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

2. Session Aim and Objectives
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 Clustering
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 Nodes Coding
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
- 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 Respondent (n=5) Overview of TT Category
Professor (researcher)
Transfer of technology from overseas into our country, Malaysia. Transfer from a company overseas to a company in our country
Cross-border transfer
TT is when we need to source for something that is not readily available in Malaysia.
Technology transfer is when we are able to implement a new technology locally through our investment or involvement in projects. Usually, the technology is obtained from abroad.
Acquisition,
Cross-border
transfer
Manager
Mega construction projects will definitely involve some new technologies from foreign countries. Technologies that we have never experienced before. These are the new material and machinery technologies, which we can learn from.
President
TT should refer to new construction techniques or materials introduced by foreign suppliers.
General Manager
TT is the process of transferring knowledge, expertise and work implementation methods from one country to another. TT can also be defined as the transfer of technology that is utilised in the construction until the completion of a project.

29. LEVEL OF ABSORPTIVE CAPACITY
Examples
LEVEL OF ABSORPTIVE CAPACITY
Company A
Company B
Company C
Company D
Company E
Company F
L 1
L 2
L 3
L 4
L 5
EMPLOYEES' ABILITY
Performance Appraisal
- Behavioral Competencies √
- Technical Competencies
Employees' Training
- Overseas Training Opportunity  x
  √ √ 
  x x 
EMPLOYEES' MOTIVATION
Performance Based Compensation  √ x
Merit Based Promotion

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