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Data Processing at ICPSR

Data Processing at ICPSR. Peggy Overcashier Senior Systems Analyst, ICPSR CESSDA Expert Seminar Neuchâtel, Switzerland September 9, 2004. What is ICPSR?. Membership organization founded in 1962 Over 500 colleges and universities 2004-2005 budget approximately $10 million (USD)

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Data Processing at ICPSR

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  1. Data Processing at ICPSR Peggy Overcashier Senior Systems Analyst, ICPSR CESSDA Expert Seminar Neuchâtel, Switzerland September 9, 2004

  2. What is ICPSR? • Membership organization founded in 1962 • Over 500 colleges and universities • 2004-2005 budget approximately $10 million (USD) (8.2 million EUR; 12.5 million CHF) • 30% from membership fees • 70% from grants and contracts • Around 100 employees; 40 data processing staff • World’s largest archive of computer-readable social science data • About 7,000 titles and 140,000 data files • Close to 300 data files available for online analysis

  3. Two Kinds of Archival Holdings: • General Archive Holdings are funded with member dues and are available only to members • Special Topic Archives are supported by foundations or federal agencies and holdings are available to all • Aging • Child Care and Early Education • Criminal Justice • Demographic Research • Education • Health and Medical Care • Substance Abuse and Mental Health

  4. Topics • What we do today and how • Current ICPSR processing pipeline • Development of aids to efficient and accurate processing • Automated scripts and tools • Semi-automated techniques • Where we’re headed • ICPSR process improvement initiative

  5. Current ICPSR Processing Pipeline

  6. Scan deposited electronic files for viruses • Inventory files and documentation received • Verify that electronic files open, are readable • Prepare acquisition form (text) • Transmit original data and documentation for preservation

  7. In consultation with processing supervisor • Determine processing level (routine, intensive) • Initial review of files • Potential disclosure risks • Completeness of variable-level metadata • Wild/undocumented codes • Discuss identified problems/solutions

  8. Resolve problems • Eliminate identified disclosure risks • Routine handling vs. full disclosure analysis • Build dataset, typically in SPSS or SAS • Recode • Add and/or delete variables • Fill in missing metadata • Identify missing values • Check full frequencies and/or descriptives • Convert data to ASCII with Data Definition Statements (archival format) • Tools used historically have been buggy • New in-house conversion tool ready for release

  9. Gather existing pieces of documentation • Methodology • Other information received from depositor • Assess what other documentation needs to be included in final products • Hand off to Electronic Document Conversion unit for conversion to PDF or hold until documentation set is completely assembled

  10. Gather and document study-level metadata • Write study summary • Enter into study description form (text) • Submit to editing staff

  11. Optional, at discretion of archive • Assess for potential problems in online analysis • Multiple weights • Outliers • Multiple linkable files • Prepare question text file in SDA native format (DDL) • Configure for online analysis system • Automated test setup; administrators * name = PREGNANT text = The next questions are about your health and health care. Are you currently pregnant? *

  12. Generate frequencies, descriptive statistics for codebook • Document variable-level metadata • Add processor notes • Source documents typically in Word, sometimes WordPerfect, ASCII, PDF, other • Create additional documents as needed • Hand off to Electronic Document Conversion unit for conversion to PDF • The two document steps are frequently combined into one

  13. Make sure all files handed off have been returned and reviewed • study description • PDF documentation • Test all data files and data definition statements • SAS, SPSS, (Stata) • UNIX, Windows • Prepare turnover form (text) • Create turnover directory, move all study files • Quality control check by another processor • Turn over study files for preservation and dissemination

  14. Tool Development • Skill/knowledge set: Programmer vs. Data Processor • Programming skills required for some tools • Fully-automated scripts • Web-based forms • Creativity and software knowledge required for others • Semi-automated techniques • Use of existing software in non-conventional ways

  15. Tools: Semi-automated Methods Regular Expressions • Search and replace using patterns rather than literal strings • Multi-Edit, TextPad: Windows-based editors • Capable of regular expressions • Can save files with UNIX formatting • Extract syntax from existing documentation • Value labels • Question text

  16. Tools: Semi-automated Methods Excel for Text Editing • VLOOKUP • List management • Variable disposition • Merging related information from multiple sources • Running counts • Remapping metadata to new variable names

  17. Tools: Semi-automated Methods SDA Conversion Utilities • Documentation • Frequency, descriptive statistics with variable-level metadata, question text embedded • Can include introductory materials, links to external documents • ASCII→PDF, XML, HTML • DDS conversion • SPSS, SAS, Stata, XML • Prepare metadata for variable-level search

  18. A Little More Technical • Macros • Automate repetitious sequences of commands, keystrokes • Recordable in many applications • Variable Arrays • Pre-define groups of variables on which the same data transformations will be performed • Loops • Repeatedly run a single set of commands as long as a condition is true

  19. Tools: A Few UNIX Script Examples • Automated QC script • Batch-test Data Definition Statements in UNIX • Disclosure analysis and processing system for the Treatment Episode Data Set • Web-based XML generator for Quick Tables configuration files • Hermes: automated batch production system • Early implementation of process improvement recommendations

  20. Process Improvement at ICPSR • Begun in spring 2003 • 4 distinct phases • Mapping the current pipeline • Designing the future • External review • Planning and implementation

  21. Phase 1: Map Current Processing Pipeline • Consultant interviewed groups and individuals • Drew and refined process maps • General agreement that the story and pictures were correct before proceeding

  22. Process Mapping: Insider’s View Overview More detailed, with processing milestones Very detailed, covers a corridor wall

  23. Phase 2: Designing the Future • Internal Process Improvement Committee formed • Brainstorming • “Evolutionary” vs. “Revolutionary” ideas • Formal reports and recommendations

  24. Process Improvement: Guiding Principles • Automation • Standardization • Centralization • Quality Control • Version Control • Focus on the User • Electronic Collection Management • Staff Development and Career Path Expansion

  25. Future Processing Framework

  26. Characteristics • More linear • Integrated; steps connected • Automated milestone tracking • Metadata migrates to database • Eliminate rekeying • Single authoritative source

  27. Phase 3: External Review Committee • Outside experts reviewed reports • Met with individuals and small groups of staff • Endorsed the PIC’s recommendations • Additional recommendations provided • Formal report written

  28. Phase 4: Planning and Implementation • Communication with staff • PIC Web site • PIC/staff information sessions • Implementation manager hired • Implementation plans developed for several recommendations • PIC reconstituted as a standing committee • Review new process improvement suggestions • Provide input for implementation plan

  29. Some Improvements in Development • Automated batch production of enhanced suite of products • Hermes for current and future releases • Retrofit project for previous releases • Web-based forms (acquisition, study description, turnover) • Replace text forms • Eliminate rekeying • Automated processing milestone tracking

  30. Issues Under Consideration • “Ready-to-go” files • How to handle missing data by default (SAS, Stata) • How to best provide SAS formats • Development of standardized bibliographic citation for online analyses • Archival vs. distribution formats • How to handle qualitative data • New formats (e.g., video, audio files) • Development of best practices, automated tools for disclosure analysis

  31. For more information: Peggy Overcashier overcash@icpsr.umich.edu +1 734 615 9529

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