Multiple Indicator Cluster Surveys Data Processing Workshop Secondary Editing MICS Data Processing Workshop.

Slides:



Advertisements
Similar presentations
SURVEY QUALITY CONTROL
Advertisements

Multiple Indicator Cluster Surveys Data Entry and Processing.
MICS4 Data Processing Workshop Multiple Indicator Cluster Surveys Data Processing Workshop Data Entry Editing.
MICS Data Processing Workshop Supervisors Menu. Purpose of the Supervisors Menu Executes supervisors applications –...and displays results Transfers and.
MICS DATA PROCESSING Data Entry Editing. REMEMBER AND REMIND YOUR FIELD STAFF: The best place to correct data is in the field where the respondent is.
MICS4 Data Processing Workshop Multiple Indicator Cluster Surveys Data Processing Workshop Supervisors Menu.
MICS Data Processing Workshop Overview. Data Processing Design Data processing is organized around clusters There is one set of data files for each cluster.
MICS4 Data Processing Workshop Multiple Indicator Cluster Surveys Data Processing Workshop Data Entry Applications with Logic.
MICS4 Data Processing Workshop Multiple Indicator Cluster Surveys Data Processing Workshop Overview of Data Processing System.
MICS Data Processing Workshop
MICS Data Processing Workshop User-Defined and Built-In Functions.
MICS DATA PROCESSING Secondary Editing. REMEMBER AND REMIND YOUR FIELD STAFF: The best place to correct data is in the field where the respondent is available.
MICS Data Processing Workshop Structure Checking and Verification.
MICS4 Data Processing Workshop Multiple Indicator Cluster Surveys Data Processing Workshop Revisiting Data Path and Error Messages in a Data Entry Application.
MICS4 Survey Design Workshop Multiple Indicator Cluster Surveys Survey Design Workshop Data Entry and Processing.
MICS4 Survey Design Workshop Multiple Indicator Cluster Surveys Survey Design Workshop Field Staff and Field Procedures.
MICS4 Survey Design Workshop Multiple Indicator Cluster Surveys Survey Design Workshop Survey Quality Control.
MICS4 Survey Design Workshop Multiple Indicator Cluster Surveys Survey Design Workshop Questionnaire for Children Under Five: Under-Five Child Information.
MICS4 Data Processing Workshop Multiple Indicator Cluster Surveys Data Processing Workshop MICS Dictionary and Forms.
MICS4 Data Processing Workshop Multiple Indicator Cluster Surveys Data Processing Workshop Secondary Editing.
1 Field Management: Roles & Responsibilities Partially Adapted from Multiple Indicator Cluster Surveys (MICS) Regional Training Workshop – Survey Techniques,
Multiple Indicator Cluster Surveys Data Processing Workshop Data Entry Applications with Logic MICS Data Processing Workshop.
Mobile Surveyor A Windows PDA/Mobile based survey Software for easy, fast and error free data collection.
Learning Objectives Copyright © 2002 South-Western/Thomson Learning Data Processing and Fundamental Data Analysis CHAPTER fourteen.
Learning Objectives 1 Copyright © 2002 South-Western/Thomson Learning Data Processing and Fundamental Data Analysis CHAPTER fourteen.
Learning Objectives Copyright © 2004 John Wiley & Sons, Inc. Data Processing, Fundamental Data Analysis, and Statistical Testing of Differences CHAPTER.
Multiple Indicator Cluster Surveys Data Processing Workshop MICS Dictionary and Forms MICS Data Processing Workshop.
Multiple Indicator Cluster Surveys Survey Design Workshop
Multiple Indicator Cluster Surveys Data Interpretation, Further Analysis and Dissemination Workshop Overview of Data Quality Issues in MICS.
MICS Data Processing Workshop Multiple Indicator Cluster Surveys Data Processing Workshop Data Quality Tables.
Multiple Indicator Cluster Surveys Survey Design Workshop Data Analysis and Reporting MICS Survey Design Workshop.
MICS Survey Design Workshop Multiple Indicator Cluster Surveys Survey Design Workshop Overview of MICS Tools, Templates, Resources, Technical Assistance.
Improving Collection of Client Identifiers July 29, 2010.
Learning Objective Chapter 13 Data Processing, Basic Data Analysis, and Statistical Testing of Differences CHAPTER thirteen Data Processing, Basic Data.
Chapter Sixteen Starting the Data Analysis Winston Jackson and Norine Verberg Methods: Doing Social Research, 4e.
McGraw-Hill/Irwin © 2004 by The McGraw-Hill Companies, Inc. All rights reserved. Chapter 9 Processing the Data.
MICS Survey Design Workshop Multiple Indicator Cluster Surveys Survey Design Workshop Interpreting Field Check Tables.
MICS4 Survey Design Workshop Multiple Indicator Cluster Surveys Survey Design Workshop Data Analysis and Reporting.
Improving Data Entry of CD4 Counts March Welcome! The State Office of AIDS (OA) is continuing to work with providers to improve the quality of data.
To add an encounter manually, click on “Add”. To upload an electronic file of encounters, click on “Data Transfer” and then “Upload.” See separate training.
Introduction to fertility In Demography, the word ‘fertility’ refers to the number live births women have It is a major component of population change.
Improving Collection of Poverty Level in ARIES April 27, 2011.
Copyright 2010, The World Bank Group. All Rights Reserved. Data Processing and Tabulation, Part I.
MICS Data Processing Workshop Multiple Indicator Cluster Surveys Data Processing Workshop Overview of MICS Tools, Templates, Resources, Technical Assistance.
Multiple Indicator Cluster Surveys Data Processing Workshop Supervisor’s Menu MICS Data Processing Workshop.
Chapter Thirteen Validation & Editing Coding Machine Cleaning of Data Tabulation & Statistical Analysis Data Entry Overview of the Data Analysis.
King Fahd University of Petroleum & Minerals Department of Management and Marketing MKT 345 Marketing Research Dr. Alhassan G. Abdul-Muhmin Editing and.
AADAPT Workshop South Asia Goa, December 17-21, 2009 Maria Isabel Beltran 1.
Multiple Indicator Cluster Surveys Regional Training Workshop I – Survey Design General Characteristics of MICS3 Questionnaires.
Improving Eligibility Documents November, Improving Data Collection The State Office of AIDS (OA) is now working with providers to improve the quality.
EPI 218 Queries and On-Screen Forms Michael A. Kohn, MD, MPP 9 August 2012.
Early Childhood Outcomes Indicator 7 Data Collection Application Review.
Unit 4: Reporting, Data Management and Analysis #4-4-1.
MICS Survey Design Workshop Multiple Indicator Cluster Surveys Survey Design Workshop Data Entry Using Tablets / Laptops.
RESEARCH METHODS Lecture 29. DATA ANALYSIS Data Analysis Data processing and analysis is part of research design – decisions already made. During analysis.
Copyright 2010, The World Bank Group. All Rights Reserved. Testing and Documentation Part II.
Copyright 2010, The World Bank Group. All Rights Reserved. Managing Data Processing Section B.
Vendor’s Malt Manufacturing Return. Log in with the user id and password provided through the EDS registration process and click on the Login button.
Lesson 4.  After a table has been created, you may need to modify it. You can make many changes to a table—or other database object—using its property.
MICS Data Processing Workshop Multiple Indicator Cluster Surveys Data Processing Workshop Creating Analysis Files: Description of Preparation Steps.
Multiple Indicator Cluster Surveys Data Processing Workshop Built-In and User-Defined Functions MICS Data Processing Workshop.
José Aponte Public Health Advisor Module 3: Adding Intelligence to Forms 12 June 2012 Epi Info™ 7 Introductory Training Office of Surveillance, Epidemiology,
Multiple Indicator Cluster Surveys Data Processing Workshop Overview of SPSS structural check programs and frequencies MICS Data Processing Workshop.
MICS4 Data Processing Workshop Multiple Indicator Cluster Surveys Data Processing Workshop Tabulation Programs.
Day 6: Supervisors’ Training This presentation has been supported by the U.S President’s Emergency Plan for AIDS Relief (PEPFAR) through the U.S. Agency.
Introduction to fertility
CHAPTER 13 Data Processing, Basic Data Analysis, and the Statistical Testing Of Differences Copyright © 2000 by John Wiley & Sons, Inc.
DATA INPUT AND OUTPUT.
Data Processing, Basic Data Analysis, and the
Discrepancy Management
Presentation transcript:

Multiple Indicator Cluster Surveys Data Processing Workshop Secondary Editing MICS Data Processing Workshop

REMEMBER AND 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.

Secondary Editing Flow Chart Backup Raw (unedited) Data File Secondary Editing Listing Backup Final (edited) Data File Resolve Inconsistencies on paper listing Enter Corrections into Raw Data File DP Supervisor Secondary Editor DP Supervisor Inconsistencies? No Yes

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 Leave the inconsistency without correction and document the inconsistency for users

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

Four Examples 1. Woman’s age and date of birth inconsistent 2. DPT2 and Polio 2 vaccination dates differ 3. Level and grade of education inconsistent 4. Polio 3 vaccination date before Polio 1 vaccination date

Example 1: Basic Information The Data [DOI] WM6 = 04/2009 = 1312 [DOB] WB1 = 09/1966 = 801 [Age] WB2 = 41 The Error Message U 1003 E Age of woman (WB2=41) and her date of birth (DOB=09/1966) inconsistent [DOI=04/2009]

Example 1: The Inconsistency The Inconsistency –Age calculated age (calcage) = 42 reported age ( WB2 ) = 41 –Date of birth calculated LDOB: 1312-(12*41)-11 = 809 calculated UDOB: 1312-(12*41) = 820 reported DOB (using 09/1966) : 801

Example 1: Resolving the Inconsistency Variables to Check –WM6, WB1, WB2, HL5(LN), HL6(LN), CM2, MA8, MA9 Steps: 1.Check for data entry errors 2.If WM6M = WB1M, and WM2 = calcage - 1, leave unchanged 3.If WB1M and WB1Y valid, set WM2 = calcage 4.If WB1M invalid, set WB1Y = 9997 (inconsistent)

Example 2: Basic Information The Data –Polio 2: IM3C = 08/08/2008 –DPT 2: IM4B = 08/08/2009 The Error Message U 2705 M Date of Polio 2 vaccination (08/08/2008) and date of DPT2 vaccination (08/08/2009) different The Inconsistency –Polio and DPT shots are often given on the same date

Example 2: Other Information All Polio and DPT Vaccination Dates: –Polio 1: IM3P1 = 16/06/2008 –Polio 2: IM3P2 = 08/08/2008 –Polio 3: IM3P3 = 13/09/2008 –DPT1: IM3D1 = 16/06/2008 –DPT2: IM3D2 = 08/08/2009 –DPT3: IM3D3 = 13/09/2008

Example 2: Resolving the Inconsistency Steps: 1.Check for data entry errors 2.See if recording mistake was made on questionnaire 3.If no obvious recording mistake, leave data unchanged 4.We’re more interested in knowing whether the child was vaccinated—the exact timing of the event is less critical

Example 3: Basic Information The Data –ED4A = 2 { secondary } –ED4B = 11 The Error Message –U 0090 E ED1=02: Level (ED4A=2) and grade (ED4B=11) of education inconsistent The Inconsistency –ED4B records grade at the current level, and for this country (UK), the highest grade at the secondary level is 7

Example 3: Other Information Other Variables –Current schooling: ED6 = notappl –Schooling last year: ED8 = notappl –Highest level (woman’s questionnaire): WB4 = 2 WB5 = 11

Example 3: Resolving the Inconsistency Steps: 1.Check for data entry errors 2.Check for interviewer errors - Does ED4B include grades passed at lower levels? 3.If available, check values of WB4 and WB5 4.If you can’t resolve inconsistency, set ED4B = 97 (inconsistent)

Example 4: Basic Information The Data –IM3P1 = 25/11/2008 –IM3P3 = 08/01/2008 The Error Message U 2704 E Date of Polio 1 vaccination (25/11/2008) after date of Polio 3 vaccination (08/01/2008) The Inconsistency –Polio 3 vaccination given before Polio 1 vaccination

Example 4: Other Information All Polio and DPT Vaccination Dates: –Polio 1: IM3P1 = 25/11/2008 –Polio 2: IM3P2 = 03/03/2009 –Polio 3: IM3P3 = 05/01/2008 –DPT1: IM3D1 = 25/11/2008 –DPT2: IM3D2 = 05/02/2009 –DPT3: IM3D3 = notappl/notappl/notappl

Example 4: Resolving the Inconsistency Steps: 1.Check for data entry errors 2.See if recording mistake was made on questionnaire 3.If no obvious recording mistake, set day, month, and year of most inconsistent date to 97, 97 and 9997 respectively

Adding an Edit Add logic to the data entry application Add message text to the message file Add message to the editing guidelines

Defining the Editing Specifications Carefully review the questionnaire Define the edit –What is the possible inconsistency? –How should the inconsistency be handled during data entry? –How should the inconsistency be handled during secondary editing?

Editing Guidelines For each inconsistency: –Describe the issue if the 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 explanation of resolution(s), list all related variables that should be examined

Modifying the Editing Guidelines Add editing guidelines for your country specific questions that were added to the MICS questionnaire 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

REMEMBER AND 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.