Distinguishing taste variation from error structure in discrete choice data

Swait́, Joffre; Bernardino, Adriana

doi:10.1016/s0191-2615(99)00009-0

Cited by 103 publications

(24 citation statements)

References 21 publications

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“…(See Swait and Bernardino, 2000 for the introduction of this informal method across multiple segments.) The hypothesis was tested that the parameters from models estimated for different choice measures were the same, and scales between the datasets were allowed to vary for logical pairs of choice measures: best with worst; best and second best; worst and second worst.…”

Section: Testing For the Pooling Of Different Choice Rankingsmentioning

confidence: 99%

“…The results suggest the differences are not only attributable to variance scale but also differences in preferences between the choice measures among adolescents, providing evidence against pooling of the different data sources. The pooling test was further relaxed to allow for partial preference heterogeneity across data sources or attributes of the CHU9D, thus allowing more noise among some attributes by data source (Swait and Bernardino, 2000). Parameters chosen to be freed included specific attribute level parameters and entire attributes with particular focus on worst and second worst data in which the test statistic was smallest.…”

Section: Choice Data Analysismentioning

confidence: 99%

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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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Section: Testing For the Pooling Of Different Choice Rankingsmentioning

confidence: 99%

Section: Choice Data Analysismentioning

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

Self Cite

115

View full text Add to dashboard Cite

show abstract

“…Because response times has been interpreted as a measure of data quality, a related branch of literature exploring response times has focused on response scale heterogeneity, and how to separate this from heterogeneity in random coefficients (Louviere et al, 2002;Louviere and Eagle, 2006;Louviere et al, 1999;Swait and Bernardino, 2000). Hess and Rose (2012) show, however, that scale heterogeneity cannot be identified separately from random heterogeneity in preferences.…”

Section: Introductionmentioning

confidence: 99%

Response time patterns in a stated choice experiment

Börjesson

Fosgerau

2015

Journal of Choice Modelling

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This paper studies how response times vary between unlabeled binary choice occasions in a stated choice (SC) experiment, with alternatives differing with respect to in-vehicle travel time and travel cost. The pattern of response times is interpreted as an indicator of the cognitive processes employed by the respondents when making their choices. We find clear signs of referencedependence in response times in the form of a strong gain-loss asymmetry. Moreover, different patterns of response times for travel time and travel cost indicate that these attributes are processed in different ways by respondents. This may be of particular relevance for choice experiments in the transportation field, where the travel time attribute is central.

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“…(e.g. Swait and Bernardino, 2000). As a collective, the literature on discrete choice analysis has devoted considerable attention towards addressing how best to incorporate random taste heterogeneity, but there is no objectively best way.…”

Section: Discussionmentioning

confidence: 99%

Random taste heterogeneity in discrete choice models: Flexible nonparametric finite mixture distributions

Vij

Krueger

2017

Transportation Research Part B: Methodological

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This study proposes a mixed logit model with multivariate nonparametric finite mixture distributions. The support of the distribution is specified as a high-dimensional grid over the coefficient space, with equal or unequal intervals between successive points along the same dimension; the location of each point on the grid and the probability mass at that point are model parameters that need to be estimated. The framework does not require the analyst to specify the shape of the distribution prior to model estimation, but can approximate any multivariate probability distribution function to any arbitrary degree of accuracy. The grid with unequal intervals, in particular, offers greater flexibility than existing multivariate nonparametric specifications, while requiring the estimation of a small number of additional parameters. An expectation maximization algorithm is developed for the estimation of these models. Multiple synthetic datasets and a case study on travel mode choice behavior are used to demonstrate the value of the model framework and estimation algorithm. Compared to extant models that incorporate random taste heterogeneity through continuous mixture distributions, the proposed model provides better out-of-sample predictive ability. Findings reveal significant differences in willingness to pay measures between the proposed model and extant specifications. The case study further demonstrates the ability of the proposed model to endogenously recover patterns of attribute non-attendance and choice set formation.3

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Distinguishing taste variation from error structure in discrete choice data

Cited by 103 publications

References 21 publications

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

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

Response time patterns in a stated choice experiment

Random taste heterogeneity in discrete choice models: Flexible nonparametric finite mixture distributions

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