Qualitative Data Analysis with NVivo 8 David Palfreyman

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Qualitative Data Analysis with NVivo 8 David Palfreyman

Qualitative Data Analysis with NVivo 8 David Palfreyman

Outline �Qualitative data and how to analyze it. �Your data �Nvivo 8 2 March

Outline �Qualitative data and how to analyze it. �Your data �Nvivo 8 2 March 2007 David Palfreyman

Types of qualitative data Interviews Text Focus groups Images Documents Audio Observation Video Artifacts

Types of qualitative data Interviews Text Focus groups Images Documents Audio Observation Video Artifacts 2 March 2007 David Palfreyman

Research questions �Do teachers in lower- and higher-resourced schools have different attitudes to discipline?

Research questions �Do teachers in lower- and higher-resourced schools have different attitudes to discipline? May 2009 David Palfreyman

Units of analysis L O T S O F D A T A (in

Units of analysis L O T S O F D A T A (in bundles) �Cases �Attributes �Data extracts (e. g. quotations) �Codes (labels) - Relations - Themes 2 March 2007 David Palfreyman M E M O S

MS Word for smaller projects �Search (CTRL+F; Shift+F 4) �Coding with formats (bold, italics,

MS Word for smaller projects �Search (CTRL+F; Shift+F 4) �Coding with formats (bold, italics, font, size, colour) �Insert comments �Collect quotations (Find formats, copy and paste) 2 March 2007 David Palfreyman

Set up your project in Nvivo �Sources: input data (documents, media files, …); memos.

Set up your project in Nvivo �Sources: input data (documents, media files, …); memos. �Nodes: store ideas and coding. �Sets: group your sources and ideas. May 2009 David Palfreyman

Analyze with NVivo � Finding bits of data (e. g. How many informants are

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? ) 2 March 2007 David Palfreyman

Seeing (and showing) the bigger picture �Memos: record your thoughts about the data while

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? ) May 2009 David Palfreyman

Online resources Grounded Theory: A thumbnail sketch http: //www. scu. edu. au/schools/gcm/ar/arp/grounded. html (A

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 theoretical assumptions they embody). Nvivo 8 tutorials http: //www. qsrinternational. com/support_tutorials. aspx? productid=18 http: //qsrinternational. fileburst. com/Document/NVivo 8/Teach_Yourself_NVivo_8_Tutorials. pdf 2 March 2007 David Palfreyman