Research is the systematic process of finding out more about something than you already know, ideally so that you can prove a hypothesis, produce new knowledge and understanding, and make evidence-based decisions. What this process looks like depends on the questions you want to answer and what techniques or strategies you use to find that information. These techniques of collecting, sorting, and analyzing data (or bits of information) are called research methods. The better the tools and more comprehensive the techniques you employ, the more effective your research will be. By extension, the more effective your research is, the more credible and persuasive your argument will be.

The typical kinds of research sources you will use can be grouped into three broad categories.

Primary Sources. Data from research you conducted yourself in lab experiments and product testing, or through surveys, observations, measurements, interviews, site visits, prototype testing, or beta testing. Primary sources can also be published statistical data, historical records, legal documents, firsthand historical accounts, and original creative works.

Secondary Sources. Sources that discuss, analyze, and interpret primary sources, such as published research and studies, reviews of these studies, meta-analyses, and formal critiques.

Tertiary Sources. Reference sources such as dictionaries, encyclopedias, and handbooks that provide a consolidation of primary and secondary information. These are useful to gain a general understanding of your topic and major concepts, lines of inquiry, or schools of thought in a field or discipline.

Categories of Data

From your sources, you will acquire primary and secondary data that you will use in your research-driven writing. Table 11.1 distinguishes between two types of data: primary and secondary.

Two other common categories of data are quantitative and qualitative data. In general terms, quantitative data is numerically based whereas qualitative data is word based. Different fields privilege different kinds of data and use them in different ways.

Quantitative data uses numbers to describe information that can be measured quantitatively. This data is used to measure, make comparisons, examine relationships, test hypotheses, explain, predict, or even control. Lab-based fields (such as many STEM fields) tend to emphasize quantitative data.

In contrast, qualitative data uses words to record and describe the data collected. This data often describes people’s feelings, judgments, emotions, customs, and beliefs that can only be expressed in descriptive words not in numbers. This data type includes “anecdotal data” or personal experiences. Text-based fields (such as many humanities fields) tend to prefer qualitative data. 

Remember, this distinction is general—there are plenty of excellent counterexamples of STEM fields effectively using qualitative data and humanities fields using quantitative data. Some fields, especially in the social sciences, even use both data types.