INVITATION TO ATTEND A WORKSHOP ON RESEARCH DESIGN, MOBILE DATA COLLECTION AND MAPPING AND DATA ANALYSIS USING NVIVO AND R ON 4TH to 15TH JULY 2022
Skills for Africa Training Institute
workshops at skillsforafrica.or.ke
Tue Jun 7 10:24:22 BST 2022
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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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Whatapp
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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training at skillsforafrica.org or
call +254-702-249-449
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: training at 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: training at skillsforafrica.org
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