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 [EMAIL PROTECTED] On Behalf Of Richard Wright [EMAIL PROTECTED] Sent: Thursday, 4 September 2008 4:53 p.m. To: [email protected] 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 <[EMAIL PROTECTED]> > Date: Thu, 4 Sep 2008 15:08:19 +1200 > To: [email protected] > >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:[EMAIL >PROTECTED] On Behalf Of Liza Rovniak >Sent: Thursday, 4 September 2008 10:40 a.m. >To: [email protected] >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: [EMAIL PROTECTED] > >---------------------------------------------- CLASS-L list. Instructions: >http://www.classification-society.org/csna/lists.html#class-l > >---------------------------------------------- >CLASS-L list. >Instructions: http://www.classification-society.org/csna/lists.html#class-l > >--_000_6085BF643DA19E43B24174695D9A63F50BE7246C33artsmail4ARTS_ >Content-Type: text/html; charset="us-ascii" >Content-Transfer-Encoding: quoted-printable > ><!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.0 Transitional//EN"> ><HTML xmlns=3D"http://www.w3.org/TR/REC-html40"; xmlns:v =3D=20 >"urn:schemas-microsoft-com:vml" xmlns:o =3D=20 >"urn:schemas-microsoft-com:office:office" xmlns:w =3D=20 >"urn:schemas-microsoft-com:office:word" xmlns:m =3D=20 >"http://schemas.microsoft.com/office/2004/12/omml";><HEAD> ><META http-equiv=3DContent-Type content=3D"text/html; charset=3Dus-ascii"> ><META content=3D"MSHTML 6.00.6000.16705" name=3DGENERATOR> ><STYLE>@font-face { > font-family: Calibri; >} >@page Section1 {size: 8.5in 11.0in; margin: 1.0in 1.0in 1.0in 1.0in; } >P.MsoNormal { > FONT-SIZE: 11pt; MARGIN: 0in 0in 0pt; FONT-FAMILY: > "Calibri","sans-serif" >} >LI.MsoNormal { > FONT-SIZE: 11pt; MARGIN: 0in 0in 0pt; FONT-FAMILY: > "Calibri","sans-serif" >} >DIV.MsoNormal { > FONT-SIZE: 11pt; MARGIN: 0in 0in 0pt; FONT-FAMILY: > "Calibri","sans-serif" >} >A:link { > COLOR: blue; TEXT-DECORATION: underline; mso-style-priority: 99 >} >SPAN.MsoHyperlink { > COLOR: blue; TEXT-DECORATION: underline; mso-style-priority: 99 >} >A:visited { > COLOR: purple; TEXT-DECORATION: underline; mso-style-priority: 99 >} >SPAN.MsoHyperlinkFollowed { > COLOR: purple; TEXT-DECORATION: underline; mso-style-priority: 99 >} >SPAN.EmailStyle17 { > COLOR: windowtext; FONT-FAMILY: "Calibri","sans-serif"; mso-style-type: > pe= >rsonal-compose >} >..MsoChpDefault { > mso-style-type: export-only >} >DIV.Section1 { > page: Section1 >} ></STYLE> ><!--[if gte mso 9]><xml> > <o:shapedefaults v:ext=3D"edit" spidmax=3D"1026" /> ></xml><![endif]--><!--[if gte mso 9]><xml> > <o:shapelayout v:ext=3D"edit"> > <o:idmap v:ext=3D"edit" data=3D"1" /> > </o:shapelayout></xml><![endif]--></HEAD> ><BODY lang=3DEN-US vLink=3Dpurple link=3Dblue> ><DIV dir=3Dltr align=3Dleft><SPAN class=3D993351123-03092008><FONT face=3DA= >rial=20 >color=3D#0000ff size=3D2>Hi, I am using correspondence analysis to examine = >degrees=20 >of homogamy/social distance in society using the occupations of husba= >nds=20 >and wives as markers of social position. I have done this over five=20 >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 >model . This indeed is the case with three of the time periods. At two poin= >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 >SAS?</FONT></SPAN></DIV> ><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:[EMAIL PROTECTED] <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 >[email protected]<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: [EMAIL PROTECTED]<o:p></o:p></P> ><P=20 >class=3DMsoNormal><o:p> </o:p></P></DIV>------------------------------= >----------------=20 >CLASS-L list. Instructions:=20 >http://www.classification-society.org/csna/lists.html#class-l </BODY></HTML= >> >---------------------------------------------- >CLASS-L list. >Instructions: http://www.classification-society.org/csna/lists.html#class-l > >--_000_6085BF643DA19E43B24174695D9A63F50BE7246C33artsmail4ARTS_-- ---------------------------------------------- CLASS-L list. Instructions: http://www.classification-society.org/csna/lists.html#class-l ---------------------------------------------- CLASS-L list. Instructions: http://www.classification-society.org/csna/lists.html#class-l
