Training Course on M & E, Data management and Analysis in Food Security & Nutrition Programmes


Datastat Research Consultancy

Training Course on M & E, Data management and Analysis in Food Security & Nutrition Programmes

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INTRODUCTION

The interest for information on the effect of approaches, projects and interventions on food and nutrition security is growing rapidly. The public sector, including the civil society and governments, frequently monitor data on food and nutrition in order to determine the existing trends and conditions, and the impact of interventions and policies.

International agencies, NGOs, governments and other agencies carry out monitoring, evaluation, and impact assessments regularly. With regard to this fact, this course lays emphasis on the need to carefully select the right set of indicators when designing information support systems at various administrative levels as well as the skills required for the analysis and interpretation of collected data.

The course adopts and interactive training approach and provides participants the chance to learn from each other as well as from the expert facilitators.

WHO SHOULD ATTEND

This course is relevant to managers, professionals, team members or consultants.

DURATION

5 days

COURSE OBJECTIVES

Understand the role of food security and nutrition for attaining the MDGs
Gain fresh insights on the values of participatory & learning-oriented design, monitoring, and evaluation with regard to food security and nutrition.
To strengthen your competence in designing an M&E-system
Have clear ideas for the improvement of M&E systems and impact assessment for food security and nutrition.
COURSE CONTENT

Introduction

Food Security frameworks and concepts
M& E Fundamentals

Data sources collection and use
Defining a good M&E system
Identifying the challenges that face Monitoring and evaluation in the Food Security and Nutrition sector
Including M&E in food security program design
M&E Frameworks
M&E Plans
Participatory M&E systems
M&E Frameworks

Developing and operationalizing M&E frameworks
Linking M&E frameworks to indicators
M&E Frameworks basics for Food Security and other programs
M&E in Food Security and Nutrition context
Monitoring results and impacts using a logical framework
Gender M&E

Exploring gender in M&E plans
Gender considerations for data collection
Introduction to M&E in Gender and Food Security
Selecting indicators to measure gender-related outputs and outcomes
Prioritizing gender in M&E plans
Step by Step approaches to M&E

Agree on and design core documents to setup an M&E system
Agree on field monitoring data collection and management process
Agree on Monitoring data analysis process
Agree on process for monitoring data utilization and reporting
Agree on process of evaluation management
Agree on the principles and purpose of the project M&E system
Establish project M&E system
Review and revise M&E plans based on progress
Interpreting and Communicating results for M&E

Communication and reporting for M&E
Contemporarily methods of dissemination
Data collection, management and data quality

Data collection methods  (quantitative and qualitative)
Data collection versus data analysis
Data quality and data management
Data quality dimensions
Double counting
Functional areas of data management systems
Increasing questionnaires response rates
M&E field trips
ICT tools for data collection, monitoring and evaluation in food security and nutrition

Case study
Dashboards; data management analytics, and stakeholders access
Data collection implementation models
ICT tool for Data processing
Key choice of application to collect data in rural areas
Using Mobile phones for data collection
Data demand for food security and nutrition programs

Data demand
Data use frameworks and key concepts
Information availability
Information use
Introduction to Data analysis Food Security and Nutrition Programs

Basic analysis
Data analysis key concepts
Introduction
Types of variables
Summarizing data

Graphs and charts for continuous variables
Graphs and charts for dichotomous and categorical variables
Graphs and charts for ordinal variables
Numerical summaries for discrete variables
Tables for categorical variables
Tables for dichotomous variables
Tables for ordinal variables
Tabulations for summary statistics for continuous variables
Introduction to qualitative data Analysis

Coding the data
Introduction to qualitative data analysis software (NVivo)
Organizing your data
Planning for qualitative data analysis
Reviewing the data
Quantitative data Analysis

Basics for statistical analysis
Choosing the correct statistical test
Comparison of Data analysis packages
Confidence intervals
Hypothesis testing
Hypothesis testing versus confidence intervals
Interpreting the data
Planning for qualitative data analysis
Testing for normality of data
Tests of statistical significance
Assessing Programme Impact on Food Security

Impact Assessment in Programme Design
Introduction to Impact Assessment
Programme Design Implications
Methods and Approaches for Assessing Impact

Overview of Methods and Approaches
Qualitative Methods
Quantitative Methods: Household Surveys
Quantitative Methods: Secondary Data
Selecting Methods and Approaches
METHODOLOGY
The instructor led trainings are delivered using a blended learning approach and comprises of presentations, guided sessions of practical exercise, web based tutorials and group work. Our facilitators are seasoned industry experts with years of experience, working as professional and trainers in these fields.

All facilitation and course materials will be offered in English. The participants should be reasonably proficient in English.

We also offer online Training.

Course Fee: Ksh 80,000, USD 900

Contact Us
Mobile No: +254724527104/ +254734969612

Email Add: [email protected]

Postal Add: 5402-00100

Head Office: Comet House , Along Monrovia Street, Nairobi, Kenya

Visit our website:www.datastatresearch.org

 

To register for this event email your details to datastatresearch1@gmail.com

Register using webmail: Gmail / AOL / Yahoo / Outlook

 

Date And Time

2021-05-17 to
2021-05-21
 

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