton, executive director of STEM programs at Southern New Hampshire University (SNHU). "Quantitative and qualitative skills are both important in today's marketplace because so much information is being tossed out at us all the time that it's sometimes hard to make sense of it all. I don't just mean data and numbers - I mean information in the form of opinions, tweets, Facebook posts, images, you name it, information is flowing everywhere all the time. We need to be able to figure out what to do with it all and then make informed decisions or solve problems based on all the information.
If you can measure data and keep within a budget using your scientific and mathematics skills - and you're also able to design or lead strong dynamic teams, you'll have an advantage over other job applicants who are only proficient in one skill set or the other. Or, if you are working in a human services setting, by combining both quantitative and qualitative skill sets, you will bring a range of people skills and data analytic skills from your psychology or sociology coursework background - and be able to balance a multi-million dollar budget or analyze raw data reports too. As a financial analyst, strong skills in problem solving, data analysis, research and math are required, but you also need to be able to work independently and as part of a team.
Collaboration, communications and management skills are essentials as you advance in any career and aspire to higher levels of responsibility, even if you started out thinking you wanted to focus on the numbers alone or that you didn't want to work with numbers at all.
Bottom line? To advance in your career by using a blend of quantitative data and qualitative analysis, you can't just live in spreadsheets. As SNHU Career advisor Cait Glennen observes, "One of my students is using her MS in Data Analytics as a fraud analyst for a major credit card company. Her degree has taught her about how numbers tell a story, and she now uses her grasp of both quantitative data and qualitative analysis to determine if the story has taken a wrong turn into fraudulent and illegal activities."
Glennen adds that many students who pursued a degree in mathematics now use their skills in business to be "amazing problem solvers. Business as a whole is moving towards quantitative data and qualitative analysis and employers are seeking people who have a strong grasp on data and its interpretation. Graduates with these skill sets tend to work in roles where they are interpreting and manipulating existing data in order to provide concrete business insights versus just working with the databases themselves."
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