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Data and Statistics: Terminology and Definitions: Data and Statistics Terminology and Definitions

Defines terms relevant to using data and statistics.

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Data Planet Statistical Datasets is available via IP (and proxy server) authentication at The Data Planet Statistical Datasets interface allows users to browse available datasets by subject and source and to manipulate variables to create customized views of the data, as well as to search for statistics of interest via Quick and/or Advanced Search.

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What Datasets Are Included in Data Planet Statistical Datasets?

To check which datasets are included in Data Planet Statistical Datasets, or to see if your institution subscribes to any premium datasets, go into Statistical Datasets and look at the Indicator column along the left. If you subscribe, you'll see the premium datasets listed, along with all available datasets in the indicator tree on the left-hand side of the screen. 

Datasets and indicators may be browsed by broad subject category and by source.


For a full listing of datasets available in Data Planet Statistical Datasets, and the sources of these datasets, see Data Planet Datasets and Sources.

Data and Statistics: Terminology and Examples

Terminology Basics 

Below you will find simple definitions of the basic terminology associated with data and statistics. The examples will link into DataSheets from Data Planet Statistical Ready Reference. From the DataSheets, you can link into Data Planet Statistical Datasets to explore the millions of datasets available in the repository.

Data Planet publishes aggregated secondary datasets:

Secondary means that the data are collected by source organizations other than Data Planet. Secondary data are contrasted with primary datasets, which refer to data that researchers have collected themselves.

Aggregated means simply that the datasets are a collection of summary data, vs microdata, which refer to the individual response items in surveys and other data collection instruments.

Data: Fundamentally, data=information. We typically use the term to refer to numeric files that are created and organized for analysis. There are two types of data: aggregate and microdata.

  • Aggregate data are statistical summaries of data, meaning that the data have been analyzed in some way.  The Data Planet repository is an excellent resource for obtaining aggregated data. 
  • Microdata: Individual response data obtained in surveys and censuses - these are data points directly observed or collected from a specific unit of observation. Also known as raw data. ICPSR is an excellent resource for obtaining microdata files.

Data point or datum: Singular of data. Refers to a single point of data. Example: 25,114 billion BTU of aviation gasoline was consumed by the transportation sector in the US in 2012

Quantitative data/variables: Information that can be handled numerically. Example: spending by US consumers on personal care products and services

Qualitative data/variables: Information that refers to the quality of something. Ethnographic research, participant observation, open-ended interviews, etc., may collect qualitative data. However, often there is some element of the results obtained via qualitative research that can be handled numerically, eg, how many observations, number of interviews conducted, etc. Example: periods when the US was in, vs was not in, a recession, 1850-2020 The quality of being in a recession is assigned a value of .01 (True) and not in a recession .0 (False), which makes it possible to display the information as a chart.

Indicator: Typically used as a synonym for statistics that describe something about the socioeconomic environment of a society, eg, per capita income, unemployment rate, median years of education.

Statistic: A number that describes some characteristic, or status, of a variable, eg, a count or a percentage. Example: total nonfarm job starts in August 2014

Statistics: Numerical summaries of data that has been analyzed in some way. Example: ranking of airlines by percentage of flights arriving on-time into Huntsville International Airport in Alabama in 2013

Time series data: Any data arranged in chronological order. Example: Gross Domestic Product of Greece, 2000-2013

Variable: Any finding that can change or vary. Examples include anything that can be measured, such as  the number of logging operations in Alabama.

  • Numerical variable: Usually referring to a variable whose possible values are numbers. Example: Bank Prime Loan Rate
  • Categorical variable: A variable that distinguishes among subjects by putting them in categories (eg, gender). Also called discrete or nominal variables. Example: Female vs Male Infant Mortality Rate of Belarus (the mortality rate is numerical - the age and gender characteristic is categorical)

Terminology Used with Collections of Data

Data aggregation: A collection of datapoints and datasets. Example: a search on the broad category "higher education" in Data Planet retrieves results from a collection of  sources. 

Dataset: A collection of related data items, eg, the responses of survey participants. This term is used very loosely – the entire Census 2010 Summary File 1 can be considered a dataset as can any individual table published in the Census 2010 Summary File 1, eg, Table P20. Households by Presence of People Under 18 Years by Household Type by Age of People Under 18 Years

Database: A collection of data organized for research and retrieval. Example: American Community Survey.

Time series: A set of measures of a single variable recorded over a period of time. Example: Hourly Mean Earnings of Civilian Workers – Mining Management, Professional, and Related Workers

"Big Data" Terminology

Big data: A popular term used to describe the exponential growth and availability of structured and unstructured data that derived from the increasing sophistication of operational and transactional systems, mobile media, and the Internet. Big data and its analysis have become key components of obtaining business intelligence in particular.

Data analytics: Generally used to refer to the analytical techniques and tools required to analyze massive amounts of data. Closely related to data mining, which refers to the extraction of information from business systems.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            

Definition References:

School of Data. School of Data Handbook. What is Data? Accessed January 5, 2015,

Troester, Mark (SAS). Big Data Meets Big Data Analytics. Accessed January 5, 2015,

Upton, Graham, and Ian Cook. 2008. A Dictionary of Statistics. Oxford University Press.

Vogt, W. Paul. 2005. Dictionary of Statistics & Methodology: A Nontechnical Guide for the Social Science, 3nd edition. SAGE Publications, Inc.

Western Libraries. Data and Statistics. Accessed January 5, 2015, .




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