This document discusses techniques for handling missing data in research studies. It recommends choosing techniques based on study objectives, literature review, and expert opinion. It describes common data collection techniques like interviews, questionnaires, and observation. It emphasizes ensuring data collection tools are valid and reliable by measuring what they are intended to measure and producing consistent results. The document provides guidance on designing questionnaires and data collection forms, conducting pilot studies, training data collectors, and using statistical techniques like Expectation Maximization to address missing data.

1. MISSING DATA
Reporting and Handling missing data
DR HARDEV SINGH s/o HARPAL SINGH

2. HOW
 How to choose the techniques:
• What the objectives to answer.
• Ideas from literature review.
• Expert opinion.
• Feasibility to implement.
 Need tool(s) for technique(s) that you choose.

3. DATA COLLECTION
TECHNIQUE
 Data collection techniques:
• Interview (face-to-face/ telephone).
• Self-administered questionnaire.
• Instruments.
• Recorded resources.
• Observation.

4. VALIDITY & RELIABILITY
 Validity & reliability
• Measure what you are supposed to measure.
• Repeatable.

5. QUESTIONNAIRE
 Interview/ self-administered questionnaire:
• If possible, use or modify a validated questionnaire
• Should contain:
• Identifier – avoid repetition & keep track of total distributed.
• Introduction – purpose, confidentiality, use of data & patient’s
agreement.
• Socio-demographic information.
• Main study questions.
• Closing statements.
• Short & precise; non-sensitive; simple language.

6. DATA COLLECTION FORM
 Recorded resources:
• Data collection form.
• Should contain:
• Identifier – avoid repetition & keep track of total used.
• Socio-demographic information.
• Main study items.
• Definition (terms/ units to use).
• Test-retest/ inter-rater reliability

7. PILOT STUDY
 Pilot study: A small scale trial of a study technique.
• Validity & reliability.
• Limitations; time/ resource requirements.
• Needs for revision.

8. DATA COLLECTORS
 Training for data collectors:
• Supervisors: preferably who would have authority over data collectors.
• Limit the number of data collectors to reduce errors.
• Clear understanding of the study and methods.
• Periodical checks to determine the quality of data.

9. EXCAMPLES

10. FOLLOW INSTRUCTIONS
1. ANALYZE
2. PILIH MISSING VALUE ANALYSIS
(CLICK)
CLICK ANALYZE
PILIH MISSING VALUE ANALYSIS

11. HIGHLIGHT VARIABLE
THE VARIABLES DROM
SAME GROUPS
1. HIGHLIGHT
VARIABLE YANG
SATU GROUP
DENGANNYA
(CONTOH
GROUP SIKAP)
2. CLICK
DISINI

12. TRANSFER THE VARIABLE
INTO QUANTITATIVE BOX
VARIABLES

13. 1. CHOOSE EM
2. CLICK EM
3. CLICK OK

14. 1. CHOOSE SAVE
COMPLETE DATA
2. INSERT NEW DATA
SET (KEY IN NEW
DATA NAME)
3. CLICK
CONTINUE

15. 1. New data set will appear on the selected variable

16. NO MORE MISSING DATA
BEFORE EM
AFTER EM

17. CONCLUSION
 NEW DATA CAN BE INSERTED BY JUST COPY AND
PASTE