Some New Developments

In the means-end approach as described in Sect. 4.2 the data are collected in a very open-ended way. A consequence of this is that the data collection and processing is laborious and that because of this often only a limited number of interviews is per-formed. The process remains laborious, even in the meaning structure approach in which the elicitation of salient dwelling features is much less open-ended and in which the meaning structures are constructed on paper during the interview.

Recently, we have administered laddering interviewing in a computer-aided tele-phone survey, of which some results are presented in Coolen (2008) and many more in Meesters (2009). In this survey the number of features was limited and the semi-structured interviewing format was again adapted. Based on previous research only eight important dwelling features were part of the questionnaire: tenure, num-ber of rooms, size of living room, dwelling type, garden, type of neighborhood, type of location, and type of architecture. For each of these features respondents

were first asked what level they preferred. Having indicated their preferred level the respondents were subsequently asked what the most important reason was for pre-ferring this level. This was an open-ended question, while for the coding of the answers ‘field coding’ was applied, where the interviewer is supplied with a set of categories within which he/she has to try to code the answer given by the intervie-wee. If an answer cannot be coded into one of the supplied categories, the inter-viewer has to note down the answer of the respondent. The set of categories for the survey was compiled on the basis of several pilot projects in which semi-structured face-to-face interviews were conducted without previously determined categories, which were subsequently transcribed and content-analyzed. The interviewers who conducted the survey were trained in field-coding the answers to the open-ended question. After the survey it turned out that between 80% and 85% of the answers were coded in one of the pre-specified categories. A content analysis was per-formed on the other answers, which resulted in very few additional categories. The remaining answers were too idiosyncratic to be categorized and were collected in the category ‘other’. It is evident that the pilot studies were instrumental in achiev-ing these results in the survey.

In the previous sections it has been indicated that there are several reasons for replacing the hierarchical value map by a network representation. The dwelling is a too complex and heterogeneous good to make the paper-and-pencil technique for constructing hierarchical value maps feasible. Moreover, as suggested by Valette-Florence and Rapacchi (1991), the paper-and-pencil method can lead to various omissions and errors. In addition, Van Rekom and Wierenga (2007) have recently shown that meaning structures are not necessarily hierarchical in nature, and that for non-hierarchical meaning structures a network representation is more appropri-ate. Up to now we have only presented meaning networks of separate housing features. Hartig (2006) has remarked that it would be interesting to integrate and aggregate the meaning networks of different housing features. Although this has not fully materialized yet, several ways of aggregating meaning networks related to housing features have been explored by Meesters (2009).

4.5 Conclusion

In this chapter the meaning structure method for measuring housing preferences has been presented. The approach focuses on what preferences people have and why they have these preferences. Because of this double focus the meaning structure method differs from most other approaches to measuring housing preferences, which only focus on what people want and ignore the why question. The meaning structure approach consists of a conceptual model, a measurement procedure, and an analysis method. The original means-end approach, as is usually applied for marketing and advertising purposes, was presented in Sect. 4.2 together with the results of a pilot study in the domain of housing. This pilot study gave rise to a revision of the conceptual framework, the measurement procedure, and the analysis

method. The meaning structure method was described in Sect. 4.3. This method makes, among other things, the presentation of meaning structures more reliable by using network displays and adds a quantitative dimension to the originally more qualitatively focused analysis of these aggregated presentations by employing net-work statistics.

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Preference and Choice, DOI 10.1007/978-90-481-8894-9_5,

© Springer Science+Business Media B.V. 2011

5.1 Introduction

When choosing between alternative places of residence, the decision-maker has to consider multiple attributes of the available alternatives at the same time, such as the preferred dwelling type, number of rooms and costs. Thus, the decision problem has multiple value dimensions, which may be in conflict, as is usually the case with difficult choices (Von Winterfeldt and Edwards 1986, p. 259). For example, the current dwelling might be relatively large and cheap but situated in a bad neighbor-hood, whereas an alternative dwelling might be situated in a better neighborhood but this comes at the cost of higher rent or less space. Should the resident move?

Multi-criteria decision-making techniques can be used to facilitate such complex decisions. Within a multi-criteria context, decision-making problems are repre-sented as a decision-maker who considers a set of alternatives and seeks to make an optimal decision considering all the factors (so-called criteria or attributes) that are relevant to the decision. One way of doing this is by measuring the decision-maker’s values separately for a set of influential attributes and by weighting these by the relative importance of these attributes as perceived by the decision-maker. It is assumed that the more important attributes will have a greater impact in deter-mining preferences or choices. Thus, given the factors we care about, what’s the best choice? Combining the importance that respondents assign to different attri-butes with their evaluation of those attriattri-butes can be achieved using Multi-Attribute Utility Theory, often referred to as MAUT.

Formally, Multi-Attribute Utility theory is a technique to support decision-making when a decision-maker has to choose from a limited number of available alternatives. For example, these alternatives could be dwellings that are available at

S.J.T. Jansen (*)

OTB Research Institute for the Built Environment, Delft University of Technology, Delft, The Netherlands e-mail: s.j.t.jansen@tudelft.nl