http://spssx-discussion.165.s1.nabble.com/PCA-for-dichotomous-data-tp4564908p4565519.html
lots of cases with zero as entry. But the respondents did not say No,
they just said nothing. It's a a sort of logic missing. Nevertheless, in
vs. all the rest) I currently work with a discriminant analysis with the
> If the data are dichotomous, conventional PCA (SPSS FACTOR procedure) is
> exactly the same as categorical PCA (SPSS CATPCA procedure). The latter is
> required when the original data are multi-categorical variables (either
> nominal or ordinal), in order to generate (iteratively) optimal scaling
> values for the categories and a Principal Component Analysis of the
> resulting (interval level) variables.
>
> I wonder whether the fact that each respondent may choose up to three
> dichotomous variables has any influence on this. It depends, I surmise, on
> the way you want to treat those data.
> (a) you may treat each CHOICE as one case. In this fashion, there would be
> one case (one row in the dataset) for each combination of respondent and
> choice, with up to three (but not necessarily three) choices per respondent.
> In this case, my above advice works, although its analysis may require a
> two-level model to distinguish between intra- and inter- respondent effects.
> (b) you may treat each RESPONDENT as a case. In this option, you may have
> different COMBINATIONS of responses per respondent. The maximum number (all
> combinations of three out of 12) is probably much higher than the number of
> respondents in your sample, and thus only a small proportion of all
> combinations will show up. These observed combinations may be treated as a
> NOMINAL multy-category variable, with many values. For this kind of approach
> CATPCA would be appropriate, but I caution that the number of distinct
> combinations observed must not be large (with N respondents and M observed
> combinations, you have N-M-1 degrees of freedom, which may result in a
> fairly low number, thus invalidating the results in statistical terms.) If
> only a few response patterns are observed, and the number of respondents is
> comparatively very large, you'd be OK, but beware of too many choices and
> too few subjects.
>
> Hector
>
> -----Mensaje original-----
> De: SPSSX(r) Discussion [mailto:
[hidden email]] En nombre de ftr
> Enviado el: Friday, July 08, 2011 11:13
> Para:
[hidden email]
> Asunto: PCA for dichotomous data
>
> Hello,
>
> Eurobarometer 66.1 provides data on social values which I would like to
> use, with other influences, to explain church going.
> The item battery of social values provides 12 questions with yes/no
> answer alternatives. The respondent can choose up to three variables.
>
> What I need is a procedure like a PCA for dichotomous data, but I don't
> have access to CATPCA. I calculated proximities with the dice algorithm
> to correct for the high probability that none of two items will be
> selected. I used PROXIMITIES to calculated the similarity of variables.
>
> PROXIMITIES v327 to v338
> /VIEW=VARIABLE
> /MEASURE= dice (1,0) .
>
> Once PROXIMITIES produces the matrix can you input this as a correlation
> matrix into FACTOR ? And how to move from this variable-based analysis
> back to the case-based analysis ?
>
> Is there a better alternative for getting a variable structure from
> dichotomous variables ?
>
> TIA,
> F. Thomas
>
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