Food Security Survey Analysis Training Course

Duration 5 days 10 modules · 33 contact hours
Level Intermediate Certificate on completion
Who attends Food Security Analysts, MEAL Officers and others
Fee from USD 1,100 KSh 95,000 for Kenyan organisations
Where 52 cities + online See dates by city
Next start 7 Nov 2026 Course code SD-FS-033

Before you join: Participants should understand basic descriptive statistics, including percentages, averages, distributions and confidence intervals, and should have previously worked with spreadsheet or survey datasets.

Dates & prices

Two start dates a month in every city, and live online on weekdays or weekends. Fees are per participant; classroom fees include tuition, materials, lunch and refreshments, and the certificate.

Suggest a date or city

Showing the next six months. Later dates, other cities and private deliveries for your team are available on request.

Course overview

Food-security survey data can identify households facing poor consumption, constrained access and harmful coping strategies, but only when analysts apply indicators consistently and account for sampling design, recall periods and data quality. Weak cleaning, undocumented recoding or unweighted estimates can misdirect targeting, obscure geographic disparities and weaken the evidence used for response planning. Analysts need to turn raw household survey files into defensible findings that programme teams, donors and government counterparts can use.

This course develops the R-based analysis skills required for food-security assessments, post-distribution monitoring, baseline studies and rapid surveys. Participants work through data import, cleaning, recoding, indicator construction, weighting, complex survey estimation and subgroup analysis. They calculate and interpret Food Consumption Score, Household Dietary Diversity Score, Household Hunger Scale, reduced Coping Strategies Index, Livelihood-based Coping Strategy Index and selected economic access measures. The course also covers WFP CARI classification, uncertainty, survey-weighted regression and the limits of drawing causal conclusions from survey results.

Teaching combines short technical briefings with guided case work, group exercises, analysis simulations and structured review of R outputs. Participants use realistic household survey datasets reflecting humanitarian and development settings, including displacement, seasonal food access and market disruption. They leave with a reusable R analysis workflow, indicator calculation scripts, quality-assurance checks, reporting tables and an action plan for improving a live survey-analysis process. Delivery is instructor-led over five days, available in classrooms in 50+ cities or live online, with a certificate of completion.

The course is suited to monitoring, evaluation, assessment, research and programme staff who already work with survey data and need a disciplined method for analysing food-security evidence in R.

What you will be able to do

  • Build reproducible R workflows for importing, cleaning and documenting food-security survey data
  • Calculate Food Consumption Score, Household Dietary Diversity Score and Household Hunger Scale indicators
  • Construct rCSI, Livelihood-based Coping Strategy Index and food-expenditure-share measures
  • Apply survey weights, strata and clusters to estimate population-level food-security prevalence
  • Classify household food-security outcomes using the WFP CARI framework
  • Conduct weighted subgroup comparisons and survey-adjusted regression analysis in R
  • Produce decision-ready tables, charts and technical reporting notes from survey results
  • Prepare a documented survey-analysis plan with quality controls and disclosure safeguards

Benefits of attending

For you

  • Produce food-security indicator estimates without relying solely on external analysts
  • Explain weighting, confidence intervals and subgroup differences to programme colleagues
  • Build auditable R scripts for repeat survey rounds and donor reporting
  • Interpret CARI classifications and coping indicators with appropriate caution
  • Present survey findings in tables and charts suitable for decision meetings

For your organisation

  • More consistent food-security indicator calculations across assessments and monitoring rounds
  • Faster conversion of household survey files into usable targeting evidence
  • Reduced risk of reporting unweighted or statistically unstable prevalence estimates
  • Clearer documentation of recoding decisions, exclusions and analytic limitations
  • Stronger evidence for response design, geographic prioritisation and programme review

Course modules

10 modules over 5 days

Participants can translate food-security assessment questions into analysable survey variables and indicator choices.

  • Food-security assessment questions translated into measurable household survey variables
  • Unit of analysis, recall periods and respondent eligibility defined correctly
  • Indicator selection aligned with WFP CARI and programme decisions
  • Household, individual and market data structures distinguished before analysis
  • Codebook fields checked against questionnaire skip logic and labels
  • Ethical handling of sensitive consumption, coping and displacement responses

Participants can create a documented R workflow for importing and validating survey data.

  • RStudio projects, scripts and relative file paths organised reproducibly
  • CSV, Excel and SPSS files imported with documented variable types
  • Variable labels and value labels preserved using haven workflows
  • Missingness patterns profiled before recoding food-security survey indicators
  • Duplicate household records identified using keys, dates and enumerator fields
  • Validation rules flag impossible ages, quantities, prices and recall values

Participants can recode raw questionnaire responses into analysis-ready food-security variables.

  • Food groups recoded from questionnaire items into FCS categories
  • Consumption frequencies capped using standard seven-day FCS rules
  • Household weights, strata and clusters prepared for design-based estimation
  • Derive adult-equivalent variables where household composition affects interpretation
  • Survey dates converted into analysis-ready recall and season variables
  • Audit trail records recodes, exclusions and derived indicator decisions

Participants can calculate and interpret core food consumption and dietary diversity indicators.

  • Food Consumption Score calculated with WFP food-group weights
  • Standard and context-specific FCS thresholds compared and documented
  • Household Dietary Diversity Score derived from 24-hour food-group consumption
  • Food Consumption Score-Nutrition computed where nutrient-rich groups are captured
  • Distribution plots expose heaping, outliers and threshold sensitivity
  • Indicator limitations interpreted alongside seasonality, markets and respondent recall

Participants can construct hunger, coping and economic access indicators from household survey data.

  • Household Hunger Scale scored from occurrence and frequency questions
  • Reduced Coping Strategies Index calculated using locally validated severity weights
  • Livelihood-based Coping Strategy Index classified by coping phase
  • Food expenditure share estimated from harmonised consumption and expenditure data
  • Economic access indicators compared without treating them as interchangeable
  • Recall windows and cultural food practices examined before conclusions

Participants can produce weighted estimates and confidence intervals from complex survey designs.

  • Probability weights applied to produce population-level food-security estimates
  • Strata, clusters and finite populations specified in survey designs
  • Design effects and effective sample sizes assessed before subgroup reporting
  • Weighted proportions and confidence intervals estimated with survey package
  • Domain analysis protects estimates for regions, sex and livelihood groups
  • Small-cell suppression rules applied to unstable disaggregated results

Participants can build transparent CARI classifications and assess their sensitivity to indicator choices.

  • CARI dimensions assembled from food consumption, economic access and coping
  • Household classification rules translated into transparent R decision logic
  • Contributing indicators checked for missing values and contradictory classifications
  • Severity categories tabulated by geography, displacement status and livelihood
  • Sensitivity checks test consequences of alternative indicator thresholds
  • Classification outputs linked to response analysis and monitoring questions

Participants can test subgroup differences and interpret survey-adjusted analytical models.

  • Cross-tabulations tested using Rao-Scott adjusted chi-square methods
  • Survey-weighted logistic regression models predictors of poor consumption
  • Odds ratios, confidence intervals and reference categories interpreted accurately
  • Model specifications account for confounding, collinearity and sparse cells
  • Interaction terms examine unequal outcomes by gender and displacement
  • Association findings separated from causal claims and programme attribution

Participants can communicate food-security findings through accurate tables, charts, maps and narrative.

  • Weighted tables formatted with denominators, confidence intervals and suppression notes
  • ggplot2 charts communicate prevalence, uncertainty and subgroup differences clearly
  • Maps join survey estimates to approved administrative boundary files
  • Executive findings written with indicator definitions and decision implications
  • Technical annexes document sampling, weighting, recoding and analytic limitations
  • Reproducible Quarto reports combine code, tables, charts and narrative

Participants can quality-assure food-security analysis outputs and plan a repeatable reporting process.

  • Peer review checklist tests scripts, outputs and indicator calculations
  • Version-controlled folders separate raw data, code, outputs and documentation
  • Disclosure controls remove identifiers before sharing analytical extracts
  • Results triangulated with market prices, seasonal calendars and programme records
  • Analysis plan prioritises decisions, audiences, deadlines and responsible owners
  • Personal action plan identifies a live survey-analysis improvement

Exercises and examples are adapted to each cohort's sectors and roles.

What you take back to work

  1. Reproducible R survey-analysis script template
  2. Food-security indicator calculation codebook
  3. Complex survey design specification checklist
  4. WFP CARI classification decision model
  5. Weighted reporting table and chart template
  6. Survey-analysis quality-assurance and action plan

Who should attend

  • Food Security Analysts — need defensible indicator estimates for assessments and response design
  • MEAL Officers — need to analyse survey findings for monitoring and evaluation reporting
  • Assessment Coordinators — need to manage consistent analysis across field data collections
  • Research Officers — need survey-weighted methods for household food-security studies
  • Programme Managers — need to interpret evidence for targeting and programme adaptation
  • Government Statistics Staff — need R methods for analysing food-security survey datasets

Skills and tools covered

R survey analysisFood-security indicatorsSurvey weightingCARI classificationStatistical interpretationReproducible reporting

Tools and standards

R, RStudio, survey package, WFP CARI

How the course runs

Training methodology

The course uses short instructor briefings followed by guided R analysis of realistic household survey files. Participants examine questionnaire structures, clean records, calculate indicators, specify survey designs and interpret outputs through case work and group exercises. Simulations test how missing data, weighting choices and threshold decisions affect reported results. Facilitated reviews connect findings to targeting, response analysis and management reporting. Each participant develops an application plan for a current or upcoming survey-analysis task.

Requirements and prerequisites

Participants should understand basic descriptive statistics, including percentages, averages, distributions and confidence intervals, and should have previously worked with spreadsheet or survey datasets. Prior exposure to food-security assessments, household surveys, monitoring data or humanitarian programme data is expected. Participants should be able to read simple R code and perform basic tasks such as loading data and running a script. Advanced programming, econometrics, GIS expertise and prior use of the survey package are not required. Participants do not need to be nutrition specialists.

A typical training day

08:30 – 10:30First session
10:30 – 10:45Refreshment break
10:45 – 12:30Second session
12:30 – 13:30Lunch and networking
13:30 – 15:00Third session
15:00 – 15:15Refreshment break
15:15 – 16:30Workshop and daily review

Live online deliveries follow the same structure in East Africa Time, with shorter screen blocks and longer breaks.

What the fee includes

  • Instruction by a practitioner facilitator
  • Full course workbook and materials
  • Exercise files, templates and case studies
  • Refreshments and lunch on classroom deliveries
  • Certificate of completion
  • Post-course application plan

Frequently asked questions

Participants should be comfortable loading data, running a basic script and reading simple R code. The course teaches the required survey-analysis workflow, but it is not an introduction to programming.

Bring a laptop with R and RStudio installed, if permitted by your organisation's IT policy. You may also bring a de-identified questionnaire, codebook or survey output from your work for use during planning discussions.

It is designed for food-security, MEAL, assessment, research and programme staff who analyse or commission household surveys. It is particularly relevant for teams working on humanitarian assessments, baseline studies, monitoring rounds and response targeting.

The course uses R specifically for food-security survey analysis rather than teaching programming in isolation. It focuses on recognised indicators, complex survey estimation, CARI classification and reporting decisions commonly required in humanitarian and development work.

Participants can adapt the scripts and checklists to clean a current survey, calculate indicators and prepare weighted results. The final application plan identifies a practical improvement to an upcoming assessment, monitoring round or reporting process.

Each training day runs from 08:30 to 16:30 and provides about 6.5 contact hours. Time is divided between instructor briefings, guided R analysis, case work, group exercises, output review and application planning.

Questions about this course?

Ask about dates, fees, group bookings or running it for your team. Our training team replies by email.

Ask on WhatsApp info@skillsetdevelopment.com

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