What is Framework? Matt Barnard National Centre for Social Research

Framework in context Two approaches to analytical process 1 – data management/ interpretation coterminous 2 – data management/ interpretation distinct NatCen follows second approach Framework is a data management tool Aim is to organise data to facilitate interpretation Interpretation not dependent on Framework But some interpretation facilitated by Framework

Objectives of data management Primary objective Re-assemble ‘fractured discourse’ Chunking Secondary objective Reduce and prioritise Create ‘map’ of data

Approaches to data management Approaches vary in how data is cut Between cases Within cases Approaches vary in mode Manual/ paper-based CAQDAS Choice depends on Requirements of research studies Objectives and ‘meta-questions’ Types of data

Theme-based approaches Data sorted by thematic categories, eg Type of crime Contact with police Impact of crime Operationalised by Applying codes in coloured pens Physically cutting up data Using CAQDAS tool for coding

Theme-based approach Crime Contact with CJS Impact

Case-based approaches Data sorted within cases Chronology of event Individuals previous/ current experiences of crime Impact of crime on various areas of life Operationalised by Annotating transcript Mapping connections within case Using mapping/ graphics software File cards

Case-based approach Jim Bob Wendy TheftContact with police Impact

Case and theme based approaches Data sorted both thematically and by case Operationalised Creating a grid/ matrix Transfer data for each case into relevant cells

Case and theme based approach

Framework Developed by Jane Ritchie and Liz Spencer Key characteristics Case and theme based approach Hierarchy of themes and sub-themes Reduces data through summarisation and synthesis Retains links to original data

Guidelines for entering data Summarise material from transcript Retain language Note page reference Use agreed abbreviations/ conventions Avoid repetition by cross referencing

What next? Framework provides well organised data set Data management not a substitute for interpretation Analyst bring questions to data set, answered by Thematic analysis Developing typologies Explanatory analysis

Thematic analysis Categories of phenomena Involves looking at a single theme In Framework looking down one or more columns Aim to describe range and diversity Output examples Sources of debt advice Coping strategies Significance of birth registration Impacts of homelessness

Developing typologies Categories of people, cases or processes Involves looking at themes and cases In Framework looking down columns and across rows Output examples Types of parents with learning difficulties Types of grant making processes Types of outcomes of contact with drug treatment

Explanatory analysis Explanations of attitudes, choices or impacts Involves looking at themes and cases In Framework looking across rows and comparing and contrasting rows Output examples Reasons people don’t like busses Factors influencing outcomes of contact with treatment services Explanations for why young people become homeless

Framework pros and cons Advantages Systematic Comprehensive Transparent Disadvantages Time and labour intensive Danger of become process rather than outcome focused

References/ resources Ritchie, J., & Lewis, J. (2003) Qualitative Research Practice: A guide for Social Science Students and Researchers, London: Sage Qualitative Follow-up of the British Gambling Prevalence Survey gambling-prevalence-survey-2007 The Drug Treatment Outcomes Research Study: Qualitative Study study

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