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WORKSHOP ON RESEARCH DESIGN, 
MOBILE DATA COLLECTION AND MAPPING AND DATA ANALYSIS USING NVIVO AND R ON 
4TH to 15TH JULY 2022

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VENUE: 
WESTON HOTEL, NAIROBI, KENYA
Office 
Telephone: +254-702-249-449
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as a group of 5 or more participants and get 25% discount on the course fee. 

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train...@skillsforafrica.org or 
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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 considerations 
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.
COURSE 
OBJECTIVES
At 
the end of course participants should be able to:
·       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
DURATION
10 
Days
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.
COURSE 
CONTENT
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
·       io
·       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
Ø  
This course is delivered by our seasoned trainers who have 
vast experience as expert professionals in the respective fields of practice. 
The course is taught through a mix of practical activities, theory, group works 
and case studies.
Ø  
Training manuals and additional reference materials are 
provided to the participants.
Ø  
Upon successful completion of this course, participants 
will be issued with a certificate.
Ø  
We can also do this as tailor-made course to meet 
organization-wide needs. Contact us to find out 
more: train...@skillsforafrica.org
Ø  
The training will be conducted at Skills for 
Africa Training Institute in Nairobi Kenya.
Ø  
The training fee covers tuition fees, training materials, 
lunch and training venue. Accommodation and airport transfer are arranged for 
our participants upon request.
Ø  
Payment should be sent to our bank account before start of 
training and proof of payment sent 
to: train...@skillsforafrica.org
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