NVivo 2025: The Complete Qualitative Data Analysis Course

NVivo is a powerful qualitative data analysis tool designed to help researchers organize, code, and analyze non-numerical data such as interviews, surveys, and text documents. This lecture provides an introduction to NVivo, explaining its core purpose, key functionalities, and how it supports qualitative research. By understanding NVivo’s role in data analysis, you will gain insight into how it can streamline the research process, improve data organization, and enhance the accuracy of qualitative findings.

In this lecture, we will explore the different types of data that NVivo can handle, the benefits of using the software, and how it compares to traditional qualitative research methods. We will also discuss real-world applications of NVivo in academic, social, and business research. By the end of this session, you will have a clear understanding of why NVivo is an essential tool for qualitative researchers and what you can achieve with it.

Creating and structuring your first project is a critical step in using NVivo effectively. In this lecture, we will guide you through the process of setting up a new project, including organizing data sources, structuring folders, and defining key elements such as cases, nodes, and classifications. You’ll gain hands-on experience with project setup, ensuring that your research is well-organized from the beginning.

Additionally, we will discuss best practices for managing your NVivo project, including tips on saving and backing up your work, avoiding common mistakes, and optimizing your workflow for better efficiency. By the end of this session, you’ll be ready to start analyzing qualitative data with a structured and organized approach in NVivo.

One of the first steps in qualitative data analysis is importing and organizing data. This lecture focuses on how to efficiently add files to NVivo, whether they are text documents, PDFs, images, audio files, or survey responses. You’ll learn the best methods for structuring and categorizing your files, allowing for seamless data retrieval and analysis.

We will also cover essential file management techniques such as creating folders, tagging data sources, and using metadata for better organization. By the end of this lecture, you’ll have a solid understanding of how to import, structure, and manage files in NVivo, setting the foundation for efficient data analysis.

Cases in NVivo represent people, organizations, or entities within your research. This lecture will introduce you to the concept of cases and explain how they are used to categorize and analyze qualitative data. You’ll learn how to create cases, assign attributes, and use them to track participants or themes across your research project.

Furthermore, we will discuss how cases enhance your analysis by allowing you to compare different groups, track changes over time, and generate meaningful insights. By the end of this session, you’ll understand the importance of cases in qualitative research and how to use them effectively in NVivo.

Auto coding in NVivo is a powerful feature that allows researchers to quickly and systematically categorize qualitative data. This lecture introduces the concept of auto coding, explaining how it can be used to streamline the coding process by automatically assigning codes based on predefined structures or content patterns. Auto coding is particularly useful for large datasets, such as interviews, surveys, or documents, where manual coding would be time-consuming. Participants will learn about the different types of auto coding, including structured coding based on headings and paragraph styles, as well as automated text searches to identify key themes.

Beyond basic auto coding, this session explores best practices for refining and validating automatically generated codes. While auto coding accelerates initial data organization, researchers must review and adjust coded content to ensure accuracy and relevance. The lecture covers strategies for assessing the quality of auto coding, merging and refining codes, and combining auto coding with manual techniques for a more comprehensive qualitative analysis. By the end of this session, participants will have the skills to leverage auto coding effectively, saving time while maintaining the rigor and depth of their research.

Case Classifications in NVivo Classifications in NVivo help organize your data by adding attributes to cases, such as demographics, locations, or other relevant details. This lecture covers the importance of case classifications, how to create them, and how they enhance your analysis by enabling structured comparisons and filtering.

We will also explore how to import classification sheets, edit case attributes, and generate classification-based queries. By the end of this session, you will have a deeper understanding of how classifications add depth to your research and enable more advanced analysis in NVivo.

Coding is a fundamental aspect of qualitative research, helping researchers organize and interpret complex datasets. This lecture introduces the principles of coding theory, explaining how qualitative coding is used to categorize data and reveal deeper insights. The session explores different types of coding approaches, such as descriptive, thematic, and analytical coding, highlighting their applications in various research methodologies.

Participants will gain an understanding of how coding structures are developed and refined over time. The lecture also discusses strategies for maintaining consistency, reliability, and rigor in coding practices. By the end of this session, learners will have a solid theoretical foundation to begin coding effectively in NVivo, ensuring that their research findings are systematically organized and meaningful.

This lecture provides a hands-on introduction to coding in NVivo, guiding users through the basic steps of creating and managing codes. It covers essential features such as manually coding text, assigning codes to multiple data sources, and working with nodes to categorize information. Learners will explore how to navigate NVivo’s coding interface, making the process intuitive and efficient.

Beyond manual coding, this session introduces auto-coding options, which streamline the classification of text data. Practical demonstrations will illustrate how to apply codes consistently and retrieve coded data for analysis. By the end of this lecture, participants will be equipped with the fundamental skills needed to start coding their qualitative data in NVivo effectively.

As researchers progress in their qualitative analysis, refining coding techniques becomes essential. This lecture focuses on developing coding structures, organizing themes, and refining nodes to ensure coherence in data interpretation. It introduces strategies for hierarchical coding, merging similar codes, and creating subcategories for deeper analysis.

Additionally, the session explores techniques for reviewing and modifying codes as research evolves. By integrating coding memos and annotations, researchers can document their thought processes and ensure transparency in their analysis. Participants will leave this lecture with the ability to refine and enhance their coding strategies for more structured and insightful data analysis.

Understanding and managing coded data is crucial for effective analysis. This lecture delves into various ways to view, explore, and assess coded content in NVivo. Users will learn how to retrieve coded segments, visualize coding structures, and use NVivo’s tools to review coding consistency.

Through practical demonstrations, participants will explore how to generate coding reports, view coded text in context, and compare coding across different data sources. The session also highlights strategies for ensuring accuracy and reliability in qualitative analysis by continuously reviewing and refining coding applications.

Expanding beyond basic coding, this lecture explores advanced coding techniques that enhance data categorization and analysis. Participants will learn about in vivo coding, matrix coding, and pattern-based coding strategies, allowing for a more comprehensive interpretation of qualitative data.

The session also covers strategies for cross-referencing coded material and integrating different types of data into a unified coding framework. By leveraging NVivo’s advanced coding features, researchers can deepen their analysis and extract more meaningful insights from their datasets.

Memos in NVivo serve as an essential tool for documenting thoughts, insights, and analytical reflections throughout the research process. This lecture explores how memos can be used to track decision-making, emerging themes, and interpretations of qualitative data. Researchers can link memos to specific nodes, cases, or sources, ensuring that their analytical journey is well-documented and structured.

The session also covers best practices for organizing memos within an NVivo project, such as categorizing them by themes or research stages. By effectively using memos, researchers can enhance the rigor of their qualitative studies and maintain a coherent narrative across different phases of analysis.

NVivo allows researchers to annotate and edit files to enhance data organization and analysis. This lecture introduces the various annotation tools available in NVivo, including highlighting, commenting, and tagging specific sections of text.

Participants will learn how to use annotations to track observations, link insights to research questions, and collaborate with team members. The session also explores editing capabilities that enable researchers to refine textual content while maintaining a structured workflow.

See Also Links in NVivo facilitate connections between different data sources, helping researchers establish relationships between concepts, themes, or supporting evidence. This lecture explains how to create and manage See Also Links to improve cross-referencing and data interpretation.

Participants will learn how to apply these links strategically to ensure a cohesive and interconnected dataset. By leveraging See Also Links, researchers can enhance the depth of their qualitative analysis and draw more meaningful conclusions.

The framework matrix in NVivo provides a structured approach to summarizing and comparing qualitative data. This lecture introduces the concept of framework matrices, explaining their role in systematic data analysis.

Participants will learn how to set up and use framework matrices to organize case-based data, track themes, and compare responses across different participant groups. This session emphasizes the value of structured summaries in drawing insights from qualitative research.

Sets in NVivo allow researchers to group related data sources for more focused analysis. This lecture explores how to create and manage sets to organize research materials based on themes, cases, or attributes.

Participants will gain insights into using sets for targeted data retrieval, improving efficiency in coding and querying. The session also covers practical strategies for maintaining well-structured sets within NVivo projects.

Dynamic sets in NVivo offer an automated way to organize and update research data based on specific criteria. This lecture explains how dynamic sets differ from static sets and how they can be used to streamline data management.

Participants will learn how to define rules for dynamic sets, ensuring that their datasets remain updated as new data is added or modified. This feature enhances the flexibility and scalability of qualitative research projects.

Queries in NVivo enable researchers to explore their data systematically, identifying patterns, relationships, and trends. This lecture introduces the concept of queries, explaining how they enhance qualitative analysis by allowing researchers to retrieve and examine coded information efficiently.

Participants will learn about different types of queries available in NVivo, including text search, coding, and matrix coding queries. Practical demonstrations will illustrate how queries can be used to answer research questions and uncover meaningful insights from complex qualitative datasets.

Text search queries allow researchers to quickly locate specific words or phrases within their datasets. This lecture explores how to perform text searches in NVivo, refine search parameters, and analyze keyword patterns across multiple data sources.

Participants will also learn how to visualize text search results using word clouds and coding stripes. The session emphasizes best practices for refining searches to ensure accuracy and relevance in qualitative research.

This lecture covers the various query options in NVivo, highlighting their applications in data exploration. Participants will learn how to customize query parameters, filter search results, and combine multiple queries to refine analysis.

By understanding different query configurations, researchers can extract more precise insights from their qualitative data. The session also demonstrates how to save and manage query results for future reference.

Word frequency queries help identify common terms and key themes in qualitative data. This lecture explains how to run word frequency queries in NVivo, analyze patterns, and interpret results using visualization tools such as word clouds and frequency tables.

Participants will also learn how to refine frequency queries by excluding stop words, adjusting search parameters, and grouping similar words. These techniques enhance qualitative data analysis by highlighting linguistic trends and dominant themes.

Coding queries allow researchers to retrieve coded content based on specific criteria. This lecture demonstrates how to create and apply coding queries to analyze relationships between different themes and categories.

The session also covers strategies for combining multiple coding criteria, enabling deeper exploration of qualitative data. By the end of the lecture, participants will have a solid understanding of how coding queries can refine and enhance their research findings.

Sentiment coding helps researchers analyze the emotional tone of textual data. This lecture introduces sentiment coding techniques in NVivo, explaining how to categorize positive, negative, and neutral sentiments within datasets.

Additionally, the session explores how to use coding queries to refine sentiment analysis. By integrating sentiment coding with other qualitative research methods, participants can gain deeper insights into respondent attitudes and perceptions.

Matrix coding queries allow researchers to compare coding across different data sources or attributes. This lecture explains how to set up and analyze matrix coding queries in NVivo, using tables and heatmaps to visualize relationships between themes.

Participants will learn how to structure queries to explore cross-cutting themes and demographic differences. These techniques are particularly useful for comparative qualitative research and mixed-methods analysis.

Crosstab queries help researchers analyze relationships between different variables in a structured format. This lecture covers how to use crosstab queries in NVivo to compare coding frequencies and patterns across multiple case attributes.

By leveraging crosstab tables, participants can identify correlations and trends in their qualitative data. The session also explores ways to refine and interpret crosstab results for robust data-driven insights.

Project maps provide a visual representation of the relationships between different components of an NVivo project. This lecture explores how to create and customize project maps to better understand data structures and thematic connections.

Participants will learn how to use project maps to illustrate coding hierarchies, relationships between sources, and conceptual linkages within their research. These visual tools enhance clarity and support more effective data storytelling.

Mind maps in NVivo help researchers brainstorm and organize ideas related to their qualitative analysis. This lecture covers the creation and customization of mind maps, demonstrating their use in thematic exploration and research planning.

By learning how to structure mind maps effectively, participants can visually organize concepts, identify key themes, and refine research questions. The session also highlights how mind maps can integrate with other NVivo analysis tools.

Concept maps allow researchers to explore relationships between different themes and ideas in their data. This lecture introduces concept mapping techniques in NVivo, explaining how to develop clear and meaningful representations of research findings.

Participants will learn how to link concepts, visualize theoretical frameworks, and generate analytical insights using concept maps. By the end of this session, researchers will have a practical approach to integrating concept maps into their qualitative analysis.

EndNote integration with NVivo allows researchers to manage and analyze academic references efficiently. This lecture explains how to export bibliographic data from EndNote into NVivo, enabling seamless integration between reference management and qualitative analysis.

Participants will learn best practices for organizing and coding imported references, enhancing their ability to synthesize literature and extract relevant themes for research projects.

This lecture explores the process of importing literature from EndNote into NVivo, focusing on technical steps and organizational strategies. Participants will learn how to structure and categorize imported references to facilitate qualitative analysis.

The session also highlights the benefits of integrating literature reviews with NVivo’s coding and querying tools, enabling a more systematic approach to evidence synthesis.

Managing literature in NVivo involves more than just importing references—it requires systematic coding and analysis. This lecture covers techniques for organizing literature sources, applying codes, and identifying key insights from scholarly works.

Participants will explore methods for cross-referencing literature with primary research data, ensuring a well-integrated and evidence-based research framework.

Queries play a crucial role in analyzing literature within NVivo. This lecture demonstrates how to use NVivo’s query tools to extract meaningful insights from academic references, identifying patterns, key themes, and knowledge gaps.

Participants will also learn strategies for refining literature searches and aligning them with their research objectives. By leveraging queries, researchers can enhance their ability to synthesize complex bodies of literature.