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Cross-over Methodology How does GDA serve as a framework for integrating different species of empirical data, compared to other such traditions? Jan Thorhauge Frederiksen, Dep. Of Psychology & Educational Studies, Roskilde University 14.

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Cross-over Methodology

How does GDA serve as a framework for integrating different species of empirical data, compared to other such traditions?

Jan Thorhauge Frederiksen,

  • Dep. Of Psychology & Educational Studies,

Roskilde University

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Multiple Empirical Forms of Data

  • Triangulation

– Different measurings of the same phenomenon

  • Bricolage (Levi-Strauss, Kincheloe)

– Adapting data production to conditions in the field, on the fly

  • Mixed Methods (Creswell & Clark, Flick,

Johnson, Brannen, Bryman)

– Different empirical phases in an epistemological - ly coherent design

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Levels of Method Integration

  • Empirical: Data relating to data

– Same individuals are reconstructed in different empirical ways

  • Analytical: constructs relating to constructs

– Analytical constructs are related to each other

  • Theoretical: relations between systems of

constructs

– relations guided by theoretical interpretations

  • Structural / Sequential: related by design

– E.g. findings guiding interviewee selection

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Integrational shortcomings

  • Triangulation: solely interpretational integration –

a theoretical claim of measurability

  • Bricolage: a claim of inherent, empirical integration

achieved on the fly (Kincheloe 2001)

  • Mixed Methods: structural integration implying

complete integration (Design towards specific forms of knowledge production) (Brannen 2005) Yet all presume data incommensurability, and method incongruence

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The production of the scientific object

Inherent assumptions of relations between data and methods.

E.g.: production of the statistical object: Cleaning, recoding, transforming – what assumptions are imposed on data in these steps?

implicit relations on all levels:

implicit theoretical assumptions relating methods Implicit empirical connections Implicit analytical consistency

The conditions of production may be uninspectable

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GDA – opportunities for explicitation

Quantitative findings often dissociated from individuals implicit a priori connections with no individual relations

Benzécri : Statistically quantities [defined] in such a manner that the final construction will be independent from arbitrary constructions due to apriori ideas (1984:128) What currents of law traverse the ocean of data? (1973:v)

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Sample Study – social educators

A study on the relations between social origin of students, and the educational strategies, and the demands of the training (Frederiksen 2010)

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Specific MCA – Active Questions

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Soc.educ. Experience:

  • Nursery/Nursery School(3)
  • Afterschool/SFO(2)
  • Special Care(2)
  • Other experience(2)
  • Daycare(2)
  • SocEduc Courses before(2)
  • Coach/Scout/Voluntary(2)

Education(7+2) Previous career(5)

  • None
  • Health/care
  • Teacher/club
  • Shop/Office
  • Craftsman/Art
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Eigenvalues and modified rates

0,0000 0,0500 0,1000 0,1500 0,2000 0,2500 1 3 5 7 9 11 13 15 17 19 21 23 25 27

Eigenvalues

Axis Eigenvalue Rate of variance Accumulated modified rate of variance

1 0,2051 10,23% 36,5% 2 0,1989 20,14% 68,6% 3 0,1691 28,57% 83,5%

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Educational Capital+ Capital of Care+ Vocational capital +

As I am mainly concerned with methods here, I won’t go into CTRs etc.

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Empirical connections

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Data relating to data: Interview-willingness and position in the space of student student trajectories Allows me to assess the interviews as empirical products in relation to the other data, e.g.in terms of validity or resistance

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Analytical connections

On the one hand:

  • Strategies, as

constructed from interviews with students, and classroom

  • bservations

On the other hand

  • Dominant and

dominated positions in social space, as created by sMCA of student admission data

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Necessary Knowledge Investment strategy dominated by Care-based educational Ascent strategy

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Interpretational connections

Biographical analysis Central transistional event in biography of a young student, Anita: Moving away from provinces, to Capital

Escaping provincial lifestyle, limited employment

  • pportunities, making use of

educational capital

Structured Data Analysis Regional recruitment differences related to

  • educational capital
  • age
  • complexity of

educational & working career

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The biographical transition from province to capital made by Anita reiterates the structural

  • pposition

between capital and provincial NISE. She literally moves to where her sort of students are to be found.

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In Conclusion…

  • Empirical relations are inherently avaiable in GDA,

providing many new avenues of analysis

  • Analytical relations may both be explicated and depic-

ted visually compelling, and the geometrical depictions allows for multidimensional appraisal of relations easily thought of as binary, or dichotomous (e.g. dominance)

  • Theoretical relations – in casu, homologies – may be

compared and assessed rigourously, without formalized testing

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