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Multiple Indicator Cluster Surveys Data Processing Workshop PowerPoint Presentation
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Multiple Indicator Cluster Surveys Data Processing Workshop

Multiple Indicator Cluster Surveys Data Processing Workshop

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Multiple Indicator Cluster Surveys Data Processing Workshop

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  1. Multiple Indicator Cluster SurveysData Processing Workshop Data Entry Editing MICS4 Data Processing Workshop

  2. REMEMBER TO REMIND YOUR FIELD STAFF: • The best place to correct data is in the field where the respondent is available to resolve inconsistencies. Once the questionnaires reach the office, the best you can do is to apply carefully specified editing guidelines consistently and carefully. MICS4 Data Processing Workshop

  3. Timing of Editing • Before data entry • Interviewer • Field editor • Office editor • During data entry • Data entry operator (with training and supervision) • After data entry • Secondary editor MICS4 Data Processing Workshop

  4. General Rules for Resolving Inconsistencies • Review all pertinent responses in the questionnaire(s) • For skips, check responses preceding and following • Refer to the editing guidelines • Do not make up an answer; if necessary, use the codes for inconsistent (7, 97, 997) or missing (9, 99, 999) • Change the fewest pieces of information • If unable to resolve, leave the inconsistency without correction and document the inconsistency for users MICS4 Data Processing Workshop

  5. Data Editing Philosophy • Field Editing • Interviewer or field editor • Using field editing manual, can be fully (almost) corrected • Office Editing - Use editing guidelines • Office editor • ID and structure errors only • DE personnel • Check for data entry errors; resolve only structural inconsistencies • Secondary editor • Investigate and resolve (sometimes by taking no action) all inconsistencies MICS4 Data Processing Workshop

  6. Defining the Editing Specifications • Carefully review the questionnaire • Define the edits • What is the possible inconsistency? • How should the inconsistency be handled during data entry? • How should the inconsistency be handled during secondary editing? MICS4 Data Processing Workshop

  7. A Simple Example The number of eligible women (HH12) can’t be larger than the number of household members (HH11) Q1. Should we check for this inconsistency during data entry? Q2. Should it be resolved during data entry? Q3. What should the editing guidelines say? MICS4 Data Processing Workshop

  8. How Do We Handle the Inconsistency? A1. Yes, we should check: PROC HH12 if HH12 > HH11 then errmsg(0015); reenter endif; A2. Yes, it must be resolved; both variables structurally important • HH11 controls entries in household listing • HH12 controls number of women’s questionnaires MICS4 Data Processing Workshop

  9. How is the Inconsistency Resolved? A3. Editing guidelines should have the data entry operator: • check for data entry errors, correcting any that are found • if no data entry error is found, then: • count number of household members in household listing • count number of eligible women in household listing • ensure all women questionnaires belong to the HH • correct HH11 and/or HH12 based on counts MICS4 Data Processing Workshop

  10. A Complex Example A woman’s age (WB2) and date of birth (WB1M and WB1Y) must be consistent Q1. Should we check for this inconsistency during data entry? Q2. Should it be resolved during data entry? Q3. What should the editing guidelines say? MICS4 Data Processing Workshop

  11. How Do We Handle the Inconsistency? A1. Yes, we should check A2. No, inconsistency should not be resolved by the data entry operators • while age and DOB are both critically important, this inconsistency is too complex and time consuming for data entry A3. Correct keying errors only • This inconsistency will be resolved during secondary editing MICS4 Data Processing Workshop

  12. Contents of the Editing Guidelines • Message number, type, and text • An explanation of the problem • Suggestions for method(s) of correction or recommendation to make no changes MICS4 Data Processing Workshop

  13. Error Message Numbers • Error message numbers have 4 positions • 1st position: questionnaire type 0 = HH, 1 = WM, 2 = Child, 3 = MN, 9=Used by all • 2nd position: module sequential order of module inside questionnaire type • 3-4th positions: unique ID within questionnaire type and module • Some exceptions to the rules MICS4 Data Processing Workshop

  14. Code Timing Status D Data entry Should be corrected W Data entry Check for keying errors E Data entry and Secondary editing Probably needs correction M Secondary editing Unusual cases; may need correcting Types of Error Message MICS4 Data Processing Workshop

  15. Editing Guidelines • For each inconsistency: • explain its nature if error message doesn’t make it clear • explain how to handle the inconsistency during data entry (if applicable) • explain how to handle the inconsistency during secondary editing (if applicable) • in resolution explanations, list all related variables that should be examined MICS4 Data Processing Workshop

  16. Modifying the Editing Guidelines • For all country-specific questions that were added to the MICS questionnaire, add editing guidelines • Modify the standard guidelines only after careful consideration by subject specialists • Document any changes to the standard guidelines • Ensure that all processing staff use the manual and apply it consistently MICS4 Data Processing Workshop

  17. Adding an Edit • Add logic to the data entry application (.app) • Add message text to the message file (.mgf) • Add message to the editing guidelines (.doc) MICS4 Data Processing Workshop

  18. REMEMBER TO REMIND YOUR FIELD STAFF: • The best place to correct data is in the field where the respondent is available to resolve inconsistencies. Once the questionnaires reach the office, the best you can do is to apply carefully specified editing guidelines consistently and carefully. MICS4 Data Processing Workshop