Identifying and analysing dominant preferences in discrete choice experiments: An application in health care

Identifying and analysing dominantpreferences in discrete choice experiments:

An application in health care

Anthony Scott *

Health Economics Research Unit, University of Aberdeen, Foresterhill, Aberdeen AB25 2ZD, Scotland, UK

Received 27 October 2000; received in revised form 3 July 2001; accepted 4 February 2002

Abstract

The assumption that goods are traded-off to maximise utility underpins many choice-based

empirical methods used to elicit stated preferences. The aim of this paper is to test and exam-

ine the implications of this assumption in the context of a discrete choice experiment that

examined stated preferences for different models of out of hours care provided by General

Practitioners. The results indicated that 45% of individuals exhibited evidence of dominant

preferences, a special case of a lexicographic ordering. Factors influencing the existence of

dominant preferences included the complexity of the choices, but also individuals’ past expe-

riences. The analysis of dominant preferences provided important information about individ-

uals with particularly strong preferences, and suggested different policy conclusions for this

group of respondents. � 2002 Elsevier Science B.V. All rights reserved.

JEL classification: C25; C99; D12; I19

Keywords: GP out of hours care; Discrete choice experiments; Hierarchical choice

1. Introduction

A methodological issue that has received little attention to date in the literatureon discrete choice experiments is whether respondents trade-off attribute levelsor have lexicographic preference orderings (where only one attribute matters and

Journal of Economic Psychology 23 (2002) 383–398

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individuals do not trade). Assuming that individuals trade-off means that indi-viduals are prepared to accept more of a specific good or characteristic in compen-sation for less of another. This is also known as compensatory decision making. Inthe case of a lexicographic ordering of goods or characterisitcs, an individual isnot prepared to trade-off and so goods or characterisitics cannot be substitutedfor one another (non-compensatory decision making). In the case of a lexicographicordering of a bundle of goods, there are no other bundles (apart from itself) towhich it is indifferent. Indifference curves cannot be formulated as no trading takesplace. Marginal rates of substitution are meaningless, and a lexicographic order-ing cannot be represented by a utility function (Deaton & Muellbauer, 1980).These concepts underpin many methods used to elicit preferences based on eco-nomic theory.

Choice situations where individuals do not trade-off (non-compensatory deci-sion making) are ignored by economic theory, yet have been the subject of muchresearch in psychology where their existence is well established empirically (Gigeren-zer & Todd, 1999; Payne, Bettman, & Johnson, 1993). If there is evidence of non-compensatory decision making then this may influence the effectiveness of policiesto improve welfare (Earl, 1983). For example, if a policy is introduced to reducewaiting times then it may not improve individuals’ utility if individuals have a mini-mum target reduction in waiting time, and the policy does not achieve this. A studythat assumes that individuals trade-off may therefore overestimate the effectivenessof such policies since it assumes any reductions in waiting time, including those be-low the target, will increase utility. This therefore has important consequences whenattempting to interpret the results of empirical work that elicits preferences usingchoice-based techniques.

The aim of this paper is to examine the extent to which respondents trade-off at-tribute levels in a discrete choice experiment. In particular, we examine the existenceof dominant preferences, a special case of a lexicographic ordering. The paper usesdata from a study that elicited the users’ and non-users’ preferences for differentmodels of out of hours care provided by General Practitioners (GPs). After defininghierarchical choice, and discussing how it can be identified empirically, the paper dis-cusses the reasons why such preferences exist. The empirical analysis illustrates theseissues and tests for the existence of one form of hierarchical preferences. In particu-lar, it uses a more strict empirical definition for hierarchical preferences, and exam-ines potential reasons for the existence of such preferences. Discussion is focussed onthe implications for future research on eliciting preferences using discrete choiceexperiments.

2. Defining hierarchical choice

The situation when preferences are not perfectly substitutable has been examinedby several authors including Drakopoulos (1994) and Fishburn (1974). At one ex-treme of this limited substitutability are lexicographic orderings. This implies thatthere is an absolute order of preferences and precludes any degree of substitution

384 A. Scott / Journal of Economic Psychology 23 (2002) 383–398

between attributes. Following Drakopoulos (1994), let x and x0 be two alternativesdefined as two bundles of attributes:

x ¼ x1; x2; . . . xnð Þx0 ¼ x01; x

02; . . . x

0n

� �

xPx0 iff

either 1Þ x1 > x01or 2Þ x1 ¼ x01; x2 > x02or 3Þ x1 ¼ x01; x2 ¼ x02; x3 > x03to xn�1 ¼ x0n�1; xn > x0n:

Attribute x1 is considered to be absolutely important and only when there is equalityof x1 between alternatives is x2 considered in the decision making process. Thus incase (1) only x1 is considered by the decision maker and the choice is made on thisbasis. In this process, although no trade-offs are made, the relative importance ofattributes can still be established by their absolute ranking. In terms of utility:

uðxÞ > uðx0Þ iffeither 1Þ x1 > x01or 2Þ x1 ¼ x01; x2 > x02:

Thus, when x1 6¼ x01, utility is a function of x1 only, with all other attributes con-sidered as irrelevant by the decision maker.

Somewhere in between a strict lexicographic ordering and perfect substitutabilitylies target setting behaviour. This form of hierarchical choice allows for some substi-tution. It operates by an individual setting a target (or threshold) for the first attri-bute and that this must be reached before the second most important attribute isconsidered. This is closely related to satiation, aspiration, and satisfactory levels,where an individual has no further interest in an attribute once a specific level hasbeen achieved (Lancaster, 1972). This type of decision rule is similar to the ‘elimina-tion by aspects’ model of Tversky, satisficing theory of Simon, and other decisionmaking models (Keeney & Raiffa, 1976; Luce, 1978; Simon, 1959; Tversky, 1972a,b).

3. Identifying dominant preferences

Underlying the discrete choice method of conjoint analysis (where respondentsare presented with a series of pairs of scenarios and, for each pair, asked to choosewhich they prefer), is random utility theory (Manski, 1977). This is the discretechoice analogue of consumer theory, and so assumes that individuals trade-off char-acteristics when making their discrete choice. The results of this form of conjointanalysis are usually interpreted as utility and marginal rates of substitution betweenattributes are calculated, both of which require the assumption of trading (Propper,1995; Truong & Hensher, 1985). Lancaster’s original work on the characteristicsapproach to consumer theory also made this assumption (Lancaster, 1966).

A. Scott / Journal of Economic Psychology 23 (2002) 383–398 385

At present, there is little consensus about whether and how to account for hierar-chical preferences in discrete choice experiments, even though their existence hasimplications for how results are interpreted and consequently the effectiveness of pol-icies aimed at improving welfare.

Those analyses conducted in the health field have made some attempt to examinehierarchical preferences. One study of local versus central orthodontic clinics foundthat every individual made responses consistent with non-compensatory decisionmaking for waiting time, and others have found very high proportions of respon-dents (between 30% and 71%) with similar preference structures (Bryan, Buxton,Sheldon, & Grant, 1997; Propper, 1995; Ryan & Farrar, 1994; San Miguel, Ryan,& McIntosh, 2000).

The method by which these studies have attempted to identify non-compensatorydecision making is when, for a discrete choice, condition (1) is met (where x and x0

are the two scenarios in the pair-wise choice and x1; . . . ; xn are the attributes). Indi-viduals who always choose the scenario where x1 is greater (or better) than x01, nomatter what the levels of the other attributes, are assumed to have a ‘lexicographic’preference for that attribute. Lancaster (1972) refers to this situation (where only oneattribute is examined) as one of ‘dominance’:

A characteristic is dominant within some group of characteristics, insome set of situations, if the consumer always prefers a collection withmore of the dominant characteristic, whatever the amounts of the othercharacteristics. . . Operationally, dominance is observable, lexicographicordering only so in rare instances.

To establish whether a lexicographic ordering exists empirically, it must also beshown that the further conditions are met. By definition, dominance places no re-strictions on the ordering or extent of trading for other attributes. Dominance ofone attribute does not necessarily mean that the others are ordered lexicographically.When the level of the dominant attribute is the same in both alternatives, the otherattributes may still be traded-off. Lancaster provides an example where dominance,trading and target setting can occur in a single multi-attribute decision (Lancaster,1977).

The identification of choice processes other than dominance is difficult using aself-administered questionnaire because it provides little information about howan individual is making a choice. Certainly, it is difficult to tell from a self-adminis-tered questionnaire which attributes the respondent is or is not considering whenchoosing between scenario A or B in a specific choice. Individuals might be usingmany different types of heuristics and ‘rules of thumb’ and these may even varyfor each discrete choice.

Furthermore, dominance can only be established for the specific scenarios pre-sented to an individual. In discrete choice experiments where there are too many sce-narios for one individual to consider, they are often randomly split giving twoversions of the questionnaire (block design), or a fractional factorial design can beused. In these situations, if dominance is found for these individuals it only refersto those scenarios in the questionnaire and not to those which have been left out.


To state that an individual has a dominant preference, then it must be assumed thatthis would be the case if the individual were presented with all choices.

4. Reasons for the existence of hierarchical preferences

A main theme in the psychology and behavioural literature on decision making isthat preferences are often constructed at the time they are being elicitied, and thatdecision makers have a variety of strategies that are used when constructing prefer-ences (Payne, Bettman, & Johnson, 2002). This is especially the case for complextasks and those that involve ‘conflict’ among values, such as tasks involving trade-offs. Such conflicts may be confronted and resolved, or avoided, reduced or modifiedusing heuristics. Task complexity implies that individuals do not have the cognitivecapacity to process multi-attribute information (Simon, 1959). Bounded rationalitymay mean that individuals are using an heuristic or ‘rule of thumb’ as a decision rule,e.g. ‘always choose the scenario where attribute x is the ‘best’ ’.

Complexity of the decision task can also be related to the design of the instrumentused to elicit preferences. Framing effects may be relevant. For example, in a study oflocal versus central orthodontic clinics, the main reason suggested as to why ‘waitingtime’ was dominant for all individuals was that the interval between the levels of theattribute was too great (4, 8, 12, and 16 months) (Ryan & Farrar, 1994). Individualswere not prepared to trade-off four months of waiting time for more of the other at-tributes. They may have been prepared to trade-off one month, but the study couldnot have detected this.

A potential solution to this is defining the interval between levels of an attribute toencourage people to trade-off. However, this may not be appropriate where the rangeof values (or the interval between values) for an attribute are pre-determined by pol-icy, or by levels that individuals experience in practice. It is important that attributesand their levels reflect what can happen in practice so that stated preferences are rel-evant to policy. If hierarchical choice is present when ‘real world’ attributes andlevels are used, then such preferences are also likely to be present in ‘real’ choicesituations. Forcing people to trade-off by altering levels may therefore mean resultsare less applicable. It therefore becomes important to identify hierarchical prefer-ences as this may reflect the structure of preferences in real markets.

There are also other properties of discrete choice experiments that may influencethe complexity of the task, which in turn may mean individuals are unlikely to trade-off (Ryan, Scott, Farrar, & Shackley, 1996). This is likely to include the number ofpairs of scenarios presented to individuals at any one time, and the number of attri-butes in each scenario (some earlier studies have presented individuals with as manyas 24 attributes (Chakraborty, Gaeth, & Cunningham, 1993). Evidence from the psy-chology literature, using methods other than discrete choice experiments, suggeststhat non-compensatory decision making is more likely the greater the number of al-ternatives presented (Payne et al., 1992).

The second reason for the existence of hierarchical preferences is that it mayreflect a very strong preference and a belief that a specific attribute was the most


important, and that individuals were not prepared to trade it off. This is closer to alexicographic ordering and could indicate a ‘rights-based’ view of choice and an eth-ical belief that individuals should be provided with a specific characteristic (Edwards,1986; Hanley & Milne, 1996; Lockwood, 1996). It is this type of dominant preferencewhich has more serious implications for the aggregation of preferences and the na-ture of the social welfare function.

5. Empirical analysis

5.1. Background to the out of hours study

The empirical analysis of those with dominant preferences is based on a study ofthe preferences of individuals (both users and non-users) for different models of outof hours care provided by GPs (i.e. primary health care delivered during evenings,nights or weekends) (Scott, Watson, & Ross, 2002). The study used a discrete choiceexperiment to analyse the preferences of parents/guardians of children under 13years old for out of hours care (frequent users of out of hours care). 6304 question-naires were posted to individuals in the urban areas of Glasgow and Aberdeen, sam-pled randomly from the records of out of hours centres and from GP lists. Theresponse rate was 68%. The scenarios described out of hours care in terms of the lo-cation of the consultation, time between initial contact and consultation, who wasseen, and whether the doctor seemed to listen (Table 1). An example of a pair of sce-narios is shown in Appendix A. These were derived from the literature on patientsatisfaction with out of hours care, and also reflected the different models of careavailable across the country. The attributes and levels were organised into scenariosusing a fractional factorial design. This reduced the total possible number of scena-rios from 48 to 16. One scenario from the 16 was chosen to be constant throughout

Table 1

Attributes and levels used in questionnaire

Attribute Levels of each attribute

Where your child is seen emergency centre run by GPs

your home

hospital accident and emergency department

Who your child sees a GP from your practice/health centre

a GP who does not work at your practice/

health centre

Time taken between the telephone call and

treatment being received

20 minutes

40 minutes

60 minutes

80 minutes

Whether the doctor seems to listen to what you

have to say

the doctor seems to listen

the doctor does not seem to listen


the questionnaire and other scenarios are compared with it. The remaining 15 sce-narios were divided randomly, giving two versions of the questionnaire, one witheight pairwise choices and one with seven. For each pair of scenarios, individualswere asked to choose which they preferred.

5.2. Empirical identification and analysis of dominant preferences

The main method used to examine the respondent’s decision making process ex-amined those scenarios where the levels of an attribute were different. For each at-tribute where the levels were different, if the respondent always chose the scenariowith the ‘best’ level (e.g. always chose the scenario with the lowest waiting time) thenit was concluded they had a ‘dominant’ preference for that attribute. This is equiv-alent to Lancaster’s definition of dominance. However, since individuals were notpresented with all possible options, this method may overestimate the proportionof people with a dominant preference. The respondent’s ranking of attributes wasalso used to help identify dominant preferences. Thus, an individual who alwayschose the ‘best’ level of a particular attribute, and who ranked that attribute asthe most important, was defined as having a dominant preference.

For each attribute, the proportion of respondents with dominant preferences wasidentified. Data for those with dominant preferences were analysed together with in-dividuals who did not have dominant preferences (Model 1). A random effects probitmodel was used, as this accounts for the correlations across responses given by eachindividual (each individual had to answer up to eight discrete choices). The depen-dent variable was whether the individual chose scenario A or B. The independentvariables were the differences between the levels of each attribute in each pair ofscenarios. A constant term was included to test and control for any unobservedattributes or the existence of ‘left’/‘right’ bias. The results of this model were thencompared to a model that excluded those with dominant preferences (Model 2). Dif-ferences between the coefficients of the two models were tested for by including in-teraction terms in a model including all individuals (Model 3). A dummy variableindicating whether the individual had a dominant preference (or not) was multipliedby each main effects attribute to create four interaction terms, thereby testing the nullhypothesis that there was no difference between coefficients.

Data for those individuals with a dominant preference were also analysed sepa-rately. Logistic regression was performed to examine factors influencing the proba-bility of a respondent having a dominant preference for a specific attribute. Thisanalysis attempted to distinguish between factors related to the complexity of thechoices, and factors related to individuals having very strong beliefs about attributes.To investigate the complexity of choices, several indicators of their understanding ofthe choices were included as independent variables (ease of completion and the ver-sion of the questionnaire). Individuals who spent less time on the questionnaire mayalso have been more likely to have a dominant preference. Given that these factorsmay depend on the respondents’ age and education, interaction terms between timeto complete and ease of completion, and age, education, and the version of the ques-tionnaire were used. If these variables were significantly associated with having a


dominant preference, then it may be related to the complexity of the choices andtrade-offs they were faced with in the questionnaire. In turn, this suggests that indi-viduals were using a ‘rule of thumb’ when making the choices. This is not necessarilyonly a function of poor questionnaire design, since individuals may also use a rule ofthumb in real choice situations.

To investigate the existence of very strong preferences as a reason for dominance,variables relating to individual’s past experiences of out of hours visits, whether theirchild had asthma or another medical condition, and factors influencing access suchas availability of a car or extra help during the night, were included as independentvariables.

All variables were entered and backward stepwise regression was used to arrive ata more parsimonious model. This follows a ‘general to specific’ method of modelbuilding, commonly used in econometrics (Maddala, 1992). Variables were excludedon the basis of the likelihood ratio test with a p-value 6 0.10. A less stringent p-valuewas used so as not to arbitrarily exclude variables that may be relevant with respectto the hypotheses being tested.

6. Results

Using the method to identify individuals with a dominant preference in Section5.2, 58% (2255/3891) of respondents had a dominant preference for ‘whether the doc-tor seemed to listen’; 10% (402/3891) had a dominant preference for waiting time,and; 7.3% (284/3891) of respondents had a dominant preference for being able tosee a GP from their own practice. However, a proportion of these did not rankthe attribute as the most important. This introduces doubts as to whether a domi-nant preference would have been present if individuals were presented with all pos-sible scenarios. Imposing the additional criteria of including only those who alsoranked the attribute as the most important, 34% (1334/3891) of respondents had adominant preference for ‘whether the doctor seemed to listen’, 9.3% (361/3891)had a dominant preference for waiting time, and 1.3% (50/3891) had a dominantpreference for being able to a GP from their own practice. Dominant preferencesfor location of care were not calculated since the ‘best’ level of this attribute is notknown a priori. Table 2 shows the descriptive characteristics of respondents.

6.1. Comparison of those with and without dominant preferences

Table 3 shows the three random effects probit regression models that were used tocompare those with and without dominant preferences. A comparison of models1 and 2 show coefficients are different. Two of the interaction terms in model 3are statistically significantly from zero, suggesting that individuals with a dominantpreference do place a different weight on these attributes.

Tables 4 and 5 show the results of logistic regressions that examined those factorsinfluencing whether an individual had a dominant preference for a specific attribute.The tables show the odds ratios and 95% confidence intervals for the final reduced


Table 2

Descriptive characteristics of the respondents

Age of parent/guardian (mean, range) 34 (16–66)

Gender of parent/guardian (percentage female) 88%

Average age of children (mean, range) 6.7 (0–24)

Gender of children (percentage female) 48.5%

Average number of children (mean, range) 2.2 (0–12)

Highest level of education of parent/guardian

University 18.3%

College 32%

Secondary 48.7%

None 0.9%

Health status of child

Excellent 47.5%

Good 41%

Fair 9.1%

Poor 2.5%

Whether child has ever suffered from asthma or other ‘wheezy’ illness 37%

Does your child have any other medical problems for which they see a

doctor regularly?

16.7%

Do you have access to a car during the night? 74%

During the night, is there anyone else at home available to help look

after your children?

73%

Percentage from Aberdeen 43%

Percentage from the samples who have used out of hours care before 64%

Characteristics of last out of hours visit

When did your child last see a doctor during the night?

Less than one month ago 2.6%

Between one and six months ago 16.4%

Between six months and one year ago 18.6%

More than one year ago 26.4%

Never 36%

Please describe the last visit

Doctor seemed to listen 87.8%

Doctor did not seem to listen 9.8%

Cannot remember 2.4%

Place of visit:

A&E 13.5%

Emergency centre 36%

Home visit 48.9%


Time between phoning and seeing a doctor:

Around 20 minutes 31%

Around 40 minutes 34.7%

Around 60 minutes 16%

Around 80 minutes 9.7%

>80 minutes 0.05%


(continued on next page)


model in each case. Cell sizes were too small to investigate those factors influencing adominant preference for seeing a doctor from their own practice.

Table 2 (continued)

Who was seen:

GP from practice 24.5%

GP not from practice 72.1%


Completion of questionnaire

Time to complete questionnaire (mean, range) 12.3 (2–60)

Ease of completion

1 (Extremely easy) 18.4%

2 29.6%

3 36.1%

4 12.6%

5 (Extremely difficult) 3.3%

Individuals who filled out version 2 of the questionnaire 51%

Individuals with at least one inconsistent response 7.4%

Table 3

Comparison of models including and excluding individuals with dominant preferences

Independent variable Model 1 (All

respondents)

Model 2

(Excluding those

with a dominant

preference)

Model 3 (All

respondents: tests

for differences

between coefficients)

Constant 0.459 (0.028)� 0.213 (0.040)� 0.451 (0.029)�

Whether the doctor listens 2.188 (0.027)� 1.538 (0.048)� 1.771 (0.054)�

Where your child is seen 0.441 (0.016)� 0.506 (0.026)� 0.580 (0.024)�

Who your child sees 0.225 (0.027)� 0.400 (0.044)� 0.227 (0.042)�

Time between phone call and

treatment

�0.036 (0.0006)� �0.032 (0.001)� �0.037 (0.001)�

Whether the doctor listens�dominant preference

– – 0.567 (0.063)�

Where your child is seen�dominant preference

– – �0.175 (0.033)�

Who your child sees�dominant preference

– – 0.017 (0.056)

Time between phone call

and treatment� dominant

preference

– – 0.0008 (0.001)

Rho 0.298 (0.017)� 0 0.312 (0.019)�

�2 log likelihood 10039 2167 9917

Model v2 (df) 7116 (5)� 1309 (5)� 7238 (9)�

Pseudo R2 41% 38% 42%

Percent 0s predicted correctly 81% 81% 78%

Percent 1s predicted correctly 77% 48% 78%

Number of individuals 3326 683 3326

Number of observations 24789 5016 24789

� ¼ p < 0:0001.


Those with a dominant preference for the doctor listening were more likely to befemale, well educated, have more children, and have had their last visit at an emer-gency centre (Table 4). Compared to those who had never visited before, thosewhose doctor did not listen at their last visit were less likely to have a dominant pre-ference. Those who were sampled from home visits were also less likely to have adominant preference, compared to those sampled from GPs’ lists (CHI).

There was also some indication that a dominant preference was related to thecomplexity of the choices in the questionnaire, suggesting that individuals may haveused ‘the doctor listening’ as a rule of thumb when answering the choices. Those whofilled out version two of the questionnaire were twice as likely to have a dominantpreference. Interaction terms were also significant. Those who found the question-naire difficult to complete and who had a university education, were less likely tohave a dominant preference, compared to those who found it difficult and were ed-ucated to secondary school standard. Those who found it difficult to complete andwere educated to below secondary level, were more likely to have a dominant pre-ference. Respondents who filled out questionnaire two and found it difficult to com-plete were less likely to have a dominant preference. Finally, older respondents whofound the questionnaire difficult to complete were less likely to have a dominantpreference.

Those with a dominant preference for waiting time were more likely to be older,less likely to have more children, and less likely to have visited before (Table 5).

Table 4

Factors influencing whether individuals had a dominant preference for the doctor listening

Variables in reduced model b (s.e.) Odds ratio (95% CI)

Constant �0.87 (0.19) –

Gender of parent 0.35 (0.12) 1.42 (1.13–1.80)

Number of children 0.08 (0.04) 1.08 (1.01–1.16)

College education (compared to secondary education) 0.16 (0.09) 1.17 (0.99–1.38)

University education (compared to secondary education) 1.16 (0.26) 3.19 (1.90–5.33)

Doctor did not listen at last visit (compared to those

who had never visited before)

�0.29 (0.16) 0.75 (0.54–1.02)

Last visit was at an emergency centre (compared to

those who had never visited before)

0.21 (0.09) 1.23 (1.04–1.47)

Sampled from those who had had a home visit

(compared to those sampled from the CHI)

�0.27 (0.10) 0.76 (0.62–0.93)

Filled out questionnaire 2 0.71 (0.20) 2.04 (1.39–3.00)

Ease of completion � university education �0.24 (0.10) 0.79 (0.65–0.95)

Ease of completion � below secondary education 0.23 (0.12) 1.26 (0.99–1.61)

Ease of completion � filled out questionnaire 2 �0.15 (0.07) 0.86 (0.75–0.98)

Ease of completion � age �0.005 (0.001) 0.995 (0.992–0.998)

Number of observations 3314

Model v2 (df) 156 (12)�

Pseudo R2 4%

Percent 0s predicted correctly 94%


� ¼ p < 0:0001.


Those who saw a GP from their own practice at their last visit were slightly morelikely to have a dominant preference than those who saw a GP who did not workin their practice, relative to those who have never visited before.

Again, there was some evidence that complexity of choices influenced the likeli-hood of a dominant preference. Respondents who found the questionnaire difficultto complete and had a university education were more likely to have a dominantpreference. Those who found the questionnaire difficult and had filled out the secondversion of the questionnaire, were also more likely to have a dominant preference fora shorter waiting time. However, this model is a poorer fit to the data that the modelin Table 4, having a lower pseudo R2, and little predictive power.

7. Discussion

This study has examined the existence and reasons for dominant preferences inthe context of a discrete choice experiment examining individuals’ preferences forGP out of hours care. Forty five per cent of respondents had a dominant preference.Their responses were analysed separately, providing richer information about the na-ture of preferences. There is evidence that both past experiences and the complexityof the decision task influenced dominant preferences. Those who had never experi-enced out of hours care before were more likely to have a dominant preferencefor both the doctor listening and for waiting time. This suggests that non-users of

Table 5

Factors influencing whether individuals had a dominant preference for a short waiting time

Variables in reduced model b (s.e.) Odds ratio (95% CI)

Constant �2.98 (0.36) –

Age of parent 0.03 (0.01) 1.03 (1.01–1.05)

Number of children �0.15 (0.06) 0.86 (0.76–0.98)

Had a visit in the last six months (compared to those

who have never had a visit)

�0.96 (0.36) 0.38 (0.19–0.78)

Had a visit between six months and one year ago

(compared to those who have never had a visit)

�1.04 (0.36) 0.35 (0.17–0.72)

Had a visit over one year ago (compared to those who

have never had a visit)

�0.96 (0.35) 0.38 (0.19–0.76)

Saw a GP from their own practice at their last visit

(compared to those who never visited before)

0.75 (0.36) 2.11 (1.05–4.27)

Saw a GP who did not work in their practice

(compared to those who never visited before)

0.62 (0.34) 1.85 (0.95–3.62)

Ease of completion �university education 0.11 (0.05) 1.11 (1.01–1.22)

Ease of completion �filled out questionnaire 2 0.12 (0.04) 1.13 (1.05–1.22)

Number of observations 3314

Model v2 (df) 49 (8)�

Pseudo R2 2%



� ¼ p < 0:0001.


the service may have higher expectations about GP out of hours care compared tousers. This has clear implications for the targeting of public education programmesabout the ‘appropriate’ use of out of hours care. It also suggests that those unfamil-iar with the service were more likely to use heurisitcs, and so may have been con-structing their preferences while filling out the questionnaire.

The complexity of the decision task was also related to having a dominant pre-ference. This confirms and adds to existing evidence on the effect of complexity ondecision making (Payne et al., 1992). This suggests that some respondents may havebeen using a ‘rule of thumb’ to answer questions. The effect of ‘ease of completion’on having a dominant preference depended on respondents’ education level, but alsoon which version of the questionnaire they filled out. Although scenarios were allo-cated randomly to the two questionnaires, this does rule out the possibility that onequestionnaire may have been more difficult to complete.

Although this study did not include cost as an attribute, and therefore did not cal-culate willingness to pay (i.e. marginal rate of substitution between each attributeand cost), future research should be aware that if a large proportion of respondentshave dominant preferences, then this may reduce the validity of findings and policyconclusions based on willingness to pay calculated from discrete choice experiments(as it would for any marginal rate of substitution calculated from a choice experi-ment). In particular, estimates of willingness to pay for specific attributes may be ‘bi-ased’ upwards, since regression coefficients for attributes will be higher if those withdominant preferences are included in the regression model. This may lead to policyrecommendations for a specific change in service provision that may in practice havelittle effect on utility relative to other changes in service provision that couldhave been made. However, the problem only becomes intractable when individualshave lexicographic orderings, rendering estimates of willingness to pay meaningless.

These arguments also apply to other methods of preference elicitation in eco-nomics, such as the direct elicitation of willingness to pay. One explanation of ‘zero’values is that individuals are not willing to trade-off their income for goods and ser-vices (Hanley & Milne, 1996).

That people have hierarchical preferences in hypothetical choice situations (evenif they are presented with all possible scenarios) does not necessarily imply that thesame will be true in real choice situations. However, it could therefore be argued thathierarchical preferences (and other heuristics) are more likely to exist in real choicesituations that may be more complex than the choices presented here.

Hierarchical choice may also be related to the argument that, for hypotheti-cal scenarios with which respondents are not familiar, preferences may be beingconstructed as they complete the valuation task (Dolan, 1997; Payne et al., 1992;Shiell, Hawe, & Seymour, 1997). The issue is therefore the underlying weakness ofpreferences, rather than the method used to elicit them. This has clear implicationsfor empirical studies that are eliciting preferences from the general public or fromindividuals who have little experience of the good or service. This will influencethe generalisability of the results presented here. Preference elicitation in health caremay be one such area where caution must be exercised because of unfamiliarity withthe topic, and where effort needs to be expended into familiarising respondents with


the topic before preferences are elicited. Enhanced consumer involvement in healthcare decision making is relevant here. It is also important to compare the structure ofpreferences and the process of choice between users and non-users of the service orintervention being valued.

There are also other factors that may influence the extent to which heuristics areused, that have not been the focus of this study. Payne et al. (1993) outline three broadfactors that influence the strategy used to solve a decision problem. These are factorsrelated to the decision problem itself (e.g. complexity), factors related to the individ-ual completing the task (e.g. ability to deal with information overload), and factorsrelated to the social and economic environment and situation (e.g. distraction andtime pressure). Further research should attempt to examine these factors in the con-text of discrete choice experiments. Such factors are likely to influence the generalis-ability of these results to other decision contexts in health care and more broadly.

In conclusion, the analysis of those with dominant preferences was important ingathering more information about the nature of respondents’ preferences, especiallythose that appeared to have particularly strong preferences. This also leads to differentpolicy conclusions for this group of respondents. The analysis of sub-groups is clearlyvery important here, and may suggest different policies for different sub-groups ofthe population. Furthermore, computer-based methods to elicit preferences mayintroduce opportunities to examine decision-making processes in more detail. Theseissues need to be explored further.

Until further research is completed researchers should attempt to identify domi-nant preferences in their analysis and be cautious about policy conclusions basedon marginal rates of substitution, especially if those with dominant preferences forma large proportion of the sample.

Acknowledgements

Many of the ideas in this paper arose from work on conjoint analysis in HERUand so the author is indebted to discussions with Mandy Ryan, Andy Lloyd, ShelleyFarrar, Emma McIntosh, Fernando San Miguel and Sandra Vick. The out of hoursstudy was funded by the NHS R&D Primary Secondary Interface Programme. Thisstudy would not have possible without the contributions of Stuart Watson and SueRoss. Thanks also to Nicola Torrance for her contribution to this study. The HealthEconomics Research Unit is funded by the Chief Scientist Office of the ScottishExecutive Department of Health. The views in this paper are, however, those ofthe author.

Appendix A. Example of a choice presented to patients

• Imagine that during the night, your child is short of breath, wheezing and cough-ing and that you decide to call a doctor. You have several options about the careyou receive. These differ according to who your child sees, where they are seen, the


time it takes between making the telephone call and receiving treatment, andwhether the doctor seems to listen to what you have to say.

• For each question below, you are asked to choose which type of consultation youwould prefer for your child during the night (Consultation A or Consultation B).

Which consultation would you prefer? (please tick box below)

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Identifying and analysing dominant preferences in discrete choice experiments: An application in health care