10. Module: QUALITATIVE DATA ANALYSIS

Module 10 offers a comprehensive guide to understanding and applying various methods for analysing qualitative data. Designed for both first-stage researchers and recognised researchers, this module provides in-depth insights into the nature of qualitative data and the analytical processes required to derive meaningful insights from it.

The module begins by introducing the fundamental concepts of qualitative data, explaining its distinctive characteristics and how it differs from quantitative data. It then explores the process of qualitative data analysis, setting the stage for a discussion of different approaches, techniques, and tools that can be employed to make sense of qualitative information. One key aspect of qualitative data analysis is the concept of triangulation, and the module emphasises its importance in ensuring the validity of research findings. The reader will be introduced to various types of triangulation, and gain an understanding of how combining multiple perspectives, data sources, or methods can enhance the credibility of the research.

The core of this module focuses on six distinct approaches to qualitative data analysis. Each approach is explored in detail, providing the reader with a clear understanding of its definitions, key features, step-by-step processes, goals, and potential limitations. These approaches include: content analysis, narrative analysis, thematic analysis, framework analysis, discourse analysis, and grounded theory analysis.

Each chapter within this module is structured around clear learning objectives to guide the reader's exploration of diverse qualitative data analysis techniques. By the end of this module, readers will have developed a well-rounded understanding of the various qualitative analysis approaches, along with practical guidance on how to apply them in their own research. Whether the research focuses on textual data, narratives, themes, or discourse, this module provides the tools to analyse qualitative data rigorously and meaningfully.

Module 10 aims to inspire confidence in qualitative data analysis and to equip researchers with the knowledge necessary to tackle complex qualitative datasets. It offers both theoretical foundations and practical applications, helping researchers to choose the most appropriate analytical method for their study, and ultimately ensuring that their research findings are insightful and credible.

In exploring these different approaches to qualitative data analysis, the module hopes to foster a deeper appreciation of the richness and complexity of qualitative research and to empower researchers to uncover valuable insights that might otherwise remain hidden in the data.

Chapter 1. INTRODUCTION

Objective 1 – Introduce qualitative data.

Objective 2 – Introduce qualitative data analysis.

Objective 3 – Highlight the importance of triangulation.

Objective 4 – Explain different types of triangulation.

Objective 5 – Discuss different approaches to the analysis of qualitative data.

Chapter 2. CONTENT ANALYSIS

Objective 1 – Define content analysis.

Objective 2 – Explain the difference between manifest and latent content analysis.

Objective 3 – Explain the difference between different stages of content analysis.

Objective 4 – Provide guidance on how to perform individual stages of content analysis.

Objective 5 – Explain the outcome validation process.

Chapter 3. NARRATIVE ANALYSIS            

Objective 1 – Highlight the essence of narrative analysis.

Objective 2 – Intoduce different information sources.

Objective 3 – Explain different steps of narrative analysis.

Objective 4 – Explain the nature of the outcome of narrative analysis.

Objective 5 – Present the narrative analysis typology.

Chapter 4. THEMATIC ANALYSIS

Objective 1 – Introduce the distinctive features of thematic analysis.

Objective 2 – Explain how inductive and deductive approach are used in thematic analysis.

Objective 3 – Explain theme identification levels.

Objective 4 – Explain different steps of thematic analysis.

Chapter 5. FRAMEWORK ANALYSIS

Objective 1 – Introduce the key terms used in framework analysis.

Objective 2 – Explain the matrix output as the framwork analysis defining feature.

Objective 3 – Explain different stages of thematic analysis.

Objective 4 – Point out to the potential pitfalls of framework analysis.

Chapter 6. DISCOURSE ANALYSIS

Objective 1 – Explain the defining features and distinctive characteristics of discourse analysis.

Objective 2 – Highlight the primary goal of critical discourse analysis.

Objective 3 – Explain different steps of discourse analysis.

Chapter 7. GROUNDED THEORY ANALYSIS

Objective 1 – Explain the defining features of grounded theory analysis.

Objective 2 – Introduce the basic principles of grounded theory analysis.

Objective 3 – Highlight the essentials of the theory construction process.

Objective 4 – Elaborate the coding process.

Module 10 focuses on qualitative data analyses. The nature of qualitative data, and the qualitative data analysis in general are first explained, followed by the explanation of the importance of triangulation, and description of different triangulation types. A thorough overview of six different appoaches to qualitative data analysis, including their definitions, distinctive features, steps they involve, goals, and potential limitations is provided in this module.

Module 10 comprises the following chapters:

Chapter 1. Introduction

Chapter 2. Content Analysis

Chapter 3. Narrative Analysis 

Chapter 4. Thematic Analysis

Chapter 5. Framework Analysis

Chapter 6. Discourse Analysis

Chapter 7. Grounded Theory Analysis

Author(s):

Ivana Marinković, MA

Ljubica Diković, PhD

Vesna Vasović, PhD

Western Serbia Academy of Applied Studies

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