Using NVivo as a Tool in the Data Management and Analysis Processes

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Using NVivo as a Tool in the Data Management and
Analysis Processes

• Dan Kaczynski
• Associate Professor, Graduate Education
• University of West Florida
• dkaczyns_at_uwf.edu

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Advances in Qualitative Research(new Knowledge
Happens)

• Moving from Acceptance to Parity
• Richer Range of Theoretical Perspectives
• Mixed Methods / Blended Designs
• Demand for Quality

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QDAS / CAQDAS

• Using software to facilitate the management and
  analysis of qualitative data
• The software is a tool that assists the
  researcher
• The researcher (not the tool) drives the process

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Past and Future of QDAS

• The past 25 years
• From skepticism to acceptance to mainstream
  adoption
• Working without computers is no longer an option
  for qualitative (or indeed any) researchers.
  (Richards, p.3)
• Qualitative Research Market 10
• Quantitative Research Market 90
• Toward the future
• Increased developer/researcher collaboration
• Demand for theory-driven tools
• NVivo 7

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Pre-QDAS Considerations

• It is important for the researcher to know where
  he or she is coming from before launching into
  the software.
• Clarity
• Purpose
• Focus
• Flexible Emergent Design
• prior planning is essential
• rigor is not rigid
• Theoretical Perspective
• How does theory shape my inquiry?

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Types of Qualitative Data

• Data Triangulation
• Interviews
• Observations
• Documents
• Digital Transcription
• Virtual Data

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Data Management

• Storage NVivo allows you to store and catalog
  varied types data sources.
• Interview transcripts (Text, Audio)
• Fieldnotes
• Documents (Internal, External)
• Retrieval Capabilities such as linking
  documents and finding passages facilitate the
  immersion process.
• Linking (Data, Codes)
• Memoing
• Searching
• Text
• Boolean
• Node
• Attribute
• Proximity

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CODING

• "Coding is a procedure that disaggregates the
  data, breaks it down into manageable segments,
  and identifies or names those segments."
• (3 Pitfalls)
• 1. Code to develop an understanding (avoid
  descriptive)
• 2. Build upon your conceptual framework (avoid
  mechanical)
• 3. Coding is a fluid process (avoid fixed
  structures)
• (Schwandt, p. 26-27)

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Data Analysis Codingstages of grounded theory
coding (Strauss Corbin, 1998)

• 1. Open
• Breaking down data in the initial stage
• Becoming immersed in the data
• Identifying properties dimensions
• Generating naming categories
• 2. Axial
• Linking data around the axis of a category in
  new ways
• Developing several main categories
• Connecting categories subcategories
• Searching for convergence and recurring
  regularities
• 3. Selective
• Seeking saturation of categories
• Searching for divergence
• Examining discrepant and deviant cases
• Integrating categories to form and refine
  theory (theory building)
• Identifying a core or central category

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Data Analysis - Memos

• Memos are cumulative and meant to be personally
  useful they should reflect thinking in progress
  rather than polished ideas intended for others.
  Make them your own. (Schram, 2003, p. 27)
• Methods
• Documents the basis for design decisions
• Provides an audit trail of design changes
• Researcher reflections
• Strengthens conceptual framework
• Enhances credibility of researcher as
  instrument
• Analytic
• Captures the researchers ideas about what the
  data means
• Illuminates the researchers interpretive
  processes