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RESEARCH METHODOLOGY- PROCESSING OF DATA

PROCESSING OF DATA- INTRODUCTION AND IMPORTANT STEPS

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RESEARCH METHODOLOGY- PROCESSING OF DATA

1. 1. RESEARCH METHODOLOGY PROCESSING OF DATA JENIFER S.K. FINAL YEAR MBA SRMC
2. 2. INTRODUCTION 1. The data, after collection, has to be prepared for analysis. 2. Collected data is raw and it must be converted to the form that is suitable for the required analysis. 3. The result of the analysis are affected a lot by the form of the data. 4. So, proper data preparation is must to get reliable result.
3. 3. IMPORTANT STEPS QUESTIONNAIRE CHECKING EDITING CODING CLASSIFICATION TABULATION GRAPHICAL REPRESENTATION DATA CLEANINGDATA ADJUSTING
4. 4. QUESTIONNAIRE CHECKING When the data is collected through questionnaires, the first steps of data preparation process is to check the questionnaires if they are accepted or not. NOT ACCEPTED IF:  Incomplete partially or fully.  Answered by a person who has inadequate knowledge.  which gives the impression that the impression that the respondent could not understand the questions.
5. 5. EDITING Editing of data is a process of examining the collected raw data (specially in surveys) to detect errors and omissions and to correct these when possible. FIELD EDITING CENTRAL EDITING Translating or rewriting Wrong and replacement
6. 6. CODING • Coding refers to the process of assigning numerals or other symbols to answers so that responses can be put into limited number of categories or classes.
7. 7. CLASSIFICATION • Classification of data which happens to be the process of arranging data in group or classes on the basis of common characteristics.
8. 8. • Attributes  only their presence and absence in an individual items can be noticed. • Class-intervals  size of each class into which a range of a variable is divided.
9. 9. TABULATION • Tabulation is the process of summarizing raw data and displaying the same in compact form( i.e., in the form of statistical tables ) for further analysis. • Tabulation is an orderly arrangement of data in columns and rows.
10. 10. GRAPHICAL REPRESENTATION • Graphs help to understand the data easily. • Most common graphs are bar charts and pie charts.
11. 11. DATA CLEANING • Checking the data for consistency and treatment for missing value.
12. 12. DATA ADJUSTING • Data adjusting is not always necessary but it may improve the quality of analysis sometimes.