Choice modelling attempts to model the decision process of an individual or segment via revealed preferences or stated preferences made in a particular context or contexts. Typically, it attempts to use discrete choices (A over B; B over A, B & C) in order to infer positions of the items (A, B and C) on some relevant latent scale (typically "utility" in economics and various related fields). Indeed many alternative models exist in econometrics, marketing, sociometrics and other fields, including utility maximization, optimization applied to consumer theory, and a plethora of other identification strategies which may be more or less accurate depending on the data, sample, hypothesis and the particular decision being modelled. In addition, choice modelling is regarded as the most suitable method for estimating consumers' willingness to pay for quality improvements in multiple dimensions.2001 - Centre for International Economics - Review of willingness-to-pay methodologies
Related terms There are a number of terms which are considered to be synonyms with the term choice modelling. Some are accurate (although typically discipline or continent specific) and some are used in industry applications, although considered inaccurate in academia (such as conjoint analysis).
These include the following:
Stated preference discrete choice modeling Discrete choice Choice experiment Stated preference studies Conjoint analysis Controlled experiments Although disagreements in terminology persist, it is notable that the academic journal intended to provide a cross-disciplinary source of new and empirical research into the field is called the Journal of Choice Modelling.
Theoretical background The theory behind choice modelling was developed independently by economists and mathematical psychologists. The origins of choice modelling can be traced to Thurstone's research into food preferences in the 1920s and to random utility theory. In economics, random utility theory was then developed by Daniel McFadden and in mathematical psychology primarily by Duncan Luce and Anthony Marley. In essence, choice modelling assumes that the utility (benefit, or value) that an individual derives from item A over item B is a function of the frequency that (s)he chooses item A over item B in repeated choices. Due to his use of the normal distribution Thurstone was unable to generalise this binary choice into a multinomial choice framework (which required the multinomial logistic regression rather than probit link function), hence why the method languished for over 30 years. However, in the 1960s through 1980s the method was axiomatised and applied in a variety of types of study.
Distinction between revealed and stated preference studies Choice modelling is used in both revealed preference (RP) and stated preference (SP) studies. RP studies use the choices made already by individuals to estimate the value they ascribe to items - they "reveal their preferences - and hence values (utilities) – by their choices". SP studies use the choices made by individuals made under experimental conditions to estimate these values – they "state their preferences via their choices". McFadden successfully used revealed preferences (made in previous transport studies) to predict the demand for the Bay Area Rapid Transit (BART) before it was built. Luce and Marley had previously axiomatised random utility theory but had not used it in a real world application; furthermore they spent many years testing the method in SP studies involving psychology students.
History McFadden's work earned him the Nobel Memorial Prize in Economic SciencesEconomics 2000 in 2000. However, much of the work in choice modelling had for almost 20 years been proceeding in the field of stated preferences. Such work arose in various disciplines, originally transport and marketing, due to the need to predict demand for new products that were potentially expensive to produce. This work drew heavily on the fields of conjoint analysis and design of experiments, in order to: Present to consumers goods or services that were defined by particular features (attributes) that had levels, e.g. "price" with levels "$10, $20, $30"; "follow-up service" with levels "no warranty, 10 year warranty"; Present configurations of these goods that minimised the number of choices needed in order to estimate the consumer's utility function (decision rule). Specifically, the aim was to present the minimum number of pairs/triples etc of (for example) mobile/cell phones in order that the analyst might estimate the value the consumer derived (in monetary units) from every possible feature of a phone. In contrast to much of the work in conjoint analysis, discrete choices (A versus B; B versus A, B & C) were to be made, rather than ratings on category rating scales (Likert scales). David Hensher and Jordan Louviere are widely credited with the first stated preference choice models. They remained pivotal figures, together with others including Joffre Swait and Moshe Ben-Akiva, and over the next three decades in the fields of transport and marketing helped develop and disseminate the methods. However, many other figures, predominantly working in transport economics and marketing, contributed to theory and practice and helped disseminate the work widely.
Relationship with conjoint analysis Choice modelling from the outset suffered from a lack of standardisation of terminology and all the terms given above have been used to describe it. However, the largest disagreement has proved to be geographical: in the Americas, following industry practice there, the term "choice-based conjoint analysis" has come to dominate. This reflected a desire that choice modelling (1) reflect the attribute and level structure inherited from conjoint analysis, but (2) show that discrete choices, rather than numerical ratings, be used as the outcome measure elicited from consumers. Elsewhere in the world, the term discrete choice experiment has come to dominate in virtually all disciplines. Louviere (marketing and transport) and colleagues in environmental and health economics came to disavow the American terminology, claiming that it was misleading and disguised a fundamental difference discrete choice experiments have from traditional conjoint methods: discrete choice experiments have a testable theory of human decision-making underpinning them (random utility theory), whilst conjoint methods are simply a way of decomposing the value of a good using statistical designs from numerical ratings that have no psychological theory to explain what the rating scale numbers mean.
Designing a choice model Designing a choice model or discrete choice experiment (DCE) generally follows the following steps: Identifying the good or service to be valued; Deciding on what attributes and levels fully describe the good or service; Constructing an Experimental design that is appropriate for those attributes and levels, either from a design catalogue, or via a software program; Constructing the survey, replacing the design codes (numbers) with the relevant attribute levels; Administering the survey to a sample of respondents in any of a number of formats including paper and pen, but increasingly via web surveys; Analysing the data using appropriate models, often beginning with the Multinomial logistic regression model, given its attractive properties in terms of consistency with economic demand theory.
Identifying the good or service to be valued This is often the easiest task, typically defined by: the research question in an academic study, or the needs of the client (in the context of a consumer good or service)
Deciding on what attributes and levels fully describe the good or service A good or service, for instance mobile (cell) phone, is typically described by a number of attributes (features). Phones are often described by shape, size, memory, brand, etc. The attributes to be varied in the DCE must be all those that are of interest to respondents. Omitting key attributes typically causes respondents to make inferences (guesses) about those missing from the DCE, leading to omitted variable problems. The levels must typically include all those currently available, and often are expanded to include those that are possible in future – this is particularly useful in guiding product development.
Constructing an experimental design that is appropriate for those attributes and levels, either from a design catalogue, or via a software program A strength of DCEs and conjoint analyses is that they typically present a subset of the full factorial. For example, a phone with two brands, three shapes, three sizes and four amounts of memory has 2x3x3x4=72 possible configurations. This is the full factorial and in most cases is too large to administer to respondents. Subsets of the full factorial can be produced in a variety of ways but in general they have the following aim: to enable estimation of a certain limited number of parameters describing the good: main effects (for example the value associated with brand, holding all else equal), two-way interactions (for example the value associated with this brand and the smallest size, that brand and the smallest size), etc. This is typically achieved by deliberately confounding higher order interactions with lower order interactions. For example, two-way and three-way interactions may be confounded with main effects. This has the following consequences: The number of profiles (configurations) is significantly reduced; A regression coefficient for a given main effect is unbiased if and only if the confounded terms (higher order interactions) are zero; A regression coefficient is biased in an unknown direction and with an unknown magnitude if the confounded interaction terms are non-zero; No correction can be made at the analysis to solve the problem, should the confounded terms be non-zero. Thus, researchers have repeatedly been warned that design involves critical decisions to be made concerning whether two-way and higher order interactions are likely to be non-zero; making a mistake at the design stage effectively invalidates the results since the hypothesis of higher order interactions being non-zero is untestable.
Designs are available from catalogues and statistical programs. Traditionally they had the property of Orthogonality where all attribute levels can be estimated independently of each other. This ensures zero collinearity and can be explained using the following example.
Imagine a car dealership that sells both luxury cars and used low-end vehicles. Using the utility maximisation principle and assuming an MNL model, we hypothesise that the decision to buy a car from this dealership is the sum of the individual contribution of each of the following to the total utility. Price Marque (BMW, Chrysler, Mitsubishi) Origin (German, American) Performance
Using multinomial regression on the sales data however will not tell us what we want to know. The reason is that much of the data is collinear since cars at this dealership are either:
high performance, expensive German cars low performance, cheap American cars
There is not enough information, nor will there ever be enough, to tell us whether people are buying cars because they are European, because they are a BMW or because they are high performance. This is a fundamental reason why RP data are often unsuitable and why SP data are required. In RP data these three attributes always co-occur and in this case are perfectly correlated. That is: all BMWs are made in Germany and are of high performance. These three attributes: origin, marque and performance are said to be collinear or non-orthogonal. Only in experimental conditions, via SP data, can performance and price be varied independently – have their effects decomposed.