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. :
EpiData Association Website:
Importing raw data into SPSS:

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.,
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
Software program
.qes
EpiInfo/EpiData questionnaire
.rec
EpiInfo/EpiData records (data)
.chk
EpiInfo/EpiData check (controls & labels)
.not
EpiData notes (data documentation)
.sav
SPSS permanent data file
.sps
SPSS syntax file (program)
.txt
Generic (flat) text data
.htm
Web Browser
.doc
Microsoft Word
.xls
Microsoft Excel

14. Selected EpiData Variable Types
Examples
Text
_ <A >
Numeric
# ##.#
Date
<mm/dd/yyyy> <dd/mm/yyyy>
Auto ID
<IDNUM>
Sondex (sanitized)
<S >

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
Fname
Lname
DOB
SEX
DEATHAGE
John
Snow
3/15/1813 1 45
George
Orwell
6/25/1903 46

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)