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

5 Conclusion We have analysed stated preference data from two different discrete choice experiments (DCEs): multi-profile case best-worst scaling (BWS) which, like traditional DCEs, in- volves choices over several profiles, and single profile case BWS which involves choices over attributes of a given profile. In our application, a profile is an entry-level nursing job. That respondents need to process only one profile at a time, and may thus under- stand single profile case tasks better, has been often advanced as an advantage of the single profile BWS method. Also, the ability to identify additional utility parameters can make the single profile case BWS a profitable alternative to multi-profile DCEs. For example, in relation to our application, suppose that hospital managers are considering how best to allocate a fixed budget to the design of new nursing jobs meant to attract nurses away from non-nursing jobs. A relevant multi-profile DCE may be hard to de- sign, because jobs in different occupations are best described by different attributes. A single profile case BWS experiment would provide useful inputs by allowing inference of attribute-levels which are more preferred than others, thereby highlighting key features an attractive nursing job needs to possess.
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DISCRETE CHOICE MODELS AND VALUATION EXPERIMENTS. AN APPLICATION TO CULTURAL HERITAGE

DISCRETE CHOICE MODELS AND VALUATION EXPERIMENTS. AN APPLICATION TO CULTURAL HERITAGE

Conjoint analysis is an umbrella designation for a number of related approaches where choices, ranks or matches between alternatives, as defined over and by attributes and levels, are involved 4 . The many dimensions, attributes and values characterising the supply and demand of cultural good and services lends itself to be analysed by mechanisms which have the capability of dealing with situations where goods and changes are multi-dimensional and trade off between them are analysed 5 , possibly identifying part-worth utilities for different components of value, associated to services and functions. Furthermore, its avoidance of an explicit elicitation of willingness to pay (WTP), by relying instead on expressed choices 6 (or rankings and ratings) between alternative “profiles” and “scenarios”, might be an advantage compared to contingent valuation (CV) 7 . Some variants of conjoint analysis can be identified in the literature according to the way preferences are measured. I wish to suggest and focus on “Choice Experiment” (CE) (Hanley and Mourato, 1999) or “Choice Modelling” (CM) (Bennett, 1999), wherein evaluation is achieved by presenting users with a series of alternative “scenarios” or “profiles” (i.e. alternative cultural “supply” of services), asking them to choose the most preferred out of the choice set, the baseline being the status quo 8 . As stated by Rolfe et al. (2000): “an alternative technique, choice modelling appears to hold some promise because it can be used to model complex situations and to frame choices consistent with “real life” choices”.
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Elicitation of Preferences for Improvements in Ostomy Pouches – A Discrete Choice Experiment

Elicitation of Preferences for Improvements in Ostomy Pouches – A Discrete Choice Experiment

Because of government intervention in the form of the co-payment system used in Sweden, the price that ostomates pay for their ostomy pouches 2 is not a market price and so ostomy pouches are not purchased in a perfectly functioning market. Public programmes in general, and explicitly in the health sector, often have an impact on non-market goods and services, for which it is typically not possible to derive complementary market good and health good relations. Accordingly, the relationship between people’s actual behaviour in a market and the price/qualities of the good in question would not be sufficient for inferring the economic value of the benefits of the non-market goods and policy. This is the case for ostomy pouches in Sweden and therefore the welfare economic value of ostomy pouches must be derived through economic valuation methods [2], which is the aim of this paper. The economic valuation in this study seeks to identify the consumption opportunities individuals would be willing to forgo in return for the opportunity to use alternative ostomy pouches with improved attributes. Different ostomy pouches may represent a change in quality for the consumer – this can be expressed in monetary terms using economic valuation and more specifically for the present case using stated preference techniques, namely the Discrete Choice Experiment (DCE).
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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

In general, the choice for including an opt-out option in DCEs, depends evidently on the research objective. When the research objective is to determine the potential participation in a health program, an opt-out option should always be included; if in real life ‘not participating’ is an option as well. By doing so, researchers stay as close as possible to the actual choices of their target population. Introducing an additional loss of power, because respondents do not make any trade-offs and chose to opt-out, should then be accepted. However, the number of respondents that opt-out for other reasons than aiming for the highest personal utility should be minimized. Based on the learning effect that was shown in this study, future DCEs that include an opt-out option may want to incorporate multiple forced choice warm-up exercises. However, since DCE questionnaire are already cogni- tively demanding and time consuming, a more efficient solution might be to use a dual response design. In such a design, respondents are forced to make a choice and immediately after choosing, respondents are asked if they would like to opt out if given the choice [53–55]. This might diminish the risk that a direct introduction of an opt-out results in large numbers of respondents avoiding to seriously weigh the different levels of attributes. Additionally, in order to minimize the proportion of respondents that chooses to opt-out because they find the choice tasks too complex or difficult, future research should empirically explore how choice sets should be presented to make them as easy and less complex as possible. Finally, additional research that uses debriefing of respondents should be conducted to explore the reasons for choosing the opt-out alternative in depth.
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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 can also include an opt-out, in the form of a “choose none” or a status quo (“choose my current job”) option [29]. Nearly one in three studies in this review (8/27, 29.6%) included such an option, compared to just one in the Lagarde-Blaauw review. Three studies presented a two stage choice to participants, one as a forced binary choice between two presented profiles and one ternary choice containing an opt-out [68-70]. The inclusion of an opt-out option can avoid a “forced choice” which assumes that one of the alternatives offered must be taken up and may falsely increase the strength of preference associated with alternatives, distorting related welfare estimates [29,31,71-74]. Indeed, the instruction to “assume these are the only options available to you” is a common way of framing a choice task. In real life, however, health workers always have many options in the labour market, including the status quo of staying in their current job or withdrawing from the health labour market altogether. This holds true even for students or new graduates. Although consumption of the good or service on offer can rarely be assumed in DCE applications in health, except for perhaps comparing new treatments versus current treatments, it is arguably more pertinent here. After all, labour market decisions are complex decisions with significant consequences, frequently associated with major disruptive effects on an individual’s status quo, and the total number made over a lifetime is comparatively few compared to other types of decisions. Maintaining this status quo by opting out of a choice between job profiles may seem very attractive, and its inclusion more closely reflects the real world market. This is especially important for measures of relative attribute impact such as willingness to pay for desirable job characteristics (see below). The disadvantage is that
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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 can also include an opt-out, in the form of a “choose none” or a status quo (“choose my current job”) option [29]. Nearly one in three studies in this review (8/27, 29.6%) included such an option, compared to just one in the Lagarde-Blaauw review. Three studies presented a two stage choice to participants, one as a forced binary choice between two presented profiles and one ternary choice containing an opt-out [68-70]. The inclusion of an opt-out option can avoid a “forced choice” which assumes that one of the alternatives offered must be taken up and may falsely increase the strength of preference associated with alternatives, distorting related welfare estimates [29,31,71-74]. Indeed, the instruction to “assume these are the only options available to you” is a common way of framing a choice task. In real life, however, health workers always have many options in the labour market, including the status quo of staying in their current job or withdrawing from the health labour market altogether. This holds true even for students or new graduates. Although consumption of the good or service on offer can rarely be assumed in DCE applications in health, except for perhaps comparing new treatments versus current treatments, it is arguably more pertinent here. After all, labour market decisions are complex decisions with significant consequences, frequently associated with major disruptive effects on an individual’s status quo, and the total number made over a lifetime is comparatively few compared to other types of decisions. Maintaining this status quo by opting out of a choice between job profiles may seem very attractive, and its inclusion more closely reflects the real world market. This is especially important for measures of relative attribute impact such as willingness to pay for desirable job characteristics (see below). The disadvantage is that
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Discrete Choice Experiments: A Guide to Model Specification, Estimation and Software

Discrete Choice Experiments: A Guide to Model Specification, Estimation and Software

variation. In the case of the MNL model the compensating variation can be expressed as: (11) where V j 0 and V j 1 are the values of the utility function, V, estimated in the choice model for each choice option j before and after the quality change, respectively, and J is the number of options in the choice set. The log sum terms in equation (10) weight the utility associated with each alternative by the probability of selecting that alternative and as such can be interpreted as the expected utility. The CV therefore calculates the change in expected utility before and after the policy change and scales this utility difference by the marginal utility of income, , to provide a monetary and therefore cardinal measure of the change in welfare. Often information on income is unavailable, in which case the coefficient on the price attribute (which represents the marginal disutility of price) can be used as the negative of the marginal utility of income. In fact any quantitative numeraire would work see for example Lancsar et al (2011) who use the marginal utility of a QALY as the numeraire. Calculation of the CV involves harnessing the coefficients estimated in the choice model along with the values of the attributes of interest and can easily be undertaken by hand or in standard software packages (e.g. using nlcom in Stata which also produces confidence intervals). The interested reader is referred to [58] for further discussion of the theory and methods for such calculations.
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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

In addition to using methods that can be used to identify unobserved preference heterogeneity, which we discussed in detail in Sect. 5.4 , the issue of segmenting DCE data to examine the preferences of defined subgroups is sometimes important. One early analysis [ 227 ] segmented the DCE data according to the severity of symptoms associated with osteoarthritis, and the importance of a joint ache attribute was seen to increase in respondents with more severe symptoms. Other analyses relating to establishing priority criteria for allocating cadaveric kidney transplants [ 71 , 72 ] have provided evidence of statistically significant differ- ences in preferences between different stakeholder groups. A major finding to emerge from this research was that whilst non-ethnic minority patients would prefer to allocate kidney transplants to recipients with a good tissue match, ethnic minorities (who would be disadvantaged by use of such priority criteria) would not. Another interesting ana- lysis relating to segmentation used segmentation because ‘‘health organizations need to understand whether the same health treatments, prevention programs, services, and pro- ducts should be applied to everyone in the relevant popu- lation or whether different treatments need to be provided to each of several segments that are relatively homoge- neous internally but heterogeneous among segments’’ [ 228 ]. Segmenting the data to facilitate subgroup analysis is particularly appropriate if policy-relevant differences in preferences between defined subgroups might be applicable.
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Quantifying user preferences for sanitation construction and use: Application of discrete choice experiments in Amhara, Ethiopia.

Quantifying user preferences for sanitation construction and use: Application of discrete choice experiments in Amhara, Ethiopia.

Keywords: preferences, discrete choice experiments, sanitation, latrine use, latrine construction, Ethiopia Introduction Poor sanitation and hygiene are responsible for considerable morbidity and mortality in low income settings (1,2); yet interventions to improve and sustain sanitation coverage and use have had limited success (3). One reason may be that interventions often target a set of aims that do not overlap with the preferences, needs, and demands of the local populations for which interventions are designed (4–6). While elucidating local preferences may be difficult, doing so is essential for the design and execution of effective water, sanitation, and hygiene (WASH) programming (7–9). WASH preferences and demand assessments, particularly those employing more rigorous econometric methods, have been lacking in rural areas in low-income settings (10). Household sanitation – the construction, repair and upgrading of latrine facilities, and the use of those facilities by all members of the household – has frequently been treated as a single behaviour mediated by a simple set of factors. Scenario-based techniques, such as discrete choice experiments (DCEs), can serve to elicit data reflecting users’ perceived needs and desires. WASH practitioners can leverage resulting DCE data to inform intervention design and further enhance traditional preference and demand assessments by pinpointing mechanisms that either facilitate or impede adoption of improved behaviours and practices. Here, we distinguish between behaviours and practices, as behaviours represent
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Issues in the Design of Discrete Choice Experiments

Issues in the Design of Discrete Choice Experiments

alternatives (here called choice tasks), and to specify which alternative they prefer. The use of choice tasks in other areas – especially, psychology, transportation, marketing and agriculture – has a more established history. Health preference studies have been conduct for about as long (2, 3); however, the relatively late uptake in preference evidence in health is surprising in some regards as patient and population values concerning health have always been key components of a range of questions from health policy to clinical practice, and often cannot be directly observed, a problem exacerbated by the lack of a perfectly competitive market (4). Though there is broad consensus that the value patients or the population place on health matters in decision-making, the methods for including them in a way that is reliable are debated.
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Effects of alternative elicitation formats in discrete choice experiments

Effects of alternative elicitation formats in discrete choice experiments

Abstract An elicitation format prevalently applied in DCE is to offer each respondent a sequence of choice tasks containing more than two choice options. However, empirical evidence indicates that repeated choice tasks influence choice behavior through institutional learning, fatigue, value learning, and strategic response. The study reported in this paper employs a split sample approach based on field surveys using a single binary elicitation format with a majority vote implementation as the baseline to expand the research on effects of sequential binary DCE formats. We provide evidence for effects caused by institutional learning and either strategic behavior or value learning after respondents answered repeated choice questions. However, we did not find any indications for strategic behavior caused by awareness of having multiple choices. The choice between a sequential and a single elicitation format may thus imply a trade-off between decreased choice accuracy and potentially increased strategic behavior due to an incentive incompatible mechanism. Further research is needed to explore strategic behavior induced by incentive incompatible elicitation formats using alternative approaches that are not compromised by a confounded baseline, that facilitate the differentiation between value learning and strategic behavior, and that allow the use of less restrictive model specifications. Such research should also investigate the effects of varying incentives induced by the order in which choice questions are presented to respondents.
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Discrete Choice Experiments in Health Economics: Past, Present and Future

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

4.1 Strengths and Limitations The current study has several strengths. First, the detailed with the total number of articles approximately divided equally among authors because of the relative short time- frame and the need to balance author burden with study quality. Additionally, a subsample of studies (20%) was double-checked by one author (V.S.) for quality control, which enhanced reliability. Second, this study identified trends in empirical DCEs by comparing outcomes from all prior reviews. Additionally, this study included aspects of empirical DCEs not investigated before, although these aspects were recognised in the literature as becoming more important in DCE research (e.g. blocking in experimental design and the type of qualitative methods used in a DCE). Third, our observation of less rapid growth in the number of empirical DCEs (compared to the growth observed in previ- ous reviews) matches the trend in the preference research to focus on the broad range of stated preference methods avail- able (rather than DCEs exclusively) [ 4 , 5 , 347 ].
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Preference Elicitation and Query Learning

Preference Elicitation and Query Learning

Driven by the same concerns as preference elicitation in combinatorial auctions, there has also been significant recent work on ascending combinatorial auctions (Parkes, 1999b,a; Ausubel and Milgrom, 2002; Wurman and Wellman, 2000; Bikhchandani et al., 2001; Bikhchandani and Ostroy, 2001). These are multistage mechanisms. At each stage the auctioneer announces prices (on items or in some cases on bundles of items), and each bidder states which bundle of items he would prefer (that is, which bundle would maximize his valuation minus the price he would have to pay for the bundle) at those prices. The auctioneer increases the prices between stages, and the auction usually ends when the optimal allocation is found. Ascending auctions can be viewed as a special case of preference elicitation where the queries are demand queries (“If these were the prices, what bundle would you buy from the auction?”) and the query policy is constrained to increasing the prices in the queries over time. Recently it was shown that if per-item prices suffice to support an optimal allocation (i.e., a Walrasian equilibrium exists), then the optimal allocation can be found with a polynomial number of queries, where each query and answer is of polynomial size (Nisan and Segal, 2003).
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Validity of Discrete-Choice Experiments - Evidence for Health Risk Reduction

Validity of Discrete-Choice Experiments - Evidence for Health Risk Reduction

4.3 Criterion validity: actual and stated choice Whereas in the preceding section, focus was on estimating MWTP for risk reduction, the objective here is to calculate WTP for the specific hip protector HIPS ® presented to respondents at the end of the experiment. This allows to relate the decision to participate in the wearing trial (which is an indicator of actual WTP) to the stated WTP value, amounting to a test of criterion validity. Ideally, one would attempt to predict participation in the trial based on estimated individual WTP. However, these values derive from utility differences, causing socioeconomic characteristics to drop out from the estimating equation unless they enter through interactions with product attributes, in particular the cost attribute, permitting variation of the marginal utility of income (Johnson and Desvousges, 1997). The present sample is limited to individuals aged 70 and more and living independently, resultingin a high degree of homogeneity in terms of measured socioeconomic characteristics. This fact may explain why these interaction terms did not attain statistical significance, precluding calculation of individual WTP values. Even if these values could have been calculated, it is doubtful whether predicting the participation decision would have been successful because only 16 percent of the sample (83 respondents out of 522) chose to participate in the trial. This makes estimation of a distribution function difficult.
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Bayesian optimal designs for discrete choice experiments with partial profiles

Bayesian optimal designs for discrete choice experiments with partial profiles

Received 2 May 2011, revised version received 7 September 2011, accepted 20 November 2011 Abstract In a discrete choice experiment, each respondent chooses the best product or service sequentially from many groups or choice sets of alternative goods. The alternatives are described by levels of a set of predefined attributes and are also referred to as profiles. Respondents often find it difficult to trade off prospective goods when every attribute of the offering changes in each comparison. Especially in studies involving many attributes, respondents get overloaded by the complexity of the choice task. To overcome respon- dent fatigue, it is better to simplify the choice tasks by holding the levels of some of the attributes constant in every choice set. The resulting designs are called partial profile designs. In this paper, we construct D-optimal par- tial profile designs for estimating main-effects models. We use a Bayesian design algorithm that integrates the D-optimality criterion over a prior dis- tribution of likely parameter values. To determine the constant attributes in each choice set, we generalize the approach that makes use of balanced incomplete block designs. Our algorithm is very flexible because it produces partial profile designs of any choice set size and allows for attributes with any number of levels and any number of constant attributes. We provide an illustration in which we make recommendations that balance the loss of statistical information and the burden imposed on the respondents.
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Discrete Choice Experiments for Health Policy : past, present, and future

Discrete Choice Experiments for Health Policy : past, present, and future

2. RESEARCH QUESTIONS 2.1 Opt-out versus neither In DCE guidelines, the term opt-out is used to encapsulate both the neither and the opt- out alternative. The only exceptions are the work by [21, 30] where the authors explicitly distinguish between these two formats. Likewise, the opt-out and neither alternatives are usually coded similarly when analysing the data, i.e. as zeros or as missing attribute levels [23]. Although some respondents may interpret choosing neither of the alternatives as a pure opt-out (not to buy the good or use the service), others may interpret neither in terms of a better alternative for which the researcher usually does not know the associated levels. For example, a DCE on patients’ hospital choice in the UK [11] included a neither alternative, and respondents were explicitly explained that: “… choosing ‘neither’ corresponded to a decision to look either for alternative treatment outside of the NHS, or opting not to have the operation”. This uncertainty surrounding respondents’ interpretation leads to challenges when modelling the neither alternative and may bias the DCE results if zero or missing values are used in cases where other levels would have been more appropriate. Previous studies that included a neither alternative also raised this point about multi-interpretability. For example, in a DCE on family planning (FP) and HIV services [31] where a neither alternative was included, it was stated that “… ‘neither’ responses could be interpreted either as a choice not to use FP or not to use any service at all. This would obscure the preferences of individuals who would like to use a service, but who find that the alternatives presented are not suitable”. And, in a DCE on preferences for key dimensions of quality of care [32], it was stated that “… the study may have not clearly specified what a ‘no’ response meant— whether it indicated seeking care at a private facility or not seeking facility-based care— which may have implications for the interpretation of the constant terms”.
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Validity of discrete-choice experiments evidence for health risk reduction

Validity of discrete-choice experiments evidence for health risk reduction

The results of the present study must be made comparable with those shown in Table 3. First, calculated WTP is for a reduction of the risk of breaking the femur rather than the risk of death. In advance of the choice experiment, it was ascertained that respondents did understand the risks associated with a fractured femur. Before making their hypothetical decisions in the DCE, respondents were informed about their risk of breaking the femur as well as the resulting risk of death. Mortality rates given fracture of the femur were used according to age class to estimate the implied relative reduction in mortality due to this particular cause (Hubacher and Ewert, 1997; Lippuner et al., 1997). The associated marginal WTP values were then integrated for a protective effect of 100 percent which allowed the computation of the value of a statistical life, amounting to 1.9 mn. US$ for individuals aged 70 –75.
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In search of a preferred preference elicitation method: A test of the internal consistency of choice and matching tasks

In search of a preferred preference elicitation method: A test of the internal consistency of choice and matching tasks

Our study confirmed the pattern found in other studies on preference reversals. In particular, choice and matching elicitation procedures caused significant differences in valuations. In addition, we have confirmed the findings of Bostic et al. (1990) that choice tasks generate fewer inconsistencies than matching tasks. Bostic et al. (1990) used a series of choices to determine a cash amount that was indifferent to a given bet. They found that this led to fewer ‘traditional’ preference reversals than a matching task where the subjects simply had to state a cash amount that caused indifference. We found that eliciting indifference through choice series also reduces another kind of preference reversal, i.e., one that is caused by using different response modes within a task. Moreover, we used other outcomes (health instead of money) than Bostic et al. (1990), indicating that this pattern holds in other domains as well, and may therefore be a more universal 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

Although Bayesian designs provide more robust design solutions than local optimal designs, as they account for the uncertainty around the possible parameter values, in health economics less attention has been paid to Bayesian designs. Designs have been mainly based on zero priors for the unknown preference parameters, which results in utility-neutral designs, to simplify design construction. However, this assumption is un- realistic, since it assumes no preference for the attribute levels across alternatives, and might reduce the choice design efficiency. In particular, design have been restricted to the optimal design principles (orthogonality, level balance, minimal overlap and util- ity balance) defined by Huber and Zwerina (1996) who state that jointly satisfying these principles returns an optimal choice design. Nevertheless, for large and more complex designs that involve real constraints (e.g. avoiding dominant and implausi- ble choices) these principles might conflict with each other, and even satisfying these principles might not produce efficient design as illustrated in Street and Burgess (2007, pp.89-91).
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Estimating health state utility values from discrete choice experiments a

Estimating health state utility values from discrete choice experiments a

The most popular methods for estimating MIXL are simulated maximum likelihood (SML) and Bayesian. Each has relative merits (Regier, et al., 2009; Train, 2003). The SML method is widely used, as most econometric and statistic software have developed standard routines to estimate MIXL based on this method. 6 However, the Bayesian approach has several clear advantages that suit our case. First, we assume all the random coefficients are correlated which leads to the estimation of a large covariance matrix. The SML method can be very time consuming in this case. And even with large number of simulation draws, convergence is not always guaranteed. In contrast, the Bayesian approach estimates correlated MIXL and uncorrelated MIXL at almost the same speed (Train, 2003). Second, the SML method cannot estimate M3.3 without fixing the bounds while the Bayesian approach may estimate the bounds and other parameters simultaneously by using informative priors (we will show this in a moment). Therefore, in this study we chose to use the Bayesian method to estimate all the models including the conditional logit which is a special case of MIXL with its  set as
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