Dirty Data: Longitudinal classification systems

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, the article argues for the increased use of ‘dirty data’ in longitudinal quantitative analysis, which allows for both qualitative and quantitative changes to and within classification systems to be explored. 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.