Choice Modelling
Encyclopedia
Choice modelling attempts to model the decision process of an individual or segment in a particular context. Choice modelling may also be used to estimate non-market environmental benefits and costs.
Well specified choice models are sometimes able to predict with some accuracy how individuals would react in a particular situation. Unlike a poll or a survey, predictions are able to be made over large numbers of scenarios within a context, to the order of many trillions of possible scenarios.
Choice modelling is believed by some to be the most accurate and general purpose tool currently available for making some probabilistic predictions about certain human decision making behavior. Many alternatives 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 modeled. In addition Choice Modelling is regarded as the most suitable method for estimating consumers’ willingness to pay for quality improvements in multiple dimensions.. The Nobel Prize
for economics was awarded to a principal exponent of the Choice Modelling theory, Daniel McFadden
.
that may be broadly termed Choice Modelling. As with any emerging technology, there are varying claims as to the correct lexicon.
These include:
research into food preferences in the 1920s and to random utility theory.
To some degree, all decisions involve choice. Individuals choose among different alternatives; commuters choose between alternative routes and methods of transport, shoppers choose between competing products for their attributes such as price, quality and quantity.
Choice modelling posits that with human choice there is an underlying rational decision process and that this process has a functional form. Depending on the behavioural context, a specific functional form may be selected as a candidate to model that behaviour. The multinomial logit
or MNL model form is commonly used as it is a good approximation to the economic principle of utility
maximisation. That is, human beings strive to maximise their total utility. The multinomial logit form describes total utility as a linear addition (or subtraction) of the component utilities in a context. Once the functional form of the decision process has been established, the parameters of a specific model may be estimated from available data using multiple regression, in the case of MNL. Other functional forms may be used or combined, such as binary logit, probit
or EBA with appropriate statistical tests to determine the goodness of fit of the model to a hold out data set.
. The reasons for this have more to do with information theory
than anything else. To understand why this is, take the following example:
Imagine a car dealership that sells both luxury cars and used low-end vehicles. Using the utility maximisation principle and an MNL model form, 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.
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:
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. The reason is that these three attributes always co-occur and in this case are perfectly correlated . That is: all BMW's are made in Germany and are of high performance. These three attributes: origin, marque and performance are said to be collinear or non-orthogonal.
These types of data, the sales figures, are known as revealed preference
data, or RP
data, because the data 'reveals' the underlying preference for cars. We can infer someone's preference through their actions, i.e. the car they actually bought. All data mining
uses RP
data. RP
data is vulnerable to collinearity since the data is effectively from the wild world of reality. The presence of collinearity implies that there is missing information, as one or more of the collinear factors is redundant and adds no new information. This weakness of data mining
is that the critical missing data that may explain choices, is simply never observed.
We can ensure that attributes of interest are orthogonal by filtering the RP
data to remove correlations. This may not always be possible, however using stated preference methods, orthogonality
can be ensured through appropriate construction of an experimental design.
s to respondents. For the same experiment, different designs could be used, each with different properties. The best design depends on the objectives of the exercise.
It is the experimental design that drives the experiment and the ultimate capabilities of the model. Many very efficient designs exist in the public domain that allow near optimal experiments to be performed.
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. Furthermore this could be achieved within a sample frame of only around 256 respondents.
Below is an example of a much smaller design. This is 34 main effects design.
This design would allow the estimation of main effects utilities from 81 (34) possible product configurations. A sample of around 20 respondents could model the main effects of all 81 possible product configurations with statistically significant results.
Some examples of other experimental designs commonly used:
More information on experimental designs may be found here.
data we are at the whim of the interrelated nature of the real world. With SP data, since we are directly asking humans about their preferences for products and services, we are also at liberty to construct the very products as we wish them to evaluate.
This allows great freedom in the creative construction many improbable but plausible hypothetical products. It also allows complete militation against collinearity
through experimental design.
If instead of using the RP
sales data as in the previous example, we were to show respondents various cars and ask
"Would you buy this car?"", we could model the same data. However, instead of simply using the cars we actually sold, we allowed ourselves the freedom to create hypothetical cars, we could escape the problems of collinearity and discover the true utilities for the attributes of marque, origin and performance. This is known as a Choice Experiment.
For example one could create the following unlikely, however plausible scenarios.
Information theory
tells us that a data set generated from this exercise would at least allow the discrimination between 'origin' as a factor in choice.
A more formal derivation of an appropriate experimental design would consequently ensure that no attributes were collinear and would therefore guarantee that there was enough information in the collected data for all attribute effects to be identified.
Because individuals do not have to back up their choices with real commitments when they answer the survey, to some extent, they would behave inconsistently when the situation really happens, a common problem with all SP methods.
However, because Choice Models are Scale Invariant
this effect is equivalent for all estimates and no individual estimate is biased with respect to another.
SP models may therefore be accurately scaled with the introduction of Scale Parameters
from real world observations, yielding extremely accurate predictive models.
tells us that there is no context-specific information here.
Instead, a choice experiment requires that individuals be forced to make a trade-off between two or more options, sometimes also allowing 'None or Neither' as a valid response. This presentation of alternatives requires that the at least some respondents compare: the cheaper, lower performance car against the more expensive, higher performance car. This datum provides the key missing information necessary to separate and independently measure the utility of performance and price.
Techniques include:
The Choice Experiment itself may be performed via hard copy with pen and paper, however increasingly the on-line medium is being used as it has many advantages over the manual process, including cost, speed, accuracy and ability to perform more complex studies such as those involving multimedia or dynamic feedback.
Despite the power and general applicability of Choice Modeling, the practical execution is far more complex than running a general survey. The model itself is a delicate tool and potential sources of bias that are ignored in general market research surveys need to be controlled for in choice models.
Many other market research techniques attempt to use ratings and ranking scales to elicit preference information.
Choice Models however do not suffer from these problems and furthermore are able to provide direct numerical predictions about the probability an individual will make a particular choice.
(or MaxDiff
as it is commonly known) is a well-regarded alternative to ratings and ranking. It asks people to choose their most and least preferred options from a range of alternatives. By integrating across the choice probabilities, utility scores for each alternative can be estimated on a ratio scale.
Choice modeling is a standard technique in travel demand modeling. A classical reference is Ben Akiva and Lerman (1989) , and Cascetta (2009) ; more recent methodological developments are described in Train (2003) .
Early applications of discrete choice theory to marketing are described in Anderson et. al. (1992)
Recent developments include a Bayesian approach to discrete choice modeling as set out in Rossi, Allenby, and McCulloch (2009)
Well specified choice models are sometimes able to predict with some accuracy how individuals would react in a particular situation. Unlike a poll or a survey, predictions are able to be made over large numbers of scenarios within a context, to the order of many trillions of possible scenarios.
Choice modelling is believed by some to be the most accurate and general purpose tool currently available for making some probabilistic predictions about certain human decision making behavior. Many alternatives exist in econometrics
Econometrics
Econometrics has been defined as "the application of mathematics and statistical methods to economic data" and described as the branch of economics "that aims to give empirical content to economic relations." More precisely, it is "the quantitative analysis of actual economic phenomena based on...
, marketing
Marketing
Marketing is the process used to determine what products or services may be of interest to customers, and the strategy to use in sales, communications and business development. It generates the strategy that underlies sales techniques, business communication, and business developments...
, sociometrics and other fields, including utility
Utility
In economics, utility is a measure of customer satisfaction, referring to the total satisfaction received by a consumer from consuming a good or service....
maximization, optimization
Optimization
Optimization or optimality may refer to:* Mathematical optimization, the theory and computation of extrema or stationary points of functionsEconomics and business* Optimality, in economics; see utility and economic efficiency...
applied to consumer theory
Consumer theory
Consumer choice is a theory of microeconomics that relates preferences for consumption goods and services to consumption expenditures and ultimately to consumer demand curves. The link between personal preferences, consumption, and the demand curve is one of the most closely studied relations in...
, and a plethora of other identification strategies which may be more or less accurate depending on the data
Data
The term data refers to qualitative or quantitative attributes of a variable or set of variables. Data are typically the results of measurements and can be the basis of graphs, images, or observations of a set of variables. Data are often viewed as the lowest level of abstraction from which...
, sample
Sample (statistics)
In statistics, a sample is a subset of a population. Typically, the population is very large, making a census or a complete enumeration of all the values in the population impractical or impossible. The sample represents a subset of manageable size...
, hypothesis
Hypothesis
A hypothesis is a proposed explanation for a phenomenon. The term derives from the Greek, ὑποτιθέναι – hypotithenai meaning "to put under" or "to suppose". For a hypothesis to be put forward as a scientific hypothesis, the scientific method requires that one can test it...
and the particular decision being modeled. In addition Choice Modelling is regarded as the most suitable method for estimating consumers’ willingness to pay for quality improvements in multiple dimensions.. The Nobel Prize
Nobel Prize
The Nobel Prizes are annual international awards bestowed by Scandinavian committees in recognition of cultural and scientific advances. The will of the Swedish chemist Alfred Nobel, the inventor of dynamite, established the prizes in 1895...
for economics was awarded to a principal exponent of the Choice Modelling theory, Daniel McFadden
Daniel McFadden
Daniel Little McFadden is an econometrician who shared the 2000 Nobel Memorial Prize in Economic Sciences with James Heckman ; McFadden's share of the prize was "for his development of theory and methods for analyzing discrete choice". He was the E. Morris Cox Professor of Economics at the...
.
Related terms for choice modelling
A number of terms exist that are either subsets of, part of the process or definition of, or overlap with other areas of econometricsEconometrics
Econometrics has been defined as "the application of mathematics and statistical methods to economic data" and described as the branch of economics "that aims to give empirical content to economic relations." More precisely, it is "the quantitative analysis of actual economic phenomena based on...
that may be broadly termed Choice Modelling. As with any emerging technology, there are varying claims as to the correct lexicon.
These include:
- Stated preference discrete choice modelling
- Discrete choiceDiscrete choiceIn economics, discrete choice problems involve choices between two or more discrete alternatives, such as entering or not entering the labor market, or choosing between modes of transport. Such choices contrast with standard consumption models in which the quantity of each good consumed is assumed...
- Choice experiment
- Choice setChoice SetA choice set is one scenario, also known as a treatment, provided for evaluation by respondents in a choice experiment. Responses are collected and used to create a choice model...
- Conjoint analysisConjoint analysisConjoint analysis, also called multi-attribute compositional models or stated preference analysis, is a statistical technique that originated in mathematical psychology. Today it is used in many of the social sciences and applied sciences including marketing, product management, and operations...
- Controlled experiments
Theoretical background
Modelling was developed in parallel by economists and cognitive psychologists. The origins of choice modeling can be traced to Thurstone'sLouis Leon Thurstone
Louis Leon Thurstone was a U.S. pioneer in the fields of psychometrics and psychophysics. He conceived the approach to measurement known as the law of comparative judgment, and is well known for his contributions to factor analysis.-Background and history:Louis Leon Thurstone was born in Chicago,...
research into food preferences in the 1920s and to random utility theory.
To some degree, all decisions involve choice. Individuals choose among different alternatives; commuters choose between alternative routes and methods of transport, shoppers choose between competing products for their attributes such as price, quality and quantity.
Choice modelling posits that with human choice there is an underlying rational decision process and that this process has a functional form. Depending on the behavioural context, a specific functional form may be selected as a candidate to model that behaviour. The multinomial logit
Multinomial logit
In statistics, economics, and genetics, a multinomial logit model, also known as multinomial logistic regression, is a regression model which generalizes logistic regression by allowing more than two discrete outcomes...
or MNL model form is commonly used as it is a good approximation to the economic principle of utility
Utility
In economics, utility is a measure of customer satisfaction, referring to the total satisfaction received by a consumer from consuming a good or service....
maximisation. That is, human beings strive to maximise their total utility. The multinomial logit form describes total utility as a linear addition (or subtraction) of the component utilities in a context. Once the functional form of the decision process has been established, the parameters of a specific model may be estimated from available data using multiple regression, in the case of MNL. Other functional forms may be used or combined, such as binary logit, probit
Probit
In probability theory and statistics, the probit function is the inverse cumulative distribution function , or quantile function associated with the standard normal distribution...
or EBA with appropriate statistical tests to determine the goodness of fit of the model to a hold out data set.
Methods used in choice modeling
Choice modeling comprises a number of specific techniques that contribute to its power. Some or all of these may be used in the construction of a Choice Model.Orthogonality
For model convergence, and therefore parameter estimation, it is often necessary that the data have little or no collinearityMulticollinearity
Multicollinearity is a statistical phenomenon in which two or more predictor variables in a multiple regression model are highly correlated. In this situation the coefficient estimates may change erratically in response to small changes in the model or the data...
. The reasons for this have more to do with information theory
Information theory
Information theory is a branch of applied mathematics and electrical engineering involving the quantification of information. Information theory was developed by Claude E. Shannon to find fundamental limits on signal processing operations such as compressing data and on reliably storing and...
than anything else. To understand why this is, take the following example:
Imagine a car dealership that sells both luxury cars and used low-end vehicles. Using the utility maximisation principle and an MNL model form, 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. The reason is that these three attributes always co-occur and in this case are perfectly correlated . That is: all BMW's are made in Germany and are of high performance. These three attributes: origin, marque and performance are said to be collinear or non-orthogonal.
These types of data, the sales figures, are known as revealed preference
Revealed preference
Revealed preference theory, pioneered by American economist Paul Samuelson, is a method by which it is possible to discern the best possible option on the basis of consumer behavior. Essentially, this means that the preferences of consumers can be revealed by their purchasing habits...
data, or RP
Revealed preference
Revealed preference theory, pioneered by American economist Paul Samuelson, is a method by which it is possible to discern the best possible option on the basis of consumer behavior. Essentially, this means that the preferences of consumers can be revealed by their purchasing habits...
data, because the data 'reveals' the underlying preference for cars. We can infer someone's preference through their actions, i.e. the car they actually bought. All data mining
Data mining
Data mining , a relatively young and interdisciplinary field of computer science is the process of discovering new patterns from large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics and database systems...
uses RP
Revealed preference
Revealed preference theory, pioneered by American economist Paul Samuelson, is a method by which it is possible to discern the best possible option on the basis of consumer behavior. Essentially, this means that the preferences of consumers can be revealed by their purchasing habits...
data. RP
Revealed preference
Revealed preference theory, pioneered by American economist Paul Samuelson, is a method by which it is possible to discern the best possible option on the basis of consumer behavior. Essentially, this means that the preferences of consumers can be revealed by their purchasing habits...
data is vulnerable to collinearity since the data is effectively from the wild world of reality. The presence of collinearity implies that there is missing information, as one or more of the collinear factors is redundant and adds no new information. This weakness of data mining
Data mining
Data mining , a relatively young and interdisciplinary field of computer science is the process of discovering new patterns from large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics and database systems...
is that the critical missing data that may explain choices, is simply never observed.
We can ensure that attributes of interest are orthogonal by filtering the RP
Revealed preference
Revealed preference theory, pioneered by American economist Paul Samuelson, is a method by which it is possible to discern the best possible option on the basis of consumer behavior. Essentially, this means that the preferences of consumers can be revealed by their purchasing habits...
data to remove correlations. This may not always be possible, however using stated preference methods, orthogonality
Orthogonality
Orthogonality occurs when two things can vary independently, they are uncorrelated, or they are perpendicular.-Mathematics:In mathematics, two vectors are orthogonal if they are perpendicular, i.e., they form a right angle...
can be ensured through appropriate construction of an experimental design.
Experimental design
In order to maximise the information collected in Stated Preference Experiments, an experimental design (below) is employed. An experimental design in a Choice Experiment is a strict scheme for controlling and presenting hypothetical scenarios, or choice setChoice Set
A choice set is one scenario, also known as a treatment, provided for evaluation by respondents in a choice experiment. Responses are collected and used to create a choice model...
s to respondents. For the same experiment, different designs could be used, each with different properties. The best design depends on the objectives of the exercise.
It is the experimental design that drives the experiment and the ultimate capabilities of the model. Many very efficient designs exist in the public domain that allow near optimal experiments to be performed.
For example the Latin square
Latin square
In combinatorics and in experimental design, a Latin square is an n × n array filled with n different symbols, each occurring exactly once in each row and exactly once in each column...
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. Furthermore this could be achieved within a sample frame of only around 256 respondents.
Below is an example of a much smaller design. This is 34 main effects design.
| 0 | 0 | 0 | 0 |
| 0 | 1 | 1 | 2 |
| 0 | 2 | 2 | 1 |
| 1 | 0 | 1 | 1 |
| 1 | 1 | 2 | 0 |
| 1 | 2 | 0 | 2 |
| 2 | 0 | 2 | 2 |
| 2 | 1 | 0 | 1 |
| 2 | 2 | 1 | 0 |
This design would allow the estimation of main effects utilities from 81 (34) possible product configurations. A sample of around 20 respondents could model the main effects of all 81 possible product configurations with statistically significant results.
Some examples of other experimental designs commonly used:
- Balanced incomplete block designs (BIBD)
- Random designs
- Main effects
- Two way effects
- Full factorial
More information on experimental designs may be found here.
Stated preference
A major advance in choice modelling has been the use of Stated Preference data. With RPRevealed preference
Revealed preference theory, pioneered by American economist Paul Samuelson, is a method by which it is possible to discern the best possible option on the basis of consumer behavior. Essentially, this means that the preferences of consumers can be revealed by their purchasing habits...
data we are at the whim of the interrelated nature of the real world. With SP data, since we are directly asking humans about their preferences for products and services, we are also at liberty to construct the very products as we wish them to evaluate.
This allows great freedom in the creative construction many improbable but plausible hypothetical products. It also allows complete militation against collinearity
Multicollinearity
Multicollinearity is a statistical phenomenon in which two or more predictor variables in a multiple regression model are highly correlated. In this situation the coefficient estimates may change erratically in response to small changes in the model or the data...
through experimental design.
If instead of using the RP
Revealed preference
Revealed preference theory, pioneered by American economist Paul Samuelson, is a method by which it is possible to discern the best possible option on the basis of consumer behavior. Essentially, this means that the preferences of consumers can be revealed by their purchasing habits...
sales data as in the previous example, we were to show respondents various cars and ask
"Would you buy this car?"", we could model the same data. However, instead of simply using the cars we actually sold, we allowed ourselves the freedom to create hypothetical cars, we could escape the problems of collinearity and discover the true utilities for the attributes of marque, origin and performance. This is known as a Choice Experiment.
For example one could create the following unlikely, however plausible scenarios.
- a low performance BMW that was manufactured in the US. "Would you buy this car?", or;
- a high performance Mitsubishi manufactured in Germany. "How about this car?"
Information theory
Information theory
Information theory is a branch of applied mathematics and electrical engineering involving the quantification of information. Information theory was developed by Claude E. Shannon to find fundamental limits on signal processing operations such as compressing data and on reliably storing and...
tells us that a data set generated from this exercise would at least allow the discrimination between 'origin' as a factor in choice.
A more formal derivation of an appropriate experimental design would consequently ensure that no attributes were collinear and would therefore guarantee that there was enough information in the collected data for all attribute effects to be identified.
Because individuals do not have to back up their choices with real commitments when they answer the survey, to some extent, they would behave inconsistently when the situation really happens, a common problem with all SP methods.
However, because Choice Models are Scale Invariant
Scale invariance
In physics and mathematics, scale invariance is a feature of objects or laws that do not change if scales of length, energy, or other variables, are multiplied by a common factor...
this effect is equivalent for all estimates and no individual estimate is biased with respect to another.
SP models may therefore be accurately scaled with the introduction of Scale Parameters
Scale parameter
In probability theory and statistics, a scale parameter is a special kind of numerical parameter of a parametric family of probability distributions...
from real world observations, yielding extremely accurate predictive models.
Preferences as choice trade-offs
It has long been known that simply asking human beings to rate or choose their preferred item from a scalar list will generally yield no more information than the fact that human beings want all the benefits and none of the costs. The above exercise if executed as a quantitative survey would tell us that people would prefer high performance cars at no cost. Again information theoryInformation theory
Information theory is a branch of applied mathematics and electrical engineering involving the quantification of information. Information theory was developed by Claude E. Shannon to find fundamental limits on signal processing operations such as compressing data and on reliably storing and...
tells us that there is no context-specific information here.
Instead, a choice experiment requires that individuals be forced to make a trade-off between two or more options, sometimes also allowing 'None or Neither' as a valid response. This presentation of alternatives requires that the at least some respondents compare: the cheaper, lower performance car against the more expensive, higher performance car. This datum provides the key missing information necessary to separate and independently measure the utility of performance and price.
Sampling and block allocation
Stated Preference data must be collected in highly specific fashion to avoid temporal, learning and segment biases.Techniques include:
- random without replacement block allocation; to ensure balanced sampling of scenarios
- in-block order randomisation; to avoid temporal and learning biases
- independent segment based allocation; to ensure balanced scenarios across segments of interest
- block allocation balancing; to ensure that non-completes do not affect overal sample balance
Model generation
The typical outputs from a choice model are:- a model equation
- a set of estimates of the marginal utilities for each of the attributes of interest; in the above example these would be (Marque, Origin, Price and Performance). In the case of an MNL model form, the marginal utilities have a specific quantitative meaning and are directly related to the marginal probability that the attribute causes an effect on the dependent variable which in the above example would be propensity to buy.
- variance statistics for each of the utilities estimated.
Choice modeling in practice
Superficially, a Choice Experiment resembles a market research survey; Respondents are recruited to fill out a survey, data is collected and the data is analysed. However two critical steps differentiate a Choice Experiment from a Questionnaire:- An experimental design must be constructed. This is a non-trivial task.
- Data must be analysed with a model form, MNL, Mixed Logit, EBA, Probit etc...
The Choice Experiment itself may be performed via hard copy with pen and paper, however increasingly the on-line medium is being used as it has many advantages over the manual process, including cost, speed, accuracy and ability to perform more complex studies such as those involving multimedia or dynamic feedback.
Despite the power and general applicability of Choice Modeling, the practical execution is far more complex than running a general survey. The model itself is a delicate tool and potential sources of bias that are ignored in general market research surveys need to be controlled for in choice models.
Strengths of choice modelling
- Forces respondents to consider trade-offs between attributes;
- Makes the frame of reference explicit to respondents via the inclusion of an array of attributes and product alternatives;
- Enables implicit prices to be estimated for attributes;
- Enables welfare impacts to be estimated for multiple scenarios;
- Can be used to estimate the level of customer demand for alternative 'service product' in non-monetary terms; and
- Potentially reduces the incentive for respondents to behave strategically.
Choice modelling versus traditional quantitative market research
Choice Experiments may be used in nearly every case where a hard estimate of current and future human preferences needs to be determined.Many other market research techniques attempt to use ratings and ranking scales to elicit preference information.
Ratings
Major problems with ratings questions that do not occur with Choice Models are:- no trade-off information. A risk with ratings is that respondents tend not to differentiate between perceived 'good' attributes and rate them all as attractive.
- variant personal scales. Different individuals value a '2' on a scale of 1 to 5 differently. Aggregation of the frequencies of each of the scale measures has no theoretical basis.
- no relative measure. How does and analyst compare something rated a 1 to something rated a 2. Is one twice as good as the other? Again there is no theoretical way of aggregating the data.
Ranking
Rankings do introduce an element of trade-off in the response as no two items may occupy the same ranking position. Order preference is captured; however, relative importance is not.Choice Models however do not suffer from these problems and furthermore are able to provide direct numerical predictions about the probability an individual will make a particular choice.
Maximum difference scaling
Maximum Difference Preference ScalingMaxDiff
Maximum difference scaling is a discrete choice model first described by Jordan Louviere in 1987 while on the faculty at the University of Alberta. The first working papers and publications occurred in the early 1990s...
(or MaxDiff
MaxDiff
Maximum difference scaling is a discrete choice model first described by Jordan Louviere in 1987 while on the faculty at the University of Alberta. The first working papers and publications occurred in the early 1990s...
as it is commonly known) is a well-regarded alternative to ratings and ranking. It asks people to choose their most and least preferred options from a range of alternatives. By integrating across the choice probabilities, utility scores for each alternative can be estimated on a ratio scale.
Uses of choice modelling
Choice modelling is particularly useful for:- Predicting uptake and refining New Product Development
- Estimating the implied willingness to pay (WTP) for goods and services
- Product or service viability testing
- Variations of product attributes
- Understanding brand value and preference
- Demand estimates and optimum pricing
- Brand value
Choice modeling is a standard technique in travel demand modeling. A classical reference is Ben Akiva and Lerman (1989) , and Cascetta (2009) ; more recent methodological developments are described in Train (2003) .
Early applications of discrete choice theory to marketing are described in Anderson et. al. (1992)
Recent developments include a Bayesian approach to discrete choice modeling as set out in Rossi, Allenby, and McCulloch (2009)