conjoint analysis issues

[jpb-8]

Hey guys, I'm having some trouble with my conjoint analysis.

Used a full-profile ratings based conjoint, with 6 variables each with 2
levels. Using a fractional factorial design, I got a orthogonal array with 8
profiles plus I created 2 holdout cases.

Each respondent was asked to rate the 10 different profiles with one
question. 236 respondents completed the questionnaire.

So I have ran the conjoint analysis, which gave me different outputs (e.g.
model description, utilities, importance values and correlations). I know
that the correlations table provides info about the relation between the
observed utilities values and the expected values. Coefficients larger than
.70 are good, right?

I also understand that the holdout cases coefficients can be used as a check
on the validity of the estimated utilities. I read in the SPSS conjoint manual:

In many conjoint analyses, the number of parameters is close to the number
of profiles rated, which will artificially inflate the correlation between
observed and estimated scores. In these cases, the correlations for the
holdout profiles may give a better indication of the fit of the model. Keep
in mind, however, that holdouts will always produce lower correlation
coefficients.

Strangely my output

Correlations(a)
Value Sig.
Pearson's R ,986 ,000
Kendall's tau ,786 ,003
Kendall's tau for Holdouts 1,000 .
a Correlations between observed and estimated preferences



My questions:
1) Does somebody know how this is possible, this 1,00 value for kendall's
tau for holdouts and a blank/dot significance value, when this correlation
coefficient is supposed to be lower than the others. Does this value
indicate a bad fit of the model? or can no inferences be made based on this
result. In other words, what might be the reason that SPSS is not able to
calculate a Kendall's tau for holdout scores with sig. value

2) How can I use the holdoutcases/or holdout utilities to determine the
validity of the model. Do I simply correlate/bivariate/thick pearson and
take the actual ratings and the utility scores?

3) Since I have one dependent variable (a rating question (how likely is it
that you will join this program) for each scenario) how can I determine the
reliability of this variable? Does it for instance make sense to do a
reliability analysis on the ratings of the 10 scenarios, and use the
Cronbachs Alpha as an indicator of the reliability of the dependent variable
measured with a 5 point likert scale.

Thanks in advance for any input

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