Course Overview

A mixed method research design allows researchers to collect and analyse both quantitative and qualitative data within the same study. With the approach overall goal of providing a better and deeper understanding, by providing a fuller picture that can enhance description and understanding of the phenomena, use of the right data collection tools and analyzing software are essential. This 10 days course will focus on designing tools for data collection that ensures real, quality, and rich data including capturing of videos, audios, and images. The course will also demonstrate both qualitative and quantitative research designs. While the course will explore quantitative data analysis using Stata 14, qualitative data will be analysed using Nvivo 14 software. Additionally, participants will be trained on interpretation of results and writing different research outputs among them project report, scientific journal articles, blogs, case studies and policy briefs

DURATION

10 Days 

COURSE OBJECTIVES

By the end of the course the learner should be able to:

• Understand qualitative analysis approaches
• Understand different qualitative data collection methods
• Set up a project in NVivo
• Create a framework for qualitative data analysis using NVivo
• Carry out qualitative data analysis using NVivo
• Write a qualitative report
• Usefulness and problems with Panel Data
• Opportunities and challenges of panel data.
• Linear models data analysis with dynamic data
• Logistic regression models with dynamic data
• Count data models with dynamic data
• Linear structural equation models with dynamic data

COURSE OUTLINE

Module 1: Introduction

Introduction to Qualitative Research

• What is qualitative research?
• Dimensions of qualitative methods
• Qualitative research approaches
• Qualitative data collection methods
• Qualitative research study design

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

 Module 2: Project Management

NVivo Projects

• Creating new projects
• Merging, importing and exporting projects
• Managing projects
• Working with different data sources

 Nodes in NVivo

• Theme codes
• Case nodes
• Relationships nodes
• Node matrices

 Classifications

• Source classifications
• Case classifications
• Node classifications

 Module 3: Coding and Analysis

Coding

• Data-driven vs theory-driven coding
• Analytic coding
• Descriptive coding
• Thematic coding
• Tree coding

 Thematic Analysis using 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

 Memos Annotations and Links

• Linked memos
• Adding annotation to selected content
• See also link

 Queries using NVivo

• Queries for textual analysis
• Queries for exploring coding

 Module 4: Analysis, interpretation and visualization

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

 Module 5: Triangulation of Data Sources and Reporting

Triangulation of Data 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

 Qualitative Report Writing

• Qualitative report format
• Reporting qualitative research
• Reporting content
• Interpretation

 QUANTITATIVE DATA ANALYSIS OF PANEL DATA USING STATA

Module 6: Introduction

Introduction to Panel Data

• Why Are Panel Data Desirable?
• Problems with Panel Data
• Examples of Time-varying and time-invariant variables 

Opportunities and challenges of panel data

• Data requirements
• Control for unobservables
• Determining causal order
• Problem of dependence
• Software considerations

 Module 7: Linear models

• Robust standard errors
• Generalized estimating equations
• Random effects models
• Fixed effects models
• Between-within models

 Module 8: Logistic regression models

• Robust standard errors
• GEE
• Subject-specific vs. population averaged methods
• Random effects models
• Fixed effects models
• Between-within models

 Module 9: Count data models

• Poisson models
• Negative binomial models

 Module 10: Linear structural equation models

• Fixed and random effects in the SEM context

Models for reciprocal causation with lagged effects