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Date:         Wed, 30 Jan 2008 14:46:05 -0600
Reply-To:     Melissa Ives <mives@chestnut.org>
Sender:       "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From:         Melissa Ives <mives@chestnut.org>
Subject:      Re: clustering variables (binary scale)
Comments: To: Gekko <habermail@HOTMAIL.DE>
Content-Type: text/plain; charset="US-ASCII"

The ideal method and measure really depends on what you are trying to do and your data. For example:

Ward's (1963) minimum distance method is a hierarchical method that groups cases to maximize between-group differences and minimize within-group differences (i.e., optimizes an F-Statistic). It keeps grouping the most similar pair of cases/clusters until there is just one cluster.

The Squared Euclidean Distance between cases/cluster centers. This places greater weights on cases that are further apart and serves to isolate high-risk groups faster.

However some method group all records into one big cluster and then breaks out records based on the measure chosen.

Try looking at: Aldenderfer, M. S., & Blashfield, R. K. (1984). Cluster analysis. In M. S. Lewis-Beck, Quantitative applications in the social sciences (SAGE University Paper 44. ). Newbury Park, CA: Sage.

Rapkin, B. D., & Luke, D. A. (1993). Cluster analysis in community research: Epistemology and practice. American Journal of Community Psychology, 21, 247-277.

Melissa

-----Original Message----- From: SPSSX(r) Discussion [mailto:SPSSX-L@LISTSERV.UGA.EDU] On Behalf Of Gekko Sent: Wednesday, January 30, 2008 3:38 AM To: SPSSX-L@LISTSERV.UGA.EDU Subject: [SPSSX-L] clustering variables (binary scale)

hi,

does anybody know, the best method and the ideal measure for clustering variables (!not respondents!) in spss?

a method like: linkage, ward, centroid, neighbour, median... a measure like: euclyd dist, phi-4, lambda, jaccard, rogers and tanimoto...

the variables are binary (0,1) and some are diseases (no, yes) out of a set of possible diseases, and some are about nutrition like was try to eat healthy (no, yes), every day the same (no, yes)...

thanks stefan

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