1. Data Preparation
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Dr. Michael R. Hyman, NMSU

2. File, Record, and Field

3. Data Matrix

4. Data Entry
Process of transforming data from research projects to computers

5. Five Steps for Data Preparation
Validation
Editing
Coding
Data entry/transcription
Machine cleaning of data

6. Validation Check that interviews conducted as specified
Ensure respondent qualified
Interviewer looked/acted professionally
Interview conducted in proper environment
All appropriate questions asked

7. Editing: Personal Interviews
Check for:
Omissions
Ambiguities
Inconsistencies
Proper skip patterns
Properly recorded answers, especially to open-ended questions

8. Editing: Self-Administered Questionnaires
Check for:
All questionnaire sections and key questions answered
Respondents understood instructions and took task seriously
No missing pages
Questionnaire returned before cutoff date

9. Solutions for Editing Problems
Re-contact respondent
Discard questionnaire
Use only good items
Data analysis implications (beyond scope of class)

10. Coding
Process of grouping and assigning numeric codes to different question responses
Closed-ended questions easier because pre-coded

11. Pre-coding Example

12. Coding an Open-Ended Question
Generate list of responses
Consolidate responses (subjective judgment)
Set response category codes
Assign independent response category and record associated numeric code

13. Portion of Travel Study Code Book

14. Data Entry Process
Validated, edited, and coded questionnaires given to data entry operator
More accurate and efficient to go directly from questionnaire to data entry device and storage medium
Skip coding sheets

15. Data Transcription

16. Intelligent Data Entry
Checking entered data for internal logic by either the data entry device or another connected device
Excel/Quattro and SPSS rely on dumb data entry
Require data cleaning

17. Machine Cleaning of Data
Computerized error check
Identifies and suggests fixes for logical errors
Marginal report
Computer-generated table of response frequencies for questions
Monitor entry of valid codes and skip patterns

18. Machine Cleaning Instructions

19. Recoding Data

20. Recoding Data
Using computers to convert original codes used for raw data into codes that are more suitable for analysis
Var1 = 8 - Var1

21. Collapsing a Five-Point Likert Scale

22. Coping with Missing Data

24. Item Non-response to Questions of Fact

25. Ways to Handle Missing Responses
Leave blank
Case-wise deletion
Pair-wise deletion
Mean response
Imputed response