Top PDF Validity of discrete-choice experiments evidence for health risk reduction

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

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

DCE for environmental goods have been tested for convergence validity. Thus, Mason and Quigley (1983) found that hedonic regression (where market prices are related to product attributes) resulted in estimates that were similar to those derived from DCE. The estimates of both methods were close to the ‘true’ values of a Monte Carlo simulation. A similar conclusion was drawn by Gegax and Stanley (1997) who compared WTP values for safety obtained from hedonic regression and DCE. They found no significant differences between the hedonic and DCE estimates. More recent results from environmental economics confirm convergence validity between RP and DCE (see e.g. Hensher et al., 1999; Louviere et al., 1999). However, juxtaposing CV and DCE results, Boxall et al. (1996) arrived at WTP estimates that were twenty times higher in the case of CV. At the same time, these authors were able to show that DCE results were in conformity with RP, i.e. with actual choice behavior. Mark and Swait (2004) come to the same conclusion with regard to physicians’ evaluation of an alcohol medication and actual prescribing decisions.Still other studies cast doubt on the validity of the CV method (see, e.g., Hausman, 1993; Nocera et al., 2002) or suggest superiority of DCE over CV (Ryan, 2004)
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Validity of discrete-choice experiments evidence for health risk reduction

Validity of discrete-choice experiments evidence for health risk reduction

DCE for environmental goods have been tested for convergence validity. Thus, Mason and Quigley (1983) found that hedonic regression (where market prices are related to product attributes) resulted in estimates that were similar to those derived from DCE. The estimates of both methods were close to the ‘true’ values of a Monte Carlo simulation. A similar conclusion was drawn by Gegax and Stanley (1997) who compared WTP values for safety obtained from hedonic regression and DCE. They found no significant differences between the hedonic and DCE estimates. More recent results from environmental economics confirm convergence validity between RP and DCE (see e.g. Hensher et al., 1999; Louviere et al., 1999). However, juxtaposing CV and DCE results, Boxall et al. (1996) arrived at WTP estimates that were twenty times higher in the case of CV. At the same time, these authors were able to show that DCE results were in conformity with RP, i.e. with actual choice behavior. Mark and Swait (2004) come to the same conclusion with regard to physicians’ evaluation of an alcohol medication and actual prescribing decisions.Still other studies cast doubt on the validity of the CV method (see, e.g., Hausman, 1993; Nocera et al., 2002) or suggest superiority of DCE over CV (Ryan, 2004)
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Validity and Reliability of Willingness-to-Pay Estimates: Evidence from Two Overlapping Discrete-Choice Experiments

Validity and Reliability of Willingness-to-Pay Estimates: Evidence from Two Overlapping Discrete-Choice Experiments

4. Experiment Design To elicit preferences of the Swiss residential population with regard to proposed changes in the health care system, two DCEs were designed featuring hypothetical insurance contracts. Their attributes should reflect the reforms that are debated at present by policy makers. These contract attributes were preselected in expert sessions with representatives of the Swiss health care system and their relevance checked in a pretest. The nine characteristics retained (plus PREMIUM as the price attribute) are listed in Table 1. With regard to those attributes that were not taken into account, participants were told that the status quo and the alternative were identical in this regards in order to avoid omitted variable bias in the econometric analysis. The possibilities considered are the following. In experiment A, free choice of physician is restricted to a list of contract providers (PHYSLIST). The list can be made up applying differ- ent selection criteria, viz. cost, quality, or efficiency, defined as the quality-cost ratio (PHY- SCOST, PHYSQUAL, and PHYSEFF). The number of hospitals available is reduced by clos- ing small local hospitals in favor of larger centralized ones (HOSPITAL). At present, long- term care is only partially covered by mandatory health insurance in Switzerland. The pro- posed change comprises full coverage of long-term care, to be financed by those over 50 years old (LTCARE). The current drug benefit is very comprehensive; it would be changed by excluding drugs for minor illnesses such as the common cold (MINOR) or reimbursing only the cheapest drug available, usually a generic (GENERICS).
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Validity and reliability of willingness-to-pay estimates: evidence from two overlapping discrete-choice experiments

Validity and reliability of willingness-to-pay estimates: evidence from two overlapping discrete-choice experiments

The two experiments were developed and implemented in a coordinated way in order to allow for a joint analysis of the data. The documentation materials accompanying the DCE were identical. Two representative telephone surveys with 1,000 persons aged over 25 years 2 were conducted independently in the German and French parts of Switzerland during September 2003. The procedure was in two steps due to the special character and information require- ments of a DCE. In a first telephone contact, people were asked if they would be willing to take part in the study. Those agreeing to participate received a package containing documen- tation materials to make sure that all respondents had the same information about the Swiss health care system and knew the deductible level and premium of their health insurance plan. In this way, respondents were given time to reflect, which may result in more consistent choices during the experiment [44].
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A systematic review of the reliability and validity of discrete choice experiments in valuing non-market environmental goods

A systematic review of the reliability and validity of discrete choice experiments in valuing non-market environmental goods

Evidence on the content validity of DCE is sparse with only 20 outcomes, which may be an artefact of our systematic review protocol, but could also imply a high level of undiagnosed protest beliefs and a need for more routine measurement. If a high number of respondents across DCE studies hold protest beliefs toward the payment vehicle or the policy scenario, this challenges the use- fulness of the method in environmental decision-making. Similar concerns apply to perceived inconsequentiality and dif ficult-to- comprehend DCE survey designs, which may result in random re- sponses instead of choices that would maximize utility. In partic- ular, the identi fication of protesters is subjective and case study speci fic, and there is no agreement on how to handle protest atti- tudes in econometric modelling ( Meyerhoff et al., 2014 ). We have observed a move towards ever more sophisticated econometric model speci fications to analyse DCE data, but argue that survey design remains very important for improving DCE's reliability and validity. The use of debrie fing questions is a simple but useful diagnostic tool to examine content validity, but we found them to be rarely reported. However, as with self-reported attribute- attendance, scholars have questioned the extent to which re- spondents' self-reported measures are reliable ( Hess and Beharry- Borg, 2012 ).
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Inferring Attribute Non-attendance from Discrete Choice Experiments: Implications for Benefit Transfer

Inferring Attribute Non-attendance from Discrete Choice Experiments: Implications for Benefit Transfer

Typical convergent validity tests of benefit transfer based on stated preference data assume that willingness to pay (WTP) estimates have been accurately measured, and that differences in WTP arise from differences in observable and unobservable characteristics between the study and the policy sites. In this paper, we conduct a convergent validity test assuming equality of underlying preferences, but allow for the possibility that transfer errors arise from differences in the way that respondents process information in the preference elicitation tasks. Using data from an identical survey instrument applied to the population of two river basins in Spain, we obtain marginal and total WTP estimates for ecological improvements of water bodies and the corresponding transfer errors across sites. Results of equality constraint latent class (ECLC) models that infer attribute non-attendance (AN-A) are compared to results from mixed logit (MXL) models in WTP space. We find large absolute and relative differences in marginal and total WTP between sites for the MXL models, and significantly reduced transfer errors for the ECLC model. This paper therefore provides further evidence that AN- A can significantly affect environmental values derived from attribute-based stated preference methods and is the first to investigate the implications for benefit transfer.
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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

4 In 2009–2012, one study explored how changing the number of responses elicited from respondents might affect estimates of WTP [ 204 ]; another looked at parents’ preferences for management of attention-deficit hyperactivity disorder [ 206 ]; one study looked at general public preferences for long-term care [ 137 ]; another two studies looked at preferences for human papillomavirus vaccine, one case looking a societal preferences [ 207 ] and the other [ 63 ] looking at mothers’ preferences; another study looked at the valuation of diagnostic testing for idiopathic developmental disability by the general population [ 208 ]; another looked at various stakeholder groups’ preferences for coagulation factor concentrates to treat hemophilia [ 145 ]; one study looked at general public preferences for tele-endocopy services [ 158 ]; another compared Dutch and German preferences for health insurance amongst their populations [ 214 ]; one paper looked at public and decision maker preferences for pharma- ceutical subsidy decisions [ 215 ]; one study explored how individuals perceive various coronary heart disease factors [ 203 ], whilst another described the relative importance of major adverse cardiac and cerebrovascular events to be used when analyzing trials [ 212 ]. Two other DCEs were performed on the area of quality improvement; one investigated how to best disseminate evidence-based practices to addiction service providers and administrators [ 205 ], while the other was used to investigate which indicators had the greatest impact on the decisions of health service inspectors concerning the assessment of quality of mental health care [ 211 ]. Other applications included a study on preferences of health workers in Burkina Faso for health- insurance payment mechanisms [ 209 ]; a study on how respondents valued mortality risk attributable to climate change reductions [ 210 ]; and a study on the preferences for reducing contaminated sites to reduce the risk for cancer [ 213 ].
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Discrete Choice Experiments for Health Policy : past, present, and future

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

Insights in the factors influencing the intention to accept or decline a pandemic vaccine may have implications for both national and international policy and for further research. When communicating public health messages regarding vaccination, one should be aware of preference heterogeneity and therefore use different sources and channels to distribute the messages [44]. The current study provides guidance on how to target public health messages, by the identification of two classes with different preferences for pandemic vaccinations. To immediately reduce the number of susceptible people, a possible strategy could be to target the message for the first phase of a vaccination program to the more vaccination minded persons, here latent class 1. This can be done by using the government and RIVM as bodies to advice the vaccine to males and focus more on the expected effectiveness of the vaccine. Next, physicians can advise females to take the vaccine. Additionally, out-of-pocket costs need to be as low as possible, as our study showed the negative relation between out- of- pocket costs and vaccination decisions. For public health messages during vaccination programs, it is also important to monitor side effects. Updates of the side effects of the vaccine need to be given on a regularly basis to make sure that an informed choice can be made and to reduce fear of the side effects of the vaccine. Furthermore, policy makers can use the expected uptake probability of hypothetical vaccinations when predicting the number of vaccinations that is needed. Although these numbers are rough estimates and it is not known if they are externally valid, the expected uptake can still be useful when other information is lacking. Additionally, these numbers can guide communication on the expected vaccination uptake. Since this is the first quantitative study in motivations for pandemic vaccinations, we do not know to what extent differences exist between countries regarding preferences for vaccinations. There is some evidence, including a questionnaire study in four countries investigating reasons why high risk people reject influenza vaccination in four countries of Europe suggests differences between respondents of the different countries [45]. Therefore, further international research is recommended.
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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.

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

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

Traditional DCEs provide information about attributes but not the overall im- pact of the attributes and their levels. In traditional DCEs, attributes are not scaled, or broken down into categorical levels; therefore, the impact they have is subjective (Flynn et al., 2007; Flynn et al., 2008; Lancsar et al., 2007). The parameter es- timates in attribute-level best-worst DCEs provide information about the impact of the attributes and attribute-levels where as in traditional DCEs the parameters describe the change in level of an attribute (Yoo and Doiron, 2013). Lancsar et al. (2007) presented five methods for determining the relative impact of attributes. Four of the five models are possible with the traditional DCEs, and the last model is the attribute-level best-worst scaling experiment. The methods for traditional DCEs are: partial log-likelihood, marginal rates substitution, Hicksan welfare measure (appro- priate in willingness to pay type problems), and probability analysis. Lancsar et al. (2007) examined the all five models in a study in a health systems research study dealing with cardiac patients and found the attribute impacts between the different methods comparable. However, it was determined that the attribute-level best-worst scaling provided greater precision in determining the impacts.
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Examining ordering effects in discrete choice experiments: A case study in Vietnam

Examining ordering effects in discrete choice experiments: A case study in Vietnam

For each choice task, while the differences across choice tasks in the deal value of the status quo are zero, the variable values related to the improvement option are calculated accordi[r]

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Nominal group technique to select attributes for discrete choice experiments: an example for drug treatment choice in osteoporosis

Nominal group technique to select attributes for discrete choice experiments: an example for drug treatment choice in osteoporosis

Results: Twenty-six osteoporotic patients participated in five NGT sessions. Most (80%) of the patients changed their ranking after the discussion. However, the average initial and final ranking did not differ markedly. In the final ranking, the most important medication attributes were effectiveness, side effects, and frequency and mode of administration. Some (15%) of the patients did not correctly rank from 1 to 12, and the order of attributes did play a role in the ranking. Conclusion: The NGT is feasible for selecting attributes for DCEs. Although in the context of this study, the NGT session had little impact on prioritizing attributes, this approach is rigorous, transparent, and improves the face validity of DCEs. Additional research in other contexts (different decisional problems or different diseases) is needed to determine the added value of the NGT session, to assess the optimal ranking/rating method with control of ordering effects, and to compare the attributes selected with the different approaches.
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Discrete Choice under Risk with Limited Consideration

Discrete Choice under Risk with Limited Consideration

8 Discussion The literature concerned with the formulation, identification, and estimation of discrete choice models with limited consideration is vast. However, to our knowledge, there is no previous work applying such models to the study of decision making under risk, except for the contemporaneous work of Barseghyan, Coughlin, et al. (2019). In particular, this paper is the first to exploit the SCP for identification purposes. As a result, several fundamental differences emerge between our work and existing papers. First, we achieve identification in the most challenging case where there is a single excluded regressor that affects the utility of all alternatives. 40 Second, we allow for consideration to depend on preferences. Third, with alternative-specific excluded regressors, this dependence can be essentially unrestricted and can be combined with dependence of consideration on (some of) the excluded regressors. Fourth, we scrutinize the large support assumption, show why it may be necessary, and when and how it is possible to make progress when it is not satisfied. Fifth, our approach comes with an easy to implement and computationally fast estimation strategy. Finally, we make a contribution specific to the study of decision making under risk by proposing a model that is immune from Apesteguia & Ballester (2018) criticism and features two sources of unobserved heterogeneity – risk aversion and limited consideration – whose distributions are identified. More generally, the paper establishes that, as long as the DMs’ preferences satisfy the SCP, allowing for limited consideration does not hinder the model’s identifiability or applicability. Hence, we view our framework as a stepping stone for studies of consumer behavior in markets where limited consideration may be present (one example is Coughlin, 2019, who builds on our framework to study consumer choice in Medicare Part D markets).
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Bayesian optimal designs for discrete choice experiments with partial profiles

Bayesian optimal designs for discrete choice experiments with partial profiles

the most important ones. They dominate the decision making process while the other attributes are ignored. The corresponding decision rule respondents apply then is non-compensatory. To accurately measure respondents’ trade-offs, it makes sense to simplify the comparison by holding the levels of some of the attributes constant in every choice set. These constant attributes need not be the same in each choice set. They can be ignored in the choice task so that the remaining attributes whose levels are varied make up the resulting choice set. The profiles in such a choice set are called partial profiles, and the number of attributes that are allowed to vary in the partial profiles is called the profile strength. The advantage of using partial profiles is that the compensatory discrete choice models remain valid because they help prevent respondents resorting to non-compensatory decision rules, such as lexicographic decision rules. Also, in the presence of a dominant attribute, we still obtain information about trade-offs made between the remaining attributes when partial profiles are used. The downside of partial profiles is that, in theory, they provide less information on the parameter values compared to full profiles that allow all attributes to vary in each choice set (Kessels et al. 2010).
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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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Using discrete choice experiments to inform the design of complex interventions

Using discrete choice experiments to inform the design of complex interventions

This paper presents how DCEs can complement conven- tional qualitative research to prioritize service compo- nents most critical to the target population of adult men in Tanzania. The DCE results led to the inclusion of two key service components. Firstly, the strong preferences among both the younger men and the older men revealed the importance of age-separated waiting areas at the VMMC facilities. Waiting areas for VMMC were rearranged to allow for younger and older men to wait separately and out of view of each other. Secondly, the strong preference for partner counselling led to the introduction of partner counselling booths, staffed by both female health workers and female community peers who provided one-on-one and small group information for female partners about circumcision care and post-circumcision abstinence. Out-of-hours service provision was preferred in Njombe, but the utility was relatively small compared with the value placed on sepa- rated waiting areas and partner counselling service. This allowed the intervention to focus less effort on promot- ing out-of-hours VMMC services, although they remained available upon request. The suggested prefer- ence for opt-out testing led to an intensification of the information campaign, ensuring clients were comfort- able requesting not to be tested for HIV and staff were re-trained to emphasize the right to opt-out of HIV test- ing to clients. Officially, however, both the intervention and the control arms maintained a policy of opt-out HIV testing.
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Comparison of benefit–risk preferences of patients and physicians regarding cyclooxygenase-2 inhibitors using discrete choice experiments

Comparison of benefit–risk preferences of patients and physicians regarding cyclooxygenase-2 inhibitors using discrete choice experiments

In this paper, using a DCE approach, we directly compared the benefit–risk trade-offs as reported by Korean patients and physicians regarding Cox-2 inhibitors as treatments for arthritis, leading to several meaningful results. Our results demonstrated the preference gap between patients and physi- cians regarding Cox-2 inhibitors. We found that patients were more concerned with the benefits of Cox-2 inhibitors, such as PAI and FUN, than with the risk of GI and CV adverse events, whereas physicians considered CV adverse events to be the most important attribute among the four tested attri- butes. In addition, we found patients were more willing than physicians to accept risk upon improvement of both beneficial attributes from their worst levels or moderate levels to their best treatment response levels. With respect to decreased PAI, patients were more willing by 1.6 ( = 2.1/1.3) times to face a GI risk and by 1.5 ( = 2.1/1.4) times to face a CV risk than physicians. Notably, with respect to improved FUN, patients Table 3 Preferences of patient group and physician group as estimated by the regression model
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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

Most DCEs in health economics are rooted in the Random Utility Theory (RUT) [3,5–7]. This theory assumes that respon- dents choose rationally and will select the scenario that generates the highest personal utility, that is, respondents will only select the opt-out option if none of the presented scenarios in that specific choice task is more attractive than the opt-out option [5,8]. Additional research shows that from this perspective, forcing respondents to make a choice induces bias, as they would not always make that same choice in real life [3,9,10]. In such a forced-choice situation, people who would rather choose to opt- out, tend to randomly select either scenario from a choice task or select the most safe/least extreme scenario [9–12]. As a consequence, the standard error of the attribute estimates will increase while the external validity decreases [9,10]. In summary, based on the RUT, an opt-out option can always be included, if this is accordance with the respondent’s real-life decision context. However, in practice, other motives than achieving the highest personal utility may be more important when people make their decisions [8–22]. This resulted in the hypothesis that only very few respondents act solely according to the assumptions of the RUT when choosing the opt-out option. Some individuals are more prone to choose the opt-out situation even before they actually evaluate the different situations in a choice task. Baron and Ritov (1992) argued that individuals choose the opt-out alternative to protect themselves from poor choices, as negative outcomes based on taking action (choosing) are perceived as worse compared to negative outcomes due to inactivity (not choosing) [19]. This finding was confirmed by many others [13,17,18], among which a theory by Luce and colleagues who suggest that if people decide to make a choice, the tendency to choose to opt-out increases as the trade-off becomes more difficult and the decision at hand is emotion-laden [12,16]. This indicates that people choose to opt- out to avoid making difficult trade-offs [12,16]. Research by Dahr and colleagues (1997 and 2003) showed that choice task complexity (i.e., large number of choice situations per choice task or comparable choice situations with respect to their attractive- ness) results in more opting out [9,11]. In summary, it seems plausible that respondents choose the opt-out option more often if they have to decide about a complex emotion-laden topic, if choice tasks are difficult, if scenarios are complex and if none of the scenarios is clearly superior. This way, respondents minimize their effort and reduce internal conflict induced by (negative) decision making.
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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

METHODS Questionnaire Design The attributes and levels describing the different consultation scenarios were identified through a review of the existing literature and semi-structured interviews to primary care managers and District managers of Tuscan Local Health Authorities and they were validated in a focus group. In order to avoid placing a significant cognitive burden on respondents that could alter the trade off between the attributes [17], the number of attributes selected was limited to the three most important factors emerged [17]. Considering also the results of previous DCEs [4, 10-12, 20], plausible levels to each of the attributes were assigned (Table 1) [18, 19]. A full factorial design has been adopted [21] and 3 3 (27) combinations were obtained. The 27
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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

Discrete choice experiments have become increasingly used in health services research, but primarily to assess patient-stated preferences and willingness to pay for dif- ferent models of health care service delivery [47-50]. There are still only a small number of studies that have used this methodology to analyse the job preferences of health care providers. The aim of this article is to review the existing literature on the use of discrete choice experi- ments to study HR issues in both developed and develop- ing countries. The intention is to draw lessons on the value of this relatively new methodology to inform HR policy development in developing countries. This paper first introduces the basic principles of DCE methods, then the methodology of our literature review is described. The main part of the paper describes the DCE studies we iden- tified and summarizes their findings. The final discussion focuses on some cross-cutting lessons as well as the advan- tages and limitations of DCE methods for HR research. Methods
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