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Research Methods. Qualitative data analysis. Session outline. Qualitative data analysis: what it is, aims, initial thoughts and common mistakes Approaches to data analysis: Theoretical propositions, Triangulation, Grounded theory, Content analysis
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Research Methods Qualitative data analysis
Session outline • Qualitative data analysis: what it is, aims, initial thoughts and common mistakes • Approaches to data analysis: Theoretical propositions, Triangulation, Grounded theory, Content analysis • 4 key steps: Speculative analysis, Data Reduction, Data Display, Drawing and verifying conclusions
field notes - descriptions of observations, events interview transcripts documents - eg. policies, syllabuses open ended questionnaire answers Decide how to analyse your data BEFORE you collect it! NOT easier than quantitative data analysis - but different Types of qualitative data 2 messages:
Data Analysis Initial thoughts: * fatigue - time/energy in collecting data * I’ve done the hard bit * where do I start?? * writers block Go back to research questions… *what did the study aim to do *what were your initial hunches
Aims/purposes of analysis • central to the production of meaningful and valid research • from which you draw your conclusions • process by which you convince your readers of these • therefore needs to be open, public process - how did you get there?
No specified conventions for analysing qualitative data in the way that exists for quantitative data. Nevertheless, still need systematic approach to examining and interpreting the results of the data gathering. Preliminary and ongoing analysis should be engaged in as data is collected distinction data collection and analysis less sharp for qualitative studies than for quantitative. Preliminary considerations
Qualitative is not as easy as it looks… • Qualitative analysis is ‘as much a test of the enquirer as it is a test of the data: • first and foremost, analysis is a test of the...ability to think - to process information in a meaningful and useful manner...qualitative analysis remains much closer to codified common-sense than the complexities of statistical analysis of quantitative data’ (Robson 1995: 374). • As a result it is important to recognise some common failings.
data overload - too much to process and remember information availability - information which is difficult to get hold of (wrongly) gets less attention positive instances - a tendency to ignore evidence which conflicts with hypotheses uneven reliability - that some sources are more reliable than others sometimes gets ignored fictional base - tendency to compare with a base or average when no base data is available confidence in judgement - excessive confidence once a judgement is made co-occurrence - interpreted as strong evidence of correlation inconsistency - repeated evaluations of the same data which differ Common mistakes (Robson, 1995: 374-5)
Approaches to analysis • Theoretical propositions • Triangulation • Grounded theory • Content analysis
Theoretical propositions • The task of analysing data can be more systematic if the study is based on particular theoretical propositions (or a conceptual framework).
Triangulation • Triangulation involves gathering data on the same theme from a variety of sources. • It can be useful in data analysis whether or not there are correspondences or discrepancies.
Grounded theory • Provides an ‘open’ approach to data analysis where the structure is derived from the data. This means systematically analysing it so as to tease out themes, patterns and categories. • Jones (1987 cited in Easterby-Smith et al 1993: 108) argues that ‘rather than forcing data within logico-deductively derived assumptions and categories, research should be used to generate grounded theory, which ‘fits’ and ‘works’ because it is derived from the concepts and categories used by social actors themselves to interpret and organise their worlds’.
Grounded theory analysis: Seven main stages (1/2) (Easterby-Smith et al,1987) • familiarisation - re-reading transcripts and preliminary impressions • reflection - questions posed might include ‘does it support existing knowledge?’‘does it challenge it?’. Cataloguing is important. There may be a range of ideas, hypotheses or explanations. • conceptualisation - a set of concepts or variables are identified as important
Grounded theory analysis: Seven main stages (2/2) (Easterby-Smith et al,1987) • cataloguing concepts - the main concepts are organised and recorded • recoding - the concepts are examined by revisiting the data to understand how they may apply in different contexts • linking - the analytical framework and explanations become more refined. Findings and ideas may be discussed with others • re-evaluation - refinement in the light of comment by others and reflection
Content analysis and hermeneutics: what it is • Detailed analysis of the contents of a certain body of texts. • Useful to analyse marketing materials such as adverts and brochures, District Council tourism strategies, and other written documents. • A research method in its own right, and also a data analysis technique for in-depth interview transcripts, focus groups…
Content analysis: what it does • Some authors emphasize frequency (e.g. number of times particular items are mentioned) but this is very mechanistic and content analysis can go further and analyse other components and also data in ways other than counting. • Frequency can be preliminary analysis to then allow other research
Qualitative data analysis: 4 Key Steps 1. Speculative analysis 2. Data Reduction 3. Data Display 4. Drawing and verifying conclusions
1. Speculative analysis • on going throughout the data collection, and confirmed through data transcription • initial hunches, thoughts, reactions • often recorded alongside the actual data * eg before and after an interview- what did you think?
reduce data to manageable and meaningful amounts reduction is NOT ignoring/getting rid of data involves coding or categorising of the data categories need to be exclusive a code is a means of classifying events, actions, ideas a means of quickly retrieving data a means to develop an analytical framework need to code data using theoretical sensitivity 2. Data Reduction
Data Reduction(Robson,1995: 401) • counting - categorising data and measuring frequency • patterning - noting recurring patterns or themes • clustering - groupings of objects, persons, activities, settings etc. with similar characteristics • factoring - grouping of variables into a small number of hypothetical factors • relating variables - explaining the type of relationship between two variables (if any) • building causal networks - chains or webs of linkages between variables • relating findings to general theoretical frameworks - find general propositions that account for the particular findings in the study
Theoretical Sensitivity • ‘Theoretical sensitivity is the ability to recognise what is important in the data and to give it meaning’ (Hitchcock and Hughes, 1995, p.298) • thorough understanding of theoretical and research literature and personal/ professional experience • continual interaction with the data
quantitative data theory- data collection to test - confirm or refute the theory qualitative data start with the data - builds theory ‘grounded theory’ more open-ended, creative process Data - Theory relationshipin Qualitative Research
3. Data Display • clear to the reader • two key ways: • narrative text - discussion of key themes, or issues, illustrated with data (eg. quotations, q’naire answers etc) • Visual presentation - eg. pie diagrams, tables etc • not either/or, can use a mixture...
Narrative Text • Key themes identified by reading and re-reading data • sifting and sorting into categories • describing and analysing the data is intertwined, drawing on theoretical framework of the study) • (analysis of data consider the implications of the data -why is what you have found important and in what ways?)
Visual Presentation • presents a clear visual image of patterns/themes • titles, scales, and reference to the diagrams in text is IMPORTANT • avoid using % if numbers are small • avoid describing what is in a table in the text..explore the implications instead
4. Drawing and verifying conclusions • representativeness of sample - don’t over generalise • your interpretation of events - • triangulation of data collection? • claiming things that are not there! • present evidence from your data to support interpretations eg. quotations, observations
Conclusions • admit limitations in data collection • strength, not weakness • contributes to the research validity • demonstrates your reflexivity as researcher • Finally ...Good qualitative research depends upon good analysis not just description.