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Introduction to NVivo Application

Introduction to NVivo Application. Dr. Roshartini Omar Faculty of Technology Management and Business (FPTP) J701-12 (Level 7). Session Aim and Objectives. Aim To introduce students to the analysis of qualitative data Objectives By the end students will have an appreciation of:

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Introduction to NVivo Application

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  1. Introduction to NVivo Application Dr. Roshartini Omar Faculty of Technology Management and Business (FPTP) J701-12 (Level 7)

  2. Session Aim and Objectives • Aim • To introduce students to the analysis of qualitative data • Objectives • By the end students will have an appreciation of: • The principles of analysing qualitative data • Qualitative Data Management Tools • How to present qualitative results

  3. Qualitative Data Analysis • Used for any non-numerical data collected as part of evaluation - observations - interviews - analysis of written documents (document sources) - focus groups transcripts - diaries

  4. Data Analysis: Content Analysis • Content analysis is a research tool used to determine the presence of certain words or concepts within the text or sets of texts (Soetanto et al. 2002; McBurney, 1998 and Neuman, 1997).

  5. The Qualitative Process of Data Analysis

  6. Developing Descriptions & Themes from the Data • Coding data • Developing a description from the data • Defining themes from the data • Connecting and interrelating themes

  7. Coding Data • Open Coding • Assign a code word or phrase that accurately describes the meaning of the text segment • Line-by-line coding is done first in theoretical research • More general coding involving larger segments of text is adequate for practical research (action research)

  8. EDUC 7741/Paris/Terry

  9. Axial Coding • The process of looking for categories that cut across all data sets • After this type of coding, you have identified your themes Clustering • After open coding an entire text, make a list of all code words • Cluster together similar codes and look for redundant codes • Objective: reduce the long list of codes to a smaller, more manageable number (25 or 30)

  10. EDUC 7741/Paris/Terry

  11. Computer help for Qualitative Data Analysis • Software Packages to help you organize data • Search, organize, categorize, and annotate textual and visual data • Help you to visualize the relationships among data • Example: NVivo

  12. NVivo • NVivo is a computer software programme designed for managing qualitative data and carrying-out qualitative analysis. • NVivo does not analyze qualitative data for the researcher but rather can enhance the researcher’s analytical capacity by providing the means to efficiently store, sort and look for patterns in diverse forms of data.

  13. Procedure Followed in Applying Nvivo Software (Adopted from Bazeley, 2007)

  14. Key Terms in NVivo • Sources • Research materials (documents, PDFs, datasets, audio, video, pictures, etc.) • Nodes • Containers for your coding. Nodes can be related to themes (Concepts) or people (Cases). • Coding • The process of gathering material by topic, theme or case. Data reduction, data organization and idea generation

  15. NVivo Workspace • Ribbon - locate all NVivo commands • Navigation View –organize and access all items in NVivo. • List View —When a folder is selected in Navigation View the contents are displayed in list view. • Detail View —When an item from list view is opened, it is displayed in Detail View. This is where you actually see the contents of the files. NOTE—the workspace can be rearranged to suite your needs.

  16. The NVivo Workspace Ribbon Detail View Navigation View List View

  17. 1. Starting a project • The first step in this stage is to create a project comprised of all the documents, coding data and associated information that can assist during the analysis process. Seeking to restrict access to the data recorded the researcher may create a confidential password in the project. • NVivo Screenshot of a Study’s Project

  18. 2. Organising your Sources • Sources - Internals (Focus Group, Interviews, Pictures) Note: Internals are sources that can be imported into NVivo; externals are sources that cannot be imported (e.g. a source only available as an object, like a book) or may be very large files that take up too much disk space.

  19. 3. Importing Sources • NVivo can import many different file types (Pdf, Words, Audios, Videos, Pictures, etc)

  20. Audio file Document file

  21. 4. Setting up Nodes

  22. 4. Setting up Nodes NVivo 8 NVivo 11

  23. Should identifies the temporary nodes in the themes based on phrases or paragraphs in each document, and organised them in a set of lists. • In the set of list, the nodes were classified more appropriately, modifying the nodes and adding new nodes into tree nodes. The proposed conceptual framework which was developed earlier was used to group and arrange the nodes.

  24. 5. Visualising Nodes and Sources • The final tree nodes were then grouped and arranged in order to further analyse and present the research data through rebuilding and displaying the relationships between nodes. • Models are used to explore, visually, ideas about how different project items might relate to each other.

  25. Controlling for Bias • We tend to see what we want to see and may miss things that do not conform to our expectations • Use well trained recorders • Evaluators review documents and code them in themes

  26. 6. Queries Use Nvivo queries to find patterns based on coding, check for coding consistency among team members, and review your progress. Click on the Query tab on the ribbon. • Text Search & Word Frequency • Coding Query: gathers all the coding at any combination of nodes. • Matrix Coding Query creates a matrix of nodes based on search criteria.

  27. Providing Visual Data Displays • Qualitative researchers often display their findings visually • Comparison table or matrix • Hierarchical tree diagram that represents themes and their connections • Boxes that show connections between themes • Physical layout of the setting • Personal or demographic information for each person or site

  28. Examples

  29. Examples

  30. Examples Factors Influencing TT Models of Case Studies-Phase 1

  31. Making comparisons with the Literature • Interpret the data in view of past research • Show how the findings both support and contradict prior studies • “These findings are consistent with other studies in regard to duration. It has been found that the length or duration of service learning projects has an impact on student outcomes, with the longer duration projects having greater impacts. However, significant differences are not found in projects lasting over 18 weeks (Conrad & Hedin, 1981). The project on which this study focused was examined over a year and a half period of time; thus it is considered to be long in duration which helps to explain its impact on student outcomes.”

  32. Examples

  33. Validating the Accuracy of Findings At the end, the qualitative researcher validates the finding by determining the accuracy or credibility of the findings. Methods include: • Prolonged engagement & persistent observation in the field • Triangulation (expert panels) • Peer Review • Clarifying researcher bias • External Audit

  34. TQ for listening shartini@uthm.edu.my

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