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Organising and classifying data

Once data has been described / collected,

the task is to render it manageable, bring

some order to it, through a process of deconstruction and reconstruction

Deconstruction

Reconstruction

Conceptualisation

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Data organisation-

ordering and sifting Initial organisation and sorting

 ± µReading¶ data (literally and analytically)

 ± Noticing relevant phenomena (to address original

research question)

 ± Rendering data manageable (some basic ordering

needed)

 ± Segmenting data (chunking data, pulling apart)

 ± Generalising into data (finding wider themes)

«very provisional grouping, sifting, ordering of data «

Deconstruction and r econstruction-the heart of 

the analytic process

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Classification and categorisation

Classifying and cr eating categories

 ± Classification-the µclasses¶ or conceptual scheme you

identify ± Category - the individual values or categories within

the classification scheme

 ± For example the classification scheme of age may

have categories relating to age bands or groups or the classification scheme of occupations may have

categories relating to particular jobs or professions

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Deconstruction involves classifying 

and creating categoriesClassification activities may involve...? µNatural¶ classes- e.g. colours, sex

Language based- e.g. surface µnames¶ given to differentphenomena / people in the data ± µScientist¶ in science and society may be divided into several

categories e.g. Biologist, Chemist, etc

Common theme across data, mor e abstract- e.g.µritual¶ dimension to activity of eating categories might be ± Preparation / getting together / sharing out food / eating etc.

M eaningful patterns across data- more abstractsimilarities and differences e.g. in social interactionduring funeral behaviours at particular times and places,categories might be ± µManner of arriving ¶, µdisplays of grief¶ amongst groups of people

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µPATTERN¶ possibilities

include...dimensions

 ± Opposites

 ± Positive/negative«orientation ± Continuum«gradual increment

 ± Variation«key social

 ± Continual«story

 ± Trajectory/ direction«process

 ± Case study«in context

 ± Comparison«systematic

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Objectives and issues for 

classification and categorisation Objectives ± Mutually exclusive (no overlapping boundaries)

 ± Comprehensive / exhaustive (of the data)

 ± Consistently applied to data Issues

 ± Thin data«knowing what it means?

 ± Category fixedness/stability

 ± Ascribing significance to data ± Data reduction«after deconstruction comes

reconstruction which reduces data to more abstractmeaning. However, there may be some trade off inloss of nuance

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Categories and data

Assigning categories

 ± Once data classes/ categories defined, need to

assign all data to the classification scheme

Coding

 ± Mechanics of reordering data segments

 ± Provides a physical linkage between actual data andcategories (i.e. ideas and concepts)

Splitting and splicing

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Reconstruction-fitting things together 

Recontextualising data- through classification

 ± removing data from its original context e.g. a statement in an

interview uttered after a previous statement and before another,

taken and coded to a distinct analytic µcategory¶ -idea of giving the

data segment a new µpool of meaning¶ (Coffey & Atkinson)

Types of classification scheme

 ± Hierarchical (relationship of linkage)

  ± Associational (horizontal linkage)

Analytic µwork¶ following categorisation ± Frequency (counting) e.g. prevalence of attitude

 ± µIntensity¶ e.g. conviction with which attitude is held

Moving up the µladder of abstraction¶

 ± More descriptive Vs. Analytic

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Conceptualisation?

Where do the µideas¶ for labelling, naming/findingthemes and patterns come from ?

Intellectual process

 ± Hunches, ideas, creativity of researcher (althoughmust be related systematically to the data as part of analysis)

 ± Data as described

 ± Literature i.e. theories of topic

 ± Interdependence of theory and data I nductive deductive

µHypothetico - deductive¶ approach

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Use of concepts during analysis?

Definitive « intended to encompass meaning/significance of data in a precise and carefullydefined way e.g. concept of µfuneral procession¶

defines specific social phenomena; sensitising « intended to signal the direction of 

meaning, a loose relationship to data to capturecharacter of trends in the data e.g. µhome

working¶ suggestive of lifestyle choices andvarious types of occupational grouping together with relevant social phenomena but impreciseand vague definitionally

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Imagine a continuum of how concepts are

used, the work they do in analysis runningfrom

Continuum

Theoretical

Construct

Empirical

.

Group data in

relevant meaningful

way

More coherent

defined meaning

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Working with the

data set as a whole?

Cross-sectional

Non-cross-sectional

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Working with preliminary

headings/groupingsWhat kinds of social phenomena do classification

and categorising refer to

Acts

Behaviors

Activities

Accounts

Meanings

Attitudes

Participation

Understanding

Key social characteristics have been found usefulin exploring distinctions and pattern?

Social Relationships

Practices

Settings

Discourses

Social Class

Ethnicity

Sex

Age

Spatial location

e.g. urban/rural