Top PDF Minimum aberration designs for discrete choice experiments

Minimum aberration designs for discrete choice experiments

Minimum aberration designs for discrete choice experiments

The design of the DCE plays a critical role because it determines how the attributes and their levels are combined to form choice sets. One common method is to use fractional factorial designs (FFDs) to construct DCEs; see Street and Burgess ( 2007 ) and Bush ( 2014 ), among others. These designs are based on a starting design that is either a full factorial or an FFD, for which the entries represent the first option in each choice set. Generators are then added component-wise to the starting design to form the remaining options in each choice set. These methods are flexible for constructing DCEs for estimat- ing main effects only. Because both main effects and two-factor interactions can jointly determine whether DCEs are successfully used to accurately assess real-life decision- making processes, designs that can also accurately estimate two-factor interactions are more desirable. Our work in this article focuses on constructing more effective designs for estimating main effects and two-factor interactions simultaneously.
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The use of discrete choice experiments to inform health workforce policy: a systematic review.

The use of discrete choice experiments to inform health workforce policy: a systematic review.

Choice tasks profiles can be generic, e.g. “Job A” versus “Job B”, or labelled e.g. “Rural clinic” versus “Urban hospital” (Figure 1). Generic designs were used by the majority of studies (74.1%, 20/27), although seven studies featuring a labelled design in the last three years [4,52-54,57,65,69]. All of these studies presented rural versus urban alternatives, except the above study by Lagarde et al. that also included jobs overseas and in private facilities [65]. The use of labelled designs in this way can enhance realism for participants by allowing alternative-specific attributes to be defined in order to avoid unrealistic combinations that might lead to participant confusion and/or disengagement with the questionnaire (for example, the availability of private practice in rural posts) [4,54,56,75]. Labelled designs can also provide choices between additional qualities associated with the labels by participants, but not captured by the limited number of attributes [75]. The drawback is that these qualities are not delineated, so researchers cannot be certain if their interpretation of the label matches that of the participants. In addition, label-specific attributes/levels are correlated with the label, and therefore their utilities cannot be distinguished in the analysis [75]. This may not be a disadvantage, however, if the policy aim is to investigate preferences for specific job types in a given market (e.g. rural/urban/overseas) or how individuals value the same attribute in different posts. In contrast, a generic choice is more appropriate where the research interest is the trade-off between different attributes for one particular type of job.
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Enhancing Agent-Based Models with Discrete Choice Experiments

Enhancing Agent-Based Models with Discrete Choice Experiments

• Evaluation of individual variables for each agent. Some variables are examined in more detail, i.e., not just minimum/average/maximum values over all agents are recorded, but the specific value for each agent. This is done for variables such as degree of capacity utilization in the warehouse or of production, and also allows the recognition of interesting patterns in the simulation results. This approach uses CSV files as well and was also applied to track a fitness variable which will be presented in the method section. • Individual agent evaluation. This approach traces some randomly selected agents in detail over the whole simulation period. Almost all important data about an agent is stored in an XML file for each simu- lated point in time, and thus permits further evaluation with a separate evaluation program. For example, this data enables an understanding of the reasons why each and every incoming or outgoing request was accepted or rejected. This approach is especially valuable if bugs are to be traced back to their source. • Visual evaluation. It is possible to run the simulation program with a GUI that includes a map containing
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Effects of alternative elicitation formats in discrete choice experiments

Effects of alternative elicitation formats in discrete choice experiments

H Comparison: Single binary (SB) – First questions of PoolRB (CrossRB1) All choice sets were created using a Bayesian D-efficient design (Bliemer et al. 2008). Bayesian D-efficient designs are statically efficient designs (see, for example, Ferrini and Scarpa 2007; Rose and Bliemer 2008; Rose et al. 2008). Statistically efficient designs aim to maximize the amount of obtained information. A commonly used measure to express the global level of efficiency is the D-error, which minimizes the determinant of variance-covariance matrix. The smaller the D-error, the more statistically efficient is the design. Therefore, a statistically efficient design can be used to increase efficiency while holding the sample size fixed. The Bayesian D-efficient designs (100 Halton draws) used in this study are developed based on the calculation of the Db-error of randomly selected designs (10,000 iterations). Attribute levels are randomly assigned to each attribute in each choice set of the change options while accounting for attribute balance. The base level (zero cost option) is held constant but included in the design process. Priors ware obtained from pilot studies targeting the population of Sydney and Canberra 20 . Following a suggestion of Rose and Bliemer (2005), the rows and columns related to the constant term are excluded from the calculation of the Db-error in order to avoid the dominance of the unproportionally large standard errors of the constant. Dominant and redundant choice sets are removed through restrictions and swapping of attribute levels marginally reducing the Db-efficiency (3%). The Bayesian D-efficient designs are developed for multinominal logit (MNL) models without accounting for covariate effects. Estimating different models may alter the design efficiency (Rose and Bliemer 2005). The results
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Discrete Choice Experiments in Health Economics: A Review of the Literature

Discrete Choice Experiments in Health Economics: A Review of the Literature

Methods This paper systematically reviews health-related DCEs published between 2009 and 2012, using the same database as the earlier published review (PubMed) to obtain citations, and the same range of search terms. Results A total of 179 health-related DCEs for 2009–2012 met the inclusion criteria for the review. We found a continuing trend towards conducting DCEs across a broader range of countries. However, the trend towards including fewer attributes was reversed, whilst the trend towards interview-based DCEs reversed because of increased computer administration. The trend towards using more flexible econometric models, including mixed logit and latent class, has also continued. Reporting of monetary values has fallen compared with earlier periods, but the proportion of studies estimating trade-offs between health outcomes and experience factors, or val- uing outcomes in terms of utility scores, has increased, although use of odds ratios and probabilities has declined. The reassuring trend towards the use of more flexible and appropriate DCE designs and econometric methods has been reinforced by the increased use of qualitative methods to inform DCE processes and results. However, qualitative research methods are being used less often to inform attribute selection, which may make DCEs more susceptible to omitted variable bias if the decision framework is not known prior to the research project.
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Comparison of designs for computer experiments

Comparison of designs for computer experiments

7. Discussion The choice of experimental points is an important issue in planning an efficient computer experiment. Various authors have suggested intuitive goals for good designs, including “good coverage”, ability to fit complex models, many levels for each factor, and good projection properties. At the same time, a number of different mathematical criteria have been put forth for comparing designs. We have proposed here a new criterion, based on the alias matrix for a simple model, and compared it to three criteria that have been advocated by other researchers, the integrated mean squared error, the design entropy and minimum distance criteria. We have compared these criteria with respect to ability to guide the choice of sample size and to produce good designs with respect to lack of replication and projective properties. We also compared several classes of designs using the criteria..
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The use of discrete choice experiments to inform health workforce policy: a systematic review

The use of discrete choice experiments to inform health workforce policy: a systematic review

Choice tasks profiles can be generic, e.g. “Job A” versus “Job B”, or labelled e.g. “Rural clinic” versus “Urban hospital” (Figure 1). Generic designs were used by the majority of studies (74.1%, 20/27), although seven studies featuring a labelled design in the last three years [4,52-54,57,65,69]. All of these studies presented rural versus urban alternatives, except the above study by Lagarde et al. that also included jobs overseas and in private facilities [65]. The use of labelled designs in this way can enhance realism for participants by allowing alternative-specific attributes to be defined in order to avoid unrealistic combinations that might lead to participant confusion and/or disengagement with the questionnaire (for example, the availability of private practice in rural posts) [4,54,56,75]. Labelled designs can also provide choices between additional qualities associated with the labels by participants, but not captured by the limited number of attributes [75]. The drawback is that these qualities are not delineated, so researchers cannot be certain if their interpretation of the label matches that of the participants. In addition, label-specific attributes/levels are correlated with the label, and therefore their utilities cannot be distinguished in the analysis [75]. This may not be a disadvantage, however, if the policy aim is to investigate preferences for specific job types in a given market (e.g. rural/urban/overseas) or how individuals value the same attribute in different posts. In contrast, a generic choice is more appropriate where the research interest is the trade-off between different attributes for one particular type of job.
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Discrete Choice Experiments in Health Economics: Past, Present and Future

Discrete Choice Experiments in Health Economics: Past, Present and Future

5 Conclusion This study provides an overview of the applications and methods used by DCEs in health. The use of empirical DCEs in health economics has continued to grow, as have the areas of application and the geographic scope. This study identi- fied changes in the experimental design (e.g. more frequent use of D-efficient designs), analysis methods (e.g. mixed logit models most frequently used), validity enhancement (e.g. more diverse use of internal validity checks), quali- tative methods (e.g. upwards trend of qualitative methods used for attribute and level selection) and outcome measures (e.g. coefficients most frequently used). However, a large number of studies not reporting methodological details were also identified. DCEs should include more complete infor- mation, for example, information about design generation, blocking, model specification, random-parameter estimation and model results. Developing reporting guidelines specifi- cally for DCEs might positively impact quality assessment, increase confidence in the results and improve the ability of decision-makers to act on the results. How and when to integrate health-related DCE outcomes into decision-making remains an important area for future research.
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Revisiting cost vector effects in discrete choice experiments

Revisiting cost vector effects in discrete choice experiments

Perhaps the most significant finding of this study is that different cost vectors can affect decision strategies employed by choice experiment survey respondents. Statistically significant differences in attribute non-attendance (AN-A) for the cost attribute point to differences in choice behaviour in response to different cost vectors. Differences in AN-A could have implications for WTP estimates, but we believe that further investigation of such effects requires a better understanding of reasons that underpin AN-A and that explain differences between alternative approaches to identify AN-A incidence. We also find significant differences across cost vector treatments for a decision strategy that might reflect unwillingness to trade-off cost and other non-cost attributes: always choosing the cheapest alternative when a non-status quo alternative was selected. Our results suggest that varying use of this decision strategy can obscure effects of cost vectors on marginal WTP estimates present for the part of the sample that did not exhibit these decision strategies. Indeed, after omitting respondents who use this decision strategy from the sample, we find a monotonic increase as the magnitude of the cost vector increases, and significant differences between all cost vector treatments for all of the attributes. This suggests that in moving forward, research investigating cost vector effects should take a more detailed look at decision strategies used. This can be considered as non-trivial: many different strategies may be used by respondents, and impacts of these on a naïve model (that does not account for them and might thus suffer from misspecification bias) might counteract systematic differences in preferences and WTP found for respondents who make trade-offs as assumed by random utility maximisation models. As an aside, our results suggest that efficient designs may not always be the ideal experimental design choice in the presence of non-compensatory strategies such as always choosing the cheapest alternative. This aspect deserves further investigation.
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The effect of including an opt-out option in discrete choice experiments

The effect of including an opt-out option in discrete choice experiments

Additionally to the effect on the data by simply including such an opt-out option, our study results indicate that choice behavior changes which influences DCE results when respondents are given the opportunity to opt-out. Including an extra choice option automatically implies reduced effectiveness, as there are more answering categories included. Specifically an opt-out option does not provide any insight on attribute level trade-offs. This is not an issue, if the choice to opt-out is due to the low perceived personal utility of the other scenarios. However, our analysis showed that it is likely to assume that a considerable number of respondents chose to opt-out for other reasons than a dislike for lifestyle programs. It can therefore be suggested that including an opt-out option in a DCE, leads to an ‘unnecessary’ loss of effectiveness. This is of special interest in the light of designing DCEs in an efficient manner (e.g., by minimizing D-error). Such designs strive to create choice sets with an optimal utility balance between the scenarios of each choice task, by optimizing the variance- covariance matrix (22–24). This designing procedure results in a DCE that requires a minimal number of choice tasks per respondent and a minimal number of respondents per experiment (aside from model specifications (e.g., level restrictions or interactions)). At the same time, this may induce complexity of the generated choice tasks. Since there are indications for higher levels of opt-out when choice tasks become more complex, the efficiency of designing DCEs in such a way may be at risk. Future research is necessary to identify subgroups among study popula- tions that are most likely to opt-out due to other reasons than solely personal utility. Moreover, it should be explored how DCEs can be designed in an efficient manner while keeping in mind this phenomenon.
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Bayesian Design of Discrete Choice Experiments for Valuing Health State Utilities

Bayesian Design of Discrete Choice Experiments for Valuing Health State Utilities

This chapter reviewed the main issues with the experimental design used to con- struct choice designs in health economics in general, and for estimating health state utilities within the QALY scale in particular, and identified the main requirements in the experimental designs for successful implementation of DCEs to estimate health state utilities. Generally, the review showed that the main design issue is that most of the choice experiments constructed for valuing health state utilities are generated using orthogonal array designs or are based on other required statistical properties such as level balance, and minimal overlap. Restricting the construction of choice de- signs to these properties might result in dominant and implausible combinations of the attribute levels that reduces the design efficiency. However, imposing constraints on the attribute level combinations together with other health evaluation design require- ments (particularly including the death state in the choice design to anchor health state utilities into the QALY scale) and the complexity of nonlinear design problems, require deviation from orthogonal design principles and more advanced design methods to construct an efficient choice design.
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Severity-Stratified Discrete Choice Experiment Designs for Health State Evaluations

Severity-Stratified Discrete Choice Experiment Designs for Health State Evaluations

2.2 Experimental Designs With/Without Severity‑Stratified Restriction We implemented heterogeneous DCE design algorithms to create for each study arm a unique experimental design com- prising 168 choice tasks, distributed over eight sub-designs [ 21 ]. The algorithm optimizes for Bayesian D error for the total design, while simultaneously optimizing for the Bayes- ian D errors of each of the eight sub-designs. In essence, this strategy produces a blocked design with eight blocks, where the design within each block is optimized in addition to the optimization of the overall design across blocks. A Latin hypercube sample optimized for maximum minimum distance between points and a greedy optimization algo- rithm was used to optimize the weighted averaged Bayesian D error with one-third of the weight assigned to the aggre- gated efficiency and two-thirds on the individual efficiencies of the sub-designs. Note that the design algorithm controlled for left–right randomization of the two states by including both options A and B in comparison with option C in the Bayesian design criterion, even though only one of the two choice options was presented (in random order) to the survey respondents.
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Methods for Analyzing Attribute-Level Best-Worst Discrete Choice Experiments

Methods for Analyzing Attribute-Level Best-Worst Discrete Choice Experiments

Although the experiments were presented in the early 1990’s, it was not until Marley and Louviere (2005) that mathematical probabilities and properties were for- mally determined and published. Marley and Louviere (2005), Marley et al. (2008), and Marley and Pihlens (2012) provided the probability and properties to best-worst scaling experiments for the three cases. Additionally, Lancsar et al. (2013) provided the probability and utility definition for case 3 experiments that include the sequen- tial best-worst choice from a set of choices. Other work found in the literature with regards to these experiments are the design of the experiments and dealing with taste heterogeneity. Louviere and Woodworth (1983) stated that orthogonal, main effects, and fractional factorial designs provide better parameter estimates than other designs. In application to best-worst scaling experiments, balance incomplete block designs (BIBD)(Louviere et al. 2013; Parvin et al. 2016) and orthogonal main effects plans (OMEPs) are popular designs (Flynn et al., 2007; Knox et al., 2012; Street and Knox, 2012). These designs and their properties are examined by Street and Burgess (2007). Louviere et al. (2013) looked at the design of experiments for best-worst scaling experiments and stated that it is possible to determine individual parameter estimates for the respondents.
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Bayesian optimal designs for discrete choice experiments with partial profiles

Bayesian optimal designs for discrete choice experiments with partial profiles

2009). These so-called utility-neutral partial profile designs are created using or- thogonal designs based on linear design principles. To improve the quality of the designs, innovative construction methods have been developed based on optimal design theory for linear models (see, e.g., Atkinson et al. 2007). Nevertheless, the most recent utility-neutral optimal designs created by Grossmann et al. (2009) only allow for the construction of DCEs with two profiles per choice set and two groups of attributes where the number of levels of the attributes is fixed in every group. There are also limitations on the allowable number of constant attributes. This approach is therefore not applicable in a wide variety of practical problems. However, a more fundamental problem with the use of utility-neutral optimal designs is that they do not match the discrete choice models. This is because discrete choice models are nonlinear in the parameters, implying that the quality of the design of a DCE depends on the unknown parameters (Atkinson and Haines 1996). One justification for the use of the utility-neutral design approach is that the nonlinear design problem can be transformed into a linear one by assuming zero prior parameter values. A key feature of the utility-neutral optimal designs is thus that they are optimal for one specific set of parameter values. Therefore, utility-neutral optimal designs belong to the class of locally optimal designs which are constructed using prior point estimates for the parameters (Huber and Zwerina 1996).
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Issues in the Design of Discrete Choice Experiments

Issues in the Design of Discrete Choice Experiments

hypothetical, the alternatives are experienced, not described using vignettes. BC’s symposium presentation focused on the potential value of experience-based methods for DCE designs. In this work, the argument was advanced that decision-makers are likely to be more interested in the views of individuals with a clear understanding of what they are trading (e.g., patients). His presentation provided two examples, asking patients in a chemotherapy clinical to prioritize their symptom relief and asking patients who used two forms of delivery (infusion and infection) in a crossover trial about their preferences (15). The use of experience-based methods has a number of important advantages; in particular that it will reduce hypothetical bias. This has to be balanced against the potential for sample selection bias, small samples for rare alternatives, and a possible incompatibility between preferences of experienced individuals and the general population (16).
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A review of the application and contribution of discrete choice experiments to inform human resources policy interventions

A review of the application and contribution of discrete choice experiments to inform human resources policy interventions

However, the application of this methodology is relatively complex, as the construction of choice experiments requires the understanding and application of advanced notions in experimental design theory [52]. Although some software programs, such as SAS [74], now provide tools to help researchers construct optimal designs, proper experimental design remains a very technical and evolving field that might limit the use of DCE methods. A large literature devoted to choice experiments has already underlined some of the limitations of certain study designs [51], while design constraints or limitations can limit the validity of results obtained from such choice experiments [66,67,75,76]. The econometric analysis of DCE data also requires fairly advanced statistical methods and there is no consensus in the literature at present on the best models to use [53,77]. Although choice experi- ments may be useful in informing decision-making in developing countries, HR researchers should be aware of the technical expertise required to use them, as well as their potential limitations.
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Comparing designs for computer simulation experiments

Comparing designs for computer simulation experiments

Deterministic and stochastic simulation models both re- quire careful planning and execution of experimental de- sign strategies. Based on the type of computer simulation – deterministic or stochastic – a researcher must make choices with respect to choice of design (i.e. space-filling or optimal) and choice of model fitting technique (i.e. li- near model or GASP model). This research demonstrated a new way of comparing designs for computer simulation experiments. Theoretical prediction performance of space-filling and optimal designs with respect to the second order polynomial model and the GASP model were illustrated. The theoretical results showed that there was a dominate design in both cases. The I-optimal design dominated the linear model and the GASP IMSE design dominated the GASP model. These results are somewhat intuitive as both of these designs are intended to minimize the variance in the design region with respect to the spe- cific model. While the FDS graphical strategy is useful for comparing design types, the FDS plotting capabilities also allow the assessment of the other effects on predic- tion variance such as sample size, dimension, and un- known theta parameters (in the case of the GASP model).
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Heterogeneity in preferences for primary care consultations: results from a discrete choice experiments

Heterogeneity in preferences for primary care consultations: results from a discrete choice experiments

Apart from the important impact of some socio- demographic variables and the health status, the characteristics connected to the participants' past experiences seem to have the greatest influence on the involvement level. Thus, our data suggest that preferences for a different involvement level could be relatively controllable by the caregivers, considering that, to a large extent, they seem to depend on the attitude of the GP in the previous visits and to the long- term ‘personal’ relationship between a patient and his/her GP, that is different from the occasional contacts with a specialist. Patients, in Italy, are in fact free to choose their GP at the age of 18 and once they make their final choice they hardly change it [37]. Consequently, this long term relationship, built over the years and most of the times based on reciprocal trust should not require patients’ total involvement during each consultation. The presence of heterogeneity was also confirmed when taking into account information preferences.
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The use of alternative preference elicitation methods in complex discrete choice experiments.

The use of alternative preference elicitation methods in complex discrete choice experiments.

The concept and importance of tradability may be related to reference-dependent preferences in the area of behavioral economics (Kahneman, 2003). Evidence from lab and field experiments shows that the point of reference influences the monetary valuation of a good, sometimes with surprising results. People may have evaluated the job in a single profile case scenario as the only offer in hand, whereas they are unlikely to use as the point of reference any particular one of the three job offers that a multi-profile case scenario presents. Once the job is perceived as given, the non-salary characteristics could increase in monetary value. 19 A test of this explanation could proceed with a random allocation of two contextual prompts to single profile case respondents, which would include one stating that the job is the only available offer and the other stating that it is but one of several, and comparing the preferences elicited by each treatment method with those elicited by the multi-profile case method.
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Joining a Cult: Religious Choice or Psychological Aberration

Joining a Cult: Religious Choice or Psychological Aberration

In this article, I will analyze the different theories about "cult" membership and conversion, specifically focusing upon whether or not conversions to cults ought to b[r]

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