Hi Richard, I am doing five seperate analyses, one for each historical point. Im not sure of your meaning about reversing the signs. If you mean standardising the scores by converting negative values to positive, then yes I am doing that. This is not the problem. If I sort the dimension score(s) low to high in value in excel, their corresponding occupations for each score is not the expected ranked postion, one that reflects other time periods. In fact the rank order of occupations/dimension scores is opposite to what it should be. Any thoughts? Many thanks for the help.
Cheers, Stephen
________________________________________
From: Classification, clustering, and phylogeny estimation [[log in to unmask]] On Behalf Of Richard Wright [[log in to unmask]]
Sent: Thursday, 4 September 2008 4:53 p.m.
To: [log in to unmask]
Subject: Re: Correspondence anaylsis inversion of ranking
Stephen
Did you do five separate analyses for each of the five historical points? Or is there a single analysis, within which you are examining order over five historical points?
If the former then you can legitimately reverse all the signs for the scores.
Perhaps I am not understanding the problem.
Richard
>
>Subject: Correspondence anaylsis inversion of ranking
> From: Stephen McTaggart <[log in to unmask]>
> Date: Thu, 4 Sep 2008 15:08:19 +1200
> To: [log in to unmask]
>
>Hi, I am using correspondence analysis to examine degrees of homogamy/social distance in society using the occupations of husbands and wives as markers of social position. I have done this over five historical points using New Zealand census data (1981-2001). I'm using the dimension scores (1 and 2) of the CA process to achieve a ranked scale of homogamy/social interaction . It is expected that the order of the ranking will be similar to that of the ranking of occupations in the issco model . This indeed is the case with three of the time periods. At two points in time however this ranking is inverted. Has anyone got tips on how to explain this/switch this around? I believe that the 'best fit model' in correspondence analysis can be a little nebulous. Greenacre talks about 'rotating the axis.' Will this work and how might I do this in SAS?
>Any help will be useful.
>Cheers, Stephen
>
>________________________________
>From: Classification, clustering, and phylogeny estimation [mailto:[log in to unmask]] On Behalf Of Liza Rovniak
>Sent: Thursday, 4 September 2008 10:40 a.m.
>To: [log in to unmask]
>Subject: cluster analysis validation technique
>
>Hi,
>
>I am hoping someone here can help me with a "how to" question on running McIntyre and Blashfield's (1980) nearest-centroid evaluation procedure to validate the stability of my cluster analysis solution. I am a newbie to cluster analysis, so this is my first time running this procedure.
>
>I have a sample of about 900 observations and have randomly split the sample in two (Sample A and Sample B). I conducted hierarchical cluster analysis and then calculated the centroid vectors for a 3-cluster solution on each of these two subsamples (i.e., steps 1 through 4 of McIntrye and Blashfield's evaluation technique).
>
>Step 5 of McIntrye and Blashfield's technique is to calculate "the squared Euclidean distance for each of Sample B's objects from each of the centroids of Sample A," and Step 6 is to assign "each object in Sample B to the closest centroid vector." At this point, I am not sure what buttons to press in SPSS to complete the analysis. One possibility I tried is to use K-means cluster analysis to achieve these two steps, but K-means uses simple Euclidean distance (not squared Euclidean distance as recommended by McIntyre and Blashfield) to assign the observations to clusters. Is this okay? (someone told me it was, but I just want to double-check). I would greatly appreciate any guidance on what buttons to press in SPSS/appropriate syntax to complete steps 5 and 6 of this analysis.
>
>Thank you.
>
>Liza Rovniak
>
>Liza S. Rovniak, PhD, MPH
>Adjunct Assistant Professor
>Center for Behavioral Epidemiology & Community Health
>Graduate School of Public Health, San Diego State University
>San Diego, CA 92123
>Phone: 858-505-4770, ext. 152; Fax: 858-505-8614
>Email: [log in to unmask]
>
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><DIV dir=3Dltr align=3Dleft><SPAN class=3D993351123-03092008><FONT face=3DA=
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>color=3D#0000ff size=3D2>Hi, I am using correspondence analysis to examine =
>degrees=20
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>nds=20
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>historical points using New Zealand census data (1981-2001). I'm using the=
>=20
>dimension scores (1 and 2) of the CA process to achieve a ranked=
>=20
>scale of homogamy/social interaction . It is expected that the order of the=
>=20
> ranking will be similar to that of the ranking of occupations in the =
>issco=20
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>ts in=20
>time however this ranking is inverted. Has anyone got tips on how to=20
> explain this/switch this around? I believe that the 'best fit model' =
>in=20
>correspondence analysis can be a little nebulous. Greenacre talk=
>s=20
>about 'rotating the axis.' Will this work and how might I do this in=20
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><DIV dir=3Dltr align=3Dleft><SPAN class=3D993351123-03092008><FONT face=3DA=
>rial=20
>color=3D#0000ff size=3D2>Any help will be useful.</FONT></SPAN></DIV>
><DIV dir=3Dltr align=3Dleft><SPAN class=3D993351123-03092008><FONT face=3DA=
>rial=20
>color=3D#0000ff size=3D2>Cheers, Stephen</FONT> </SPAN></DIV><BR>
><DIV class=3DOutlookMessageHeader lang=3Den-us dir=3Dltr align=3Dleft>
><HR tabIndex=3D-1>
><FONT face=3DTahoma size=3D2><B>From:</B> Classification, clustering, and p=
>hylogeny=20
>estimation [mailto:[log in to unmask]] <B>On Behalf Of </B>Liza=20
>Rovniak<BR><B>Sent:</B> Thursday, 4 September 2008 10:40 a.m.<BR><B>To:</B>=
>=20
>[log in to unmask]<BR><B>Subject:</B> cluster analysis validation=20
>technique<BR></FONT><BR></DIV>
><DIV></DIV>
><DIV class=3DSection1>
><P class=3DMsoNormal>Hi,<o:p></o:p></P>
><P class=3DMsoNormal><o:p> </o:p></P>
><P class=3DMsoNormal>I am hoping someone here can help me with a “how=
> to” question=20
>on running McIntyre and Blashfield’s (1980) nearest-centroid evaluati=
>on=20
>procedure to validate the stability of my cluster analysis solution. I am a=
>=20
>newbie to cluster analysis, so this is my first time running this procedure=
>..=20
><o:p></o:p></P>
><P class=3DMsoNormal><o:p> </o:p></P>
><P class=3DMsoNormal>I have a sample of about 900 observations and ha=
>ve=20
>randomly split the sample in two (Sample A and Sample B). I conducted=20
>hierarchical cluster analysis and then calculated the centroid vectors for =
>a=20
>3-cluster solution on each of these two subsamples (i.e., steps 1 through 4=
> of=20
>McIntrye and Blashfield’s evaluation technique). <o:p></o:p></P>
><P class=3DMsoNormal><o:p> </o:p></P>
><P class=3DMsoNormal>Step 5 of McIntrye and Blashfield’s technique is=
> to calculate=20
>“the squared Euclidean distance for each of Sample B’s objects =
>from each of the=20
>centroids of Sample A,” and Step 6 is to assign “each object &n=
>bsp;in Sample B=20
>to the closest centroid vector.” At this point, I am not sure what bu=
>ttons to=20
>press in SPSS to complete the analysis. One possibility I tried is to use=20
>K-means cluster analysis to achieve these two steps, but K-means uses simpl=
>e=20
>Euclidean distance (not squared Euclidean distance as recommended by McInty=
>re=20
>and Blashfield) to assign the observations to clusters. Is this okay? (some=
>one=20
>told me it was, but I just want to double-check). I would greatly=20
>appreciate any guidance on what buttons to press in SPSS/appropriate syntax=
> to=20
>complete steps 5 and 6 of this analysis. <o:p></o:p></P>
><P class=3DMsoNormal><o:p> </o:p></P>
><P class=3DMsoNormal>Thank you.<o:p></o:p></P>
><P class=3DMsoNormal><o:p> </o:p></P>
><P class=3DMsoNormal>Liza Rovniak<o:p></o:p></P>
><P class=3DMsoNormal><o:p> </o:p></P>
><P class=3DMsoNormal>Liza S. Rovniak, PhD, MPH<o:p></o:p></P>
><P class=3DMsoNormal>Adjunct Assistant Professor<o:p></o:p></P>
><P class=3DMsoNormal>Center for Behavioral Epidemiology & Community=20
>Health<o:p></o:p></P>
><P class=3DMsoNormal>Graduate School of Public Health, San Diego State=20
>University<o:p></o:p></P>
><P class=3DMsoNormal>San Diego, CA 92123<o:p></o:p></P>
><P class=3DMsoNormal>Phone: 858-505-4770, ext. 152; Fax:=20
>858-505-8614<o:p></o:p></P>
><P class=3DMsoNormal>Email: [log in to unmask]<o:p></o:p></P>
><P=20
>class=3DMsoNormal><o:p> </o:p></P></DIV>------------------------------=
>----------------=20
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