Purpose
In order for initiatives carried out by organizations to be successful, they must be in line with their goals. This training is designed to help development organizations evaluate projects accurately and appropriately, then explain and use the findings.
Course Objectives
By the end of this course the participants will be able to:
Recognize the distinction between evaluation and monitoring
Determine the project’s evaluation indicators.
Plan and design an evaluation
During evaluation, use a variety of data collection techniques.
Analyze data in both quantitative and qualitative ways.
Utilize the data to provide evaluation judgments and suggestions.
DURATION 10 DaysÂ
Target Audience
Researchers, project workers, managers, development practitioners, policy makers in government, NGOs, and development organizations who want to assess the efficacy of their activities are the target audience for this course.
COURSE OUTLINE
TOIPC 1: Basics of M&E for Project Evaluation Overview
Project Indicators of the Results Framework Logical Framework
Project Evaluation Introduction
What project evaluation means
Monitoring vs. evaluation: the significance of project evaluation
Evaluation of Project Design
TOPIC 2: Creating an evaluation strategy
Evaluation’s Goals Evaluations Categories
Evaluation Standards Evaluation Criteria Development
Organizing the Collection of Data
Sources of Evaluation Data Identification
Data Gathering Techniques
Methods of Data Analysis
Finished Evaluation Matrix
TOPIC 3: Managing the Evaluation Process
Distribution of Tasks Evaluation Budget Workplan Evaluation
Executing the Evaluation Plan
Evaluation Benchmarks Recognition and Evaluation Instruments
Monitoring data use for evaluation
Mixed techniques for data management
TOPIC 4: Mobile Data Gathering
Paper-based test questions into ODK forms
Setting up the ODK server aggregation
ODK collect is used for field data collection.
Data export from ODK for additional analysis
TOPIC 5: Analysis of Quantitative Data Using SPSS/Stata
Introducing topics in statistics
Creating variables and entering data
Data reconstruction
Changing the variables
Descriptive data analysis
Data averaging
Regression analysis, the T-test, the ANOVA, and inferential statistics
TOPIC 6: Analyzing qualitative data
Principles of qualitative data analysis
Getting data ready for qualitative analysis
Connecting and integrating many data sets in various formats
Quality information Thematic examination
Quality information Content evaluation
Data manipulation and analysis with Nvivo
Triangulation of data
TOPIC 7: Creating Evaluation Results
Assessment matrix consolidation
Triangulation of data
Creating grading criteria and questions
Assessment report
Conclusions
Recommendations
Report of final evaluation
Publishing evaluation results