Framework of Statistical Information

This is a typology of the categories or classes of statistical information. Remember the relationship between statistics and data, however, is causal. Statistics are created from data.

Framework of Statistical Information An overlap occurs in this chart between Statistics: Databases and Data: Aggregate, which will be discussed below.

Framework of Statistical Information

In print

In Print Rely on yearbooks, statistical abstracts, catalogues, and indexes to locate statistics in print. Examples of online indexes to print resources: – Statistical Universe (U.S., international, government and private) – Tablebase Example of online catalogues that include print resources: – U.S. Census Bureau Sales Catalog U.S. Census Bureau Sales Catalog – Statistics Canada’s Online Catalogue Statistics Canada’s Online Catalogue

Framework of Statistical Information Online

Online Statistics Example of e-publications – Statistical Abstract of the United States Statistical Abstract of the United States – Statistics Canada Downloadable Publications (DSP) Example of e-tables – Tables [and publications] containing U.S. Consumer Price Indexes Tables – Canadian Statistics (STC Website) Example of statistical databases – American Fact Finder and Data Ferrett American Fact Finder Data Ferrett – CANSIM II (STC Website, E-STAT, CHASS)

E-Publications Tend to be available in PDF format Can use the “Select Text” Tool in the Adobe Reader and copy columns to another application

Statistical Information

E-Tables Tend to be displayed in HTML May provide a pull-down list to view other categories in the table Some e-tables will provide an alternate format for the table that can be downloaded (e.g., the Canadian Census tables are available in comma- separated ASCII, IVT, and print-friendly formats)

Databases Often use HTML forms to define the statistics to be retrieved May offer a variety of output formats for the retrieved statistics (e.g., E-STAT provides IVT format for Beyond 20/20, graphs, charts, maps, and ASCII formats for spreadsheets and databases)

Framework of Statistical Information Aggregate Data

Aggregate Data Aggregate data consist of statistics that are organized into a data structure and stored in a database or in a data file. The data structure is based on tabulations organized by time, geography, or social content.

Aggregate Data Data Structure – Time – Geography – Social Content Example: CANSIM II

Aggregate Data Time series data have long fueled econometric models based on macro- economic indicators. Comma-separate values (CSV) have become an important format for time series data, which is often manipulated in Excel if not analyzed in a spreadsheet.

Aggregate Data Example: CENSUS Data Structure – Time – Geography – Social Content

Aggregate Data Increased availability of GIS software has created greater demand for Census statistics organized as aggregate data. Beyond 20/20 has become a popular tool for reshaping census statistics from 1996 and 2001 for use with GIS software. DBF is the most commonly used format to share census statistics with GIS software.

Aggregate Data A map from E-STAT of Montreal Census Tracts

Aggregate Data “Small area statistics” are a special category of aggregate data. These data files consist of statistics for small geographic areas usually calculated from a population or manufacturing census or an administrative database with enough cases to create accurate summaries for small areas.

Aggregate Data Example: Cause of Death (HID) Data Structure – Time – Geography – Social Content

Aggregate Data Also known as “cross-classified” tables, these files tend to be made of statistics constructed from social-content variables. Examples of cross-classified tables in DLI are found in education and justice.

Framework of Statistical Information Microdata

This is raw data organized in a file where the lines in the file represent a specific unit of observation and the information on the lines are the values of variables. There are different types of microdata files, which will now be discussed.

Confidential Microdata Master files: these files contain the fullness of detail captured about each case of the unit of observation. This detail is specific enough that the identify of a case can often be disclosed easily. Therefore, these files are treated as confidential.

Confidential Microdata Share files: these are confidential files in which the participants in the survey have signed a consent form permitting Statistics Canada to allow access to their information for approved research. These files consist of a subset of the cases in the master file.

Confidential Microdata In summary, confidential microdata get grouped into two types: – master files and share files.

Public Use Microdata These microdata are specially prepared to minimize the possibility of disclosing or identifying any of the cases in a file, i.e, participants in a survey. The original data from the master file are edited to create a public use microdata file.

Public Use Microdata Steps in Anonymizing Microdata – Remove of all personal identification information (names, addresses, etc); – Include only gross levels of geography; – Collapse detailed information into a smaller number of general categories; – Cap the upper range of values of variables with rare cases; – Suppress the values of a variable; or – Suppress entire cases.

Public Use Microdata Statistics Canada PUMFs – Only available for select social surveys that undergo a review of the Data Release Committee, an internal Statistics Canada committee. – No ‘enterprise’ public use microdata.

Public Use Microdata Statistics Canada PUMFs – Almost all PUMFs consist of cross-sectional samples, that is, samples where the data have been collected from respondents at one point in time. – Longitudinal samples, where data are collected from the same individuals two or more times, are difficult to anonymize and maintain any useful information.

Synthetic Microdata These data files have been created to assist with the analysis of confidential data files. – The files provide the full variable structure of the confidential microdata but do not contain any real cases. – They are intended to be used by researchers wanting to submit a file of commands in a statistical package’s language for remote job submission.

Synthetic Microdata – They are also being used by those with approved projects in Research Data Centres to help prepare their analysis strategies prior to working in an RDC. – Synthetic files are also commonly referred to as “dummy files,” although a more technical use of this term does exist for this specific type of synthetic file.

Synthetic Microdata A variety of synthetic file types are being created and tested by author divisions. – One type has no real data but does contain a complete set of real variables. This type is the more technical reference to a dummy file. – Another type has a mix of real data but no real cases. The purpose of this type is to provide -- in the aggregate -- results that should be close to an analysis of the real microdata file.

Synthetic Microdata Users of these files must be advised that none of the analytic results from these files should ever be reported. Their only purpose is to help researchers construct their statistical analysis programs to guard against syntax errors that might exist in their setup.

Framework of Statistical Information

Framework Summary This framework provides a way of thinking about the types of statistical information that exist. Is the information Statistics or Data? – If Statistics, is the information in print or online? If online, is it in an e-pub, e-table, or database? – If Data, is the information aggregate data or microdata?