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Dirty data : longitudinal classification systems

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Uprichard, Emma (2011) Dirty data : longitudinal classification systems. Sociological Review, Vol.59 (Suppl.S2). pp. 93-112. doi:10.1111/j.1467-954X.2012.02058.x ISSN 0038-0261.

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Official URL: http://dx.doi.org/10.1111/j.1467-954X.2012.02058.x

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Abstract

Typically in longitudinal quantitative research, classifications are tracked over time. However, most classifications change in absolute terms in that some die whilst others are created, and in their meaning. There is a need, therefore, to re-think how longitudinal quantitative research might explore both the qualitative changes to classification systems as well as the quantitative changes within each classification. By drawing on the changing classifications of local food retail outlets in the city of York (UK) since the 1950s as an illustrative example, an alternative way of graphing longitudinal quantitative data is presented which ultimately provides a description of both types of change over time. In so doing, this article argues for the increased use of ‘dirty data’ in longitudinal quantitative analysis, a step which allows for the exploration of both qualitative and quantitative changes to, and within, classification systems. This ultimately challenges existing assumptions relating to the quality and type of data used in quantitative research and how change in the social world is measured in general.

Item Type: Journal Article
Divisions: Faculty of Social Sciences > Centre for Interdisciplinary Methodologies
Journal or Publication Title: Sociological Review
Publisher: Wiley-Blackwell Publishing Ltd.
ISSN: 0038-0261
Official Date: December 2011
Dates:
DateEvent
December 2011Published
Volume: Vol.59
Number: Suppl.S2
Page Range: pp. 93-112
DOI: 10.1111/j.1467-954X.2012.02058.x
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Description:

Special Issue: Sociological Review Monograph Series: Measure and Value, edited by Lisa Adkins and Celia Lury

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