Data Collection Methods Training Course

Participants can select appropriate methods based on purpose, population, resources and risk.

• Method selection criteria: purpose, population, time, cost, access and risk
• Surveys used for structured estimates and comparable respondent data
• Key informant interviews for specialist knowledge and institutional perspectives
• Focus group discussions for shared norms, experiences and contested views
• Direct observation for behaviours, facilities, services and physical conditions
• Document review for administrative records, policies, reports and case files
• Mixed-method designs combining breadth, explanation, triangulation and validation

Participants can define measurable variables and specify consistent indicator measurement procedures.

• Indicator reference sheets defining numerator, denominator, source, frequency and responsibility
• Operational definitions preventing inconsistent interpretation of key variables
• Nominal, ordinal, interval and ratio scales matched to analysis needs
• Construct validity assessed through observable measures and respondent understanding
• Recall periods selected to reduce memory error and reporting bias
• Disaggregation variables specified for sex, age, disability and location
• Response categories designed to be mutually exclusive and collectively exhaustive

Participants can produce a sampling plan suited to their data collection purpose.

• Target populations, sampling frames and units of analysis clearly defined
• Simple random sampling selected when complete population lists exist
• Systematic sampling applied using ordered lists and fixed selection intervals
• Stratified sampling designed for priority groups and required disaggregation
• Cluster sampling planned for dispersed populations and fieldwork efficiency
• Purposive, quota and snowball sampling limits explicitly documented
• Sample-size assumptions covering precision, design effect and anticipated nonresponse

Participants can design clear, sequenced questionnaires that generate analysable quantitative data.

• Questionnaire flow organised around eligibility, consent, modules and closing
• Closed questions written with precise wording and defined reference periods
• Likert scales balanced with labelled response options and neutral positions
• Skip logic mapped to eligibility, household composition and service use
• Sensitive questions positioned carefully with nonresponse options and interviewer guidance
• Translation protocols using forward translation, back translation and reconciliation
• Cognitive interviewing used to test comprehension, recall and response processes

Participants can design and conduct qualitative methods that produce credible thematic evidence.

• Semi-structured interview guides built from questions, probes and sequencing
• Open-ended questions invite descriptions, examples, exceptions and causal explanations
• Probing techniques clarify meanings without leading respondents toward answers
• Focus group composition planned around power, safety and shared experience
• Facilitation roles separated between moderator, note-taker and observer
• Saturation assessed through recurring themes, variation and unanswered questions
• Interview notes structured with verbatim quotes, context and analyst reflections

Participants can use observation and participatory approaches to gather contextual and community-generated evidence.

• Structured observation checklists define behaviours, conditions, intervals and scoring
• Time-and-motion observation records service flow, waiting time and bottlenecks
• Transect walks document hazards, facilities, access routes and seasonal conditions
• Community mapping identifies resources, risks, service points and excluded areas
• Seasonal calendars capture livelihood patterns, disease cycles and workload changes
• Ranking exercises compare community priorities using transparent scoring criteria
• Facilitator neutrality protects minority views during participatory group processes

Participants can establish ethical procedures that protect participants and support inclusive data collection.

• Informed consent scripts cover purpose, risks, voluntary participation and withdrawal
• Assent and guardian consent procedures applied when engaging children
• Do-no-harm assessments identify distress, retaliation, stigma and exposure risks
• Referral pathways prepared for disclosures of protection, health or safeguarding concerns
• Confidentiality procedures separate identifiers from responses and field notes
• Washington Group Short Set used for disability-disaggregated data collection
• Accessible formats planned for language, literacy, mobility, sensory and communication needs

Participants can configure a practical mobile data collection workflow with appropriate controls.

• KoboToolbox forms structured with question types, constraints and skip logic
• ODK Collect workflows configured for offline interviewing and secure synchronisation
• Validation constraints prevent impossible values, missing fields and invalid dates
• Unique identifiers designed for households, respondents, facilities and follow-up rounds
• GPS, timestamps and media fields used only with justified safeguards
• Form version control records changes, release dates and enumerator instructions
• Device management procedures cover charging, custody, loss and access passwords

Participants can prepare an operational fieldwork plan that assigns resources, timing and responsibilities.

• Fieldwork plans sequence recruitment, training, piloting, collection and closeout
• Enumerator workloads calculated from interview duration, travel and daily targets
• Route plans account for access constraints, security windows and transport availability
• Contact protocols define call attempts, revisit rules and respondent replacements
• Materials lists include devices, consent forms, identification and referral information
• Risk registers assign mitigations for weather, insecurity, outbreaks and communications failure
• Daily reporting routines track completed interviews, refusals, incidents and shortages

Participants can establish enumerator standards, training content and supervision arrangements.

• Enumerator selection criteria cover language, conduct, availability and technical competence
• Training agendas address protocol, consent, tools, neutrality and safeguarding
• Role-play assessments test introductions, probing, difficult responses and closing procedures
• Interviewer manuals document standard wording, definitions, skips and escalation routes
• Supervisor ratios set realistic spans of control for dispersed teams
• Accompanied interviews assess adherence, rapport, pacing and recording accuracy
• Performance feedback uses documented observations, corrective actions and retraining decisions

Participants can apply preventive and corrective controls throughout data collection.

• Data quality dimensions: validity, reliability, completeness, timeliness, integrity and precision
• Pilot tests identify unclear wording, failed skips and unrealistic interview length
• Spot checks verify sampled interviews, locations and key response fields
• Back-checks recontact respondents using short confirmation questionnaires
• Daily review flags duplicates, outliers, straight-lining and missing values
• Inter-rater reliability tested when multiple observers assign categorical scores
• Correction logs record detected errors, decisions, edits and unresolved issues

Participants can prepare secure data management procedures from collection through handover.

• Data management plans specify files, owners, locations, access and retention
• Data dictionaries define variable names, labels, coding and permissible values
• De-identification removes direct identifiers before analysis and data sharing
• Role-based access controls restrict viewing, editing, export and administration rights
• Encryption protects devices, transfers, backups and stored datasets
• File naming conventions identify project, version, date and responsible analyst
• Data sharing agreements define purpose, permitted users, security and disposal

Participants can identify major threats to data credibility and specify practical mitigations.

• Selection bias assessed through coverage gaps, nonresponse and replacement practices
• Social desirability bias reduced through neutral wording and private interviews
• Interviewer effects monitored through recording patterns and supervisor observation
• Recall bias managed through bounded periods, event cues and records
• Courtesy bias considered when service providers collect beneficiary feedback
• Triangulation compares findings from surveys, interviews, observation and records
• Limitations statements distinguish evidence constraints from unsupported conclusions

Participants can adapt data collection designs for remote, insecure and access-constrained settings.

• Telephone surveys designed around call schedules, verification and shortened instruments
• Interactive voice response assessed for literacy, language and phone-access limitations
• Remote key informant interviews require identity checks and private call arrangements
• Satellite and secondary data sources assessed for resolution, timeliness and bias
• Gatekeeper engagement managed without compromising voluntary participation or confidentiality
• Adaptive sampling plans document access changes, exclusions and revised assumptions
• Security protocols define check-ins, movement tracking and incident escalation procedures

Participants can run a pilot review and revise instruments using documented evidence.

• Pilot objectives distinguish feasibility testing from statistical pretesting requirements
• Debrief protocols capture enumerator observations, respondent reactions and timing
• Paradata review examines duration, break-offs, edits and device problems
• Problem logs classify wording, sequencing, translation, logic and operational issues
• Revision decisions balance measurement quality, respondent burden and comparability
• Change-control tables document original items, amendments, rationale and approval
• Final readiness reviews confirm instruments, protocols, staffing and contingency arrangements

Participants can prepare collection outputs for analysis, reporting and programme learning.

• Cleaning rules identify duplicates, invalid values, missingness and inconsistent responses
• Codebooks prepare quantitative datasets for handover and reproducible analysis
• Qualitative coding frameworks link transcripts, themes, subthemes and evidence excerpts
• Disaggregation tables compare findings by relevant demographic and geographic groups
• Data visualisation choices match indicator types, audience needs and uncertainty
• Feedback loops return findings to communities, teams and decision makers
• Collection lessons documented for future rounds, evaluations and partner capacity plans

Participants can build, review and present a complete data collection package for a defined programme evidence need.

• Evidence question matrix links decisions, indicators, sources and collection methods
• Sampling plan specifies population, frame, approach, sample assumptions and limitations
• Collection instruments include survey, interview, observation or participatory method materials
• Ethics protocol documents consent, safeguarding, confidentiality and referral arrangements
• Fieldwork plan assigns staffing, routes, schedule, supervision and logistics
• Quality assurance plan defines piloting, checks, back-checks and correction procedures
• Peer review tests feasibility, inclusiveness, data credibility and operational readiness