Quantitative data are data represented numerically, including anything that can be counted, measured, or given a numerical value. Quantitative data can be classified in different ways, including categorical data that contain categories or groups (like countries), discrete data that can be counted in whole numbers (like the number of students in a class), and continuous data that is a value in a range (like height or temperature). Quantitative data are typically analyzed with statistics.
(From the Data Glossary, National Center for Data Services, National Library of Medicine)
Quantitative Research Methods in Translation and Interpreting Studies
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Quantitative Research Methodology in the Health Sciences
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Quantitative Research Methods for Professionals in Education and Other Fields
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Analyzing Quantitative Data
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Quantitative and Statistical Research Methods
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Research Design
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Research Design
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Interpretations related to mixed (sometimes called merged) methods vary; be wary of jargon! Gery Ryan, of the Kaiser Permanente School of Medicine, gives these definitions, while arguing that we should be thinking of the purposes of the research rather than the methodological labels:
Mixed methods research: “Combines elements of qualitative and quantitative research approaches (e. g., use of qualitative and quantitative viewpoints, data collection, analysis, inference techniques) for the broad purposes of breadth and depth of understanding and corroboration.”
Multimethod research: “Either solely combine multiple qualitative approaches or solely combine multiple quantitative approaches.”
Data triangulation: “Uses multiple sources of data or multiple approaches to analyzing data to enhance the credibility of a research study.”
(From "Mixed Methods Research Designs and Data Triangulation" by Gery Ryan, Kaiser Permanente School of Medicine)
Data Analysis Software:
Survey Data Collection Software:
(Descriptions from Wikipedia, except where noted)
Resources:
Library Data Services Program Referral Guide
Training:
Code-Free Data Analysis (video): In this video you'll get started with analyzing data without needing to know any coding. Includes many resources and tips. From the 2024 University of California "Love Data Week" workshops.
Library Event Calendar (search "data' or other keywords)
Research Data Management events and training page
Python Bites: mini-tutorials from the Dartmouth University Library
Be sure to consult the UC Berkeley Library Research Guides for your discipline of interest! Below are social sciences examples, but this can work for other fields as well!
1) When searching, consider using search words that will connect with quantitative analysis: these are options generated by AI--"quantitative," "statistical," "numerical," and "data"; research design terms such as "experiment," "survey," "correlational," and "longitudinal"; statistical method terms like "regression," "ANOVA," "t-test," and "Chi-Square"; and terms related to the process like "measurement," "sampling," and "validity"
2) Explore which quantitative data sources are most used by researchers in your field of interest, and include the names of those data sources in your searching. Some examples are GSS (General Social Survey), NHANES (National Health and Nutrition Examination Survey), ACS (American Community Survey)/Census data, etc.
3) Search within a discipline that is quantitatively oriented, such as demography (population studies)
4) Consult a data repository or collection, and see if you can find papers that are linked from datasets. The best for this is ICPSR-- most studies will have a tab for "data-related publications."
EXAMPLES:
1) Google Scholar search <immigration higher education regression analysis survey>
--Citation found:
Hou, F., Lu, Y. International students, immigration and earnings growth: the effect of a pre-immigration host-country university education. IZA J Develop Migration 7, 5 (2017). https://doi.org/10.1186/s40176-017-0091-5
2) Google Scholar search <sociology of aging general social survey>
--Citation found:
Barkan, S. E., & Greenwood, S. F. (2003). Religious Attendance and Subjective Well-Being among Older Americans: Evidence from the General Social Survey. Review of Religious Research, 45(2), 116–129. https://doi.org/10.2307/3512578
3) Google Scholar search <demography of maternal mortality>
--Citation found:
Bomela, N.J. Maternal mortality by socio-demographic characteristics and cause of death in South Africa: 2007–2015. BMC Public Health 20, 157 (2020). https://doi.org/10.1186/s12889-020-8179-x
4) ICPSR Search <Crime>
--Study found:
https://www.icpsr.umich.edu/web/ICPSR/studies/38666 National Crime Victimization Survey: School Crime Supplement, [United States], 2022 (ICPSR 38666) (then click on “Data-related publications”)
--Citation found:
Thomsen, E., Henderson, M., Moore, A., Price, N., and McGarrah, M.W. (2024). Student Reports of Bullying: Results From the 2022 School Crime Supplement to the National Crime Victimization Survey (NCES 2024-109rev). U.S. Department of Education. Washington, DC: National Center for Education Statistics. Retrieved [9-26-2025] from https://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2024109rev.