Efficient Data Handling for Successful Analysis
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Ensure accuracy by editing, handling gaps, and conducting thorough data checks. Utilize coding and categorization for organized analysis. Understand mean, range, and reliability for hypothesis testing.
Efficient Data Handling for Successful Analysis
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Presentation Transcript
Getting Data Ready for Analysis • Editing Data • Open-ended questions • Questionnaire data have to be checked for incompleteness and inconsistencies
Getting Data Ready for Analysis(Cont’d) • Handling blank responses • If substantial number of questions have been left unanswered: throw out the questionnaire • If only few items are left blank: • Use midpoint • Allow the computer to ignore the blank responses • Mean value of responses of all those who have responded to that particular item • Mean of the responses of this particular respondent to all other questions measuring this variable • Random number
Getting Data Ready for Analysis(Cont’d) • Coding • Use coding sheet first • Categorization • Set up a scheme for categorizing variables such that several items that measuring a concept are all grouped together • Entering data
Data Analysis • Feel for the data • Mean, range, standard deviation, variance • Testing goodness of data • Reliability • Validity • Hypothesis testing
