1 / 10

Qualitative Data Analysis with NVivo 8

Qualitative Data Analysis with NVivo 8. David Palfreyman. Outline. Qualitative data and how to analyze it. Your data Nvivo 8. Types of qualitative data. Text Images Audio Video. Interviews Focus groups Documents Observation Artifacts. Research questions.

lparnell
Download Presentation

Qualitative Data Analysis with NVivo 8

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Qualitative Data Analysis with NVivo8 David Palfreyman

  2. Outline • Qualitative data and how to analyze it. • Your data • Nvivo 8 David Palfreyman

  3. Types of qualitative data Text Images Audio Video Interviews Focus groups Documents Observation Artifacts David Palfreyman

  4. Research questions • Do teachers in lower- and higher-resourced schools have different attitudes to discipline? David Palfreyman

  5. Units of analysis • Cases • Attributes • Data extracts (e.g. quotations) • Codes (labels) • LOTS OF DATA (in bundles) MEMO S - Relations - - Themes - David Palfreyman

  6. MS Word for smaller projects • Search (CTRL+F; Shift+F4) • Coding with formats (bold, italics, font, size, colour) • Insert comments • Collect quotations (Find formats, copy and paste) David Palfreyman

  7. Set up your project in Nvivo • Sources: input data (documents, media files, …); memos. • Nodes: store ideas and coding. • Sets: group your sources and ideas. David Palfreyman

  8. Analyze with NVivo • Finding bits of data(e.g. How many informants are over 25? OR: Who mentions “commitment” in their interview?OR: I had a document and some information about Mary – or was it Maria…? – and did I make a note about her?) • Coding: labelling bits of data(e.g. This person finds ZU students “difficult” –like my previous interviewee) • Queries: asking questions of your data(e.g. How has “commitment” been referred to in the focus groups?OR: Do any of the participants make a connection between “family” and “motivation”?OR: Do men and women tend to differ in their priorities?) David Palfreyman

  9. Seeing (and showing) the bigger picture • Memos: record your thoughts about the data while you remember them! • Models: visualize what is going on in the data.(e.g. concept maps, processes, categories, dimensions) • Links: connect data items and content. • Quantifying codes(e.g. Is there a statistically significant difference between men’s and women’s comments on this issue?) David Palfreyman

  10. Online resources Grounded Theory: A thumbnail sketch http://www.scu.edu.au/schools/gcm/ar/arp/grounded.html (A clear summary of Grounded Theory as applied to actual data). CAQDAS: A primer http://www.lboro.ac.uk/research/mmethods/research/software/caqdas_primer.html (a detailed review of various softwares for QDA, comparing their features and also the theoretical assumptions they embody). Nvivo 8 tutorials http://www.qsrinternational.com/support_tutorials.aspx?productid=18 http://qsrinternational.fileburst.com/Document/NVivo8/Teach_Yourself_NVivo_8_Tutorials.pdf David Palfreyman

More Related