Course Date: 23rd September – 04th, October 2019 for 10 Days
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Organizer: Foscore Development Center
INTRODUCTION
New developments in data science offer a tremendous opportunity to improve decision-making. In the development world, 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. How can organizations better manage the process of converting the potential of data science to real development outcomes This ten days hands-on course is tailored to put all these important consideration into perspective. It is envisioned that upon completion, the participants will be empowered with the necessary skills to produce accurate and cost effective data and reports that are useful and friendly for decision making.
It will be conducted using ODK, GIS, NVIVO and R
DURATION
2 Weeks
LEARNING OBJECTIVES
Understand and appropriately use statistical terms and concepts
Design and Implement universally acceptable Surveys
Convert data into various formats using appropriate software
Use mobile data gathering tools such as Open Data Kit (ODK)
Use GIS software to plot and display data on basic maps
Qualitative data analysis using NVIVO
- Analyze t data by applying appropriate statistical techniques using R
- Interpret the statistical analysis using R
- Identify statistical techniques a best suited to data and questions
- Strong foundation in fundamental statistical concepts
- Implement different statistical analysis in R and interpret the results
- Build intuitive data visualizations
- Carry out formalized hypothesis testing
- Implement linear modelling techniques such multiple regressions and GLMs
- Implement advanced regression analysis and multivariate analysis
Write reports from survey data
Put strategies to improve data demand and use in decision making
WHO SHOULD ATTEND?
This is a general course targeting participants with elementary knowledge of Statistics from Agriculture, Economics, Food Security and Livelihoods, Nutrition, Education, Medical or public health professionals among others who already have some statistical knowledge, but wish to be conversant with the concepts and applications of statistical modeling.
TOPICS TO BE COVERED
Module1: Basic statistical terms and concepts
Introduction to statistical concepts
Descriptive Statistics
Inferential statistics
Module 2:Research Design
The role and purpose of research design
Types of research designs
The research process
Which method to choose?
Exercise: Identify a project of choice and developing a research design
Module 3: Survey Planning, Implementation and Completion
Types of surveys
The survey process
Survey design
Methods of survey sampling
Determining the Sample size
Planning a survey
Conducting the survey
After the survey
Exercise: Planning for a survey based on the research design selected
Module 4:Introduction
Introduction to Mobile Data gathering
Benefits of Mobile Applications
Data and types of Data
Introduction to common mobile based data collection platforms
Managing devices
Challenges of Data Collection
Data aggregation, storage and dissemination
Types of questions
Data types for each question
Types of questionnaire or Form logic
Extended data types geoid, image and multimedia
Module 5:Survey Authoring
Design forms using a web interface using:
ODK Build
Koboforms
PurcForms
Hands-on Exercise
Module 6:Preparing the mobile phone for data collection
Installing applications: ODK Collect
Using Google play
Manual install (.apk files)
Configuring the device (Mobile Phones)
Uploading the form into the mobile devices
Hands-on Exercise
Module 7: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
Module 8:Advanced survey Authoring
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
Module 9:Hosting survey data (Online)
ODK Aggregate
Formhub
KoboToolbox
Uploading forms to the server
Module 10: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 11: 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.
Module 12:Understanding Qualitative Research
- Qualitative Data
- Types of Qualitative Data
- Sources of Qualitative data
- Qualitative vs Quantitative
- NVivo key terms
- The NVivo Workspace
Module 13: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
Module 14:Introduction to NVIVO
- NVIVO Key terms
- NVIVO interface
- NVIVO workspace
- Use of NVIVO ribbons
Module 15:NVIVO Projects
- Creating new projects
- Creating a new project
- Opening and Saving project
- Working with Qualitative data files
- Importing Documents
- Merging and exporting projects
- Managing projects
- Working with different data sources
Module 16:Nodes in NVIVO
- Theme codes
- Case nodes
- Relationships nodes
- Node matrices
- Type of Nodes,
- Creating nodes
- Browsing Nodes
- Creating Memos
- Memos, annotations and links
- Creating a linked memo
Module 17:Classes and summaries
- Source classifications
- Case classifications
- Node classifications
- Creating Attributes within NVivo
- Importing Attributes from a Spreadsheet
- Getting Results; Coding Query and Matrix Query
Module 18: Coding
- Data-driven vs theory-driven coding
- Analytic coding
- Descriptive coding
- Thematic coding
- Tree coding
Module 19:Thematic Analytics in NVIVO
- Organize, store and retrieve data
- Cluster sources based on the words they contain
- Text searches and word counts through word frequency queries.
- Examine themes and structure in your content
Module 20:Queries using NVIVO
- Queries for textual analysis
- Queries for exploring coding
Module 21: Building on the Analysis
- Content Analysis; Descriptive, interpretative
- Narrative Analysis
- Discourse Analysis
- Grounded Theory
Module 22: Qualitative Analysis Results Interpretation
- Comparing analysis results with research questions
- Summarizing finding under major categories
- Drawing conclusions and lessons learned
Module 23: Visualizing NVIVO project
- Display data in charts
- Creating models and graphs to visualize connections
- Tree maps and cluster analysis diagrams
- Display your data in charts
- Create models and graphs to visualize connections
- Create reports and extracts
Module 24: Triangulating results and Sources
- Triangulating with quantitative data
- Using different participatory techniques to measure the same indicator
- Comparing analysis from different data sources
- Checking the consistency on respondent on similar topic
Module 25: Report Writing
- Qualitative report format
- Reporting qualitative research
- Reporting content
- Interpretation
MODULE 26:Basics of Applied Statistical Modelling using R
- Introduction to the Instructor and Course
- Data & Code Used in the Course
- Statistics in the Real World
- Designing Studies & Collecting Good Quality Data
- Different Types of Data
MODULE 27: Essentials of the R Programming
- Rationale for this section
- Introduction to the R Statistical Software & R Studio
- Different Data Structures in R
- Reading in Data from Different Sources
- Indexing and Subletting of Data
- Data Cleaning: Removing Missing Values
- Exploratory Data Analysis in R
MODULE 28: Statistical Tools
- Quantitative Data
- Measures of Center
- Measures of Variation
- Charting & Graphing Continuous Data
- Charting & Graphing Discrete Data
- Deriving Insights from Qualitative/Nominal Data
MODULE 29: Probability Distributions
- Data Distribution: Normal Distribution
- Checking For Normal Distribution
- Standard Normal Distribution and Z-scores
- Confidence Interval-Theory
- Confidence Interval-Computation in R
MODULE 30: Statistical Inference
- Hypothesis Testing
- T-tests: Application in R
- Non-Parametric Alternatives to T-Tests
- One-way ANOVA
- Non-parametric version of One-way ANOVA
- Two-way ANOVA
- Power Test for Detecting Effect
MODULE 31: Relationship between Two Different Quantitative Variables
- Explore the Relationship Between Two Quantitative Variables
- Correlation
- Linear Regression-Theory
- Linear Regression-Implementation in R
- Conditions of Linear Regression
- Multi-collinearity
- Linear Regression and ANOVA
- Linear Regression With Categorical Variables and Interaction Terms
- Analysis of Covariance (ANCOVA)
- Selecting the Most Suitable Regression Model
- Violation of Linear Regression Conditions: Transform Variables
- Other Regression Techniques When Conditions of OLS Are Not Met
- Regression: Standardized Major Axis (SMA) Regression
- Polynomial and Non-linear regression
- Linear Mixed Effect Models
- Generalized Regression Model (GLM)
- Logistic Regression in R
- Poisson Regression in R
- Goodness of fit testing
MODULE 32: Multivariate Analysis
- Introduction Multivariate Analysis
- Cluster Analysis/Unsupervised Learning
- Principal Component Analysis (PCA)
- Linear Discriminant Analysis (LDA)
- Correspondence Analysis
- Similarity & Dissimilarity Across Sites
- Non-metric multi-dimensional scaling (NMDS)
- Multivariate Analysis of Variance (MANOVA)
Module 33: 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
- Preparing a report for the survey, a communication and dissemination plan and a demand and use strategy.
- Presentations and joint action planning
General Notes
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All our courses can be Tailor-made to participants needs
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The participant must be conversant with English
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Presentations are well guided, practical exercise, web based tutorials and group work. Our facilitators are expert with more than 10 years of experience.
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Upon completion of training the participant will be issued with Foscore Development Center certificate (FDC-K)
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Training will be done at Foscore Development Center (FDC-K) in Nairobi Kenya. We also offer more than five participants training at requested location within Kenya, more than ten participant within east Africa and more than twenty participant all over the world.
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Course duration is flexible and the contents can be modified to fit any number of days.
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The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and a Certificate of successful completion of Training. Participants will be responsible for their own travel expenses and arrangements, airport transfers, visa application dinners, health/accident insurance and other personal expenses.
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Accommodation, pickup, freight booking and Visa processing arrangement, are done on request, at discounted prices.
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One year free Consultation and Coaching provided after the course.
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Register as a group of more than two and enjoy discount of (10% to 50%) plus free five hour adventure drive to the National game park.
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Payment should be done two week before commence of the training, to FOSCORE DEVELOPMENT CENTER account, so as to enable us prepare better for you.
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For any enquiry to: [email protected] or +254712260031
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Website: www.fdc-k.org
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Click to view Research and Data Analysis courses calendar 2019