Data Analytics & Business Intelligence · Physical

SPSS for Monitoring, Evaluation and Impact Assessment

5 Days 2 sessions available Certificate on completion

SPSS for Monitoring, Evaluation and Impact Assessment is a practical course designed to equip M&E professionals, researchers, NGOs and development practitioners with skills to analyse programme data, evaluate outcomes, compare baseline and endline results, conduct statistical tests, measure programme effectiveness and produce evidence-based evaluation reports using SPSS.

Course objectives

Use SPSS to manage and analyse M&E and programme datasets.
Develop and manage variables for programme indicators and evaluation studies.
Analyse baseline, midline and endline datasets.
Generate descriptive statistics for programme indicators.
Analyse demographic and beneficiary characteristics.
Conduct cross-tabulations to compare programme groups and outcomes.
Analyse Likert-scale and questionnaire data.
Apply appropriate hypothesis-testing techniques to evaluation questions.
Conduct t-tests, chi-square tests and ANOVA for programme comparisons.
Perform correlation and regression analysis to investigate factors associated with programme outcomes
Analyse relationships between programme participation and measured outcomes
Assess questionnaire and measurement-scale reliability using Cronbach's Alpha.
Identify and interpret trends, differences, associations and patterns in programme data.
Create professional charts, tables and visualizations for M&E reports. Interpret SPSS outputs and translate statistical findings into programme insights.
Assess evidence of programme effectiveness and outcomes.
Prepare statistical findings for evaluation reports, donor reports and management decision-making.
Complete an end-to-end M&E/impact assessment analysis using SPSS.
Apply appropriate statistical methods to impact-assessment datasets.

Who should attend

Monitoring and Evaluation (M&E) Officers and Managers Project and Programme Managers Research Officers and Researchers Data Analysts and Statistical Analysts NGO and Non-Profit Organisation Professionals Development Practitioners and Programme Officers Government Monitoring and Evaluation Officers Impact Assessment Specialists Evaluation Consultants Policy and Planning Officers Health and Social Development Professionals Donor-funded Project Teams Academics, Lecturers and Postgraduate Students Survey and Field Data Collection Professionals Data Management and Reporting Officers Project Coordinators and Programme Administrators

Course content

Module 1: Introduction to SPSS for M&E Role of SPSS in monitoring and evaluation M&E data and statistical analysis Programme indicators Outputs, outcomes and impacts Evaluation questions and hypotheses SPSS interface and workflow
Module 2: M&E Data Management Importing Excel/CSV datasets Variable definition and coding Indicator variables Beneficiary and project datasets Data validation Missing values Duplicate records Data-quality checks
Module 3: Survey and Questionnaire Data Preparation Coding questionnaire responses Likert-scale variables Demographic variables Multiple-response questions Recoding variables Computing indicators Creating composite scores
Module 4: Data Cleaning and Transformation Identifying inconsistent records Managing missing data Outlier detection Filtering cases Selecting programme groups Creating derived indicators Preparing analysis-ready datasets
Module 5: Descriptive Analysis for M&E Frequencies Percentages Means and medians Standard deviation Minimum and maximum Beneficiary profiles Indicator summaries Disaggregation by gender, age, location and other variables
Module 6: M&E Data Visualization Bar charts Pie charts Histograms Boxplots Line charts Trend analysis Comparative charts Visualizing programme indicators
Module 7: Cross-Tabulation and Disaggregated Analysis Gender-disaggregated analysis Geographic comparisons Age-group comparisons Programme-group comparisons Row and column percentages Chi-square analysis Interpreting relationships between variables
Module 8: Baseline, Midline and Endline Analysis Structuring longitudinal evaluation data Comparing measurement periods Pre- and post-intervention analysis Change analysis Mean and percentage changes Statistical significance of changes Presenting evaluati
Module 9: Hypothesis Testing for Programme Evaluation Evaluation hypotheses Null and alternative hypotheses p-values Confidence intervals Statistical significance Practical significance Selecting appropriate statistical tests
Module 10: T-Tests and ANOVA for Programme Comparisons Independent-samples t-test Paired-samples t-test Comparing intervention and comparison groups Pre/post comparisons One-way ANOVA Post-hoc tests Effect sizes Interpretation
Module 11: Correlation and Regression for M&E Pearson correlation Spearman correlation Simple regression Multiple regression Identifying factors associated with outcomes Regression coefficients R² Model interpretation
Module 12: Reliability and Measurement Analysis Reliability concepts Cronbach's Alpha Questionnaire-scale reliability Item analysis Composite indicators Construct measurement Interpreting reliability results
Module 13: Impact Assessment Using SPSS Impact-assessment concepts Intervention and comparison groups Outcome measurement Difference analysis Confounding factors Regression-based assessment Interpreting evidence of programme effects Limitations of observa
Module 14: M&E Reporting and Evidence Presentation Interpreting SPSS output Statistical tables Indicator dashboards and charts Evaluation findings Evidence-based conclusions Recommendations Donor reporting Communicating statistical findings to non-statist
Module 15: Practical M&E/Impact Assessment Capstone Participants analyse a complete synthetic programme evaluation dataset, covering: Programme Data → Data Cleaning → Indicator Construction → Descriptive Analysis → Disaggregation → Baseline/En

Frequently asked questions

It is a practical course focused on using IBM SPSS Statistics to manage, analyse and interpret programme, survey, monitoring, evaluation and impact-assessment data.

The course is ideal for M&E officers, project managers, researchers, NGO professionals, development practitioners, government officers, programme officers, data analysts and evaluation consultants.

No. The course starts with the fundamentals and progressively introduces more advanced statistical techniques relevant to M&E.

No. Statistical concepts are explained in a practical way, with emphasis on when to use a technique, how to perform it in SPSS and how to interpret the results.

Yes. The training is particularly relevant to NGOs, development agencies, government programmes and donor-funded projects.

Yes. The course introduces practical approaches for analysing programme outcomes and impacts, including group comparisons, pre/post analysis and regression-based analysis.

Yes. Participants complete an end-to-end M&E/impact-assessment project, from data cleaning and indicator construction through statistical analysis, visualization and reporting.

Yes. Practical exercises can use realistic programme, beneficiary, survey, baseline/endline and impact-assessment datasets.