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DATA MANAGEMENT. Using EpiData and SPSS. References. Public domain (pdf) book on data management: Bennett, et al. (2001). Data Management for Surveys and Trials. A Practical Primer Using EpiData . The EpiData Documentation Project. : http://www.epidata.dk/downloads/dmepidata.pdf

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  1. DATA MANAGEMENT Using EpiData and SPSS

  2. References Public domain (pdf) book on data management: Bennett, et al. (2001). Data Management for Surveys and Trials. A Practical Primer Using EpiData. The EpiData Documentation Project. : http://www.epidata.dk/downloads/dmepidata.pdf EpiData Association Website: http://www.epidata.dk/ Importing raw data into SPSS: http://www.ats.ucla.edu/stat/spss/modules/input.htm

  3. Data Management • Planning data needs • Data collection • Data entry and control • Validation and checking • Data cleaning and variable transformation • Data backup and storage • System documentation • Other

  4. Types of Data Base Management Systems (DBMSs) • Spreadsheets (e.g., Excel, SPSS Data Editor) • Prone to error, data corruption, & mismanagement • Lack data controls, limited programmability • Suitable only for small and didactic projects • Also good for last step data cleaning • Commercial DBMS programs (e.g., Oracle, Access) • Limited data control, good programmability • Slow & expensive • Powerful and widely available • Public domain programs (e.g., EpiData, Epi Info) • Controlled data entry, good programmability • Suitable for research and field use

  5. We will use two platforms: • EpiData • controlled data entry • data documentation • export (“write”) data • SPSS • import (“read”) data • analysis • reporting

  6. What is EpiData ? • EpiData is computer program (small in size 1.2Mb) for simple or programmed data entry and data documentation • It is highly reliable • It runs on Windows computers • Runs on Macs and Linus with emulator software (only) • Interface • pull down menus • work bar

  7. History of EpiInfo & EpiData • 1976–1995: EpiInfo (DOS program) created by CDC (in wake of swine flu epidemic) • Small, fast, reliable, 100,000+ users worldwide • 1995–2000: DOS dies slow painful death • 2000: CDC releases EpiInfo2000 • Based on Microsoft Jet (Access) data engine • Large, slow, unreliable (resembled EpiInfo in name only) • 2001: Loyal EpiInfo user group decides it needs real “EpiInfo for Windows” • Creates open source public domain program • Calls program “EpiData”

  8. Goal: Create & Maintain Error-Free Datasets • Two types of data errors • Measurement error (i.e., information bias) – discussed last couple of weeks • Processing errors = errors that occur during data handling – discussed this week • Examples of data processing errors • Transpositions (91 instead of 19) • Copying errors (O instead of 0) • Additional processing errors described on p. 18.2

  9. Avoiding Data Processing Errors • Manual checks (e.g., handwriting legibility) • Range and consistency checks* (e.g., do not allow hysterectomy dates for men) • Double entry and validation* • Operator 1 enters data • Operator 2 enters data in separate file • Check files for inconsistencies • Screening during analysis (e.g., look for outliers) * covered in lab

  10. Controlled Data Entry • Criteria for accepting & rejecting data • Types of data controls • Range checks (e.g., restrict AGE to reasonable range) • Value labels (e.g., SEX:1 = male, 2 = female) • Jumps (e.g., if “male,” jump to Q8) • Consistency checks (e.g., if “sex = male,” do not allow “hysterectomy = yes”) • Must enters • etc.

  11. Data Processing Steps • File naming conventions • Variables types and names • QES (questionnaire) development • Convert .QES file to .REC (record) file • Add .CHK file • Enter data in REC file • Validate data (double entry procedure) • Documentation data (code book) • Export data to SPSS • Import data into SPSS

  12. Filenaming and File Management • c:\path\filename.ext • A web address is a good example of a filename, e.g., http://www2.sjsu.edu/faculty/gerstman/StatPrimer/data.ppt • Some systems are case sensitive (Unix) • Others are not (Windows) • Always be aware of • Physical location(local, removable, network) • Path (folders and subfolders) • Filename (proper) • Extension • Demo Windows Network Explorer: right-click Start Bar > Explore

  13. File extensions you should know

  14. Selected EpiData Variable Types

  15. EpiData Variable Names • Variable name based on text that occurs before variable type indicator code • EpiData variable naming default vary depending on installation • Create variable names exactly as specified To be safe, denote variable names in {curly brackets} • For example, to create a two byte numeric variable called age, use the question: What is your {age}? ##

  16. Demo / Work Along • Create QES file [demo.qes] • Convert QES to REC [demo.rec] • Create CHK file [demo.chk] • Create double entry file [demo2.rec] • Enter data • Validate data

  17. We will stop here and pick up the second part of the lecture next week “Stay tuned”

  18. Codebooks • Contain info that helps users decipher data file content and structure • Includes: • Filename(s) • File location(s) • Variable names • Coding schemes • Units • Anything else you think might be useful

  19. EpiData codebook generators

  20. File Structure Codebook Full codebook contains descriptive statistics (demo)

  21. Full Codebook Notice descriptive statistics

  22. Conversion of Data File • Requires common intermediate file format • Examples of common intermediate files • .TXT = plain text • .DBF = dBase program • .XLS = Excel • Steps • Export .REC file  .TXT file • Import .TXT file into SPSS • Save permanent SAV file

  23. Current Export Formats Supported by EpiData

  24. Plain (“raw”) TXT data • plain ASCII data format • no column demarcations • no variable names • no labels

  25. TXT file with codebook tox-samp.txt tox-samp.not

  26. SPSS Data Export / Import TXT (raw data) SAV REC SPS (syntax)

  27. Top of tox-samp.sps Lines beginning with * are comments (ignored by command interpreter) Next set of commands show file location and structure via SPSS command syntax

  28. Bottom part of tox-samp.sps file Labels being imported into SPSS Delete * if you want this command to run

  29. Opening the SPS (command) file

  30. Running the SPS file

  31. Ethics of Data Keeping • Confidentiality (sanitized files – free of identifiers) • Beneficence • Equipoise • Informed consent (To what extent?) • Oversight (IRB)

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