Einführung in die Clusteranalyse mit SPSS-X für Historiker und Sozialwissenschaftler

Abstract

In dem vorliegenden Skript werden unter den Verfahren der Clusteranalyse das 'hierarchisch-agglomerative Verfahren' und ein 'allokatives Verfahren' behandelt. Das Schwergewicht liegt auf der konkreten Umsetzung und technischen Realisierung methodologischer Regeln anhand von Übungen im Statistikprogrammpaket SPSS-X. In zwei weiteren Kapiteln werden Verfahren der Stabilitätsprüfung und Verfahren der Behandlung fehlender Werte diskutiert. Die theoretisch vorgestellten Klassifikationsprobleme werden anhand eines Beispiels durchgerechnet. Das Ziel der vorgestellten Klassifikation besteht darin, familiale Haushaltstrukturen in einer osttiroler Gemeinde am Ende des 18. Jahrhunderts zu bestimmen. (pmb)

'This article is addressed to users of classification procedures in the social historical sciences. According to this aim an example from historical family research is used to describe the steps necessary to solve a classification task. These steps are: (1) Selection of classification attributes and units. (2) Treatment of missing data. (3) Transformation of classification attributes to comparable scales. (4) Standardization of classification units. (5) Selection of dissimilarity and similarity measures. (6) Selection of classification procedures. (7) Calculation of cluster solutions. (8) Validition of cluster solutions by stability and sensitivity analysis. As can be seen from the previous list some steps - especially step (2), (3) and (8) - are neglected or underestimated in most books on cluster analysis, although they are of practical importance: How can missing data be treated? What are the effects of different treatments of missing data on classification results? Is it better to transform classification attributes to comparable scales by empirical or theoretical procedures? How do these different methods of data transformation influence the results of cluster analysis? Finally, how can the validity of a cluster analysis be tested? The article tries to answer these questions. Furthermore standard text books on cluster analysis pay little attention, how a user of statistical program packages can realize methodological rules within the program used: How can certain types of dissimilarity measures be calculated without specific option in the program used? How can data transformation be realized? Or, how can a sensitivity analysis be performed, when there is no specific program to do this? In the article the statistical program package SPSS-X is used to demonstrate the realization of methodological rules. This investigation shows, that a wide variety of methodological rules can be realized within SPSS-X, if the user writes small programs. However there are certain limitations, expecially to the treatment of missing data. Exercises complete the represantation of the single steps. They can be solved without any computer.' (author's abstract)