SPSS for Monitoring, Evaluation and Impact Assessment

Course overview

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.

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

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

Schedule & fees