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Early Adopters of NVivo7

TAGG ORAM PARTNERSHIP. Early Adopters of NVivo7. Sarah Edwards, Karen Miller, Sharon Millar - The Health Foundation (www.health.org.uk) Dr Clare Tagg - Tagg Oram Partnership (clare@taggoram.co.uk). The Problem. Complex evaluation Multi-level: scheme, cohort, individual Longitudinal

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Early Adopters of NVivo7

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  1. TAGG ORAM PARTNERSHIP Early Adopters of NVivo7 Sarah Edwards, Karen Miller, Sharon Millar - The Health Foundation (www.health.org.uk) Dr Clare Tagg - Tagg Oram Partnership(clare@taggoram.co.uk)

  2. The Problem • Complex evaluation • Multi-level: scheme, cohort, individual • Longitudinal • Existing quantitative database (GIFTS) • Team project • 3 researchers with different approaches • Variable experience and commitment to N6/NVivo2 software • Organisational pressures • Sound analytical background • Decision to go for NVivo7 • Size of project (ie not suitable for NVivo2) • Complexity of documents not suitable for N6

  3. Setting up the project • Use of training/consultancy • Software proved easy & intuitive to learn • Used 3 sessions for structuring the project - an outsider’s view • Project structure & merging • Each person has their own documents - based on tracking individuals to provide continuity • Shared folder, node & attribute structure • Project merged to give overall picture of cohorts & schemes • Merge also used for importing new decisions about node trees etc

  4. Documents • Documents structure • stored in folders for each scheme/cohort • names chosen to order documents by scheme, cohort, individual • Case node structure reflects cohort/scheme/individual • Complex structure but easy to rectify mistakes • Attributes • Attributes applied on document import using case inheritance • Difficult to apply using casebook • Memos & annotations • Memos based on individuals • Annotations added as researcher thoughts • ‘See also’ found to be too complex

  5. Conceptual coding • Stage 1 - autocoding based on document structure • Structured interviews & documents indicated auto-coding for speed • Stage 2 - tree based on model • Conceptual tree structure grew out of model • Overlaps between conceptual coding & auto-coding • Stage 3 - tree and attributes based on sticky analysis • Used a brainstorm to develop new conceptual tree & value-laden attributes • Auto-coding abandoned • Analytic coding easy because of researcher experience but some software issues

  6. Future • Individual case studies • Increased use of memoing to support individual case study approach • Theory building • Modelling • Searching • Relationships • Realistic • Time - limited time, not an academc environment • Personal approach - can stifle creativity • Avoid software leading to a process oriented approach

  7. A good decision? • Yes • Project structure & merge • Document structure • Niggles • Financial cost: hardware, time • Coding: needs a mouse, not straightforward • Case nodes add complexity

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