1. Data Processing

2. Editing
Coding
Classification
Tabulation
Analysis

3. Editing of Data After the data is edited, it becomes information
If data is not edited, it would take more time and would be difficult for the researcher to arrive at results
Meaning
Checking or testing of the importance of data
Removing unnecessary unrequired information
To provide accurate, complete and consistent info.

4. Editing of Data Micro and Macro Level Editing
Field and Central Editing
Objectives to ensure
Accuracy of data
Consistency of data
Completeness of data
Coherence of aggregated data
The best possible data

5. Editing of Data

6. Coding of Data Process of assigning figures or symbols to answers
Responses to be put into a number of categories
Characteristics
Exhaustiveness
Mutually Exclusive
Unidimensionality
Netting or bucketing
What do you like best in BMS?
Questionnaires could be precoded

7. Classification of Data
Data classified into homogenous groups to get meaningful data
Grouping of data into common groups or classes based on common characteristics
Example:
Data being classified as per target groups requested by clients

8. Classification of Data
Types
Qualitative (Gender, Religions)
Quantitative (number of members in family, pets, TV sets)
Geographical area
Class Interval (ranges: age, income)

9. Tabulation
Process of summarizing raw data and displaying it in a compact form for analysis
Orderly arrangement of data in a logical order
While tabulation on the computer the data is converted to a numeric form
Binary data sheets made

10. Tabulation 64 36 70 30

11. Definitely Would not buy
Tabulation
Question: Would you buy this product?
General Population (458)
TG1
(156)
TG2
(84)
Definitely would buy
28.6
19.2
8.6
Probably Would buy
34.5
50.3
5.7
May or May not buy
9.4
20.1
16.3
Probably would not buy
22.8
6.4
35.7
Definitely Would not buy
4.7 4
33.7

12. Importance of Tabulation
Facilitates process of comparison
Preserves space and reduces explanatory and descriptive statements at minimum
Helps to detect errors and omissions
Identity to data
Helps simplify complex data
Basis for statistical processing

13. Data Analysis Descriptive Analysis Inferential Analysis
Unidimensional Analysis
Bivariate Analysis (Correlation and Causal)
Multivariate Analysis
Inferential Analysis
Statistical confidence
Determine validity of data with conclusion

14. Data Interpretation Converting the small stories to bigger ones
Explanations, reasons, causes can be provided
Interpretations
Generalisations
Hypotheses testing
Applying extraneous information
Expert Opinions