Professional Research and Data Management Approaches for Effective Decision Making Training Course

Developments in data science offer a tremendous opportunity to improve decision-making. There has been an increase in the number of data gathering initiative such as baseline surveys, Socio-Economic Surveys, Demographic and Health Surveys, Nutrition Surveys, Food Security Surveys, Program Evaluation Surveys, Employees, customers and vendor satisfaction surveys, and opinion polls among others, all intended to provide data for decision making.

It is essential that these efforts go beyond merely generating new insights from data but also to systematically enhance individual human judgment in real development contexts.

This training is designed to help your organization better manage the process of converting the potential of data science to real development outcomes,  beyond merely generating new insights to changing behaviors and deliver real value from research.

Training Objectives

By the end of the training, attendees will be able to:

• define and use statistical and research terms and concepts
• design and Implement universally acceptable Surveys void of bias
• construct a research strategy, research plan, specification and objectives access, store and convert data into various formats using appropriate databases
• articulate how to visualize, plot, display and transform data
• perform qualitative and quantitative data analysis
• perform simple to complex data management tasks and analysis
• draw insights from survey data
• write reports from survey data
• explore strategies to improve data demand and use in decision making

Training Contents

Day 1

Module1: Basic statistical terms and concepts

• Introduction to statistical concepts
• Descriptive Statistics
• Inferential statistics
• Data Types and Why are They Important

• Qualitative vs Quantitative
• Qualitative Data
• Types of Qualitative Data
• Sources of Qualitative data

Module 2: Preliminaries of Qualitative data Analysis

• What is qualitative data analysis
• Approaches in Qualitative data analysis; deductive and inductive approach
• Points of focus in analysis of text data
• Principles of Qualitative data analysis
• Process of Qualitative data analysis
• jResearch Data Sources

• Factors Affecting the Choice of Research Method
• Data Analysis Types
• Descriptive Analysis.
• Diagnostic Analysis.
• Predictive Analysis.
• Prescriptive Analysis.

Day 2

Module 3: Research Methodologies

• The role and purpose of research design
• Understanding the business
• Types of research designs
• The research processes
• Method to choose?
• Research metrics
• Exercise: Identify a project of choice and developing a research design

Module 4: Data Collection Strategy: Survey Planning, Implementation and Completion

• Types of surveys
• The survey processes
• Survey design
• Methods of survey sampling
• Determining the Sample size
• Planning a survey
• Conducting the survey
• After the survey 
• Types of questions

• Data types for each question
• Types of questionnaire or Form logic
• Extended data types geoid, image and multimedia

Day 3

Module 5: Survey Authoring

• Design forms using a web interface using:
• ODK Build
• Koboforms
• PurcForms
• Preparing the mobile phone for data collection
• Hands-on Exercise

• Exercise: Planning for a survey based on the research design selected

Module 6: Data recognition, Data capture, database design and coding

• Managing devices
• Challenges of Data Collection
• Data aggregation, storage and dissemination

• Installing applications: ODK Collect
• Using Google play
• Manual install (.apk files)
• Configuring the device (Mobile Phones)
• Uploading the form into the mobile devices
• Designing forms manually: Using XLS Forms

• Introduction to XLS forms syntax
• New data types
• Notes and dates
• Multiple choice Questions
• Multiple Language Support
• Hints and Metadata
• Hands-on Exercise

• Conditional Survey Branching
• Required questions
• Constraining responses
• Skip: Asking Relevant questions
• The specify other
• Grouping questions
• Skipping many questions at once (Skipping a section)
• Repeating a set of questions
• Special formatting
• Making dynamic calculations

• Hands-on Exercise 

Day 4

Module 7: Data collection and Capture

 Hosting survey data (Online)

• R/Python
• Excel
• Tableau
• ODK Aggregate
• Formhub
• ona.io
• KoboToolbox
• Uploading forms to the server

Module 8: Hosting Survey Data (Configuring a local server)

• Configuring ODK Aggregate on a local server
• Downloading data
• Manual download (ODK Briefcase)
• Using the online server interface

Module 9 : Structuring: coding, processing, cleaning and conversion

Module 10 : GIS mapping of survey data using QGIS

• Introduction to GIS for Researchers and data scientists
• Importing survey data into a GIS
• Mapping of survey data using QGIS
• Exercise: QGIS mapping exercise. 

Day 5

Module 11 : Report writing for surveys, data dissemination, demand and use

• Writing a report from survey data
• Communication and dissemination strategy
• Context of Decision Making
• Improving data use in decision making
• Culture Change and Change Management
• Exercise: Preparing a report for the survey, a communication and dissemination plan and a demand and use strategy.
• Presentations and joint action planning