Mountains

Research Design, Mobile Information Collection and Data Analysis using NVIVO and R

What Will I Learn?

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 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. 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 will be conducted using ODK, GIS, NVIVO and R

Fee In Different Currencies
RWF 70,000 Or USD 0 Or EURO 0
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Indicative Content

    • Basic statistical terms and concepts

      • Introduction to statistical concepts

      • Descriptive Statistics

      • Inferential statistics

    • 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

    • 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

    • 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

    • Survey Authoring

      • Design forms using a web interface

      • ODK Build

      • Kobo forms

      • PurcForms

      • Hands-on Exercise

    • 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

    • 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

    • 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

    • Hosting survey data (Online)

      • ODK Aggregate

      • Formhub

      • ona.io

      • Kobo Toolbox

      • Uploading forms to the server

    • 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

    • GIS mapping of survey data using QGIS

      • Introduction to GIS for Researchers and data scientists

    • Understanding Qualitative Research

      • Qualitative Data

      • Types of Qualitative Data

      • Sources of Qualitative data

      • Qualitative vs Quantitative

      • NVIVO key terms

      • The NVIVO Workspace

    • 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

    • Introduction to NVIVO

      • NVIVO Key terms

      • NVIVO interface

      • NVIVO workspace

      • Use of NVIVO ribbons

    • 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

    • 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

    • 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

    • Coding

      • Data-driven vs theory-driven coding

      • Analytic coding

      • Descriptive coding

      • Thematic coding

      • Tree coding

    • 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

    • Queries using NVIVO

      • Queries for textual analysis

      • Queries for exploring coding

    • Building on the Analysis

      • Content Analysis; Descriptive, interpretative

      • Narrative Analysis

      • Discourse Analysis

      • Grounded Theory

    • Qualitative Analysis Results Interpretation

      • Comparing analysis results with research questions

      • Summarizing finding under major categories

      • Drawing conclusions and lessons learned

    • 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

    • 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

    • Report Writing

      • Qualitative report format

      • Reporting qualitative research

      • Reporting content

      • Interpretation

    • 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

    • 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

    • Statistical Tools

      • Quantitative Data

      • Measures of Center

      • Measures of Variation

      • Charting & Graphing Continuous Data

      • Charting & Graphing Discrete Data

      • Deriving Insights from Qualitative/Nominal Data

    • Probability Distributions

      • Data Distribution: Normal Distribution

      • Checking for Normal Distribution

      • Standard Normal Distribution and Z-scores

      • Confidence Interval-Theory

      • Confidence Interval-Computation in R

    • 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

    • 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

    • 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)

    • 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

70,000
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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 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

70,000
Enroll Now

Course Features

  • Lectures 0
  • Duration 90 Days
  • Certificate Yes
    • Enroll Now

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