Creating Something from Nothing: Synthetic and Dummy files

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Creating Something from NothingSynthetic and
Dummy files

• Bo Wandschneider
• University of Guelph
• Chuck Humphrey
• University of Alberta

DLI Training Ottawa, May, 2003
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Outline

• Types of data Files
• Implications for analysis
• Where do we get access
• Which file is appropriate
• Providing service with synthetic files
• NPHS an exercise
• SLID an exercise

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Types of Data Files

• Microdata
• Confidential Microdata Products
• Master Files
• Share Files
• Public Access Microdata Products
• Public Use Anonym zed microdata (PUMFS)
• Synthetic Files

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Microdata Products

• Microdata
• raw data organized in a file where the records or
  lines in the file are observations of a specific
  unit of analysis and the information on the lines
  are the values of variables
• requires some form of processing or analysis to
  be used

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Microdata Products

• Microdata - SCF Example
• 00001103100002560700000002560700033700000000
  0000000000000000000000000000000000000000000
  00000000000000000000002594400648101946323310
  000000000909222012000000000002220232111000000000
  0000003000000000000000002228233411412190638749500
  575211004600132 000021031000000000000000000000
  0000000000000000000000000000000000000000000
  0000000000000000000000000000000000000000000
  000000000016630000000000608244322000000000006320
  000000000000000000000000000000000000000311612111
  1435481500777500570033004300110
  00003103100000000000000000000000000000000000
  0000000000000000000000000000000000000000000
  00000000000000000000000000000000000000016630
  000000000405211122000000000004320206261110000636
  0000003000000000000000002228213411436491600778500
  570033004200085 000041031000002080000000002080
  0000000005750005220000000000000025740000000
  0000000367100314900052200000000000000575100
  000000575145510000000000608244322000000000005320
  220101021000575000522300000000000000000224022341
  1431251000774500571622361600065
  00005103100001805000000001805000000000028800
  0261000000000000000000000000000000000549000
  28800026100000000117901977800246301731522210
  000000000505222012000000000004320000001011000288
  0002611000000000000000001246123411411440748739500
  575011021600046 000061031000001500000000001500
  0000000000000000000000000000000000000000000
  0000000000000000000000000000000000000150000
  000000150025510000000001010245012000000000006310
  000000000000000000000000000000000000000312326341
  1431071300773500571612004300094
  00007103100000000000000000000000000000000000
  0000002540000000000000000000000000002540002
  54000000000000000000000254000000000254041520
  000000000103402012000000000002220121134000000000
  0000003000000000000000002269233411436491600778500
  570033004200041 000081031000008400000000008400
  0000000000000000000000000000000000000000000
  0000000000000000000000000000000000000840000
  085800754225510000000000808233012000000000003320
  000000000000000000000000000000000000000311813341
  1411210848739500575211004600055
  00009103100002600000000002600000000000028700
  0156000000000000000879000000000000001322001
  16600015600000000000002732200433502298722310
  000000000708234222000000000006420000001012000287
  0001561000000000000000001248113411431400300774500
  564512071600060 000101031000000000000000000000
  0001570000000000000050430000000000000000000
  0000000504300254100250200000000000000520000
  000000520046520000000000406223122000000000006420
  000000000000000000000200000000000000000437621341
  1436491600778500570033004400076
  00011103100000000000000000000000000000000000
  0000000000000000000000000000000000000000000
  00000000000000000000000000000000000000016630
  000000000203412131000000000004620000000000000000
  0000000000000000000000003119213411435481500777500
  570033004500040 000121031000000991000000000991
  0000000000000000000000000000000000000000000
  0000000000000000000000000000000000000099100
  000000099125510000000000203433221000000000004330
  000000000000000000000000000000000000000311712131
  1432231400773500571222004300244
  00013103100002771600000002771600000000028800
  0000000000000000000000000000000000000288000
  28800000000000000000002800400624302176122210
  000000000707222012000000000003310034071100000288
  0000001000000000000000001226163411411431138739500
  575211004600156 000141031000010000000000010000
  0000000006000000000000000000000000000000000
  0000000060000060000000000000000000001060000
  068600991423310000000000404222012000000000004330
  077001011000600000522100000000000000000126012341
  1411440636719500573012221600148
  00015103100000075000000000075000000000000000
  0370000000000000000000000000000000000370000
  00000037000000000000000112000000000112025510
  000000000808233132000000000006330323511032001126
  0003703000000000000000002245223411411261318529500
  575222004600132 000161031000007012000000007012
  0001650000000000000000000000000030820000000
  0000000308200308200000000000000000001025900
  135600890325410000000000708244322000000000005310
  000000000000000000000000000000000000000311812341
  1421320320439500573522171600111
  00017103100000202700000000202700000000000000
  0000000000000000000000000000000000

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Confidential Microdata

• Master Files
• These files contain the fullness of detail
  captured about the unit of observation. The
  information in these files can identify the
  individual who provided the original information
  and, therefore, are considered confidential.

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Confidential Microdata

• Master File Example

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Confidential Microdata

• Master File - Personal identifiers

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Confidential Microdata

• Master File Geography (SLID)

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Confidential Microdata

• Master File - Fullness of Data (NPHS)

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Confidential Microdata

• Master File - Fullness of Data

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Confidential Microdata

• Master File - Fullness of Data (SLID)

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Confidential Microdata

• Master File - Fullness of Data

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Confidential Microdata

• Share Files
• these are confidential files in which the
  respondents have signed a consent form permitting
  Statistics Canada to allow access to their
  information for approved research.
• Used with NPHS and NLSCY

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Public Access Microdata

• Anonymized Microdata
• these microdata are specially prepared to
  minimize the possibility of disclosing or
  identifying any of the cases or observations
• the original data from the master file are
  edited to create a public use microdata file

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Public Access Microdata

• Steps in Anonymizing Microdata
• removal of all personal identification
  information (names, addresses, etc)
• include only gross levels of geography
• collapse detailed information into a smaller
  number of general categories
• suppress the values of a variable

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Public Access 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 Access Microdata

• Statistics Canada PUMFs
• almost all are cross-sectional, that is,
  represent data collected at one point in time
• longitudinal data are difficult to anonymize
  while maintaining any useful information

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Public Access Microdata

• PUMFs personal identifiers

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Public Access Microdata

• PUMFs gross geography

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Public Access Microdata

• PUMFs collapsed data

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Public Access Microdata

• PUMFs suppressed data

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Public Access Microdata

• Synthetic Files
• These microdata do not contain actual real
  cases but are pseudo-cases that provide aggregate
  results close to the real cases

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Public Access Microdata

• Synthetic Files
• They have been prepared to create analysis runs
  with the master file without possibly disclosing
  or identifying any of the cases

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Public Access Microdata

• Synthetic Files
• The results are not to be reported strictly to
  be used to prepare analyses of master files
• Usually associated with longitudinal files

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Public Access Microdata

• Steps in creating Synthetic Files
• Observations are transformed
• No records actually exist
• Keep fullness of detail

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Public Access Microdata

• Synthetic Files NPHS example

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Public Access Microdata

• Synthetic Files NPHS 1999 general file

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Public Access Microdata

• Synthetic Files NPHS 1999

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Public Access Microdata

• Synthetic Files NPHS 1999

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Implications for Analysis

• What are the implications in doing analysis with
  these different types of microdata files?

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Implications for Analysis

• Master File
• All observations
• Has the most variables with the most detail
• Lots of geography and personal characteristics
• Little grouping or capping of categories

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Implications for Analysis

• Master File
• Restricted access only available to authorized
  Statistics Canada employees, which includes
  deemed employees

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Implications for Analysis

• Master File
• Includes linkage variables across files within a
  study, e.g., NLSCY linkage among the files for
  different units of analysis (kids, parents,
  teachers)

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Implications for Analysis

• Public Use Microdata (PUMF)
• Suppressed observations
• Suppressed variables removed from the file
• Suppressed content
• Gross geography
• Collapsed categories
• Capped values

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Implications for Analysis

• Public Use Microdata (PUMF)
• Licensed product agree to certain terms of use
• No linkage to multiple units of analysis, with a
  few exceptions (GSS Time Use and Family)

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Implications for Analysis

• Synthetic Files
• Looks like a duck and quacks like a duck, but
  it isnt a duck or any other type of fowl.

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Implications for Analysis

• Synthetic Files
• Looks like master files
• Lots of observations
• Lots of variables
• Little grouping or capping of categories
• Lots of geographic detail

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Synthetic Files

• Precautions
• Results not authentic but close in the
  aggregate
• Use for testing analysis setups only
• Still need the master files for publishable
  results

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Where do we get Access?

• Master File
• Restricted access governed under the Statistics
  Act
• Remote Job Submission
• Research Data Centres
• Apply to SSHRC to obtain a peer-reviewed proposal
  and STC for security clearance

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Where do we get Access?

• Public Use Microdata Files (PUMF)
• Get from DLI
• Analyze where ever is convenient
• Can use a variety of analysis software, including
  SAS, SPSS, Stata, HLM, LISREL, etc.
• Slidret sans data

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Where do we get Access?

• Synthetic Files
• Author Divisions may create it
• Most relevant when dealing with new Panel Data,
  but not necessarily, e.g., the Census has
  potential
• NPHS synthetic files on DLI FTP site

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Where do we get Access?

• Synthetic files
• SLID, WES, YITS coming ????
• Do we need to encourage them?
• Work with locally
• Build SAS and SPSS setups

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Which File is Appropriate?

• 1st stop is still the PUMF
• This file has the easiest access for us
• Probably meets the needs of most clients
• Not as administratively burdensome as synthetic
  or master file
• Perfect for clients just looking for data
  courses in quantitative analysis

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Which File is Appropriate?

• If more detail is needed, refer to the Master
  File Documentation (similar to Synthetic File
  Documentation)
• Make them aware that the cost of use is higher,
  both in terms of accessibility and analytical
  requirements
• Interest most likely to come from grad students
  and experienced researchers

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Which File is Appropriate?

• Download the Synthetic files from DLI
• Make them aware of problems with synthetic files
  RESULTS ARE NOT PUBLISHABLE
• Encourage them to submit an application for RDC
  access there is a time lag

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Which File is Appropriate?

• RDC

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Which File is Appropriate?

• Some of you may work with client using synthetic
  files before passing her/him off to RDC

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Services for Synthetic Files

• DLI Contacts can provide four basic services with
  synthetic files.
• Build SPSS and SAS system files from the raw
  synthetic data files that are distributed through
  DLI
• Provide information about the use of Remote Job
  Submission (a.k.a, Remote Access) and RDCs

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Services for Synthetic Files

• Assist with finding variables in the synthetic
  files
• Provide instruction about ways of capturing SPSS
  or SAS code from dummy analysis runs with the
  synthetic files. It is this code that is then
  submitted to STC through remote job submission.

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Services for Synthetic Files

• 1. Building SPSS and SAS system files for
  synthetic data
• The NPHS synthetic data are distributed as a raw
  ASCII file with accompanying command files for
  SPSS and SAS
• Separate synthetic data files exist for the
  master file setup and for bootstrapping analysis

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Services for Synthetic Files

• 1. Building SPSS and SAS system files for
  synthetic data
• The synthetic data for the 2000-2001 NPHS has
  4,138 variables and 17,276 fabricated cases.
  Creating the SPSS and SAS system files from this
  file is not difficult, but it does take time.
  DLI Contacts may wish to create these products
  for their patrons.

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Services for Synthetic Files

• 2. Information about Remote Job Submission (RJS)
• The author divisions supporting RJS have
  established their own guidelines and have
  different operating procedures. Not all
  divisions supporting longitudinal surveys
  currently support RJS.
• Therefore, there is a need to track down this
  information for our patrons.

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Services for Synthetic Files

• 2. Information about Remote Job Submission (RJS)
• For example, the sources for information about
  RJS include the Centre for Education Statistics
• http//www.statcan.ca/english/edu/rda/index.htm

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Services for Synthetic Files

• 2. Information about Remote Job Submission (RJS)
• Where do you find this information?
• Ask the DLI Team via the DLI List
• The EAC has asked for a description of RJS on the
  DLI website, which should be on the DLI Teams
  to-do list

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Services for Synthetic Files

• 2. Information about Research Data Centres
• The collection of master files available through
  RDCs is listed on the STC website for RDCs
• Each RDC has its own website describing its
  services
• http//www.statcan.ca/english/rdc/index.htm

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Services for Synthetic Files

• 3. Data Reference for the content of the
  synthetic files
• Helping researchers identify variables over
  longitudinal files is an important service
• Need to keep the unit of analysis straight
• Need to understand the mnemonic naming convention
  for variables over cycles
• Develop indexing aids for you and your patrons

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Services for Synthetic Files

• 4. Provide helpful tips for preserving the code
  from dummy analysis runs in SPSS and SAS
• Researchers will run analyses on the synthetic
  file to generate the code that they will
  subsequently email for Remote Job Submission
• Providing information about how to do this easily
  will be helpful to your patrons

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An Example Using the NPHS

• Lets look at an example of these four services
  using the synthetic files from the NPHS,
  2000-2001.

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An Example Using SLID

• Lets look at an example of a dummy file using
  SLIDRET, a retrieval system developed to extract
  data from the cycles of the SLID. A data-less
  version of SLIDRET is available through DLI to
  help identify variables for RJS.

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Location of Slides and Exercices

• http//drc.uoguelph.ca/DATA/WKSHPS/IASSIST2003