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Course Overview

The ‘Data Analysis in Monitoring and Evaluation’ course is a comprehensive program that covers the standards and procedures for results-based monitoring and evaluation for end-to-end project lifecycles.

This course endows participants with competences in setting up and implementing results-based monitoring and evaluation systems involving data management, analysis, and reporting.

The participants will benefit from the latest M&E philosophies and practices including the results and participatory approaches.

What is the key benefit of this ‘Data Analysis in Monitoring and Evaluation’ course? This program is designed to enable its participants to become experts in monitoring and evaluating their development projects.

The Data Analysis in Monitoring and Evaluation course covers all the key elements of robust M&E systems along with hands-on practical projects to provide experience in the M&E concepts being taught.

This Zoe training course will provide you with monitoring and evaluation (M&E) lessons and workshops on how to integrate data demand and use data analysis tools and techniques with M&E concepts.

Course Objectives

Upon completing this Data Analysis in Monitoring and Evaluation course successfully, participants will be able to:

  • Develop a comprehensive monitoring and evaluation plan
  • Utilise data analysis software
  • Build project results levels
  • Construct a project using logical frameworks
  • Use indicators and targets to measure success
  • Monitor and track performance indicators over the life of the project
  • Conduct impact evaluation
  • Evaluate a project against key measures
  • Collect data using mobile data collection tools

Training Methodology

This is an interactive Data Analysis in Monitoring and Evaluation training program and will consist of the following training approaches:

  • Lectures
  • Seminars & Presentations
  • Group Discussions
  • Assignments
  • Case Studies & Functional Exercises

Similar to all our courses, this program also follows the ‘Do-Review-Learn-Apply’ model.

Organisational Benefits

Companies who send in their employees to participate in this Data Analysis in Monitoring and Evaluation course can benefit in the following ways:

  • Give your employees the ability to manage large data volumes using the latest tools
  • Provide your workforce with flexible and cost-effective professional development opportunities
  • Analyse case studies in this domain and be able to apply successful techniques in your organisation
  • Comprehend the principles and practice of data analysis for project M&E and the context in which this operates

Personal Benefits

Professionals who participate in this Data Analysis in Monitoring and Evaluation course can benefit in the following ways:

  • Learn and work with data analysis and management tools that are used widely
  • Study each of the major fields of data analytics in an organised and logical manner
  • Increase your demand as a professional with experience in data analytics because most organisations are now looking for ways to exploit the power of big data
  • Recognise how to apply big data analytics across various industries
  • Obtain certification in emerging concepts like data analytics and management, that will show potential employers and professional peers that you are an individual who takes your career seriously
  • Keep yourself updated with the latest industry trends in technology and use them to make better decisions at your workplace, thereby increasing your chance of success, and improving your credibility subsequently

Who Should Attend?

This Data Analysis in Monitoring and Evaluation course would be suitable for:

  • Researchers, development staff, process practitioners, project managers and decision-makers
  • Anyone involved in the processes of research, supervise, manage, plan, implement, monitor and evaluate development project

Course Outline

MODULE 1: INTRODUCTION TO RESULTS BASED PROJECT MANAGEMENT

  • Fundamentals of Results-Based Management
  • Why is RBM important?
  • Results based management vs traditional projects management
  • RBM Lifecycle (seven phases)
  • Areas of focus of RBM

MODULE 2: FUNDAMENTALS OF MONITORING AND EVALUATION

  • Definition of Monitoring and Evaluation
  • Why Monitoring and Evaluation is important
  • Key principles and concepts in M&E
  • M&E in project lifecycle
  • Participatory M&E

MODULE 3: PROJECT ANALYSIS

  • Situation Analysis
  • Needs Assessment
  • Strategy Analysis

MODULE 4: DESIGN OF RESULTS IN MONITORING AND EVALUATION

  • Impact, outcomes, outputs and activities
  • Results framework
  • M&E causal pathway
  • Standards in planning, monitoring and evaluating for results

MODULE 5: M&E INDICATORS

  • Indicators definition
  • Indicator metrics
  • Linking indicators to results
  • Indicator matrix
  • Tracking of indicators

MODULE 6: LOGICAL FRAMEWORK APPROACH

  • LFA – Analysis and Planning phase
  • Design of logframe
  • Risk rating in logframe
  • Horizontal and vertical logic in logframe
  • Using logframe to create Activity and Budget schedules
  • Using logframe as a project management tool

MODULE 7: THEORY OF CHANGE

  • Overview of theory of change
  • Developing a theory of change
  • Theory of Change vs Log Frame
  • Case study: Theory of change

MODULE 8: M&E SYSTEMS

  • What is an M&E System?
  • Elements of M&E System
  • Steps for developing a Results-based M&E System

MODULE 9: M&E PLANNING

  • Importance of an M&E Plan
  • Documenting M&E System in the M&E Plan
  • M&E Plan:
    • Monitoring
    • Evaluation
    • Data management
    • Reporting
  • M&E plan vs Performance Management Plan (PMP)

MODULE 10: BASE SURVEY IN RESULTS BASED M&E

  • Importance of baseline studies
  • Process of conducting baseline studies
  • Baseline study vs evaluation

MODULE 11: PROJECT PERFORMANCE EVALUATION

  • Process and progress evaluations
  • Evaluation research design
  • Evaluation questions
  • Evaluation report Dissemination

MODULE 12: M&E DATA MANAGEMENT

  • Different sources of M&E data
  • Qualitative data collection methods
  • Quantitative data collection methods
  • Participatory methods of data collection
  • Data Quality Assessment

MODULE 13: M&E RESULTS USE AND DISSEMINATION

  • Stakeholder’s information needs
  • Usage of M&E results to enhance and boost projects
  • M&E lessons learnt and best practices
  • Organisation knowledge champions
  • M&E reporting format
  • M&E results in communication strategies

MODULE 14: GENDER PERSPECTIVE IN M&E

  • Importance of gender in M&E
  • Integrating gender into program logic
  • Setting gender-sensitive indicators
  • Collecting gender-disaggregated data
  • Analysing M&E data from a gender perspective
  • Appraisal of projects from a gender perspective

MODULE 15: DATA COLLECTION TOOLS AND TECHNIQUES

  • Sources of M&E data – primary and secondary
  • Sampling during data collection
  • Qualitative data collection methods
  • Quantitative data collection methods
  • Participatory data collection methods
  • Introduction to data triangulation

MODULE 16: DATA QUALITY

  • What is data quality?
  • Why data quality?
  • Data quality standards
  • Data flow and data quality
  • Data Quality Assessments
  • M&E system design for data quality

MODULE 17: ICT IN MONITORING AND EVALUATION

  • Mobile-based data collection using ODK
  • Data visualisation – infographics and dashboards
  • Using the latest tools and technologies for real-time monitoring and evaluation

MODULE 18: QUALITATIVE DATA ANALYSIS

  • Principles of qualitative data analysis
  • Data preparation for qualitative analysis
  • Linking and integrating multiple data sets in different forms
  • Thematic analysis for qualitative data
  • Content analysis for qualitative data
  • Manipulation and analysis of data using NVivo

MODULE 19: QUANTITATIVE DATA ANALYSIS – (USING SPSS/STATA)

  • Introduction to statistical concepts
  • Creating variables and data entry
  • Data reconstruction
  • Variables manipulation
  • Descriptive statistics
  • Understanding data weighting
  • Inferential statistics: hypothesis testing, T-test, ANOVA, regression analysis

MODULE 20: IMPACT ASSESSMENT

  • Introduction to impact evaluation
  • Attribution in impact evaluation
  • Estimation of counterfactual
  • Impact evaluation methods: Double difference, Propensity score matching

MODULE 21: GIS IN M&E

  • Introduction to GIS in M&E
  • GIS analysis and mapping techniques
  • Data preparation for geospatial analysis
  • Geospatial analysis using GIS software (QGIS)
Note
Customized Schedule is available for all courses irrespective of dates on the Calendar. Please get in touch with us for details.
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