Quantitative Data Analysis Training Course

Duration 5 days 10 modules · 33 contact hours
Level Intermediate Certificate on completion
Who attends Monitoring and Evaluation Officers, M&E Managers 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-ME-068

Before you join: Participants should understand basic M&E concepts, including indicators, baselines, targets, data sources and disaggregation.

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

Monitoring and evaluation teams are often expected to turn survey, routine monitoring and assessment data into defensible findings under tight reporting deadlines. The challenge is not merely producing tables or charts: analysts must define indicators correctly, check data quality, select suitable statistical methods, explain uncertainty, and distinguish observed differences from meaningful evidence. Weak analysis can lead to incorrect programme adjustments, unreliable donor reporting and poorly targeted humanitarian response decisions.

This course develops the quantitative analysis skills used in development and humanitarian M&E. Participants work through the full analysis process, from indicator definitions and sampling decisions to data cleaning, descriptive statistics, confidence intervals, hypothesis tests, cross-tabulations, regression and clear reporting. The course uses realistic M&E datasets such as household surveys, post-distribution monitoring, service-delivery records and baseline-endline studies. Participants learn to interpret results against evaluation questions, disaggregation requirements and OECD DAC criteria.

Teaching combines short technical briefings with guided case work, group analysis tasks, data interpretation discussions and reporting simulations. Participants use spreadsheet-based analysis methods and are shown how the same logic transfers to statistical software. They leave with a practical quantitative analysis pack containing an indicator analysis plan, data-quality checklist, analysis table shell, chart-selection guide and reporting structure. The five-day instructor-led course is available in classrooms in more than 50 cities or live online, runs from 08:30 to 16:30 each day, and includes approximately 6.5 contact hours daily. Participants receive a certificate of completion.

The course is suited to M&E personnel, programme staff, researchers and managers who work with quantitative evidence and need to assess, commission, review or communicate analysis with greater confidence.

What you will be able to do

  • Construct an indicator analysis plan linking evaluation questions, variables and statistical methods
  • Apply probability and non-probability sampling methods to M&E data collection designs
  • Clean and document quantitative datasets using reproducible data-quality checks
  • Calculate and interpret descriptive statistics, disaggregation tables and data visualisations
  • Estimate confidence intervals and conduct hypothesis tests for programme monitoring questions
  • Use cross-tabulation, correlation and regression to investigate relationships between variables
  • Produce defensible findings using analysis table shells, charts and evidence statements
  • Develop an action-focused quantitative analysis plan for an active M&E assignment

Benefits of attending

For you

  • Greater confidence selecting appropriate methods for M&E questions and datasets
  • Ability to identify misleading averages, unsupported comparisons and data-quality risks
  • Practical skill producing disaggregated indicator tables for donor and management reports
  • Stronger ability to explain statistical findings without overstating certainty
  • Reusable analysis planning materials for baseline, endline and monitoring assignments

For your organisation

  • More credible indicator reporting supported by documented analytical decisions
  • Earlier detection of data-quality problems before findings reach decision-makers
  • Better programme adjustments based on disaggregated evidence and tested assumptions
  • Consistent analysis specifications across country teams, partners and commissioned studies
  • Clearer communication of results to donors, boards and operational managers

Course modules

10 modules over 5 days

Participants can frame quantitative analysis around decision-focused M&E questions and indicators.

  • Results frameworks, theories of change and quantitative evidence requirements
  • Evaluation questions translated into measurable variables and analytical comparisons
  • OECD DAC criteria linked to relevant quantitative evidence sources
  • Indicator reference sheets specifying numerator, denominator, unit and disaggregation
  • Baseline, target and endline values interpreted within programme assumptions
  • Causal attribution, contribution and plausible alternative explanations distinguished

Participants can identify data structures and measurement choices that determine valid analysis.

  • Unit of analysis defined for individuals, households, facilities and communities
  • Nominal, ordinal, interval and ratio measurement scales compared
  • Independent, dependent, confounding and control variables identified
  • Codebooks built from questionnaire fields, response values and missing codes
  • Composite indices constructed using documented items, scoring rules and weights
  • Disaggregation categories selected for sex, age, disability and location

Participants can assess sampling approaches and interpret their implications for M&E findings.

  • Target populations, sampling frames and eligibility criteria clearly defined
  • Simple random, systematic and stratified sampling methods compared
  • Cluster sampling implications for fieldwork cost and statistical precision
  • Sample size drivers: prevalence, precision, confidence level and design effect
  • Non-response bias assessed through response rates and respondent profiles
  • Purposive and convenience samples bounded by explicit inference limitations

Participants can prepare an auditable dataset using systematic data-quality checks.

  • Data-quality dimensions: validity, reliability, completeness, timeliness and integrity
  • Range checks identify impossible values and questionnaire routing errors
  • Duplicate records detected using unique identifiers and matching fields
  • Missing-data patterns profiled before deletion, imputation or recoding decisions
  • Outliers investigated using frequency distributions, box plots and source records
  • Cleaning logs document corrections, assumptions, versions and approval decisions

Participants can calculate and interpret descriptive statistics for monitoring and survey data.

  • Frequencies and percentages calculated with correct denominators and exclusions
  • Mean, median and mode selected according to distribution characteristics
  • Range, variance, standard deviation and interquartile range interpreted
  • Rates, ratios and proportions calculated for programme performance indicators
  • Weighted estimates distinguished from unweighted sample summaries
  • Disaggregated results compared without masking small subgroup sample sizes

Participants can judge whether observed differences are likely to reflect population patterns.

  • Sampling error explained through confidence intervals and margin of error
  • Null and alternative hypotheses framed for programme comparison questions
  • Chi-square tests applied to associations between categorical variables
  • T-tests used to compare means between two independent groups
  • P-values interpreted alongside effect sizes, confidence intervals and sample sizes
  • Statistical significance separated from practical and operational significance

Participants can examine relationships between variables while recognising analytical limitations.

  • Cross-tabulations reveal patterns by sex, age, location and programme exposure
  • Row percentages, column percentages and expected counts correctly interpreted
  • Pearson and Spearman correlation selected by variable measurement properties
  • Simple linear regression estimates relationships between continuous variables
  • Logistic regression models binary outcomes such as adoption or attendance
  • Regression coefficients interpreted without claiming causation from observational data

Participants can select defensible comparison approaches for programme performance evidence.

  • Baseline-endline comparisons account for changes in samples and context
  • Before-after designs distinguished from experimental and quasi-experimental designs
  • Difference-in-differences logic explained using intervention and comparison groups
  • Matched comparison groups assessed for similarity on relevant baseline characteristics
  • Seasonality, shocks and implementation variation considered during interpretation
  • Contribution claims aligned with available evidence and design strength

Participants can communicate quantitative findings accurately through tables, charts and narrative.

  • Analysis table shells specify indicators, disaggregation, comparisons and denominators
  • Bar charts, line charts and histograms matched to data types
  • Chart titles state the finding, population, period and source
  • Confidence intervals displayed when comparing estimated population values
  • Evidence statements distinguish findings, interpretation, limitations and recommendations
  • Dashboard metrics prioritised according to decisions, thresholds and reporting frequency

Participants can produce a practical analysis plan and quality-assurance process for an M&E assignment.

  • Analysis plans link questions, indicators, datasets, methods and outputs
  • Data protection requirements applied to identifiable survey and monitoring records
  • Version control procedures preserve traceable datasets, syntax and reporting files
  • Peer review checklists test calculations, labels, denominators and interpretations
  • Findings prioritised by decision relevance, certainty and affected populations
  • Application plans schedule analysis milestones, review points and stakeholder briefings

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

What you take back to work

  1. Indicator analysis plan template
  2. Quantitative data-quality checklist
  3. Survey dataset cleaning log
  4. Analysis table shell pack
  5. Statistical test selection guide
  6. Evidence reporting and chart checklist

Who should attend

  • Monitoring and Evaluation Officers — need to analyse indicator data and prepare credible reports
  • M&E Managers — need to review analysis methods and challenge unsupported findings
  • Programme Managers — need evidence to adjust implementation and resource allocation
  • Research Officers — need practical methods for survey and assessment data analysis
  • Humanitarian Assessment Staff — need to interpret needs-assessment and post-distribution data
  • Donor Reporting Specialists — need to present indicator results with appropriate caveats

Skills and tools covered

Indicator analysis planningSurvey data cleaningDescriptive statisticsStatistical inferenceRegression interpretationEvidence visualisation

Tools and standards

OECD DAC evaluation criteria, Indicator Reference Sheet, Data Quality Assessment, Microsoft Excel

How the course runs

Training methodology

The course combines short instructor briefings with guided work on development and humanitarian M&E datasets. Participants interpret household survey results, routine monitoring records and post-distribution monitoring findings; compare analysis choices; and discuss the consequences of poor data handling. Group exercises cover indicator specification, data cleaning, table interpretation and evidence reporting. Simulations require participants to brief a programme manager on findings, uncertainty and recommended follow-up. Each participant prepares an application plan for a current or forthcoming M&E assignment.

Requirements and prerequisites

Participants should understand basic M&E concepts, including indicators, baselines, targets, data sources and disaggregation. Experience using spreadsheets to enter data, sort records and create simple formulas is assumed. Participants should have worked with, or expect to work with, survey, monitoring or assessment data. Prior study of statistics is not required; the course introduces required concepts from first principles before applying them. Statistical software programming, advanced mathematics, database administration and prior experience with R, SPSS, Stata or Python are not required.

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

You should understand basic M&E terms such as indicators, baselines, targets and disaggregation, and be comfortable with everyday spreadsheet tasks. You do not need prior statistics training, programming ability or experience with statistical software.

Bring a laptop with spreadsheet software and, where permitted, an anonymised dataset or indicator report from your work. Course datasets and materials are provided, so bringing organisational data is optional.

It is designed for M&E officers, programme managers, researchers, assessment staff and reporting specialists who use quantitative evidence. It is especially useful for people who must interpret analyses prepared by others as well as those producing analyses themselves.

The course focuses on decisions commonly faced in development and humanitarian M&E: indicators, surveys, disaggregation, baseline-endline comparisons and donor reporting. It teaches enough statistical reasoning to make and review defensible analysis choices rather than training participants as software programmers or specialist statisticians.

Participants can use the analysis plan, cleaning log, table shells and reporting checklist on current monitoring, survey or evaluation assignments. The methods apply to routine indicator tracking, needs assessments, post-distribution monitoring and baseline-endline studies.

Each day runs from 08:30 to 16:30 and provides about 6.5 contact hours. Time is divided between instructor input, case-based analysis, group interpretation tasks, reporting simulations and planning for workplace application.

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

Related courses in Monitoring & Evaluation