Data Management Lab: Session 3 Data Entry Best Practices (more details at http://ulib.iupui.edu/digitalscholarship/dataservices/datamgmtlab) What you will learn: 1. Build awareness of research data management issues associated with digital data. 2. Introduce methods to address common data management issues and facilitate data integrity. 3. Introduce institutional resources supporting effective data management methods. 4. Build proficiency in applying these methods. 5. Build strategic skills that enable attendees to solve new data management problems.

1. IUPUI University Library Center for Digital Scholarship
Data Management Lab: Pilot [January 2014]
Data Coding Best Practices
Data Coding
Guidelines adapted from the ICPSR Guide to Social Science Data Preparation and Archiving
1. Use common coding conventions
a. Assure that all statistical software packages can handle the data
b. Promote greater measurement comparability
2. Check out Federal Information Processing Codes (FIPS) - standard schemes.
3. Identification variables - provide fields at the beginning of each record to accommodate all
identification variables (e.g., unique study number and respondent number).
4. Code categories - should be mutually exclusive, exhaustive, and precisely defined.
5. Preserving original information - code as much detail as possible; recording original data, such as
age and income is more useful than collapsing or bracketing the information.
6. Closed-ended questions - responses to survey questions that are pre-coded in the questionnaire
should retain the coding scheme to avoid errors and confusion.
7. Open-ended questions - either use a predetermined coding scheme or review the initial survey
responses to construct a coding scheme based on major categories that emerge; any coding
scheme and its derivation should be reported in study documentation.
8. User-coded responses - must be reviewed for disclosure risk; if necessary, treated to protect
confidentiality prior to dissemination.
9. Check-coding - it's a good idea to verify or check-code some cases during the coding process;
i.e., repeat the process with an independent coder.
10. Series of responses - if a series of responses requires more than one field, organizing the
responses into meaningful major classifications is helpful; permits analysis of the data using
broad groupings or more detailed categories.
11. Missing data
a. Codes should match the content of the field (i.e., numeric, alphanumeric,).
b. Codes should be standardized such that the same code is used for each type of
missing data for all variables.
c. Blanks should not be used as missing data codes unless there is no need to differentiate
types of missing data such as "don't know" or "refused" etc.
d. If an entire sequence of variables is blank due to inapplicability or another reason, an
indicator field should be used.
e. Skip patterns & "not applicable" - not applicable and inapplicable should be distinct
from other missing data codes.
References
1. ICPSR. (2012). Guide to Social Science Data Preparation and Archiving, University of Michigan,
Ann Arbor, MI. From http://www.icpsr.umich.edu/files/deposit/dataprep.pdf.
Heather Coates, 2013