Some properties of structural equivalence measures derived from sociometric choice data

1988 ◽  
Vol 10 (1) ◽  
pp. 1-28 ◽  
Author(s):  
Ronald S. Burt
Keyword(s):  
Choice Data ◽  
Structural Equivalence ◽  
Sociometric Choice

Reliability and Accuracy of Bootstrap and Monte Carlo Methods for Demand Distribution Modeling

2011 ◽  
Author(s):  
Swithin S. Razu ◽  
Shun Takai
Keyword(s):  
Monte Carlo ◽  
Product Development ◽  
Conjoint Analysis ◽  
New Product Development ◽  
Demand Uncertainty ◽  
New Product ◽  
Product Development Process ◽  
Choice Data ◽  
Product Concept ◽  
Demand Distribution

Estimation of demand is one of the most important tasks in new product development. How customers come to appreciate and decide to purchase a new product impacts demand and hence profit of the product. Unfortunately, when designers select a new product concept early in the product development process, the future demand of the new product is not known. Conjoint analysis is a statistical method that has been used to estimate a demand of a new product concept from customer survey data. Although conjoint analysis has been increasingly incorporated in design engineering as a method to estimate a demand of a new product design, it has not been fully employed to model demand uncertainty. This paper demonstrates and compares two approaches that use conjoint analysis data to model demand uncertainty: bootstrap of respondent choice data and Monte Carlo simulation of utility estimation errors. Reliability of demand distribution and accuracy of demand estimation are compared for the two approaches in an illustrative example.

scholarly journals Valuation Survey of EQ-5D-Y Based on the International Common Protocol: Development of a Value Set in Japan

2021 ◽  
pp. 0272989X2110018
Author(s):  
Takeru Shiroiwa ◽  
Shunya Ikeda ◽  
Shinichi Noto ◽  
Takashi Fukuda ◽  
Elly Stolk
Keyword(s):  
Discrete Choice ◽  
Health Care Policy ◽  
Mixed Logit ◽  
Health State ◽  
Choice Data ◽  
Social Functions ◽  
Value Set ◽  
A Value ◽  
Value Sets ◽  
Utility Scale

Background EQ-5D-Y is a preference-based measure for children and adolescents (aged 8–15 y). This is the first study to develop an EQ-5D-Y value set for converting EQ-5D-Y responses to index values. Methods We recruited 1047 respondents (aged 20–79 y) from the general population, stratified by gender and age group, in 5 Japanese cities. All data were collected through face-to-face surveys. Respondents were asked to value EQ-5D-Y states for a hypothetical 10-y-old child from a proxy perspective using composite time tradeoff (cTTO) and a discrete choice experiment (DCE). The discrete choice data were analyzed using a mixed logit model. Latent DCE values were then converted to a 0 (death)/1 (full health) scale by mapping them to the cTTO values. Results The mean observed cTTO value of the worst health state [33333] was 0.20. Analysis of the DCE data showed that the coefficients of the domains related to mental functions (“Having pain or discomfort” and “Feeling worried, sad, or unhappy”) were larger than those for the domains related to physical and social functions. By converting latent DCE values to a utility scale, we constructed a value set for EQ-5D-Y. No inconsistencies were observed. The minimum predicted score was 0.288 [33333], and the second-best score was 0.957 [12111]. Conclusion A value set for EQ-5D-Y was successfully constructed. This is the first survey of an EQ-5D-Y value set. Interpreting the differences between EQ-5D-Y and EQ-5D-5L value sets is a future task with implications for health care policy.

scholarly journals Gaze Amplifies Value in Decision Making

2018 ◽  
Vol 30 (1) ◽  
pp. 116-128 ◽  
Author(s):  
Stephanie M. Smith ◽  
Ian Krajbich
Keyword(s):  
Response Times ◽  
Data Sets ◽  
Choice Data ◽  
Data Set ◽  
Choice Process ◽  
The Gaze ◽  
Large Sets ◽  
Subjective Value ◽  
Amplifying Effect ◽  
Familiar Stimuli

When making decisions, people tend to choose the option they have looked at more. An unanswered question is how attention influences the choice process: whether it amplifies the subjective value of the looked-at option or instead adds a constant, value-independent bias. To address this, we examined choice data from six eye-tracking studies ( Ns = 39, 44, 44, 36, 20, and 45, respectively) to characterize the interaction between value and gaze in the choice process. We found that the summed values of the options influenced response times in every data set and the gaze-choice correlation in most data sets, in line with an amplifying role of attention in the choice process. Our results suggest that this amplifying effect is more pronounced in tasks using large sets of familiar stimuli, compared with tasks using small sets of learned stimuli.

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