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Sitakhela Likusasa Impact Evaluation
Evaluating the Effectiveness of Incentives to improve HIV Prevention
Outcomes for Young Females in Eswatini
Standard Operating Procedure - # 20
Data Quality Assurance and Quality Control
Procedures for the Sitakhela Likusasa Impact Evaluation
Document 20 in a series of 20 Standard Operating Procedures
Version date 10 Aug 2018
Status Final
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Standard Operating Procedure - # 20
Data Quality Assurance and Quality Control
Procedures for the Sitakhela Likusasa Impact Evaluation
NERCHA – National Emergency Research Council on HIV and AIDS authors: Khanyakwezwe Mabuza,
Muziwethu Nkambule, Tengetile Dlamini and Mbuso Mabuza
World Bank authors: Marelize Görgens, Damien de Walque, Andrew Longosz, Sosthenes Ketende; Nigel Herath
and Wendy Heard
IHM Southern Africa authors: Vimbai Tsododo, Tendai Chipepera, Nontobeko Fakudze and Leroy Shongwe
MINISTRY OF EDUCATION AND TRAINING
KINGDOM OF ESWATINI
MINISTRY OF HEALTH National Reference Laboratory, and Swaziland National AIDS Programme (SNAP)
KINGDOM OF ESWATINI
For baseline survey For baseline survey Main study implementation partner SGBV counselling and follow up
1
Table of Contents
Abbreviations and definitions ............................................................................................................. 2
1. Purpose ....................................................................................................................................... 4
2. Data quality assessment approach .............................................................................................. 4
3. Scope and Applicability ............................................................................................................... 4
4. Personnel Roles and responsibilities ........................................................................................... 5
5. General procedures for various types of SL QA/QC activities ...................................................... 8
5.1. Name of person(s) or organization that will be performing the quality assurance ..................... 8
5.2. Details of timing of data QA\QC activities................................................................................. 8
5.3. Preparation needed by Impact Evaluation staff prior to a quality assurance visit ...................... 9
5.4. Details of the QA/QC visits/activities ...................................................................................... 10
5.5. Individual data QA (Self-Review)............................................................................................. 13
5.6. Team QA ................................................................................................................................ 14
7. QA\QC indicators for IHM reporting to the WB......................................................................... 15
8. Survey Solutions Data Quality Assurance Process ..................................................................... 18
6.1. Data management hierarchy .................................................................................................. 18
6.2. Completion of a Survey in the field or by Phone ...................................................................... 18
6.3. Rejecting a survey .................................................................................................................. 18
6.4. Survey completion process ..................................................................................................... 19
6.5. Supervisor accepting/rejecting submitted Surveys .................................................................. 20
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Abbreviations and definitions
Terms Definition
Co-Principal Investigators
Are the three persons (two from the World Bank and one from NERCHA) nominated by their respective institutions to share the responsibility of Principal Investigator of the Sitakhela Likusasa impact evaluation.
Co-Investigators Are researchers working on aspects of the Sitakhela Likusasa impact evaluation, as per agreement from the co-Principal Investigators
Data Management Team
This is the group of individuals who are responsible for cleaning and analyzing data throughout the impact evaluation period. The DMT consists of data-handlers who interact with the data at some point in time during the execution of the impact evaluation (i.e. before and after final impact evaluation results have been published).
Data Use Committee
A governance committee designed to review requests, determines and approves access and use of Sitakhela Likusasa data while ensuring that confidentiality, privacy and integrity of data, and anonymity of participants.
Confidentiality Prevention of disclosure, to other than authorized individuals, of a sponsor’s proprietary information or of a subject’s identity
Data Dictionary This is a codebook that describes how the data that has been collected about the impact evaluation, has been captured in the database, as well as the variable names, labels and all value options and value option definitions. This includes the entities, variables, relationships to other data, values and formats of all data elements throughout the impact evaluation rounds.
Data Management The development, implementation and supervision of policies relating to the management of data. This includes mechanisms to protect the data.
Data Governance The overall agreements in terms of data management, data security, dissemination, data use, and data access. The DUC is responsible for developing a data governance policy – and this SOP on data management will be referenced in the Sitakhela Likusasa Data Governance policy
Standard Operating Procedure (SOP)
Detailed written instructions designed to achieve uniformity of the performance of a specific function
Data Quality Assurance
Activities which focus on detecting and preventing data defect or data quality issues. The operational techniques and activities undertaken within the Quality Assurance system to assess whether the requirements for quality of the data have been fulfilled.
Data Quality Control
Real-time monitoring and review of data to verify the accuracy and validity by impact evaluation team involved in the research.
WB World Bank
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IHM Institute for Health Measurement
NERCHA The National Emergency Response Council on HIV and AIDS
DQC/QC Data quality control/Quality control
QA Quality control
coPI Co-Principal Investigator
coI Co-Investigator
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1. Purpose
Data Quality Assurance (QA) is planned and systematic activity implemented as part of a quality system to
ensure that quality requirements (validity) of the data generated during the research will be fulfilled. Data
Quality Control (QC) is a real-time review (monitoring) of data to verify the accuracy and validity by
Impact Evaluation staff involved in the research.
Due to the scale of the Sitakhela Likusasa Project, it is evident that there is dire need for standard and systematic data quality assurance (QA) and quality control (QC) processes and practices across all levels of data flow –from data collection to data reporting. The purpose of this standard operational procedure (SOP) is to stipulate the routine, standardized and systematic procedures that are to be followed by the Impact Evaluation staff within the Sitakhela Likusasa project.
The SOP will minimize variation and promote data quality through consistent and systematic manner application of all tools for data collection, management, analysis and reporting within the project, even if there are temporary or permanent changes in terms of personnel who are trained to conduct the survey and handle data.
Furthermore, the aim of this document is to outline the specific steps and requirements for monitoring data quality and act immediately to address issues.
2. Data quality assessment approach
The following is a typical data quality approach:
1. Identify which data items need to be assessed for data quality, typically this will be data items
deemed as critical to the intervention and associated evaluation reporting
2. Assess which data quality dimensions to use and their associated importance
3. For each data quality dimension, define values or ranges representing good and bad quality data.
Please note, that as a data set may support multiple requirements, a number of different data quality
assessments may need to be performed
4. Apply the assessment criteria to the data items
5. Review the results and determine if data quality is acceptable or not
6. Where appropriate take corrective actions e.g. clean the data and improve data handling processes
to prevent future recurrences
7. Repeat the above on a periodic basis to monitor trends in data quality
3. Scope and Applicability This SOP applies to all stakeholders in the project who are generating and handling data at all levels of the Sitakhela Likusasa project – most notably the research and data collection teams, contracting staff, the coPIs and coIs (Sitakhela Likusasa ‘Impact Evaluation team’). The procedures outlined in this SOP are applicable to the handling and management of data at all levels of Sitakhela Likusasa project data flow framework. This SOP also references the Data Management Procedures (SOP8), Endline Field Manual
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(SOP10) and the Data Governance Policy (SOP12), which stipulates data curatorship duties and data ownership agreements.
4. Personnel Roles and responsibilities
In accordance to ethical consideration and Good Clinical Practice appropriately trained and qualified individuals are responsible for the overall conduct of the study, handling of the data, verifying the data, and conducting statistical analyses. Their responsibilities are classified in table 2. The classification of roles and responsibilities are geared at ensuring that best practices of data collection and management are adhered to, to deliver reliable and high-quality data necessary for valid Impact Evaluation results. The following table depicts the breakdown of basic roles and responsibilities needed to ensure the collection, handling, managing and storage of high-quality data. Table 3 includes names and QA\QC responsibilities of all personnel in the study. Table 2: Personnel overall responsibilities
Personnel Personnel role QA/QC Responsibility
Impact Evaluation Staff at IHM
Impact Evaluation staff with who work directly or indirectly with participants and have Participant file documentation responsibilities, e.g. Research Assistants (RA), Help desk clerks
Conduct self-review of data and documentation quality.
Correct data and documentation issues after supervisor, PI or team lead review.
Adhere to SOPs and performance objectives.
Supervisor(s) and PIs
Supervisors, who manage impact evaluation staff and who have primary responsibility and accountability for overall data and documentation quality
Train impact evaluation team on participants file documentation, data collection, and data quality assurance responsibilities.
Review results of self-review and spot check select
cases.
Communicate findings to team and IHM project manager
Support impact evaluation team in meeting SOPs and performance objectives.
Hold impact evaluation team accountable for addressing data quality issues.
IHM project manager
IHM project manager, who oversees the Supervisor(s)
Holds direct responsibility and is accountable for implementing the SOPs and achieving performance objectives.
Makes necessary changes to program policies and design based on data QC review results.
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Personnel Personnel role QA/QC Responsibility
- QA\QC coordinator I
- IHM team member
- Coordinate all QA/QC activities and makes sure that QA/QC activities are conducted as stipulated
- Collates all QA\QC reports and submits to the WB (with copies to NERCHA?)
- QA\QC coordinator II
- WB team member
- Verifies that all QA/QC reports are produced by IHM - Coordinates review of QA/QC reports by the WB team -
Table 3: Personnel and responsibilities
Role Responsibility in terms of Data QA/QC
Co-Principal Investigator - Review QA/QC reports
- Conduct QA\QC visits
- Meet with Impact Evaluation Lead to review and discuss QA\QC reports
- Deal with issues relating to breach of protocol
Co-Investigator - Conduct QA/QC visits
- Report to PIs
- Participate in QA/QC meetings
Contractor Project Director
- Review QA/QC reports
- Participate in QA\QC review meetings at IHM
- Reports to World Bank
Contractor Project Manager
- Supervise Impact Evaluation team
- Conduct QA/QC visits
- Report to Contractor Project Director and to World Bank
- Participate in QA/QC meetings
Contractor Data Manager - Supervise Impact Evaluation team
- Conduct QC tasks
- Report to Impact Evaluation Lead
- Participate in QA/QC meetings
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CAPI officer - Supervise Impact Evaluation team
- Conduct QC tasks
- Report to Impact Evaluation Lead
- Participate in QA/QC meetings
Contractor Database Administrator
- Conduct QC tasks
- Report to Impact Evaluation Lead
- Participate in QA/QC meetings
Contractor Project Assistant
- Manage QC activity calendar
- Support QA/QC coordinator
- Participate in QA/QC meetings
Contractor Field Supervisor
- Supervise field teams and assist with field work
Contractor Research assistant
- Conduct QA\QC self-review
- Reports to QA\QC coordinator
Contractor Biomedical Staff
- Conduct QA\QC self-review
- Reports to QA\QC coordinator
Medical Supervisor - Conduct QA\QC self-review
- Reports to QA\QC coordinator
Data analysts - Conduct QA\QC self-review
- Conduct QA visits
- Conduct and supervise QC
- Reports to Impact Evaluation Lead
QA\QC coordinator II - Conduct QA\QC self-review
- Conduct QA/QC visits and review
- Conduct and supervise QA\QC
- Reports to Impact Evaluation Lead
QA\QC coordinator II - Conduct QA\QC self-review
- Conduct QA/QC visits and review
- Conduct and supervise QA\QC
- Reports to Impact Evaluation Lead
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5. General procedures for various types of SL QA/QC activities
5.1. Name of person(s) or organization that will be performing the quality assurance There will two QA/QC coordinators/champion from IHM, and from the WB. The QA/QC coordinators will take a lead in conducting and reporting on QA\QC results. They may appoint other team members to conduct and report QA\QC activities.
Table 4: Roles and responsibilities of QA\QC coordinators
Role Responsibilities
IHM QA\QC coordinators
- Coordinate all QA\QC activities at IHM level - Implement changes following issues identified by QA\QC review - Enforce QA\QC activities schedule - Prepare QA\QC reports for supervisors and Impact Evaluation Lead
WB QA\QC coordinators
- Coordinate all QA\QC activities at WB level - Conduct QA visits - Conduct QC review - Review QA\QC reports - Enforce QA\QC activities schedule - Prepare QA\QC reports for supervisors and Impact Evaluation Lead
5.2. Details of timing of data QA\QC activities Monthly data QA\QC reports should be prepared and submitted to supervisors monthly. Reports covering
a previous calendar month are due on every first Wednesday of the month. Daily QA\QC self-review
reports are due on the same day. Table 5 below describes the reports, timing, and responsible personnel
Table 5: Report types and timing of QA/QC activities
Timing Report type and content Personnel responsible
Reports to
Monthly
-Due every first Wednesday of the
QA\QC monthly reports - List of all QA\QC findings -Remedial action recommendations
IHM QA\QC coordinators
Supervisors and Impact Evaluation
-
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Timing Report type and content Personnel responsible
Reports to
month -Implemented remedial action and date
Daily QC\QA daily report -Daily self-review -Syncing of Survey Solutions forms
Impact Evaluation staff
IHM coordinators
Daily
QC\QA serious event report -Loss of documents containing participant’s identifiable data -Failure or breach of QC procedures -Failure of data skip patterns and logical checks -Failure of electronic data collection forms -Failure to access documents which are necessary for day to day activities as a result of system or hardware failure -Failure to sync survey solutions forms
Failure to archive impact evaluation documents correctly
IHM QA\QC coordinators
Supervisors and Impact Evaluation Lead
Two working days following QA\QC site visit
QA\QC site visit report - List of all QA\QC findings -Remedial action recommendations -Implemented remedial action and date
Supervisors and WB coordinators
Supervisors and Impact Evaluation Lead
5.3. Preparation needed by Impact Evaluation staff prior to a quality assurance visit
To confirm data is valid and correct it is necessary to cross check against the original record. This is called the source data. So to confirm participant’s school registration and attendance, for example, records entered in the SEAV form must be checked against submitted school registers, and a participant’s withdrawal must be accompanied by a completed and signed withdrawal form; OOSY payment to have the required documents indicating proof of registration and the school/institution bank account details, along with a proof of payment from the bank to the institution (after payment is made by IHM). All impact evaluation documents, forms and data bases should be up to date prior to a visit. All QA measures to be made against documented procedures outlined in the relevant SOPs. A room or quiet desk should be booked for the use during the visit. The impact evaluation team should be aware of the planned visits and be able to make available the necessary time and assistance. Some QA visits might be an announced.
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5.4. Details of the QA/QC visits/activities The objective of each QC\QA visit may vary depending on findings reported in daily and monthly reports,
recommendations from previous visits, prescribed by the Impact Evaluation Lead, or a combination of all
the above. Tables 6 and 7 provide QC and QA activities, procedures and tasks to be performed by a data
quality reviewer.
Table 6: QC activities and procedures list
QC activity Procedures
Task
Completed
Corrective Measure
Taken
Name/
Initials Date
Supporting
Documents (List
Document
Name)
Date
Data Gathering, Input, and Handling Checks
Check for
transcription
errors in data
input and
reference.
Cross-check a sample of school and STU
registration, school attendance, raffle
and education incentive attendance
entered in electronic data files against
paper records such as proof of
payment, for transcription errors.
Check built automated checks, such as
computational checks for calculations,
or range checks for input data work as
intended.
Check the
integrity of SQL
server
database files.
Confirm that the appropriate data
processing steps are correctly
represented in the database.
Confirm that data relationships are
correctly represented in the database.
Ensure that data fields are properly
labeled and have the correct design
specifications.
Ensure that adequate documentation of
database
Check for
consistency in
data between
categories.
Identify parameters (e.g., activity data,
constants) that are common to multiple
categories and confirm that there is
consistency in the values used for these
parameters in the emissions/removal’s
calculations.
Check that the
flow of Check that education and raffle
incentive, as well as OOSY intervention
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participant’s
data along
processing
steps is correct
and observed.
data collected from various sources are
prepared and entered in appropriate
forms
Check that there are no lost education
registers before and after data
extraction and entry
Data Documentation
Review of
internal
documentation
and archiving.
Check that every primary scanned or
electronic document has a reference ID
via document upload form (BI) and
stored on OneDrive
Check that all primary documents are
archived and stored to facilitate
detailed review
Check that the archive is closed and
retained in secure place following
completion of the review
Calculation Checks
Check
methodological
and data
changes
resulting in
recalculations.
Check for temporal consistency in time
series input data for variables such as,
but not limited to history of pregnancy,
age at first sex, highest education level,
and date of birth or data within
academic calendar.
Check for consistency in the
algorithm/method used for calculations
of key indicators throughout the time
series.
Check
completeness Confirm that estimates are reported for
key indicators such HIV incidence and
treatable STI prevalence are
reproducible from baseline onwards
For raffle rounds, check that there is a
final/terminal outcome for every
randomly selected participant for a
given raffle round. This includes
treatable STI results, indication is
selected as a winner or not and
payment of raffle incentive to those
who won.
Trend checks Check if there any unusual or
unexplained trends noticed for key
variables in each module of the midline,
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MOMP and endline questionnaires
Data security
Check that all tablets used for
communications and data collection
are passcode enabled and locked
Check that tables and other electronic
devices such as laptops, memory sticks,
and desktop computers are password
protected
Check that all electronic devices are
wiped of data before being disposed or
reassigned
Table 7: QA activities and procedures list
QA Activity Procedures
Task
Completed
Corrective Measure
Taken
Name/
Initials Date
Supporting
Documents
(List Document
Name)
Date
Data Gathering, Input, and Handling Checks
Check that
relevant SOPs
are being
adhere to
For each activity, check that the
relevant SOP procedures are adhered
to
Check that the movement of
participant’s data among processing
steps is correct and observed.
Check that there are no lost education
registers before and after data
extraction and entry
Check that education incentive data
collected from various sources are
prepared, meet the required standards
and conditions, and entered in
appropriate forms
Check and confirm that education
original data documents are
appropriately stored and archived
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messages,
email, and text
messages
Check that these are securely archived
Data Documentation
Review of
internal
documentation
and archiving
Check that primary document uploaded
on the SQL server and one drive folder
has a reference ID and PID or other
identifiers
Check that all primary documents are
archived and stored to facilitate
detailed review
Check that the archive is closed and
retained in secure place following
completion of the review
Data security
Check that all tablets and telephones
used for communications and data
collection are passcode enabled and
locked
Check that tablets and other electronic
devices such as laptops, memory sticks,
and desktop computers are password
protected
Check that all electronic devices are
wiped of data before being disposed or
reassigned
5.5. Individual data QA (Self-Review) Every impact evaluation staff member to conduct a daily self-review to make sure that whatever they
have done would meet all standards stipulated in relevant SOPs. For example, RAs should check that all
tablets, phones, printed participants list, and notebooks are stored in orderly and secure manner at the
end of their working shift, and that there are no loose papers left in a working area. The work area and
project documentation need proper close out when RAs or FWs leave and resign
Frequency: daily.
Purpose:
1. To ensure quality data and documentation practices early.
2. To facilitate communication between Impact Evaluation Staff and Supervisor(s) regarding both
data and documentation quality.
3. To reduce the number of cases requiring second-level data QA.
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5.6. Team QA
Included a QA in team meeting agenda and discuss any issue identified, solutions and changes prevent
future assurances of events which may affect data quality.
Frequency: Biweekly Team Meeting
Purpose:
1. Connect data and documentation quality to quality of intervention delivery.
2. Support team approach to data quality; highlight examples of quality data.
3. Show areas where improvements maybe needed.
Supervisor(s)
i. Discuss each case’s data and documentation quality. Identify actions needed to address gaps or errors (such as blank columns for demographics or outcome data).
ii. Discuss overall team trends, scores, and goals for upcoming period. Email summary to Impact Evaluation Lead.
Impact Evaluation staff
i. Email QA\QC issues identified to Supervisor prior to meeting so they can be compiled into single document to be discussed during team QA.
ii. Compete follow-up actions identified above.
iii. During the next meeting, provide updates on actions taken for each case.
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7. QA\QC indicators for IHM reporting to the WB The format of QA\QC report will vary depending on the type of assessment. Table 6.1. Raffle round 8
# Task Who will do it
Indicators to be reported to the WB Additional requirements/task description
1 Supervisor approval of survey solutions & confirmation of activities undertaken
IHM core team & help desk staff
i. Cumulative number of forms approved
Numbers to match existing records in survey solutions form(s) Reviews to be conducted within 24/48? Hours This excludes those who were not reached during raffle fieldwork
ii. Number of forms rejected/sent back to RAs
iii. Number of forms not approved within expected time frame
iv. 10% of RR8 participants who were not selected as winners called by Help Desk to see if they were tested under the raffle.
v. 10% of RR8 winners called by Help desk to see if they were paid their winnings
vi. Compare original winner selection file to those paid as raffle winners
2 Verification trich results through confirmatory testing
NRL with IHM providing samples & IHM AFM/BM
i. Cumulative number of completed confirmatory tests
Numbers to match existing records in survey solutions form(s) Written report explaining that discordant results have been discussed with relevant participant
ii. Cumulative number of discordant results
iii. Number of follow up visits concluded to those participants with discordant results
3 Verification of follow up testing for STI positive participants
IHM AFM/BM
i. Cumulative number and proportion of completed follow-up tests
Numbers to match existing records in survey solutions form(s)
ii. Cumulative number of positive follow-up tests
A report describing steps taken and number refusal of treatment if any
IHM helpdesk iii. List PIDs, date of the call and outcome whether follow-up was done within 2 weeks or not
Phone calls to random 10% of STI positive participants to determine whether follow up was done within 2 weeks of initial positive result
Table 6.2. Education incentives
# Task Who will Indicators to be reported to Additional requirements/ task
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do it the WB description
1 Verify 10% of education incentive documentation received every round
IHM assistant field manager
i. Report of education incentive data quality assurance
30% should be for STU and 70% should be for basic education
2 Paper trail check for 7 in school and 3 in STU randomly selected participants & confirmation calls
IHM assistant field manager
ii. List of selected PIDs, date of paper trail check, outcome of QA check, corrective measures taken in terms of discrepancies
Confirm that: data on registers and school attendance documents match data entered in relevant forms; that STU information matches the data entered in relevant data files; that education incentive payment and confirmation of payment documents match data entered in relevant forms Call selected participants to confirm if they received their money
Table 6.3 Endline data collection
# Task Who will do it Indicators to be reported to the WB
Additional requirements/task description
1 Survey Solutions-based verification of all data
IHM DQA officers (8 will be appointed for the 15 weeks of endline data collection)
i. Cumulative number of forms approved
Included in Survey Solutions and reports available from there ii. Number of forms
rejected/sent back to RAs
2 Report of issues encountered by Supervisors
8 Supervisors i. Report of issues encountered, and corrective measures taken if any
Approval/rejection weekly reports with issues encountered During first 2 weeks of Endline data collection – should be daily reviews and meetings to deal with implementation issues if relevant.
3 Reports by Project Manager
Project Manager i. Reports by supervisors Supervisors weekly checklists to be completed by supervisors every week During first 2 weeks of Endline data collection – should be daily reviews and meetings to deal with implementation issues if relevant.
5 Field visit report 8 Supervisors i. Field visit report Report for every field visit to be prepared separately, using the supervisor field visit checklist
6 Withdrawals 8 Supervisors ii. Cumulative number of -
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withdrawals
iii. Cumulative proportion of signed withdrawal forms
iv. Cumulative proportion of uploaded withdrawal forms
-
v. Number of signed withdrawal forms within the reporting period
Scanned of all pages of withdrawal pages
7 Field tracing Field tracing teams i. List of PIDs and reason for field tracing
ii. List of PIDs from previous reporting period, reason for field tracing and outcome of field tracing
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8. Survey Solutions Data Quality Assurance Process
Data management hierarchy
HQ accounts – WB Team
Supervisor accounts – IHM Field manager and assistant field managers and IHM data team
Interviewer accounts – IHM RAs
6.2. Completion of a Survey in the field or by Phone
1) Review of completed surveys ON tablets by the RAs – to check and make sure that there were no
skipped questions or sections that should have been completed, then clicking ‘sync’ button to submit to supervisors for review/approval
2) IHM (Field managers and assistant field managers) to review submissions using their supervisor accounts and supervisor android applications on tablets. They will then either approve, or reject submissions.
a. If they approve a submitted form, it is then ready for review by the WB. b. If they reject a submitted form, the form is then sent back to the responsible RA to
amend the flagged questions before resubmitting.
3) The WB will be responsible for FINAL approval of forms. Using the HQ accounts, they will log into Headquarters and review all approved forms from IHM. If there are any forms that require further changes, they will reject. The forms then get sent back to the RAs tablets for action before re-submitting.
6.3. Rejecting a survey Rejection of a submitted survey could be due to one or more of the following reasons:
- Incomplete questions - Incomplete sections - Incorrect entries:
o Typographical errors including numbers and figures
As we progress with data collection using the Endline Survey, there will be a guide developed that will
indicate specific questions and sections of the survey that could be problematic and which should be
reviewed carefully.
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6.4. Survey completion process
RA Completes Survey
RA Submits Survey
IHM Supervisor decides:
Is survey complete?
IHM Supervisor
reviews Survey
IHM Supervisor
reviews survey
IHM supervisor decides: should
questionnaire be accepted?
WB checks questionnaire:
should it be approved?
WB decides: should
questionnaire be approved?
WB admin does final
check and data exports
daily
NO
NO
NO
YES
YES
YES
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6.5. Supervisor accepting/rejecting submitted Surveys
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