Two ideal types of data can be distinguished in housing research: structured and less-structured data.
Questionnaires and official statistics are examples of structured data, while less-structured data arise for instance from
open interviews and documents. Structured data are sometimes labelled quantitative, while less-structured data are
called qualitative. In this paper structured and less-structured data are considered from the perspective of measurement
and analysis. Structured data arise when the researcher has an a priori category system or measurement scale available
for collecting the data. When such an a priori system or scale is not available the data are called less-structured.
It will be argued that these less-structured observations can only be used for any further analysis when they contain
some minimum level of structure called a category system, which is equivalent to a nominal measurement scale. Once
this becomes evident, one realizes that through the necessary process of categorization less-structured data can be analyzed
in much the same way as structured data, and that the difference between the two types of data is one of degree
and not of kind. In the second part of the paper these ideas are illustrated with examples from my own research on the
meaning of preferences for dwelling features in which the concept of a meaning structure plays a central part. Until
now these meaning structures have been determined by means of semi-structured interviews which, even with small
samples, result in large amounts of less-structured data.
Keywords : Less-Structured Data, Qualitative Data Analysis, Meaning of a Dwelling, Housing Preference.
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