Framework of Statistical Information

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Framework of Statistical Information
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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.
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Framework of Statistical Information
An overlap occurs in this chart between
Statistics Databases and Data Aggregate, which
will be discussed below.
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Framework of Statistical Information
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Framework of Statistical Information
In print
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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
• Statistics Canadas Online Catalogue

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Framework of Statistical Information
Online
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Online Statistics

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

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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

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Statistical Information
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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)

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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)

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Framework of Statistical Information
Aggregate Data
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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.

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Aggregate Data

• Data Structure
• Time
• Geography
• Social Content

• Example
• CANSIM II

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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.

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Aggregate Data

• Data Structure
• Time
• Geography
• Social Content

• Example
• CENSUS

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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.

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

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Aggregate Data

• Data Structure
• Time
• Geography
• Social Content

• Example
• Cause of Death (HID)

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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.

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Framework of Statistical Information
Microdata
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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.

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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.

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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.

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Confidential Microdata

• In summary, confidential microdata get grouped
  into two types
• master files and share files.

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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.

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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.

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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.

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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.

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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 packages language for remote job
  submission.

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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.

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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.

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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.

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Framework of Statistical Information
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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?