mrspace wiki - Recently Updated Pages

Conjoint Analysis and Discrete Choice Modelling

RayPoynter — Fri, 15 Aug 2008 14:02:47 CDT

Conjoint Analysis is a way of assessing why people make the choices they make and of being able to predict what they will do if given an set of choices. Discrete Choice Modelling, DCM, is an alternative approach, to Conjoint type problems.

The table below shows a typical situation where Conjoint Analysis or DCM might be used.

A typical Conjoint problem

A manufacturer can make a laptop with the following options
• 2 sizes of screen
• 3 sizes of hard disk size
• 2 different memory sizes
• 5 prices to test
Which product(s) should the manufacturer make, and at what price(s)?

This laptop manufacturer can make 2 * 3 * 2, 12 different combinations, and if they are combined with the 5 price points there are 60 options to test.
60 options is too many to test by dividing all the options into different concepts and testing each one. Conjoint Analysis allows the manufacturer work out the value of each attribute (for example size of screen versus size of hard disk), and each level of each attribute (for example 2MB of memory versus 4MB).

How Are the Values Calculated?

Conjoint Analysis recognises that people cannot accurately say how much each factor determines their final choice. Therefore, Conjoint Analysis values are created by giving people different sets of choices and asking them to make decisions.

The choices the respondent has to make are designed so that no one option is good in every way. The respondent has to trade-off something they want, in order to get something even better.

For example if I offer you a great meal at a really high price, or an OK meal at a lower price, there are two options available to you. If you pick the great meal, you have traded away the price; i.e. you are a less price sensitive person. But, if you pick the cheaper meal, you will have traded away the quality of the meal, and you are more price sensitive.

In a Conjoint study the respondent is presented with many choices and by processing these choices we can understand the value that a respondent places on each level of each attribute.

Once we know the attribute values for each individual we can conduct a wide range of analyse, including needs based segmentation, product optimisation, and what-if modelling.

What-if Modelling

Most market research only provides answers to the question that were specifically asked during the research. What-if modelling allows marketers to pose a wide range of questions and to see how customers would have reacted.

Choosing the right technique for the job

There are a wide variety different ways of doing Conjoint Analysis and Discrete Choice Analysis. At Virtual Surveys we have concentrated on two approaches, namely bespoke Discrete Choice Models and Adaptive Conjoint Analysis.

The two examples below show the relative advantages of these two techniques.

Example 1 – Adaptive Conjoint Analysis

Skiing Holiday

Attributes: This study had a large number of attributes, including flight time, transfer time, type of accommodation, type of accommodation, number of black runs, red runs, blue runs, snow board park, après ski, etc.

Because there were a large number of attributes, and different attributes were of different importance to different people, ACA was used.
In the first stage of the ACA the respondents put into order those attributes that did not have a ‘natural’ order.

For example, they told use whether they preferred self-cater, chalet, B&B, or hotel. But we did not ask them to tell us whether they preferred the holiday to be cheaper, or for the transfer time to be shorter – we can infer which of these is best.

The second stage of the interview asks the respondent to say how import the best and worst levels for each attribute.

For example, if two resorts were equal in every other way, how important would it be that one resort had a snowboard park and the other didn’t?
The first two sections of the ACA interview give us an idea of what is important to that respondent. This allows the software to create choices for the next stage that will ask questions that are relevant to each particular respondent.

The third and main stage of the interview creates 12 to 20 paired choices and asks the respondent how much they prefer one option to the other.
For example the screen below shows two options, each showing levels from five attributes.

Option A	Option B
French Alps Self-Cater 5 Black runs Quite night life £750 per person	Italian Dolomites Chalet 30 Black runs Great bars and disco £800 per person
Strongly prefer A Strongly Prefer B 1-----------2-----------3-----------4-----------5-----------6----------7-----------8-----------9

The computer creates a variety of choice tasks, using different attributes on different quesitions.

Option A	Option B
2 hour transfer Self-Cater English speaking ski school Ski right to accommodation £800 per person	4 hour transfer Chalet No ski school Bus to and from ski area £500 per person
Strongly prefer A Strongly Prefer B 1-----------2-----------3-----------4-----------5-----------6----------7-----------8-----------9

The results of the study were used to create a needs based segmentation, to identify how many types of skier there are. Segments varied from people who were skiing-focused, 8-to-an-apartment, looking to combine a low budget with extreme hills, to people for whom four afternoons of skiing, combined with good food and shopping was the ideal.

Example 2 – Discrete Choice Modelling

Petrol Station

In this study the number of attributes could be constrained to just four, these were: brand, price, convenient location, and shop (including co-location with superstore).

The study was designed to evaluate the future impact of petrol stations co-located with superstores. The client was aware that there were planning applications for many more petrol stations. A secondary aim of the research was to evaluate strategies for competing with the co-located petrol stations.

The study was conducted as a DCM project. The respondents were shown a set of tasks with 5 cards per task, set out as below:

Brand 1

£1.20 a litre

On your daily route

No shop

Brand 2

£1.25 a litre

15 minutes out of your way

With a superstore

Brand 3

£1.15 a litre

5 minutes out of your way

With a small shop

Brand 4

£1.30 a litre

30 minutes out of your way

With a mini-supermarket

None of these

Compared with ACA, the DCM tasks are very simple, the respondent simply has to make a choice. This process is very close to the customers behaviour in the real market. People don’t rate choices in stores, they select items from a set of choices.

DSM also introduces the idea of None of These, which often creates a better context for questions.

Each respondent saw 16 of these screens, and made 16 choices. The data were processed using Hierarchical Bayes (see next sub-heading).
What-if modelling was used to assess:
• What would happen if co-location became more convenient (which is what happens when more stores open)?
• What price differential can the branded stations maintain over the unbranded options?
• What level of convenience is “convenient enough”?

Hierarchical Bayes

Hierarchical Bayes, or HB as it is more often referred to, is a powerful statistical technique to deal with sparse data sets. HB can be used for a number of purposes, but the most common use in market research is in the calculation of Conjoint and DCM utilities.

In many DCM studies, the number of choice tasks we ask each respondent is not sufficient to work at respondent level values for all the attributes and levels. HB allows us to use a small number of choices per respondent, typically in the range 12 to 20. It does this by using a large amount of computing power to estimate what respondents would have done if they had answered a larger number of choices.

What sort of products are Conjoint and DCM used for?

This section illustrates a few of the case where ACA or DCM has proved useful.

Laptop

Studies researching laptops are very suitable for both Conjoint and DCM approaches. Typically, studies look at: brand, size, weight, price, disk size, memory.

Care needs to be taken about interactions in the area of brand and styling. An Apple laptop that is very innovative may be seen as attractive, but a Dell with the same form may be seen as simply odd. DCM can be used to create different attribute sub-sets for different brands. For example a Dell or HP might be shown with XP or Vista operating systems, whereas Apple would be shown as having the Mac operating system.

Washing machine

White goods research typically looks at: brand, price, capacity, design*, functions, eg spin speed.

* Attributes like style and design are not always easy to include in a choice study. In a washing machine study one might include 4 photos of machines, showing them as basic, classic well built, stylish, and modernistic. The photos would not be branded, and would be sufficiently vague so as not to invalidate issues such as functionality.

Car

Within the area of car research, there are many areas that have been explored by choice modelling approaches. For example, some studies look at the drivers of choice (doors, brand, engine size, etc.). Other studies focus on details such as just in car entertainment or purchase options.

Hotel

Hospitality studies can be conducted with different types of groups, for example business travellers or holiday makers. These different samples produce very different questionnaires.

A common mistake in this type of study is to use attributes that have levels which are too precise for respondents to really appreciate. For example, check-in takes less than 2 minutes, 2-4 minutes, 5-7 minutes etc.

Another potential trouble-spot with hotel studies is to try to define non-tangible attributes such as atmosphere or style. Attributes such as traditional and minimalist convey some impression of the hotel, but only to a limited extent. Phrase such as boutique hotel are very fashionable, but if you find out 10% of the respondents are highly motivated towards boutique hotels, you still have no idea if they meant the same thing as each other, or how to achieve it.

Travel Insurance

Insurance in general is a popular field for choice modelling, and travel insurance is a good example. Attributes in travel insurance studies tend to include: annual premium, activities covered, whether family also covered, excesses, and items covered.

One issue with travel insurance is to be clear about when the respondent makes their typical purchase decision. Do they have an annual contract, in which case they only rarely make a decision? Do they buy it with their holiday purchase; if so this will also inhibit the chance of a preferred scheme being bought. Do they buy it at the airport? Or, do they look around, before going on holiday, and make a considered purchase. This last group are the most likely to be approximated by the choice or conjoint model.

FMCG products

Because Conjoint tends to deal with cases where there are tangible attributes, it is less often applied to the majority fast moving consumer goods research, such as soft drinks, detergents, or fish fingers. That is because most of the research in these areas concentrates on emotional scales, brand personalities, communications and advertising.

When conjoint and choice modelling is used with FMCG products it tends to be issues such as packaging (glass versus plastic versus can, size, with or without handle), elements of multi-packs, and pricing.

When to use Conjoint, and when not

Conjoint Analysis and DCM work best when the attributes can be clearly described and where attraction of the whole is a least approximated by the attraction of the parts. For example, you may like Tea and you may like vodka, but there is no reason to suspect you will like tea flavoured vodka. This is a case of where the attributes interact and where conjoint would not normally be helpful.

Similarly, attributes such as fun, happy, attentive staff, tend to be difficult to work with because respondents do not agree with each other about the definition. In a choice study we ideally want the respondents to agree about what the attributes mean, and then for them to have different importances for those attributes.

A simple way to assess whether the attributes are suitable for conjoint is to think of a matrix of attributes and products. How likely is it that most consumers would agree about which attributes apply to which products. The more they agree, the more likely it is that conjoint is appropriate.
For example, if we were conducting an instant coffee study, the attributes such as container (pouch, can, or jar), size (200g, 400g), with or without caffeine, brand, and price are all very clear.

By contrast attributes such as bitter, modern, strong, dark, and happy are less consistently applied, and therefore less suitable for choice modelling.
Choosing Between Conjoint and DCM

In general DCM is a more powerful solution than Conjoint Analysis. If a problem can be fitted to its limitations, then DCM should be chosen. The main limitation of DCM relates to the number of attributes it can handle. Most marketing scientists prefer to keep the number of attributes to seven or fewer.
In all but the most simple of cases, DCM is usually dependent on processing the data with Hierarchical Bayes.

If the number of attributes is greater than seven, and if the number really can’t be reduced, then Adaptive Conjoint Analysis is the most appropriate technique.

One limitation of the Adaptive Conjoint Analysis method is that it can only be administered via a computer, usually via online surveys, but CAPI (i.e. using laptops). DCM can be conducted via a computer, or via paper and pencil.

MRPedia

RayPoynter — Fri, 15 Aug 2008 14:02:24 CDT

MRPedia is going to be like a Wikipedia for market researchers.

ascribe - Verbatim management system

Conjoint Analysis and Discrete Choice Modelling

Juster Scale

mspace

MVSP - Multi Variate Statistical Package

Pricing research

ascribe

nigel.legg — Thu, 14 Aug 2008 03:53:55 CDT

Online system for handling verbatims

User reviews

Nigel Legg's review - very full, written from a users point of view

ascribe: user review

RayPoynter — Wed, 13 Aug 2008 18:52:12 CDT

ascribetm – Verbatim Management system from Language Logic. The ascribe verbatim management system runs on Language Logic’s servers, and access is through a web browser. This means that the speed of the system depends on your connection speed – loading a large data file through a slow dial-up connection will not be as fast as on a broadband connection – but this contributes to the low cost of entry for using the system. In my case, I run projects from a laptop, and I have a remote team of coders using a variety of PCs to access the system. In switching from using Excel for coding, there is no additional hardware or infrastructure cost. After the initial set-up charge, pricing is transaction-based, making ascribe a true SAS system. At the same time, the Language Logic team have always been responsive to support requests when needed.

The first, and probably most important, thing to note about ascribe is that it is easy to use. While this means that it is also easy to make a complete hash of a project using ascribe, there are a number of fail-safe systems in place to stop you doing so – for example, under “Edit Codeframe”, you can delete a code frame after coding has started, but a pop up window appears, asking you to type “OK” in a box before it does this. In general, the system is fast, easy to use, and, in my experience, most people can get the hang of what they should be doing in under an hour. This is of primary importance when you are using humans to code verbatim, as you want them to be thinking about the words and concepts they are dealing with, not where to click or how to use the system.

Verbatim responses can be loaded directly from Excel, or from text files created using the LLUTILS Add-In for Excel which Language Logic provide. Coded Data can be exported in a range of formats; the ones I use most commonly are SPSS-style 1/0, and Horizontal data, which produces spread field multiple response data.

Ascribe holds the data in a SQL database, and this allows you to do a lot of things which would not be possible in paper or Excel based coding, such as reordering and renumbering the codeframe after coding is complete (something we do all the time, in order to group codes which are added out of synch in the course of the project).

In terms of developing a virtual, off-site team of coders, the main failing of ascribe is that it is based on Microsoft Technology, which means that all users have to have a Windows PC running Internet Explorer 5 or above; a number of the coders working with me have had to switch over from Firefox or Opera in order to work, and I have had to turn down people with impressive language skills because they were Mac users.

This is, however, a small limitation, and, for larger companies where all coding is done on-site, it will not be a problem. Compared to the option of providing each coder with an Excel spreadsheet, and waiting impatiently for them to return it, ascribe is a vast improvement. For example, I just logged into ascribe to check a project, and found that no one had done any coding in the last hour, but someone was amending the codeframe and someone else was reviewing coding already done. From my point of view, the central control of codeframes is the most useful feature of ascribe: the ability to have a single codeframe, in use for multiple languages, and even multiple studies, at the same time; if someone updates that codeframe in any language or study, that change will immediately be available in every language or study.

Translations and transcriptions can be done within ascribe, as well as coding; this means that, for all projects, you have the option of using the text typed in, a recording of the answer given, or a translation of the text for the coding - and you get the reports you need on the translation and transcription work done. Ascribe holds the translation and the original text together, so for exports or reporting in Cross Word (see below) you can select which you want to use. While Language Logic have assured me that Ascribe can handle all languages and scripts supported by IE6 and above, most of our work has been in European languages using the Roman alphabet; we have done a Chinese project, which the system handled fine, and hope to do some work in Hindi and other Asian languages in the near future.

Ascribe has a wealth of reporting functions, but from a management perspective, the time accounting functions and top-line reports are the most useful; these are functions you would not be able to replicate if all your coding was done offline. Time accounting is very detailed; you can see, to two decimal places, how many hours each coder spent on coding, reviewing work, or updating codeframes, by question. In terms of insight into the data, the Cross Word application (an internet-connected PC application) allows you to produce simple cross tabs of the data, and then view all the verbatim responses which were coded into a particular cell; this makes drilling down into the unstructured verbatim data quick and simple, though it does not have any statistical functions. You can, for example, get sets of verbatim determined by age, gender, job type, or any other relevant variable. If the responses have been translated within ascribe, you can use this function to compare the responses by country.

Following Language Logic LLC’s licensing of the advanced Verbatim Coding System and text categorization technology from the institute of Information Science and Technologies in Pisa, Italy, last year, ascribe VCS is now becoming available. Using this, you train the system and then allow it to code questions that have the same code frame; the system uses complex text recognition algorithms to find matches and likely matches, and codes the responses accordingly. I have not yet used this system myself, but will do in the future on large projects, as it potentially allows for faster turn around. I believe, however, that this will not totally replace human coders, as they will always be required to code the last few responses that the system could not cope with, and to code new data-sets prior to training the system.

All in all, once we had the correct procedures in place and enough coders trained to use ascribe, it has made the coding process simpler and faster, without incurring too much cost.

Nigel Legg (nigel.legg@katugas-research-services.co.uk; www.katugas-research-services.co.uk) uses ascribe for English and multi-lingual verbatim coding.

MR Resources

RayPoynter — Tue, 05 Aug 2008 18:12:02 CDT

A list of resources that are useful to the market researcher.

Blogs

Papers and references

Inspiration

Researchers on Twitter

Companies and Services

RayPoynter — Tue, 05 Aug 2008 18:11:48 CDT

A list of all companies and services that might be of interest to market researchers. Each company should ideally be a new page. Services can either be listed on the company's page, or on sub pages.

Katugas Research Services

The Future Place

Virtual Surveys

VisionsLive.com

Virtual Surveys - Jobs

RayPoynter — Tue, 05 Aug 2008 18:10:57 CDT

A Growing Company

Virtual Surveys is a growing team.
As such we are continually looking for quality people to join the team.
At present we are specifically looking for:-

Senior Research Executives
Market Research Assistant Forum Moderator

If you're looking for a new challenge and fancy joining us, please contact us

Current Vacancies

Please click here for the current vacancies

Twitter

RayPoynter — Tue, 05 Aug 2008 17:55:04 CDT

A list of researchers who are on Twitter

jenniebeattie
marcsbrenner
mario
RayPoynter
nigellegg

visionslive

Visionslive — Tue, 05 Aug 2008 13:10:57 CDT

VisionsLive.com is a set of easy-to-use tools for online qualitative research. It has been built from the ground-up with the assistance of senior qualitative researchers across the globe, and can be used to implement online qualitative research projects in multiple languages.

Check out Our Blog for the latest news and big releases!

Katugas Research Services

nigel.legg — Tue, 05 Aug 2008 13:03:46 CDT

Katugas Research Services is a young, Bristol (UK) based company, providing verbatim coding and translation services to market research agencies. We use the ascribe verbatim management system from Language Logic (www.languagelogic.info), and have a team of coders able to work in around 25 European and Asian languages. Many of our coders are also trained translators, which means we can also provide translations (and transcriptions) of questionnaires, focus groups, depth interviews, and other research materials. Website just up-dated, in English, French and German.
website: www.katugas-research-services.co.uk
Contact: sales@katugas-research-services.co.uk

Blogs

RayPoynter — Tue, 05 Aug 2008 12:21:45 CDT

Blackbeard Blog	Blog on marketing and community-based research.
Curiously Persistent	Blog on research and branding issues. A mixture of own thought and accumulation of blogs elsewhere.
msurveys.com	Mario's thoughts and experiments, sitting at the intersection of market research and cutting-edge technology and trends, with a special emphasis on virtual worlds and social networking.
Nigel Hollis	Nigel is Chief Global Analyst at Millward Brown and his blog reviews a wide range of research topics.
Ray Poynter	The Future Place blog, focusing on thought leadership and future related topics.
Virtual Surveys.	Blog focusing on leading edge issue, particularly Research 2.0.
Web Strategist.	Jeremiah Owyang, Senior Analyst at Forrester Research, on social computing.

These blogs need annotating
http://artofconversation.typepad.com/art_of_conversation/
http://blog.msurveys.com
http://customerlistening.typepad.com/customer_listening/
http://francoisabiven-gb.blogspirit.com
http://freakytrigger.co.uk/blackbeard/
http://insights20.com
http://paab.typepad.com/furtherandfaster/
http://researchreinvented.blogspot.com
http://technobabble2dot0.wordpress.com/
http://www.dubstudios.com
http://www.gmiblog.com
http://www.marketresearchtech.com
http://www.researchtalk.co.uk/rt/
http://www.rockresearch.co.nz/blog/
http://www.surveyxtreme.com/
http://www.virtualsurveysdiscussion.com/vslblog/
http://www.web-strategist.com/blog/

MVSP - Multi Variate Statistical Package

RayPoynter — Tue, 05 Aug 2008 03:16:09 CDT

MVSP is a statistical package available from Kovach Computing Services.

Review by Ray Poynter
MVSP is my tool of choice for correspodence maps, I find it easy to easy, easy to teach other people to use. I tend to make the maps in MVSP, copy them and paste them into PowerPoint. By ungrouping the image twice you can move all the labels etc to make the final map easier to read.

mrspace wiki Home

RayPoynter — Tue, 05 Aug 2008 03:11:41 CDT

MRPedia How to conduct market research - like a wikipedia devoted to how to do semiotics, Gabor Grainger, NLP, and Discrete Choice Models. Created, written, and used by market researchers. This section includes software reviews.		Companies and services i.e. self-advertising.

Situations Vacant Alist of jobs seeking people. At present this works really badly on mrspace, so hopefully we will be able to create something useful here.		MR Resources A list of all the places and people that you might want to search for, from blogs, to papers, to twitters. This list is bound to evolve, but this is the initial plan.

This wiki is created in association with the mrspace network.

Anybody can view and search this wiki, but to edit it you need to be a member. There is a list of the wiki pages in the left column, and there is the search box.

Inspiration

RayPoynter — Mon, 04 Aug 2008 17:47:22 CDT

Presentation Zen. Useful blog exploring presentation techniques with plenty of videos and practical examples
TED. Great videos of top presenters in action

Papers

RayPoynter — Mon, 04 Aug 2008 17:44:59 CDT

Papers and references
Market Vision Research. List of papers covering DCM, Conjoint Analysis, and a range of advanced quantitative topics.
Sawtooth Software. A great list of papers on Conjoint Analysis and a range of associated topics such as DCM and MaxDiff.

UK

RayPoynter — Mon, 04 Aug 2008 17:24:48 CDT

Put all UK jobs here, one page per company, with sub-pages for each job.

Virtual Surveys

Situations Vacant

RayPoynter — Mon, 04 Aug 2008 17:24:02 CDT

Jobs are organised by country (with special folders for regional or global). Please create a page for you company and sub-pages for each job.

UK

Juster Scale

RayPoynter — Wed, 09 Apr 2008 20:58:49 CDT

The Juster Scale is a verbal probability scale designed to estimate future behaviour. It was developed by FT Juster in 1966 and appears in the academic literature fairly often. However, it does not seem to be used very often by European MR agencies. It appears to be more suitable for product category purchase, as opposed to specific brand purchase – this may explain why it is less popular with agencies.

The main criticism of the Juster scale is that it tends to over-predict share, something which might be reduced by re-scaling or by using just the top-end of the scale. For example the 0-10 scale is sometimes squared and the results used as percents (10→100%, 9→81%, 8→64%,….., 2→4%, 1→1%, 0→0%).

Consideringthe published papers, the Juster scale appears to be cited much more often in Australia and New Zealand, compared with other markets. A paper was presented to the 1999 MRSA Annual Conference (note MRSA is now AMSRS) which looked at the adoption in Australia of the Bass Model, and used the Juster scale as a comparator.

The standard Juster scale is an 11 point scale, like the one shown below.

10 Certain, practically certain (99 in 100)
9 Almost sure (9 in 10)
8 Very probable (8 in 10)
7 Probable (7 in 10)
6 Good possibility (6 in 10)
5 Fairly good possibility (5 in 10)
4 Fair possibility (4 in 10)
3 Some possibility (3 in 10)
2 Slight possibility (2 in 10)
1 Very slight possibility (1 in 10)
0 No chance, almost no chance (1 in 100)

Dianne, Gan, Gendall, Esslemont produced a very good summary of the Juster scale in an article in the New Zealand Marketing Bulletin in 1991. They also reported on two experiments they had carried out in New Zealand. A paper that links the Juster scale with the context of tourism in Australia was presented in Canberra at this year’s CAUTH conference. The paper by Ryan and Huyton, used the Juster scale to help estimate drivers of choice. References Juster, F T (1966). Consumer buying intentions and purchase probability. Occasional Paper 99, National Bureau of Economic Research, Colombia University Press.

Juster, F T (1969). Consumer anticipations and models of durable goods demand. In Mincer, J (1969). Economic Forecasts and Expectations. National Bureau of Economic Research.

Dianne Day, Boon Gan, Philip Gendall and Don Esslemont (1991). Predicting Purchase Behaviour, Marketing Bulletin (NZ), 1991, Volume 2

Chris Ryan, Jeremy Huyton (2001). Balanda Visitors to Central Australia: Their Perceptions, 2001 CAUTHE National Research Conference, Canberra

Research 2.0

RayPoynter — Sat, 05 Apr 2008 21:10:57 CDT

Just as many of us were getting to grips with the Internet - it has changed! Web 2.0* has created a new discourse... replacing the static, top-down nature of Web 1.0. Customers are posting, reviewing, re-selling, discussing, sharing, and creating via the Internet. The impact of Web 2.0* will change business and marketing in ways which dwarf many other recent changes.
It has huge implications for research. Historically, research has adopted 'command and control' and 'commission and forget' approaches to research and this will not work in a Web 2.0* world.
Virtual Surveys is at the forefront of using Participatory Web approaches for research purposes, under the banner Research 2.0. Using our customcommunity platform we can meet a range of long term and short term objectives. Find out more about

V-Community - our ongoing research community approach
V-Discussion - our approaches to specific research objectives
Video-Blog Booth

* O'Reilly Media 2004

Virtual Surveys

RayPoynter — Sat, 05 Apr 2008 21:08:13 CDT

Virtual Surveys are the Website Research Experts. Our research skills and on-line expertise combine to provide the insight you require to drive your business forward.

Visit our blog and our Facebook page.

Virtual Surveys is recruiting, click here for more information.

mrspace wiki - Recently Updated Pages

Conjoint Analysis and Discrete Choice Modelling

How Are the Values Calculated?

What-if Modelling

Choosing the right technique for the job

Example 1 – Adaptive Conjoint Analysis

Skiing Holiday

Example 2 – Discrete Choice Modelling

Petrol Station

Hierarchical Bayes

What sort of products are Conjoint and DCM used for?

Laptop

Washing machine

Car

Hotel

Travel Insurance

FMCG products

When to use Conjoint, and when not

MRPedia

ascribe

User reviews

ascribe: user review

MR Resources

Companies and Services

Virtual Surveys - Jobs

Current Vacancies

Twitter

visionslive

Katugas Research Services

Blogs

MVSP - Multi Variate Statistical Package

mrspace wiki Home

MRPedia

Companies and services

Situations Vacant

MR Resources

Inspiration

Papers

UK

Situations Vacant

Juster Scale

Research 2.0

Virtual Surveys