1. Transforming data into information

2. Turning data into information is just a beginning

3. Data - This can be described as the raw data that would typically support or part of an operational processes.
Information - Transformed information is consumable for reporting and analytical needs. - "What does it mean?"
Knowledge - Taking the Information we have prepared in the previous step and enriching it with relationships and correlations that start to "tell the story" of what the data contains. - "What do we already know and why?"
Intelligence - The holy grail, we have leveraged all the previous stages to put all this data to address the problems/issues or challenge - "What do we do?"

4. Stages of data analysis
Editing
Coding
Data file
Analysis approach
Descriptive analysis
Univariate analysis
Bivariate analysis
Multivariate analysis

5. Editing
The process of checking the completeness, consistency, and legibility of data and making the data ready for coding and transfer to storage.

6. Field Editing
Field supervisors often are responsible for conducting preliminary field editing on the same day as the interview.
Field editing is used to
Identify technical omissions such as a blank page on an interview form
Check legibility of handwriting for open-ended responses
Clarify responses that are logically or conceptually inconsistent.

7. in-house editing Editing for Completeness
A rigorous editing job performed by a centralized office staff
Editing for Completeness
item nonresponse
Plug value
Impute
Editing Questions Answered Out of Order

8. Facilitating the Coding Process
Eding and Tabulating “DON’T KNOW” answer
Pitfalls of editing
Pretesting edit

9. Coding Qualitative Responses
Unstructured qualitative responses (Long Interviews)
Structured qualitative reponse
dummy coding Numeric “1” or “0” coding where each number represents an alternate response such as “female” or “male.”
Data file terminology The way a data set is stored electronically in spreadsheet-like form in which the rows represent sampling units and the columns represent variables.

10. Summary Know when a response is really an error and should be edited
Appreciate coding of pure qualitative research
Understand the way data are represented in the data file
Understand the coding structure of responses including a dummy variable approach
Appreciate the ways that technological advances have simplified