LibAnswers/LibChat: Database FAQ

What is Data?

Data vs. Statistics

Data are raw ingredients from which statistics are created. Statistics are useful when you just need a few numbers to support an argument (ex. In 2003, 98.2% of American households had a television set--from Statistical Abstract of the United States). Statistics are usually presented in tables. Statistical analysis can be performed on data to show relationships among the variables collected. Through secondary data analysis, many different researchers can re-use the same data set for different purposes.

Aggregate/Macro Data vs. Microdata

Aggregate or Macro Data are higher-level data that have been compiled from smaller units of data. For example, the Census data that you find on AmericanFactfinder have been aggregated to preserve the confidentiality of individual respondents. Microdata contain individual cases, usually individual people, or in the case of Census data, individual households. The Integrated Public Use Microdata Sample (IPUMS) for the Census provides access to the actual survey data from the Census, but eliminates information that would identify individuals.

Data Sets, Studies, and Series

In data archives like ICPSR, a data set or study is made up of the raw data file and any related files, usually the codebook and setup files. The codebook is your guide to making sense of the raw data. For survey data, the codebook usually contains the actual questionnaire and the values for the responses to each question. The setup files help will not display properly.

ICPSR uses the term series to describe collections of studies that have been repeated over time. For example, the National Health Interview Survey is conducted annually. In the ICPSR archive, you will find a description of the series that provides an overview. You will also find individual descriptions of each study (i.e. National Health Interview Survey, 2004). The study number in ICPSR refers to the individual survey.

Types of Data

Cross-Sectional describes data that are only collected once.

Time Series study the same variable over time. The National Health Interview Survey is an example of time series data because the questions generally remain the same over time, but the individual respondents vary.

Longitudinal Studies describe surveys that are conducted repeatedly, in which the same group of respondents are surveyed each time. This allows for examining changes over the life course. The Project on Human Development in Chicago Neighborhoods (PHDCN) Series contains a longitudinal component that tracks changes in the lives of individuals over time through interviews.

(Originally from Sue Erickson at Vanderbilt University)

• Data and Statistics: Terminology and Definitions

  Defines terms relevant to using data and statistics.

• Data Basics from Data Planet

  This comprehensive module covers a variety of topics that will get you started on the basics of data.

Statistics Basics

Statistics can help provide concrete examples of a larger trend, give the basis for an important chart or graph, or make theoretical arguments tangible.

There are two main branches of statistics:

• Descriptive--concerns the numerical or quantitative data alone, and can help draw conclusions about a sample, rather than a population.
• Inferential--concerns the conclusions drawn about an entire population which are inferred through the results of a random sampling of a population

Be aware of bias in statistics! Generally, bias is defined as “prejudice in favor of or against one thing, person, or group compared with another, usually in a way considered to be unfair.” Numbers can be manipulated, and charts and graphs can be arranged to give a certain impression. Always double-check your facts!

Datasets & Statistics

1. Think about who might collect the data.

• Could it have been collected by a government agency?
• A nonprofit/nongovernmental organization?
• A private business or industry group?
• Academic researchers?

2. Look for publications that cite the dataset

• e.g. scholarly articles or government reports.

3. Once you know that what you want exists, it's time to hunt it down.

• Is it freely available on the web? Check Google—you never know!
• Or part of a package to which the library already subscribes?
• Is it something we can buy? (And is it within the library's budget and can the purchase be made quickly enough to fit your timeframe?)
• Can it be requested directly from the researcher? There’s a reason articles usually include author contact information