Numerical data and statistics are components of quantitative research – one of the three major research paradigms. Quantitative research utilizes data and statistics "to describe, to predict, and to explain human phenomena. Quantitative researchers often try to study behavior under controlled conditions via experiments, in order to isolate the causal effects of independent variables. Popular methods of quantitative research are experimental research, survey research, and structured observational research. Quantitative data are collected based on precise measurement of variables using structured, standardized, and validated data collection instruments and procedures. Data are analyzed using descriptive and inferential statistics. The desired product is research findings that generalize broadly" (The SAGE Glossary of the Social and Behavioral Sciences).
Data can be found in a number of different locations. One major place to look for existing data is in data-specific repositories, which are listed throughout this guide. Another way to locate data is through quantitative research articles. To identify whether a research article is quantitative, you can ask the following primary questions:
Some keywords related to quantitative data include quantitative, correlation, discrete, distribution, pretest, posttest, reliability, statistical, survey, validity, and variance.
Quantitative data is analyzed using statistics. "Statistics is the mathematical approach to organizing and interpreting numerical information....The results of statistical analyses help researchers describe patterns in the data, analyze relationships, make comparisons, and even predict future outcomes" (Conducting Survey Research: A Practical Guide).
A powerful and freely available tool for performing statistical analyses is the programming language, R. Some useful resources for learning and using R include:
Open data is "freely available on the internet permitting any user to download, copy, analyze, re-process, pass to software or use for any other purpose without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself" (SPARC). Open data allows for the independent verification of research results by users, as well as innovative ways of using existing data in new research. Primary goals of sharing data are to make science more accurate and robust.
Federal policy in the United States, coming most recently in the 2022 Public Access Policy Guidance from the Office of Science and Technology Policy, has mandated that the public have free availability of federally funded research materials, including data. If research is funded through public tax dollars, then the public should not have to pay to access it a second time. This and similar mandates have resulted in the rapid growth of digital repositories for the open sharing of data.