Multifactorial Survey Experiments (Factorial Surveys, Choice Experiments and Conjoint Analysis) 2 |
|
Convenor | Professor Katrin Auspurg (Goethe-University Frankfurt ) |
Coordinator 1 | Dr Carsten Sauer (Bielefeld University) |
Coordinator 2 | Professor Peter M. Steiner (University of Wisconsin-Madison) |
We introduce a new procedure for the interval level measurement of preferences in factorial surveys: The attribute difference scaling method which enables the implementation of factorial surveys without assuming inter-individual homogeneous rating behavior. This study compares an attribute scaled and a standard factorial survey with respect to their interval scaling quality and their out-of-sample predictive validity regarding a set of discrete choices. Furthermore, this comparison is carried out with four different rating measurement instruments, enabling a comprehensive assessment of instrument effects. As test case a five dimensional, full factorial design measuring students’ preferences regarding internships is used.
It has been argued that factorial survey designs can help to overcome social desirability bias when potentially sensitive dimensions are ‘hidden’ in vignette texts. In order to evaluate which implementation works best for sensitive topics, we implemented a split half experiment in an online factorial survey module, which was answered by a general population sample. Our vignette module assesses respondents’ judgments on just fees for early childcare and includes the child’s religious denomination (Christian, Muslim, none) as a sensitive dimension. This set up enables us to compare the widely used within-respondent variation with an alternative between-respondent implementation.
Empirical research on discrimination grapples with the social undesirability of its object. In many studies using regular survey methods, estimates are biased, and the social context of discrimination is not taken into account. Several methods have been developed, especially to deal with the first problem. In this regard, the estimation of the ‘true value’ of discriminatory attitudes is at the centre of interest. However, methodological contributions focusing on the social context of attitude communication and discriminatory behaviour, as well as the correlation between both, are rare. We present two experimental methods which address those issues: factorial surveys and choice experiments.
This paper proposes a flexible method for exploring and accommodating situations where respondents exhibit elimination and selection by aspect decision rules in discrete choice experiments, whilst addressing preference heterogeneity. We present an empirical case study on the public's preferences for health service innovations. We show that allowing for elimination-by-aspects and/or selection-by-aspects behavioural rules leads to substantial improvements in model fit and, importantly, has implications for willingness to pay estimates and scenario analysis.
The panel study 'Crime in the modern City' focusses on the emergence and development of deviant and delinquent behaviour of adolescents. A verbal scenario is implemented in the questionnaire since 2013. It serves as a measurement for (hypothetical) reactions to deviant behaviour in a specific conflictual situation. As this type of measurement should trigger certain scripts for reactions, the evocation of external social norms, carrying aspects of Social Desirability is possible, too. With the application of Latent Class Analysis this potential response bias can be anticipated: It gives the opportunity to separate honest from desirable answering patterns for further analyses.