Advanced MMBR

Advanced MMBR

Conjoint analysis (1)

Advanced Methods and Models in Behavioral Research

Conjoint analysis -> Multi-level models You have to understand:

- What it is- Which different kinds of Conjoint

Analysis there are- How it can be of use in typical TIW

research- How it can lead to different kinds of

statistical analyses (of the repeated measures or multi-level kind)

For this, you can use these slides AND the literature online

In the laptop exam, running a repeated measures analysis is part of the requirements.

The logic of the course

• binary Y

logistic regression

• conjoint analysis: way of data collection that might come in handy

"repeated measures" / "multi-level" data

We practice on self-collected data some practice/training in survey design and execution



Conjoint Analysis

Underlying assumption: for each user, the "utility" of a product can be written as

U(x1,x2, ... , xn) = c0 + c1 x1 + ... + cn xn

- 10 Euro p/m- 2 year minimum- free phone- ...

How do you rate this proposal? (-5 ... +5)


Two kinds of research questions

• Which phone do you prefer?

• How do different attributes of a proposition affect the utility of a proposition?


Why is this important?

It is an important tool in social science when you want to investigate how someone’s behavior depends on circumstances

and

a useful tool in typical TIW Master’s Theses -> example on next slide


One of the main advantages = more data: Example - Adoption of technology

1. If you ask for behavior only (“did you adopt” etc), then you get one piece of info per person, and the rest you have to infer by comparing different persons. -> Good but data are sparse.

2. If you offer different scenarios and then ask whether someone would adopt, you get how adoption depends on the context for this person.-> Richer data per person, but not behavioral

Given that sample sizes of >200 are often necessary, often option 2 is more feasible than option 1.

Or, use it on ...

- size of mole- color of mole- age of patient- patient in sun a lot- ...Is this skin cancer? (-5 ... +5)

medical decisions

- strongly favor issue personally- core of party's strategy- many other parties against it- gets a lot of media attention- ...Do I submit a motion? (-5 ... +5)

political decisions

-...-...-...-...-...

??


Example: properties of mobile phones1. Give each person 200 of these

cases (“vignettes”)2. Per person, run multiple

regression on their 200 answers --> you get (personal) values for each of the dimensions.

You could then:1. Create groups of people with the

same kind of values, or …2. … get an estimate of the average

trade-off between dimensions, or …

3. … compare different groups of respondents

- large b/w screen- long battery life- not flashy- costs 12 Euro/month- free - monthly contract How do you rate this option? (-5 ... +5)

Suppose you get a phone that has …


The data would look like this:Y D1 D2 D3 D4 D5 id …

+4 -1 -1 0 1 0 1-3 1 1 1 0 -1 10 0 0 1 0 -1 10 1 0 -1 1 0 1+1 … … … … … 1+1 … … … … … 1-1 … … … … … 1+4 … … … … … 1… … … … … … …

Other considerations- How many dimensions?- How many levels per dimension?- How many cases per person?- Which cases from all possible cases per person?- How many persons?- Judgment or choice?

NOTE

• The D-values are chosen by the researcher -> experiment

• 200 is way too much• 35 = 243 > 200• (or even: 410 = …)• If you look at the

complete data set, this is repeated measures, with within-persons effects -> standard multiple regression will not work

• Judgment vs choice

Alternatives (1)• Ask people directly what their favorite phone is

If you could choose, which phone (and monthly plan) would you prefer?

+ much easier to collect - you will end up with as many suggestions as you

have respondents- does not capture trade-offs between attributes


Alternatives (2)• Ask people directly for "their" utility model

For instance:Which of the following factors do you find important when it comes to <buying a phone> / <adopting an innovation> / <choosing a buyer on eBay> / ...?

Please divide 100 points over the following factors:- price- battery life - ...

+ much easier to collect - asks for introspection about choice process


Usually, the comparison of interest is within persons

person 1person 2person 3person 4person 5

But if you consider only the <sold phones >, you get the comparison between persons

Advantage of conjoint analysis (sophisticated)

Disadvantages of conjoint analysis

• All fictituous decisions ("what would you do if ...")

• (Often) assumes weighted average. This does not allow "all or nothing" weights

• Quite complicated for the respondents

• Can be quite complicated for the researcher, both to implement and to analyze

• ...


Kinds of Conjoint Analysis1. CVA: Conjoint Value Analysis Choosing wisely from the set of all possible cases.

2. ACA: Adaptive Conjoint AnalysisAdapting the cases you offer based on previous answers of the respondent.

3. CBC: Choice Based ConjointAsking for preferences between 2 (or 3 or 4) cases.

4. PP-CBC: Partial-profile Choice Based ConjointComparing only part of the attributes of the cases.(This last one we disregard completely)

- 10 Euro p/m- 2 year minimum- free phone- ...

How do you rate this proposal? (-5 ... +5)


1. CVA: Conjoint Value Analysis

Choosing wisely from the set of all possible cases.

• Researcher chooses the dimensions• The variance of the multiple regression estimator equals

(X’X)-1

(with X the matrix of D’s)

• This implies that choosing the subset of cases that you are going to use, affects how broad your confidence intervals are.

• -> Experimental design literature: full-factorial design, D-optimal designs, etc


2. ACA: Adaptive Conjoint Analysis

Adapting the cases you offer based on previous answers of the respondent.

• You give, say, 10 cases to each respondent.• Which cases the respondent gets, depends on his answers to the

first couple of cases.

Much more efficient than just randomly choosing cases

Butimpossible to do off-line or by phone, and even online quitedifficult to implement

Sawtooth software


Sawtooth software / SKIM Research

www.sawtoothsoftware.com

www.skimgroup.com/software


3. CBC: Choice Based ConjointAsking for preferences between 2 (or 3 or 4) cases.

Which of these three offers do you prefer? Or …

Rate these three offers Or …

Distribute 10 points over these 3 offers

- 10 Euro p/m- 2 year minimum- free phone- internet = per Mb

- 15 Euro p/m- 1 year minimum- phone costs 70- internet = per Mb

- 30 Euro p/m- 2 year minimum- free phone- internet = free


3. CBC: Choice Based Conjoint (2)

Issues:

• Which of these kinds of questions works best? We don’t know.

• If you have only choice data (or ordinal data), how can you arrive at values for the different dimensions?

Y D1 D2 D3 D4 D5 id set

1 -1 -1 0 1 0 1 1

0 1 1 1 0 -1 1 1

0 0 0 1 0 -1 1 1

0 1 0 -1 1 0 1 2

1 … … … … … 1 2

0 … … … … … 1 2

1 … … … … … 1 3

0 … … … … … 1 3

… … … … … … … 3

Y = 0/1 => Logistic regression, but …… that does not use all the available information and if we use the data for all persons we have dependencies in the data (more cases per person) and … what if we have ordinal data?


Which method to choose when?We have rules of thumb only …

www.sawtoothsoftware.com/products/advisor/


Statistical issues

We need to know how we can deal with “nested data” (for instance, more than one answer per person)

Can't the Sawtooth

people take care of this?

Marketing vs other social science

Marketing ("conjoint analysis")

Try to come up with the weights for each dimension for each respondent(e.g. to then segment the population)

Other social science ("vignette study")

Try to come up with the average weights for groups of people(so other aim, and you need less choices per person)


To Do

• Read and understand the literature on the course website on Conjoint Analysis.

Advanced MMBR

Documents

Transcript of Advanced MMBR