NVivo Data Analysis for Agriculture Training Course

Participants can create an NVivo project with a clear, scalable structure for agricultural evidence.

• NVivo project properties configured for study purpose, team, and version control
• Folder architecture separates raw sources, working files, outputs, and archives
• Source naming convention combines location, respondent type, date, and identifier
• Project description records objectives, methods, boundaries, and reporting audiences
• User roles and file permissions planned for collaborative analysis teams
• Backup schedule protects current project files and completed analytical records

Participants can prepare field materials for reliable import, coding and retrieval in NVivo.

• Transcript formatting preserves speaker labels, timestamps, translations, and interviewer notes
• Field observation records distinguish description, interpretation, and reflective comments
• Photograph metadata captures site, activity, consent status, and collection date
• Survey open responses cleaned without removing meaningful local language expressions
• Translation decisions documented when analysing multilingual farmer and stakeholder interviews
• De-identification procedures protect respondents while retaining analytical context

Participants can import diverse agricultural evidence sources and apply retrievable source metadata.

• Import Word, PDF, spreadsheet, image, audio, video, and dataset files
• Source classifications distinguish interviews, focus groups, reports, and observations
• Bibliographic details recorded for policy papers, studies, and market reports
• External sources linked when files cannot be stored inside projects
• File descriptions capture collection method, provenance, and analytical relevance
• Import checks identify missing pages, unreadable scans, and duplicate materials

Participants can build case structures that support meaningful comparisons among agricultural actors.

• Cases created for farmers, households, cooperatives, traders, and extension agents
• Case classifications assign respondent roles, farm types, and institutional positions
• Attributes record gender, age group, location, production system, and market access
• Household and individual cases linked without conflating respondent identities
• Classification sheets imported from structured participant and site registers
• Attribute values checked for consistent categories, blanks, and coding errors

Participants can develop a practical codebook for agricultural research and programme evidence.

• Deductive codes derived from theory of change and evaluation questions
• Inductive codes capture unanticipated farmer experiences and local explanations
• In vivo coding retains participant language for locally meaningful concepts
• Code definitions specify inclusion criteria, exclusion criteria, and examples
• Parent and child codes represent themes, subthemes, conditions, and consequences
• Codebook version log records additions, mergers, revisions, and retirement decisions

Participants can code interviews, focus groups and documents using consistent analytical rules.

• Descriptive coding identifies crops, services, constraints, actors, and locations
• Process coding captures planting, selling, borrowing, adapting, and decision-making actions
• Emotion coding identifies concerns, aspirations, trust, uncertainty, and perceived risks
• Segment boundaries preserve sufficient context for later evidence interpretation
• Multiple coding captures linked themes such as credit, inputs, and adoption
• Coding stripes reveal overlaps, gaps, and inconsistent application of codes

Participants can analyse photographs, audio, video and field observations alongside textual evidence.

• Audio coding links spoken passages to time-coded analytical references
• Video coding identifies demonstration practices, interactions, and observable service conditions
• Image regions coded for storage facilities, inputs, crops, and market infrastructure
• Observation notes coded separately from participant statements and researcher interpretations
• Annotations preserve contextual information without altering original source materials
• Linked memos explain visual evidence, collection conditions, and analytical limitations

Participants can document analytical reasoning and maintain an auditable NVivo project record.

• Analytical memos record emerging patterns, questions, assumptions, and alternative explanations
• Methodological memos document sampling changes, translation issues, and field disruptions
• Annotations flag uncertain passages, missing context, and follow-up requirements
• Decision log records code changes, query choices, and interpretation revisions
• Source memos assess credibility, bias, relevance, and evidential limitations
• Audit trail connects raw evidence, coded extracts, findings, and recommendations

Participants can assess coding consistency and improve shared analysis procedures.

• Intercoder agreement plan defines sample, code set, thresholds, and review process
• Coding comparison query identifies agreement, disagreement, and absent coding patterns
• Cohen's kappa interpreted alongside code prevalence and contextual judgement
• Code calibration meetings resolve ambiguous definitions and boundary disagreements
• Shared codebook governance assigns approval rights for structural changes
• Quality assurance checklist reviews metadata, coding, memos, and project completeness

Participants can use text-based queries to identify language patterns and retrieve relevant evidence.

• Word frequency query identifies recurring terms in farmer and stakeholder accounts
• Stop words tailored to local languages, transcription conventions, and study terms
• Stemmed word searches capture related forms of production and marketing terms
• Text search query retrieves context around inputs, prices, pests, and credit
• Spread coding captures surrounding passages without coding irrelevant material
• Word trees inspect how participants describe causes, effects, and responses

Participants can retrieve evidence that tests relationships between coded themes and cases.

• Coding query retrieves passages coded to adoption barriers and extension advice
• Proximity operators examine connections between rainfall shocks and coping strategies
• Compound queries combine textual terms, codes, attributes, and source classifications
• Coded evidence reviewed for contradictory accounts and negative cases
• Relationship coding maps perceived links between services, trust, and uptake
• Query criteria documented for reproducibility and review by project colleagues

Participants can compare coded experiences across farmer, household and market participant groups.

• Matrix coding query compares constraints across gender, farm size, and location
• Rows define cases, classifications, or themes for targeted comparative analysis
• Columns represent outcomes, barriers, services, perceptions, or coping responses
• Cell contents inspected before drawing conclusions from frequency differences
• Attribute filters isolate rainfed, irrigated, commercial, and subsistence farming groups
• Matrix exports prepared for review, reporting, and further qualitative interpretation

Participants can summarise case evidence in structured matrices while retaining source links.

• Framework matrix rows represent individual cases, households, communities, or enterprises
• Columns align with domains such as production, finance, markets, and resilience
• Case summaries distinguish direct evidence, analyst interpretation, and unanswered questions
• Linked source references preserve traceability from summaries to original passages
• Matrix comparisons identify common patterns, exceptions, and context-specific explanations
• Summary quality checks test completeness, neutrality, and evidential support

Participants can use NVivo visualisations to inspect patterns and communicate qualified findings.

• Hierarchy charts display coding distribution across themes and subthemes
• Cluster analysis groups sources using coding similarity or word similarity
• Comparison diagrams examine overlap between farmer groups, institutions, and issues
• Sociograms map reported relationships among producers, buyers, lenders, and advisers
• Charts display attribute distributions without treating coded counts as prevalence estimates
• Visual findings interpreted with source context, sampling limits, and negative evidence

Participants can integrate qualitative NVivo evidence with structured agricultural and programme data.

• Dataset import links open responses with survey variables and respondent identifiers
• Classifications connect qualitative cases with farm, household, and intervention characteristics
• Joint displays compare quantitative trends with explanatory qualitative evidence
• Convergent findings strengthen confidence through agreement across evidence types
• Divergent findings prompt investigation of sampling, measurement, and contextual differences
• Mixed methods memo records integration decisions and limits on inference

Participants can analyse programme evidence against agriculture intervention theories of change.

• Theory of change codes distinguish activities, outputs, outcomes, assumptions, and risks
• Contribution analysis examines plausible links between interventions and observed changes
• Outcome harvesting codes reported changes, contribution claims, and supporting evidence
• Most significant change stories assessed for selection bias and evidential strength
• Gender analysis compares access, control, workload, voice, and benefit distribution
• Do no harm considerations identify exclusion, conflict, and unintended programme effects

Participants can use NVivo evidence to analyse agricultural market systems and value chain constraints.

• Value chain coding identifies actors, functions, relationships, services, and governance
• Market system maps distinguish core exchange, supporting functions, and rules
• Constraint analysis separates symptoms, root causes, incentives, and capability gaps
• Buyer requirements coded for quality, volume, timing, traceability, and payment
• Power analysis examines price setting, information access, and bargaining relationships
• Market evidence triangulated across producers, traders, processors, and service providers

Participants can produce clear, evidence-based reports for agricultural programme and business decisions.

• Finding statements distinguish evidence, interpretation, inference, and recommendation
• Representative quotations selected without exposing participant identities or sensitive details
• Evidence tables link themes, cases, quotations, and analytical implications
• Negative cases reported to qualify dominant patterns and avoid overclaiming
• Recommendations specify action, owner, timeframe, resources, and monitoring indicator
• Exported reports combine coded extracts, matrices, charts, and source references

Participants can build, review and present a complete NVivo analysis of an agriculture evidence set.

• Project scope defines decision question, users, evidence sources, and analytical boundaries
• Imported sources organised with classifications, attributes, metadata, and secure identifiers
• Codebook applies deductive, inductive, and in vivo codes consistently
• Queries test priority questions across farmer segments and market participants
• Findings integrate matrices, coded extracts, visualisations, and contradictory evidence
• Presentation defends recommendations using traceable evidence and documented limitations