Introduction

In today’s data-driven landscape, Monitoring and Evaluation (M&E) professionals are expected to harness advanced analytical tools to transform raw data into actionable insights. SPSS (Statistical Package for the Social Sciences) is a leading software that empowers M&E practitioners to perform quantitative data analysis, predictive modeling, and trend forecasting, ensuring evidence-based program decisions. SPSS for M&E Training Course provides an in-depth, hands-on approach to SPSS, equipping participants with practical skills in data cleaning, statistical testing, visualization, and reporting tailored to complex M&E contexts.

Designed for professionals seeking to enhance program effectiveness, impact measurement, and data integrity, this training emphasizes real-world applications of SPSS in survey analysis, evaluation studies, impact assessments, and policy monitoring. Participants will gain proficiency in data-driven decision-making, reporting automation, and performance optimization, positioning them as valuable assets in both governmental, NGO, and private sector M&E teams.

Programme Curriculum

SPSS for M&E Training Course

Introduction

In today’s data-driven landscape, Monitoring and Evaluation (M&E) professionals are expected to harness advanced analytical tools to transform raw data into actionable insights. SPSS (Statistical Package for the Social Sciences) is a leading software that empowers M&E practitioners to perform quantitative data analysis, predictive modeling, and trend forecasting, ensuring evidence-based program decisions. SPSS for M&E Training Course provides an in-depth, hands-on approach to SPSS, equipping participants with practical skills in data cleaning, statistical testing, visualization, and reporting tailored to complex M&E contexts.

Designed for professionals seeking to enhance program effectiveness, impact measurement, and data integrity, this training emphasizes real-world applications of SPSS in survey analysis, evaluation studies, impact assessments, and policy monitoring. Participants will gain proficiency in data-driven decision-making, reporting automation, and performance optimization, positioning them as valuable assets in both governmental, NGO, and private sector M&E teams.

Course Duration

5 days

Course Objectives

By the end of this course, participants will be able to:

  1. Master SPSS interface navigation for M&E applications.
  2. Conduct data cleaning, transformation, and management efficiently.
  3. Apply descriptive and inferential statistics for program evaluation.
  4. Design customized dashboards and visualizations for stakeholders.
  5. Implement trend analysis and predictive modeling for program forecasting.
  6. Conduct correlation, regression, and ANOVA analyses for evidence-based insights.
  7. Utilize advanced data coding and variable transformations.
  8. Integrate survey and longitudinal data for comprehensive evaluations.
  9. Enhance report generation and automated statistical summaries.
  10. Ensure data quality, validation, and reliability in M&E datasets.
  11. Interpret findings to influence policy and program decisions.
  12. Develop M&E frameworks supported by robust statistical evidence.
  13. Apply case-based learning to real-life program evaluation scenarios.

Target Audience

  1. M&E Officers and Specialists
  2. Program Managers and Coordinators
  3. Data Analysts in NGOs and International Agencies
  4. Policy Analysts and Government M&E Practitioners
  5. Research Assistants and Evaluators
  6. Social Scientists and Public Health Professionals
  7. Development Consultants
  8. Graduate Students in Social Sciences and Data Analytics

Course Modules

Module 1: Introduction to SPSS and Data Management

  • Overview of SPSS interface and functionalities
  • Importing and exporting data from multiple sources
  • Data cleaning, validation, and error detection
  • Variable creation, recoding, and labeling
  • Case Study: Cleaning and preparing household survey data

Module 2: Descriptive Statistics and Data Visualization

  • Measures of central tendency and dispersion
  • Frequency tables and cross-tabulations
  • Histograms, pie charts, and bar charts
  • Visual exploration of trends and outliers
  • Case Study: Visualizing nutrition survey outcomes

Module 3: Inferential Statistics for M&E

  • Hypothesis testing fundamentals
  • T-tests, Chi-square, and ANOVA
  • Interpreting p-values and confidence intervals
  • Linking results to program objectives
  • Case Study: Evaluating training effectiveness in health programs

Module 4: Correlation and Regression Analysis

  • Pearson and Spearman correlation
  • Simple and multiple linear regression
  • Predicting program outcomes
  • Model diagnostics and assumption checking
  • Case Study: Predicting enrollment rates in educational programs

Module 5: Advanced Data Transformations

  • Computing new variables and indices
  • Handling missing values and outliers
  • Standardization and normalization techniques
  • Creating categorical variables from continuous data
  • Case Study: Constructing socio-economic status indices

Module 6: Longitudinal and Survey Data Analysis

  • Handling repeated measures and panel data
  • Survey weighting and stratification
  • Trend analysis over multiple periods
  • Data aggregation and disaggregation techniques
  • Case Study: Monitoring vaccination coverage over 5 years

Module 7: Reporting and Dashboard Creation

  • Generating automated tables and charts
  • Creating SPSS output for stakeholder reports
  • Exporting results to Excel, Word, and PDF
  • Visual dashboards for program performance tracking
  • Case Study: Developing a program performance dashboard

Module 8: Applied M&E Case Studies in SPSS

  • Integrating all skills in real-world scenarios
  • Analyzing project impact data
  • Identifying program strengths and gaps
  • Formulating recommendations based on statistical evidence
  • Case Study: Evaluating water sanitation project impact

Training Methodology

This course employs a participatory and hands-on approach to ensure practical learning, including:

  • Interactive lectures and presentations.
  • Group discussions and brainstorming sessions.
  • Hands-on exercises using real-world datasets.
  • Role-playing and scenario-based simulations.
  • Analysis of case studies to bridge theory and practice.
  • Peer-to-peer learning and networking.
  • Expert-led Q&A sessions.
  • Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@fineskilltrainingcenter.com or call +254769199797 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to FINESKILL TRAINING CENTER account, as indicated in the invoice so as to enable us prepare better for you.

Available Sessions

Aug 10 2026

10 Aug β€” 14 Aug 2026

online β€’ Virtual session β€’ Limited Availability
Aug 17 2026

17 Aug β€” 21 Aug 2026

online β€’ Virtual session β€’ Limited Availability
Aug 24 2026

24 Aug β€” 28 Aug 2026

online β€’ Virtual session β€’ Limited Availability
Aug 31 2026

31 Aug β€” 04 Sep 2026

online β€’ Virtual session β€’ Limited Availability
Sep 07 2026

07 Sep β€” 11 Sep 2026

online β€’ Virtual session β€’ Limited Availability
Sep 14 2026

14 Sep β€” 18 Sep 2026

online β€’ Virtual session β€’ Limited Availability
Sep 21 2026

21 Sep β€” 25 Sep 2026

online β€’ Virtual session β€’ Limited Availability
Sep 28 2026

28 Sep β€” 02 Oct 2026

online β€’ Virtual session β€’ Limited Availability
Oct 05 2026

05 Oct β€” 09 Oct 2026

online β€’ Virtual session β€’ Limited Availability
Oct 12 2026

12 Oct β€” 16 Oct 2026

online β€’ Virtual session β€’ Limited Availability
Oct 19 2026

19 Oct β€” 23 Oct 2026

online β€’ Virtual session β€’ Limited Availability
Oct 26 2026

26 Oct β€” 30 Oct 2026

online β€’ Virtual session β€’ Limited Availability
Nov 02 2026

02 Nov β€” 06 Nov 2026

online β€’ Virtual session β€’ Limited Availability
Nov 09 2026

09 Nov β€” 13 Nov 2026

online β€’ Virtual session β€’ Limited Availability
Nov 16 2026

16 Nov β€” 20 Nov 2026

online β€’ Virtual session β€’ Limited Availability
Nov 23 2026

23 Nov β€” 27 Nov 2026

online β€’ Virtual session β€’ Limited Availability
Nov 30 2026

30 Nov β€” 04 Dec 2026

online β€’ Virtual session β€’ Limited Availability
Dec 07 2026

07 Dec β€” 11 Dec 2026

online β€’ Virtual session β€’ Limited Availability
Dec 14 2026

14 Dec β€” 18 Dec 2026

online β€’ Virtual session β€’ Limited Availability
Dec 21 2026

21 Dec β€” 25 Dec 2026

online β€’ Virtual session β€’ Limited Availability
Dec 28 2026

28 Dec β€” 01 Jan 2027

online β€’ Virtual session β€’ Limited Availability