In addition, choice modelling is regarded as the most suitable method for estimating consumers' willingness to pay for quality improvements in multiple dimensions.
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).
The origins of choice modelling can be traced to Thurstone's research into food preferences in the 1920s and to random utility theory.
[4] In economics, random utility theory was then developed by Daniel McFadden[5] and in mathematical psychology primarily by Duncan Luce and Anthony Marley.
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;[7] furthermore they spent many years testing the method in SP studies involving psychology students.
[9][10] 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: 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.
David Hensher and Jordan Louviere are widely credited with the first stated preference choice models.
[10] 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, 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.
[2] 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.
This has the following consequences: 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.
For example the Latin square 1617 design allows the estimation of all main effects of a product that could have up to 1617 (approximately 295 followed by eighteen zeros) configurations.
This design would allow the estimation of main effects utilities from 81 (34) possible product configurations assuming all higher order interactions are zero.
The aim of these designs is to reduce the sample size required to achieve statistical significance of the estimated utility parameters.
This may impose cognitive burden on the respondent, leading him/her to use simplifying heuristics ("always choose the cheapest phone") that do not reflect his/her true utility function (decision rule).
Analysing the data from a DCE requires the analyst to assume a particular type of decision rule - or functional form of the utility equation in economists' terms.
Dividing all estimates by one other – typically that of the price variable – cancels the confounded lambda term from numerator and denominator.
[23] This solves the problem, with the added benefit that it provides economists with the respondent's willingness to pay for each attribute level.
Major problems with ratings questions that do not occur with choice models are: Rankings do tend to force the individual to indicate relative preferences for the items of interest.
By subtracting or integrating across the choice probabilities, utility scores for each alternative can be estimated on an interval or ratio scale, for individuals and/or groups.
In some versions of the model, an individual chooses that occupation for which the present value of his expected income is a maximum.