1. PA430 - Data coding
March 7/8, 2000

2. Codebook
Purpose
Guide data coding process
Guide to locate variables and interpret findings during data analysis
Given the code book and data base, any researcher should be able to understand your data collection and do analysis using the data

3. Preplanning
As for all stages of research, pre-planning of data coding saves you many headaches later.
Issues
Type of analysis
Availability of computer applications
Other resources: time, expertise, computer
Record storage

4. Preplanning
It is advisable to lay out a draft of the codebook, data coding sheet, etc. before collecting data
Surveys
edge coding
consistency
Choice of computer application
spreadsheet vs SPSS

5. Codebooks Key elements
introductory information including researcher’s name and institution, purpose of data collection, name of data base, etc.
variable names (3-8 characters)
sometimes a numerical code is used instead
full definition/description of variable or wording of question
variable attributes (response set) with codes

6. Data Cleaning All coders will make mistakes
Sometimes even necessary for data obtained from other sources - always check
Plan for data cleaning should involve multiple steps/re-checks

7. Helpful hints Save file every 10 minutes or so as you code
Keep more than one copy of the data (i.e., on a floppy and on a hard drive)
Save a copy of the original data. If you recode, save the file under a new name.
Save original surveys, coding sheets, etc. for a reasonable period of time

8. Data cleaning Step 1 - “eyeball” the data
Step 2 - run frequency table for each variable
look for “impossible” response codes
Possible response 1,2,3, and you find a 4
look for unexpected patterns
I.e., all cases coded the same for a variable
Step 3 - run descriptive statistics for each variable
again, looking for inconsistencies