Discussion Papers In Economics
And Business

A Review of Stated Choice Method
Junyi Shen

Discussion Paper 05-27

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANA Review of Stated Choice Method
Junyi Shen∗

October 2005
Abstract
This paper reviews Stated Choice Method (SCM), paying particular attentions on its
theoretical background, application, empirical models, experimental design, andprocedure to execute. The review suggests that comparing to other stated preference(SP) methods, SCM has a major advantage that it meets the objective of a statedpreference analysis to simulate actual consumer behavior by allowing simultaneous
evaluations of a number of alternatives or a choice between alternatives. Someadvanced models based on the degrees of relaxation of the Independently and
Identically Distributed (IID) assumption on error terms are introduced. More complex
model seems to be more plausible than relatively simple specifications. Two tests fornested and non-nested models are also discussed to help judge that one model issuperior to another model. Finally, this paper introduces the procedure of executing aStated Choice (SC) experiment.
Keywords: Stated Choice Method (SCM), Stated Preference (SP) method, Independentand Identical Distribution (IID), Extreme Value type I (EV1) distribution
JEL classifications: C35, C81, C93∗

The author would like to thank Yoshizo Hashimoto for his helpful comments. Financial
support from the Japanese Ministry of Education, Culture, Sports, Science andTechnology (Grant-in-Aid for Scientific Research A(2) 16203020) is gratefully
acknowledged. All views expressed here and any errors are the sole responsibility of theauthor.
Correspondence to: Osaka School of International Public policy, Osaka University,
1-31 Machikaneyama, Toyonaka, Osaka 560-0043, Japan. Tel/Fax: +81-6-68505652
E-mail: junyi@osipp.osaka-u.ac.jp

11. Introduction
Stated Choice Method (SCM) is a research technique in the family of Stated
Preference (SP) methods. In stated preference studies, information about decision
makers’ preferences is elicited by using specifically designed hypothetical situation.
Hence the data generated in stated preference studies are derived from decisionexperiments, which is the main difference from an analysis of revealed preferences. In
Reveal Preference (RP) studies, such as those using the travel cost method and thehedonic pricing method, decision makers’ preferences are revealed in their decisions inreal choice situations. There are various reasons why a stated preference study may be
preferred to an analysis of preferences that are revealed in actual choices. Louviere et al.
(2000, p.21-22), for example, mentioned that:“Despite well-developed economic theory for dealing with real market choices, there
are a number of compelling reasons why economists and other social scientists
should be interested in stated preference (SP) data, which involve choice responses
from the same economic agents, but evoked in hypothetical (or virtual) markets:
・ Organisations need to estimate demand for new products with new attributes orfeatures.
・ Explanatory variables have little variability in the marketplace.
・ Explanatory variables are highly collinear in the marketplace.
・ New variables are introduced that now explain choices
・ Observational data cannot satisfy model assumptions and/or contain statistical‘nasties’ which lurk in real data.
・ Observational data are time consuming and expensive to collect.
・ The product is not traded in the real market.”
Similarly, Kroes and Shelden (1988, p.13) also stated that:“It [reveal preference method] is against the backdrop of such problems that the useof stated preference methods became an attractive option in transport research.
Broadly, these methods are easier to control (because the researcher defines theconditions which are being evaluated by the respondents); they are more flexible(being capable of dealing with a wider variety of variables); and they are cheaper toapply (as each respondent provides multiple observations for variations in the
explanatory variables which interest the analyst).”A stated choice survey employs a carefully designed questionnaire in which
respondents are given a sequence of questions or choice sets. In each choice set, they are
asked to indicate their preferred option from a set of alternatives. Each alternative2

option is described in terms of a number of key attributes that are specified at different
levels. The configuration of attribute levels that describe the alternatives follows anexperimental design and varies between choice sets. The response data, which usuallyalso include individuals’ socio-economic characteristics, enable not only the estimationof the relationships between attribute levels and the choice probabilities, but also theestimation of the extent of the trade-offs between the attributes made by respondents.
The SCM is one of a number of different stated preference methods. Others include
the contingent valuation method (CVM) and conjoint rating and ranking. A majoradvantage of SCM compared with the other stated preference methods is that it meetsthe objective of a stated preference analysis to simulate actual consumer behavior. It
pertains to respondents making a choice between a number of alternatives on offer. This
is in contrast to CVM that has been applied to derive welfare estimates in the context of
non-market environmental values. In CVM, respondents are asked to evaluate a“current situation” and one alternative option only, and to indicate their willingness topay (WTP) for the change in the environmental situation. However, because it does not
elicit choices, and because it does not involve the simultaneous evaluation of various
options, CVM is not an appropriate method for choice-based analyses. Another concern
with comparison between CVM and SCM is that CVM relies very heavily on theaccuracy of descriptions, in contrast, SCM relies less on the accuracy and completenessof any particular alternative, but more on the accuracy and completeness of the productcharacteristics and attributes used to describe alternatives (Louviere et al. 2000, ch.12).
Conjoint analysis is the generic term for the attribute-based analysis of consumer
decision making (see Green and Srinivasan 1978). Respondents are asked to evaluatevarious options that are described in terms of a set of attributes1. Conjoint methods
include conjoint rating and conjoint ranking2. In a conjoint rating study, respondents
are asked to rate their likelihood of purchasing a particular attribute combination.
Using multiple ratings data for each respondent, the relationship between the ratings
and the individual attributes can be established in a regression analysis, and marginal
rates of substitution between attributes can be estimated. As with CVM, a major
drawback of this method is that it does not entail simultaneous evaluations of a numberof alternatives or a choice between alternatives. On the other hand, conjoint ranking

In the Marketing literatures, these combinations of attributes are referred to as“profiles”.
2 Several literatures include SCM as one approach of conjoint analysis. However, due tothe differences between judgment data (from conjoint rating and ranking) and choicedata (form stated choice method), we define SCM as an additional method other thanconjoint analysis in this study. For further discussion in this issue, see Louviere (1988).
1

3

involves respondents ranking multiple sets of a number of alternative options. The
response data allow the estimation of the marginal rates of substitution between the
different option attributes. Since the alternatives are evaluated simultaneously, thismethod is closer to SCM than is conjoint rating. However, similar to the conjoint rating
case, choices are not observed directly. Instead, choices between alternative options areinferred from the ranking data. Due to this essential difference between SCM andconjoint rating and ranking, Louviere and Timmermans (1990) classified rating andranking as “stated preference models” as opposed to “stated choice models”. One moredifference between SCM and conjoint rating and ranking is the theoretical basis withrespects to consumer behavior. SCM has a firm theoretical foundation in Random
Utility Theory (RUT), which will be discussed in the next section, unlike conjoint rating
and ranking.
Stated choice method has been applied in many different fields. It was originallydeveloped in marketing research in the early 1970s, and have become widely used aftera very good overview of the methods is provided by Green and Srinivasan (1978) (see,for instance, Louviere and Hensher 1983, Louviere and Woodworth 1983, Gensch 1985,
Ben-Akiva et al. 1991, Batsell and Louviere 1991, Anderson and Wiley 1992, Ben-Akiva
and Boccara 1995, Erdem and Swait 1998, Louviere et al. 1999). Stated choice method
did not receive increasing attention in transport economics until the early 1980s. Since
then, a number of important studies on demand forecast, route choice, mode choice, etc.
have made use of this approach (see, for instance, Ben-Akiva and Lerman 1985,
Berkovec and Rust 1985, Daly 1985, Hensher 1989, Hensher et al. 1989, Bradley andGunn 1990,Ben-Akiva et al. 1993, Hensher 1994, Swait et al. 1994, Bhat 1995, Yai et al.
1997,Hensher 1998, Hensher 2001,

Hensher and Greene 2002, Greene and Hensher

2003, Koppleman and Sethi 2005). Stepping into 1990s, economists started to apply
stated choice method on the field of environmental valuation, although until now there
are not many literatures as in marketing and transport studies (see, for instance,
Adamowicz et al. 1994, Hausman et al. 1995, Boxall et al. 1996, Hanley et al. 1998,
Blamey et al. 2000)
The remainder of this paper is organized as follows: Section 2 describes SCM andrandom utility theory, Section 3 introduces some advanced models in stated choicemodeling, based on the degrees of relaxation of the Independently and IdenticallyDistributed (IID) assumption on error terms, Section 4 discusses the procedure of astated choice study, and Section 5 concludes the paper.

4

2. Stated choice method and random utility theory
Stated choice model is based on random utility theory. The basic assumption
embodied in the random utility approach to choice modeling is that decision makers are
utility maximizers, i.e., given a set of alternatives the decision maker will choose thealternative that maximizes his/her utility. Since the utility of an alternative for anindividual U cannot be observed, it is assumed to consist of a deterministic component V
and a random error term ε . Formally, individual q ’s utility of alternative i can be
expresses as:

U iq = Viq + ε iq

(1)Hence the probability that individual q chooses alternative i from a particular set J,
which comprises j alternatives, can be written as:

Piq = P (U iq > U jq ; ∀i ≠ j ∈ J ) = P (ε jq < ε iq + Viq − V jq ; ∀i ≠ j ∈ J)

(2)

To transform the random utility model into a choice model, certain assumption about
the joint distribution of the vector of random error terms are required. If the random
error terms are assumed to follow the extreme value type I (EV1) distribution3 and be
independently and identically distributed (IID) across alternatives and cases (orobservations), the multinomial (or conditional) logit (MNL) model (McFadden 1974) is
obtained. In the MNL model, the choice probability in Equation (2) is expressed as:
J

Piq = exp(µViq ) / ∑ exp(µV jq )

(3)

j =1

Then, making further assumption for the deterministic component of utility to be linear
in parameters, Viq = β ′ Xiq, the probability in Equation (3) is given as:
J

Piq = exp(µβ ′X iq ) / ∑ exp(µβ ′X jq )

(4)

j =1

where µ represents a scale parameter that determines the scale of the utilities, which
is proportional to the inverse of the distribution of the error terms. It is typicallynormalized to 1 in MNL model. Xiq are explanatory variables of Viq, normally including
alternative-specific constants (ASCs), the attributes of the alternative i and thesocial-economic characteristics of the individual q , β ′ is the parameter vector
associated with the vector Xiq.
The attributes enter the utility functions at the various levels at which they are
3 Historically, EV1 distribution has been referred to by a number of names, including

Weibull, Gumbel and double-exponential.
5specified in the experimental design. Including only ASCs and attributes is sufficient if
individuals have homogeneous preferences. However, it is possible and frequently
necessary to capture preference heterogeneity in the model by interacting respondents’socio-economic characteristics with the choice attributes or the ASCs. This involves
multiplying them by either the choice attributes, which makes them attribute-specific,
or by the ASCs, which makes them alternative-specific.
An important assumption of the MNL model is the independence of irrelevantalternatives (IIA) property. This property, which follows from the independencecomponent of the IID assumption, implies that the relative choice probabilities between
any two alternatives of choice set J are not affected by the inclusion or exclusion of otheralternatives in that set. The IIA property is a strict assumption of the MNL model and a“reasonable approximation of more complex relationships” (Ben-Akiva and Lerman1985). A test has been developed by Hausman and McFadden (1984) for testing thevalidity of the IIA assumption4. It is to say, if the IIA property is violated, estimating the
choice model by MNL specification which exhibits IIA assumption will lead to biased
estimates, therefore necessitating other model specifications. There are several
advanced models that have been developed to relax the IIA assumption, which will be
discussed in the next section.

3. Some models relaxing the IID assumption5
Substantial progress has been made in stated choice modeling, primarily through therelaxation of one or more dimensions of the IID assumption of the MNL model, resulting
in more flexible model structures. In general, the additional flexibility of these advanced
models comes at the cost of increased computational burden, and in some cases losing
the mathematically amenable closed-form structure. These models include Nested Logit(NL) model, Heteroscedastic Extreme Value (HEV) model, Covariance Heterogeneous
Nested Logit (COVNL) model, Random Parameters Logit (RPL) or Mixed Logit (ML)model, and Latent Class Logit (LCL) model6.
4 For more details on this issue, see Hausman and McFadden (1984), Louviere et al.

(2000), Greene (2003), etc.

5 This section is based on various sources including Bhat (1995), Allenby and Ginter(1995), Bhat (1997), Revelt and Train (1998), Louviere et al. (2000), Greene andHensher (2000), McFadden and Train (2000), Hensher and Greene (2002), and Greene(2003).
6 The Multinomial Probit (MNP) model can also be considered as a natural alternative
to eliminate the IIA restriction by allowing the errors being correlated across63.1 Nested Logit model
One way to relax the homoscedasticity assumption in the MNL model is to group thealternatives into subsets that allow the variance to differ across the subsets while
maintaining the IIA assumption within the subsets. This specification defines a Nested
Logit (NL) model. A primary role for NL is to allow the variances of the random
components of utility to vary across subsets of alternatives (subject to the overallvariance of unobserved random components of all alternatives being constant) (Louviere

et al. 2000).
To derive the mathematical form of the model, consider a two-level NL structure7.
Suppose an individual faces a choice of branches indexed i = 1,2,..., I and elemental
alternatives indexed j = 1,2,..., J i in branch i . The choice probability of alternative jin branch i by individual q can be expressed as:

Pijq = Pj|i ,q ⋅ Piq(5)

The conditional probability Pj|i,q can be given as:
Ji

P j |i , q = exp( µ β ′X ijq ) / ∑ exp( µ β ′X ijq )

(6)

j =1

and
Ji

I

Ji

Piq = ∑ exp(λ (α ′Yiq + µβ ′X ijq )) / ∑∑ exp(λ (α ′Yiq + µβ ′X ijq ))
i =1

j

j

Ji

I

I

Ji

j =1

i =1

i =1 j =1(7)= exp(λα ′Yiq )∑ exp(λµβ ′X ijq ) / ∑ exp(λα ′Yiq )∑∑ exp(λµβ ′X ijq )where Xijq is the vector of attributes that vary with both branch and elemental levels.

Yiq is the vector of attributes that vary only with branch level. α ′ and β ′ are vectorsof unknown parameters. λ and µ are scales parameters for branch and elemental
levels, respectively. Define an Inclusive Value (IV)8 for the i th branch asalternatives and/or observations. However, due to the reason that the MNP model isequivalent in form to the RPL or ML model with certain restrictions (which will be
discussed in section 3.4) on the latter model, therefore we omit a review on the MNPmodel and focus discussions on the other advanced models in a logit family including
RPL/ML model. For more details on the MNP model, see Hausman and Wise (1978),Maddala (1983), McFadden (1989), Keane (1994), Louviere et al. (2000).
7 The extension of a two-level NL structure to three-level or four-level ones can be donewith the same methodology used in this paper. See Maddala (1983), Louviere (2000),
Hensher and Greene (2002) for more details on the issue of three-level tree structure NL
model.
8 Inclusive Value is also termed as logsum or expected maximum utility.
7

Ji

IViq = log(∑ exp(µβ ′X ijq )

(8)

j =1

Then we can rewrite Equations (6) and (7) as

Pj|i , q = exp( µβ ′X ijq ) / exp( IViq )(9)I

Piq = exp(λ (α ′Yiq + IViq )) / ∑ exp(λ (α ′Yiq + IViq ))

(10)

i =1

Note that the scale parameter λ associated with the branch level is often normalized
to be 1. Then the scale parameters for the elemental level are left to be estimated.
Alternatively, one could set µ = 1 and allow λ to be estimated9.
The IV parameter plays an important role in the NL model. It is often interpreted as ameasure of dissimilarity, capturing correlations among unobserved components ofalternatives in the partition. This correlation supports the claim that NL provides
relaxation of independence (for alternatives sharing a partition) as well as the identical
distribution assumption between alternatives in different partitions (Louviere et al.
2000).

3.2 Heteroscedastic Extreme Value model
The Heteroscedastic Extreme Value (HEV) model developed by Bhat (1995) andAllenby and Ginter (1995) allows different scale parameters for all alternatives in achoice set. This model is based on the same random utility structure as before andsimply relaxes the assumption of equal variances. A nested logit model with a unique
inclusive value parameter for each alternative (with one arbitrarily chosen variance to 1
for identification) is equivalent to an HEV specification. In mathematical term, thechoice probability of alternative i from a choice set J by individual q is expressedasJ

Piq = exp(µ i β ′X iq ) / ∑ exp(µ j β ′X jq )

(11)

j =1

where

µi

denotes the different scale parameters across alternatives. Xiq are

explanatory variables including alternative-specific constants and the attributes of thealternative i and the social-economic characteristics of the individual q . β ′ is theparameter vector associated with the vector Xiq.
The HEV model avoids the pitfalls of the IID property by allowing different scale
9 Hensher and Greene (2002) mention that the impact of normalization on the scales ofsome parameters may produce internal inconsistency of the model if not handledproperly. Typically, if the same parameter appears in several nests, normalization fromthe bottom may cause problems, as the parameter will be scaled differently in each nest.
8

parameters across alternatives. Intuitively, we can explain this by realizing that the
random term represents unobserved attributes of an alternative; that is, it represents
uncertainty associated with the expected utility (or the observed part of utility) of analternative. The scale parameter of the error term, therefore, represents the level of
uncertainty (the lower the scale, the higher the uncertainty) (Louviere et al. 2000).

3.3 Covariance Heterogeneous Nested Logit model
Bhat (1997) proposed a modification to the nested logit model that allows
heterogeneity across individuals in the covariance of nested alternatives, termed as
Covariance Heterogeneous Nested Logit (COVNL) model. As an alternative
specification of NL logit and HEV models, COVNL model estimates a model in which
the similar scale parameters across alternatives are a function of individual-specificand/or alternative-specific variables as sources of scale decomposition. Mathematically,
the function of similar scale parameter µ i is given as:µ i = F (ψ + γ ′Z iq )(12)

where Ziq is a vector of individual and/or alternative related characteristics, ψ and γ ′are parameters to be estimated, and F is a transformation function that ensures µ i isbounded between 0 and 110. Then, COVNL choice probabilities are given by Equation(13), while µ iq is given by Equation (12):J

Piq = exp(µ i β ′X iq ) / ∑ exp(µ i β ′X jq )

(13)

j =1

If γ ′ = 0 in Equation (12), covariance heterogeneity is absent and the COVNL reduces
to a NL model. The COVNL model is more complex than the simple NL model but stillretains a closed form structure. Due to both the introduction of additional variables andthe incorporation of the covariance structure, this model is statistically andbehaviorally superior to the corresponding NL and HEV models. For example, the NL
and HEV models can partially capture the heteroscedasticity by specifications of thescale parameters, however, the origin of the variability would not be explicit without
formulating a covariance structure of scale parameters as COVNL does.

3.4 Random Parameters Logit or Mixed Logit model
Other than HEV and COVNL models, the Random Parameters Logit (RPL) model
10 McFadden (1981) noted that a global sufficiency condition of the nested choice model

to be consistent with random utility maximization is that the parameters of inclusive
value be in the 0-1 range.
9(also be termed as Mixed Logit (ML) model) allows for a more heightened level offlexibility by specifying taste coefficients to be randomly distributed across individuals(see Revelt and Train 1998, McFadden and Train 2000, Louviere et al. 2000).
Additionally, RPL/ML model has a considerable advantage not available in any of theother models mentioned above. It is that RPL/ML model can account for potential
correlation over repeated choices made by each individual, although imposing afirst-order autoregressive (AR1) process is extremely complex.
The model is a generalization of the MNL model, summarized as below:
J

Piqt = exp(α ′ + β ′X iqt + ϕ ′Fiqt ) / ∑ exp(α ′ + β ′X jqt +ϕ ′F jqt )

(14)

j =1

where

α′

is a vector of fixed or random alternative-specific constant associated withi = 1,..., J alternatives and q = 1,..., Q individuals, and one of these ASCs should
be identified as 0.

β′
ϕ′is a vector of non-random parameters.

Xiqt

is a vector of individual-specific characteristics and alternative-specific attributes

is a parameter vector that is randomly distributed across individuals.

at observation t, and is estimated with random parameters.

Fiqt

is a vector of individual-specific characteristics and alternative-specific attributes
at observation t, and is estimated with fixed parameters.

In this specification, a subset or all of α ′ and the parameters in the β ′ vector canbe assumed to be randomly distributed across individuals11. These random parameters
can then be defined as a function of characteristics of individuals and/or other attributes
that are choice invariant. Based on these defined attributes, the mean and standard
deviations of specified random parameters and contributions from these choiceinvariant attributes on random parameters are estimated by using Maximum
Simulated Likelihood (MSL) method. The RPL/ML model is sufficiently flexible that itprovides the modeler a tremendous range within which to specify individual unobserved
heterogeneity. To some extent, this flexibility offsets the specificity of the distributional
assumptions (Greene and Hensher 2003).
A further important issue should be mentioned here is that the RPL or ML model isequivalent in form to the Multinomial Probit (MNP) model, even though the variancesof the random component take on a different distribution (i.e., EV1 compared to normal),
The distributions of random parameters can be considered, for example, normal
distribution, lognormal distribution, and triangular distribution, etc.
1110

if we assume (a) that the alternative-specific constants are random, (b) choice invariant
characteristics variables that produce individual heterogeneity in the averages of therandomly distributed parameters are excluded, and (c) that the full (i.e., including thevariances) lower triangular matrix of covariance is unrestricted. This equivalence isvery important, since this special case of the RPL or ML model provides an alternative
method of estimation to MNP (Louviere et al. 2000, ch. 6).

3.5 Latent Class Logit Model
The Latent Class Logit (LCL) model, unlike RPL/ML model which specifies therandom parameters to follow a continuous joint distribution, assumes that a discrete
number of classes are sufficient to describe the joint function of the parameters.
Therefore, the unobserved heterogeneity is captured by these latent classes in the
population, each of which is associated with a different parameter vector in thecorresponding utility. The LCL has often been used in marketing research instead of
RPL/ML model, while there are few studies in other fields such like transportation andenvironmental valuation.
The choice probability of individual q of class s is expressed as:
J

Piq|s = exp(µ s β s′ X iq ) / ∑ exp(µ s β s′ X jq )s = 1,..., S

(15)

j =1

which is a simple MNL specification in class s . Additionally, one can construct aclassification model as a function of some individual-specific attributes to explain theheterogeneity across classes. The LCL model simultaneously estimates Equation (15)for S classes and predicts the probability Hqs as individual q being in class s . Then,the unconditional probability of choosing alternative i is given as:
S

Piq = ∑ Piq|s H qs

(16)

s =1

An issue to be noted is the choice of S, the number of classes. Since this is not aparameter, hypotheses on S cannot be tested directly. However, as Louviere et al. (2000,
ch. 10) mentioned that a number of methods to decide S have been used based on the
Akaike Information Criterion (AIC) and its variants. AIC and Consistent AIC (CAIC),
which are given in Equations (17) and (18), are used to guide model selection.

AIC = −2( LL(θˆ) − S ⋅ K s − ( S − 1) K c )(17)

CAIC = −2( LL(θˆ) − ( S ⋅ K s + ( S − 1) K c − 1)(ln(2 N ) + 1)

(18)

where LL (θˆ) is the log likelihood at the estimated parameters θˆ , Ks is the number of11elements in the utility function of the class-specific choice models, Kc is the total numberof parameters in the classification model, and N is the number of observations in thesample. The value of S that minimizes each of the measures of AIC and CAIC suggests
which model should be preferred (Louviere et al. 2000, ch. 10).

4. Procedure of a Stated Choice study
A Stated Choice (SC) study comprises a number of stages. Many literatures introduce
the common steps for the SC practices (see, for instance, Louviere and Timmermans
1990, Hensher 1994, Louviere et al. 2000). The steps suggested in these references canbe grouped into four broad stages: questionnaire design, data collection, model
estimation and assessment, and application of model results.

4.1 Questionnaire design
The design of the survey instrument is crucial for the quality of the survey results. A
number of important issues should be identified. These are the selection of choice
attributes and levels, the experimental design, and presentation of choice tasks.

Selection of choice attributes and levels
The first task in the choice context is to select the set of choice attributes which are
likely to be most important. The set normally includes those which are commonly foundto be important (for example, cost, access time, in-vehicle time, etc. in a transportation
mode choice analysis), plus any instrumental factors in the policies or scenarios to bestudied. Sometimes, however, the set of choice attributes important to the respondent is
not the same as might be deduced from most existing models.
It is noted that different choice attributes may be important to different people, so
that, for some, important attributes are missing from the experiment. One may consider
adding attributes as many as accountable into the choice experiment. However, as thenumber of attributes increase, task complexity increases because of the number ofthings to which respondents must attend. Several studies have shown that taskcomplexity leads to preference instability as the experiment progresses (see, forinstance, Hensher 2004, etc.). As task complexity of a choice experiment increases,
respondents will find more and more difficult to finish the questionnaires. The cognitive
burden on respondents is very much determined by the topic of the study at hand.
Carson et al. (1994) state that the average number of attributes included in

12questionnaires is around seven. Generally, an investigation into individual choices
related to familiar decisions allows a greater number of attributes to be evaluatedcompared with a more unfamiliar choice situation. On the other hand, if something is
missing which is very important to the respondent and which affects the credibility ofthe other variables, the results may also be less valid. Therefore, from this view, thepilot test for examining the validness of choice attributes is necessary.
Issues involved in the selection of the levels of the chosen attributes include the range
and the measurement of the attributes. With respect to the range, an importantconsideration is the current range experienced by respondents. A often used design is to
identify these ranges at both the extreme high and low ends, as Louviere et al. (2000)
suggest that the wider the range of levels, the more likely it will be that more subjects
agree that some levels are “high” whereas others are “low”. However, it is mostimportant to detect that these extreme levels are realistic and acceptable by the
respondents. An excessively limited range for the attributes hamstrings subsequent
analyses if respondents find them unbelievable.
With respect to the measurement of the attributes, a distinction is made between
subjective and objective attributes. The latter can be objectively defined, for instance interms of distance or dollar amounts. Subjective attributes, such as environmental
quality, are more difficult to be defined. An ordinal scale of high, medium and low for an
attribute like cleanliness at a destination is suggested in literatures. However, for thiskind of ordinal definition, a careful description of each level is required to make them beunderstandable by respondents.

Experimental design
After the choice attributes and their level are determined, and the choice alternatives
are selected, developing an experimental design is sequentially the next step. The
experimental design produces the choice sets for the Stated Choice Model questionnaire
to enable the estimation of the contributions of the attributes to the utility function of
the alternatives.
A full factorial design is most ideal due to the reason that it enables the estimation of
the main effects and all attribute interactions. However, as the number of attributes
and/or their levels increase, the number of choice sets increase dramatically. For
instance, if there are six attributes each defined at three levels, the total number ofchoice sets is 729 (36). Clearly, it would be impossible for respondents to assess all choicesets. Hence, a fractional factorial design is often used. Fractional factorials are
generally orthogonal and allow the estimation of at least all main effects. Some or all

13

two-way or high orders interactions can also be estimated by applying fractional
factorial designs. However, since this is done based on increasing the number of choicesets in the design, the task complexity would also increase. Therefore, the selection of
the experimental design by the analyst involves a consideration of the trade-off between
cognitive complexity and analytical sophistication. Carson et al. (1994) note that inmost studies respondents evaluate between one and sixteen choice sets, with theaverage being somewhere around eight choice scenarios per respondent.
In some cases, the number of choice sets may still be too demanding for survey
respondents even after fractional factorial design. To deal with this, the design iscommonly divided into subsets. This procedure is refereed as “blocking” in the design
literature. It therefore creates a number of different versions of questionnaires witheach respondent exposed to one version only. Blocking can be done randomly or in asystematic fashion.

Presentation of questionnaires
The presentation of the SCM questionnaires can range from hard copy to various
multi-media modes. The choice of which mode to use is often determined by budget
constraints. Regardless of the mode selected, the overall presentation of thequestionnaire requires careful consideration. The objective is to present the choice
experiment as an approximation of actual choice situations. To that end, backgroundinformation needs to be provided. It is important that this information is consistent
with the information that respondents normally have in order to make actual choices.
In other words, the frame must be appropriate to the decision context.
The approximation of actual situations in the questionnaire, and the manner in which
the questionnaire is presented requires careful testing before the questionnaire is put infront of the survey respondents. Conducting focus group is an usual way for exploratory
researches. Focus groups are conducted to gain an understanding about the researchissue by obtaining feedback from the target audience in a small group environment.
Focus groups can also be used to test draft questionnaires on cognitive issues.

4.2 Data collection
Data collection mainly involves the issues on sampling strategy and response
collection method.

Sampling strategy
The choice of survey population obviously depends on the objective of the survey. Given

14the survey population, a sampling strategy has to be determined. Possible strategies
include a simple random sample, a stratified random sample or a choice-based sample.
A simple random sample is generally a reasonable choice. One reason for choosing amore specific sampling method may be the existence of a relatively small but important
sub-group which is of particular interest to the study. Another reason may be to increase
the precision of the estimates for a particular sub-group. In practice the selection ofsample strategy and sample size is also largely dependent on the budget available forthe survey (Alpizar et al. 2003).
Louviere et al. (2000) provide a formula to calculate the minimum sample size. The
size of the sample, n , is determined by the desired level of accuracy of the estimatedprobabilities, p̂ . Let p be a true proportion of the relevant population, a is thepercentage of deviation between p̂ and p that can be accepted and α is the confidence
level of the estimation such that: Pr(| pˆ − p |≤ ap) ≥ α for a given n . Given this, theminimum sample size is defined as:n≥

1 − p −1 1 + α
Φ (
)
2
pa 2

(19)

where Φ is the standard normal Cumulative Distribution Function (CDF). Note thatn refers to the size of the sample and not the number of observations. Since eachindividual makes r succession of choices in a choice experiment, the number ofobservations will be much larger (a sample of 400 individuals answering 8 choice sets
each will result in 3200 observations). From this view, one of the advantage of choice
experiments is that the amount of information extracted from a given sample size ismuch larger than, for example, using referendum-based methods and, hence, theefficiency of the estimates is improved. The formula in Equation (19) is only valid for a
simple random sample and with independence between the choices12.

Response collection method
Once the questionnaire is designed and tested, the sampling strategy is decided and thesample size is calculated, the survey can be put into the field. The selection of response
collection methods depends on the type of respondents, the complexity of the choicedecision or of the product being studied, and the budget available for the study.
A singularly cost-effective method of response collection is the mail survey, which ismost effective when respondents can be recruited by telephone or other means 13 .
12 For more details on this issue, see Ben-Akiva and Lerman (1985).

A similar method with mail survey, termed as posting, can be also used in some
simple and familiar products’ choice experiments. It is executed by delivering
questionnaires into the respondents’ posts and asking the respondents to mail them
13

15

However, mail survey suffers the problems such as low response rate, and relative moreinvalid answers compared with other methods due to the cognitive difficulty on survey.
A telephone survey often used in other surveys is seldom considered as an effective
method in stated choice experiments, since interviewers are quite difficult to explain
the choice sets to the respondents by telephone. However, a mail survey combined withan advance telephone call and/or a follow-up telephone call is often used to raise up the
response rates. In addition, in some cases, face-to-face interview may be applied.
Although personal interview has a number of advantages compared with mail survey, it
may be very expensive to execute depending on sample sizes.
Recently, computer-based interviews are developed very fast. They come in several
forms: (1) a self-completion survey is sent to respondents on floppy disk or CD-ROM,and mailed back to the research upon completion; (2) personal interviews are conductedusing a computer, with interviewers and/or respondents keying in responses toquestions. The former method is often more useful in business-to-business applications,whereas the latter is more often used for interviewing consumers. Computer-based
interview has the advantages of flexibility (i.e., questionnaire flow can be altered in realtime) and improved data quality (i.e., error checking occurs at the time of response)(Louviere et al. 2000, ch. 9).

4.3 Model estimation and assessment
To analyze the response data, a statistical choice model is required. As discussed in
Section 2 and Section 3, different models such as MNL, NL, HEV, COVNL, RPL/ML,
and LCL, are obtained from different error term assumptions and can be used for
estimation. To assess that one model is more superior to another one, two tests fornested and non-nested models are introduced as follows14.
The most common test undertaken to compare any two nested models is the likelihood
ratio (LR) test. The formula of LR statistics is given as:

LR = −2( LL1 − LL 2)(20)

where LL1 and LL2 are the log likelihood at convergence for model 1 and model 2 usingsame data set. Define n as the difference in the degrees of freedom for two models. The
calculated LR is compared to the critical value from a chi-squared test table at an
appropriate level of statistical significance (e.g. 0.05 being the most used level inliteratures) for the number of degrees of freedom n . If LR is greater than the criticalback upon completion.

14 A test for choice-based samples for nested and non-nested models is not introduced inthis review. A detailed discussion on this issue can be found in the study of Louviere etal. (2000).
16

value, then we can conclude that the two models are statistically different, rejecting thenull hypothesis of no difference.
A test for non-nested choice models based on the Akaike Information Criterion (AIC)
has been proposed by Ben-Akiva and Swait (1986). Suppose model 1 explains choices
using K1 variables, while model 2 explains the same choices using K2 variables; assume
that K1 ≥ K2 and either the two models have different functional forms or the two sets ofvariables are different by at least one element. Define the fitness measure for model j,

j=1, 2:ρ 2j = 1 −Lj − K j
L(0)

(21)

where Lj is the log likelihood at convergence for model j and L(0) is the log likelihood forconstants only. Ben-Akiva and Swait (1986) show that under the null hypothesis thatmodel 2 is the true model, the probability that the fitness measure in Equation (21) for
model 1 will be greater than that of model 2 is asympototically bounded by a function
given in Equation (22):Pr(| ρ 22 − ρ12 |≥ Z ) ≤ Φ(− − 2ZL(0) + ( K1 − K 2 ))(22)

where Z is the difference of the fitness measures between model 1 and model 2 andassumed larger than zero, Φ is the standard normal Cumulative Distribution
Function (CDF). Therefore, Equation (22) sets an upper bound for the probability that
one incorrectly selects model 1 as the true model if model 2 is the true model.

4.4 Application of model results
Once the statistical choice model has been estimated and assessed, the model output
is applied to the research problem at hand. The estimated coefficients of the explanatory
variables are interpreted in the context of the study. The marginal contribution to theutility of the alternatives can be compared for the different attributes included in themodel. The influence of individual socio-economic characteristics on choice utilities canalso be assessed.
One approach of application of the model results is using the relevant non-priceattribute level coefficients and price coefficients to estimate the implicit price change in
the non-price attributes. For instance, regarding the transport economics, one
important policy analysis item, the value of time saving, can be calculated by the ratioof the estimated time and cost coefficients. Examples in this application can be found inrecent studies such as Louivere et al. (2000, ch. 11), Lam and Small (2001), Hensher(2001), Greene and Hensher (2003), Hensher (2004), Brownstone and Small (2005), and17

Hess et al. (2005) etc.
A further application of the model results elasticity analysis, which expresses thepercentage change in a response (e.g., market share) caused by a 1 per cent change in acertain variable (e.g., price). Such applications can be found, for instance, in Louivere etal. (2000, ch. 11), Ortuzar and Gonzalez (2002), Greene and Hensher (2003), andMenendez et al. (2004) etc.
Finally, for some fields like transportation planning and environmental economics,
which both deal with public policy, the Compensating Variation (CV) can be calculated
by application of the model results for welfare analysis. See, for instance, Louivere et al.
(2000, ch. 12), Alpizar et al. (2003), and Li et al. (2004) etc.

5. Conclusion
This paper has reviewed Stated Choice Method. Attentions have been focused on thetheoretical background of SCM, the application and empirical modeling of SCM, thedesign of stated choice experiments, and the procedure of SCM. Stated Choice Method,
as one of the stated preference modeling techniques, has been developed to be capable ofanalyzing a number of possible choice situations by requiring variability of choice
attributes through the use of an appropriate experimental design. Further, incomparison with the other stated preference models, a SCM application simulates moreclosely actual choice behavior and is firmly grounded in the behavioral foundations ofrandom utility theory. As a result, SCM has become an important method for analyzing
various policy impacts.
However, a good method does not necessarily ensure that an application of it would bealways successful. To succeed in an application of stated choice method, analysts shouldcarefully deal with every step of the procedure. In most cases, failure of one step maylead to unsuccessfulness of the project, since the failure usually cannot be compensatedby other steps. For example, as for the issue on sampling and design, a good design will
not compensate inadequate sampling, and vice versa. Therefore, as a topic for future
research, a kind of guideline for SCM such like NOAA guideline for CVM (Contingent
Valuation Method) is worthy of being developed.

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