Data & Statistics

Terminology Basics 

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 Sage Data 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, e.g., how many observations, number of interviews conducted, etc. Qualitative variables include categorical or nominal data, which differentiates responses by classes or categories, e.g., by gender. 

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

Statistic:A number that describes some characteristic, or status, of a variable, e.g., a count or a percentage. Example: total number of full-time degree-seeking undergraduates in the United States .

Statistics:Numerical summaries of data that has been analyzed in some way. Example: ranking of Arizona universities by enrollment of full-time degree-seeking undergraduates .

Time series data: Any data arranged in chronological order. Example: total number of full-time degree-seeking undergraduates 2010-2020 .

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. 
• Categorical variable : A variable that distinguishes among subjects by putting them in categories (e.g., gender). Also called discrete or nominal variables. For example gender, race, religion, or nationality. 

Terminology Used with Collections of Data

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

Dataset:A collection of related data items, e.g., 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, e.g., 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. 

Data visualization: Any tool or technique for representing data visually.

Infographics: Formatted displays that provide a data visualization and textual content.

Open Data: Open data is data that can be freely used, re-used and redistributed by anyone - subject only, at most, to the requirement to attribute and sharealike.

Time series: A set of measures of a single variable recorded over a period of time. Example: CDC comparison of birth rates in the United States 2009-2019 .

Definition References:

Cramer, D., & Howitt, D. (2004). The Sage dictionary of statistics  (Vols. 1-0). Sage Publications, Ltd.  https://doi.org/10.4135/9780857020123

Herzog, D. (2015).  Data literacy: A user’s guide . Sage Publications, Inc.  https://doi.org/10.4135/9781483399966

Vogt, W. P. (2005).  Dictionary of statistics & methodology . Sage Publications, Inc.  https://doi.org/10.4135/9781412983907

Sage Skills: Data Literacy

Sage Skills provides self-paced learning modules on important academic skills. Each topic below includes articles, videos, and suggested readings to introduce you to these important concepts.

Sage Research Methods Datasets

Sage Research Methods Datasets provide users with sample data taken from government sources and academic research to demonstrate both qualitative and quantitative methods, including step-by-step guides and sample data to best demonstrate how the method is applied. Datasets cover a wide range of topics, from the social sciences to data management.