Qualitative Data Analysis

About qualitative data analysis.

Qualitative data analysis is a systematic process of examining non-numeric study results researchers find contexts and themes that talk about what participants experienced. To work with qualitative data researchers, need to become familiar with their findings and establish an approach to grouping and labelling them. We connect our discovered themes with recognized frameworks and prior research data. Data reliability depends on three approaches including the use of multiple sources, participant feedback, and colleague review. Researchers explain their results in an arranged method including thorough descriptions and analysis of the data. You need to maintain these intelligence factors from start to finish of your analysis work.

Course details

Purpose

By the end of this training, participants will:

Master essential knowledge about qualitative studies and data examination.
Confidently navigate QDA MINER lite interface and tools.
Take in and sort multiple kinds of qualitative data sources through QDA MINER lite management tools.
Set up powerful ways to encode data when examining qualitative research.
Learn to use QDA MINER’s/ professional tools including search functions, analytical frameworks, and data displays.
Showcase quality research results in a clear way.

Topics

Module 1: Foundations of Qualitative Analysis and QDA MINER Basics (2hrs)

Introduction to Qualitative research
QDA MINER interface
Importing, exporting and organizing data

Module 2: Coding and Categorization in Qualitative Data Analysis (2hrs)

Introduction to coding qualitative data
Thematic Analysis in Qualitative Data

Module 3: Visualization. (2hrs)

Generating word clouds, tree maps, and charts.
Developing conceptual models and diagrams.
Exporting visualizations for use in presentations.
Practical Activity: Creating visual representations of themes from focus group discussions.

Module 4: Reporting and Presenting Qualitative Findings (2hrs)

Producing summary reports and exporting coded data.
Designing custom reports tailored to stakeholder needs.
Preparing materials for publications or presentations.
Practical Activity: Creating and exporting project reports

Module 5: Advance application and final project (2hrs)

Advanced Features in QDA MINER
Frameworks for longitudinal studies.
Merging projects for collaborative analysis.
Integration with tools like Excel and SurveyMonkey.
Activity: Hands-on collaborative coding exercise.

Module 6: Final Project and Presentation (4hrs)

Work on a mini-project with provided datasets.
Steps include:
Data import and organization.
Coding and data analysis.
Utilizing queries and visualizations for insights.
Group presentation of findings.
Wrap-Up and Feedback
- Recap of key learning points.
- Participant feedback and evaluation.

Key Deliverables