Online measurement of mental representations of complex spatial decision problems: Comparison of CNET and hard laddering |
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| Affiliation: | 1. Eindhoven University of Technology, Urban Planning Group, PO Box 513, 5600 MB Eindhoven, The Netherlands;2. Erasmus University Rotterdam, Erasmus School of Economics, Department of Business, Economics, Marketing Section, PO Box 1738, 3000 DR Rotterdam, The Netherlands;1. Postdoctoral Fellow, Canada Research Chair in Environment, Society and Policy, Department of Geography, University of Ottawa, Canada;2. Geography Department, Laval University; Director, Quebec Council for Geopolitical Studies, Canada;1. College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, PR China;2. Traffic Operations Division Texas Department of Transportation Austin, TX 78701-2483, USA;1. Swedish University of Agricultural Sciences, Department of Economics, P.O. Box 7013, 750 07 Uppsala, Sweden;2. The Swedish Institute for Food and Biotechnology, P.O. Box 5401, 402 29 Gothenburg, Sweden;1. DIPF | Leibniz Institute for Research and Information in Education, Frankfurt am Main, Germany;2. Center for Research on Individual Development and Adaptive Education of Children at Risk (IDeA), Frankfurt am Main, Germany;3. Institute of Psychology, Heidelberg University, Heidelberg, Germany;4. Institute of Medical Psychology at the Center for Psychosocial Medicine, University Hospital Heidelberg, Heidelberg, Germany |
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| Abstract: | This paper introduces the online Causal Network Elicitation Technique (CNET), as a technique for measuring components of mental representations of choice tasks and compares it with the more common technique of online ‘hard’ laddering (HL). While CNET works in basically two phases, one in open question format and one as guided linking of attributes and benefits, HL works completely structured with revealed attributes and benefits. Mental representations of two activity travel tasks were collected with both techniques among members of a nationwide Dutch household panel. The results confirm the hypothesis that the revealed format of variables in HL has an effect on the indication of variables as the elicited mental representations are almost twice as big for HL than for CNET. Furthermore, it turned out that CNET is more sensitive in measuring shifts among attributes in the mental representations for situational changes of the activity-travel task. |
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| Keywords: | Mental representations Online qualitative data collection Causal Network Elicitation Hard laddering Activity-travel scheduling |
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