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Suppose I would like to model the light-bulb preference of respondents. I would like to ask their preferences between existing bulbs on the market. And when you choose a bulb in a shop you usually see a package with many information gathered togheter such as:

  • technology (halo, fluo, led)
  • price
  • energy label EURO norm (from E to A++)
  • shape (pear, candle, spiral, steam)
  • watts.
  • brand (philips,osram , etc)

As there are too many combination respondents should only see max three combinations and check [i said check NOT rate...] the one they prefer (of course many iterations are needed per respondent to understand how he choose his bulb) At the end, respondent should also give socio-demo infos such as.

  • Gender
  • Age
  • Income

I would like to see the global effect of attribute. Like what's in general the most important criteria to choose a bulb. I would like to get a kind of econometrical analysis to know if there is difference of choice between the socio demo infos.

I've heard about conjoint analysis or discrete choice analysis methods. In my case which one would be more appropiate?

Which software(s) would also be more appropriate for the overall process (from the design to the analyis)?

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    $\begingroup$ Hello, while I am nox expert on both methods I can say that market research does a lot of such questions and mostly uses conjoint analysis for it. If you are unsure, I would recommend consulting the book amazon.de/Using-Multivariate-Statistics-Barbara-Tabachnick/dp/…. It contains excellent "when to use method [x] " texts which are really helpful to decide such questions. $\endgroup$ – Christian Sauer Jun 6 '14 at 6:36
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You can use a few packages in R, such as described in this answer. You would probably need to used a hierarchical Bayes model for this, so the package bayesm would be required.

Another way would be to use off-the-shelf SaaS software, such as Conjoint.ly, which can handle the required task.

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