Attribute Range Effects in Binary Response Tasks
By Tobias Ohler, Aihong Le, Jordan J. Louviere, Joffre Swait. 2000
86 citations recorded by the source corpus.
Subfields: Economics and Econometrics, General Decision Sciences, Marketing
View Paper: https://www.semanticscholar.org/paper/d1eec82cc22270e56050946edb1ab6e50040615f
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Jordan J. Louviere
- S. B. Olsen
- Riccardo Scarpa
- David A. Hensher
- John M. Rose
- Stephane Hess
- Ian J. Bateman
- Richard T. Carson
In General Decision Sciences
- Jordan J. Louviere
- David A. Hensher
- S. B. Olsen
- Riccardo Scarpa
- John M. Rose
- Richard T. Carson
- Stephane Hess
- Ian J. Bateman
In Marketing
- Jordan J. Louviere
- S. B. Olsen
- David A. Hensher
- Riccardo Scarpa
- John M. Rose
- Stephane Hess
- J. Ladenburg
- Richard T. Carson
Citations in the corpus
- Dissecting the Random Component of Utility (2002, background): “Ohler et al. (1999) studied how attribute range differences impacted binary response outcomes and random component variability in a conjoint analysis task by varying attribute ranges over two levels (a wide range, and middle levels in the wide range).”
- Design efficiency for non-market valuation with choice modelling: how to measure it, what to report and why* (2008, unknown): “…and alternatives (DeShazo and Fermo 2002; Arentze et al. 2003), the impact of attribute level range upon response (Cooke and Mellers 1995; Ohler et al. 2000; Verlegh 280 R. Scarpa and J. Rose © 2008 The Authors Journal compilation © 2008 Australian Agricultural and Resource Economics…”
- Gender-specific starting point bias in choice experiments: Evidence from an empirical study (2008, background): “Hanley et al. [32] and Ohler et al. [ 64 ] find no effect of changing the price vector used whereas Carlsson and Martinsson [21] and Ryan and Wordsworth [68] find the opposite.”
- Ordering effects and choice set awareness in repeat-response stated preference studies (2012, methodology): “First, our full-factorial design allows us to estimate interaction effects in preferences for attributes, avoiding a known source of misspecification bias [43].”