Social Science Data Resources: Introduction
Online Data Analysis without Downloading
Many of the data resources listed in this guide can be analyzed online through the host Web site without your having to download files to software such as SPSS or SAS. The two resources listed below are good examples.
What's in this Guide
This research guide identifies electronic datasets to support statistical research in the social sciences.
- The first six tabs above provide general information and assistance for using datasets.
- The remainder of the tabs, from "Arts and Culture" through "Utah Data" provide links to quality web sites with datasets relating to the topic area of that tab.
- ICPSR is membership based and many, but not all, of their datasets are only available to University of Utah students, faculty, and staff.
Data files are typically provided in ASCII, SAS, SPSS or STATA format. Using the associated metadata, users import data to a spreadsheet or statistical software program for statistical analysis. Certain data manipulation and data analysis skills are required to download and use these datasets.
Social science data are primarily data gathered from social surveys, polls, interviews, experiments, census or administrative records. Essential for researchers in conducting quantitative and qualitative social research, social science data are commonly used for secondary analyses, testing theories and hypotheses, methodology evaluations, longitudinal and cross-sectional studies.
Understanding some basic terminology will help you to determine whether or not you need statistics, data or both.
Statistics are in a format where the data have already been analyzed and processed to produce information in an easy to read format such as charts, tables, and graphs. An example of this is Statistical Abstract of the United States. If you're looking for a quick number, it's best to start with statistics. See the "I Need Statistics" box on this page.
Data are typically raw data that need to be manipulated using software. Data can be quantitative, qualitative, spatial, etc. The difference between data and statistics can be confusing because in everyday language, the terms statistics and data are often used interchangeably.
Numeric Data is a type of data made up of numbers. Numeric Data are processed using statistical software like SPSS, Stata, or SAS.
Qualitative Data are data that describe a property or attribute. Examples of qualitative data are interviews, case studies, comments collected on a questionnaire, etc
Codebook provides information on the structure, contents, and layout of a data file.
Microdata are data on the lowest level of observation such as individual answers to questions. For example, the U.S. Census Bureau's Public-Use Microdata Samples (PUMS files) is a data set of individual housing unit responses to census questions.
Primary Data are data collected through your own research study directly through instruments such as surveys, observations, etc.
Raw Data are the actual observations that are made when the data is collected.
Secondary Data are data from a research study conducted by someone else. Usually when you are asked to locate statistics on a topic you are using secondary data. An example of secondary data are statistics from the Census of Population and Housing.
Summary Data is another way of describing data that has been processed, or summarized (see statistics). For example, the tables you are reading when using statistical sources are summary data.
Time Series is a sequence of data points spaced over time intervals.
If statistics, rather than data, will meet your needs, see these research guides:
Demographic Statistics from the U.S. Census and other sources
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Many thanks to Kathleen Murphy, Northwestern University; Wendy Mann, George Mason University; and Grace Gu, George Washington University for generosly sharing content from their excellent research guides.
Thank you to Linda Keiter for creating this Library Guide. Linda's efforts to gather important and crediable resources has aided many students and faculty in their research.