The Role of the Scale Parameter in the Estimation and Comparison of Multinomial Logit Models

Swait́, Joffre; Louviere, Jordan J.

doi:10.2307/3172883

Cited by 643 publications

(444 citation statements)

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“…Uncertainty around experimental design and cognitive ease: the variance scale factor between the BWS and DCE data was obtained by plotting one set of estimates against the other [22]; the variance scale factor is concerned with how consistent individuals are in making their choices and informs the question of cognitive ease. For the experimental design more generally, pilots 1and 2 provided estimates of the two "extreme" levels; pilot 3 enabled preliminary estimates of intermediate levels.…”

Section: Discussionmentioning

confidence: 99%

Complex Valuation: Applying Ideas from the Complex Intervention Framework to Valuation of a New Measure for End-of-Life Care

et al. 2015

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Section: Discussionmentioning

confidence: 99%

Complex Valuation: Applying Ideas from the Complex Intervention Framework to Valuation of a New Measure for End-of-Life Care

et al. 2015

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“…Swait and Louviere (1993) show that for the MNL model, under the hypothesis of preference homogeneity and scale differences between two data sources, plotting the preference parameters on a X-Y plot should result in proportional and positively sloped distribution points, whereby the slopes are related to the ratio of the scale factors in the two choice data sources.…”

Section: Testing For the Pooling Of Different Choice Rankingsmentioning

confidence: 92%

“…As is the case for exploded ranking data, models estimated from different ranks may not be pooled if both variance scale factors and parameters differ by rank level (Ben-Akiva et al, 1992). However, if adolescent preferences are similar across the different choices but vary in their error variance such that they are more or less consistent in making choices, then it is possible to pool the data sources with appropriate accounting for scale differences across context to estimate the attribute level utilities (Swait and Louviere, 1993).…”

Section: Testing For the Pooling Of Different Choice Rankingsmentioning

confidence: 99%

Valuing the Child Health Utility 9D: Using profile case best worst scaling methods to develop a new adolescent specific scoring algorithm

Ratcliffe

Huynh

Chen

et al. 2016

Social Science & Medicine

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115

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In contrast to the recent proliferation of studies incorporating ordinal methods to generate health state values from adults, to date relatively few studies have utilized ordinal methods to generate health state values from adolescents. This paper reports upon a study to apply profile case best worst scaling methods to derive a new adolescent specific scoring algorithm for the Child Health Utility 9D (CHU9D), a generic preference based instrument that has been specifically designed for the estimation of quality adjusted life years for the economic evaluation of health care treatment and preventive programs targeted at young people. A survey was developed for administration in an online format in which consenting community based Australian adolescents aged 11 to 17 years (N=1982) indicated the best and worst features of a series of 10 health states derived from the CHU9D descriptive system. The data were analyzed using latent class conditional logit models to estimate values (part worth utilities) for each level of the nine attributes relating to the CHU9D. A marginal utility matrix was then estimated to generate an adolescent-specific scoring algorithm on the full health = 1 and dead = 0 scale required for the calculation of QALYs. It was evident that different decision processes were being used in the best and worst choices. Whilst respondents appeared readily able to choose 'best' attribute levels for the CHU9D health states, a large amount of random variability and indeed different decision rules were evident for the choice of 'worst' attribute levels, to the extent that the best and worst data should not be pooled from the statistical perspective. The optimal adolescent-specific scoring algorithm was therefore derived using data obtained from the best choices only. The study provides important insights into the use of profile case best worst scaling methods to generate health state values with adolescent populations.3

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“…It is noteworthy that the models have different scales hence the coefficients cannot be directly compared (Swait & Louviere, 1993), but we can look at the relative size of the differences by using one of the domain levels as a common denominator and scaling all others relative to this. In this case, we have chosen the highest level of need of the control domain (i.e., Control_4) which was strongly estimated in all models 3 .…”

Section: Comparison Of Values Between Dce and Bwsmentioning

confidence: 99%

Best–worst scaling vs. discrete choice experiments: An empirical comparison using social care data

Potoglou

Burge

Flynn

et al. 2011

Social Science & Medicine

129

111

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Key messages• This study illustrates key issues that are important in choosing between profile-case best-worst scaling and discrete choice experiment studies • Empirical research on the value of outcomes of social care reveals similar patterns in the preference weights obtained from the two approaches • In the majority of cases examined, preference weights are not significantly different once the weights have been appropriately normalised/rescaled 3 Abstract This paper presents empirical findings from the comparison between two principal preference elicitation techniques: discrete choice experiments and profile-based best-worst scaling. Best-worst scaling involves less cognitive burden for respondents and provides more information than traditional "pick-one" tasks asked in discrete choice experiments. However, there is lack of empirical evidence on how best-worst scaling compares to discrete choice experiments. This empirical comparison between discrete choice experiments and best-worst scaling was undertaken as part of the Outcomes of Social Care for Adults project, which aims to develop a weighted measure of social care outcomes. The findings show that preference weights from best-worst scaling and discrete choice experiments do reveal similar patterns in preferences and in the majority of cases preference weights -when normalised/rescaled -are not significantly different.

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The Role of the Scale Parameter in the Estimation and Comparison of Multinomial Logit Models

Cited by 643 publications

References 0 publications

Complex Valuation: Applying Ideas from the Complex Intervention Framework to Valuation of a New Measure for End-of-Life Care

Complex Valuation: Applying Ideas from the Complex Intervention Framework to Valuation of a New Measure for End-of-Life Care

Valuing the Child Health Utility 9D: Using profile case best worst scaling methods to develop a new adolescent specific scoring algorithm

Best–worst scaling vs. discrete choice experiments: An empirical comparison using social care data

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