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Title: The reflection of hierarchical cluster analysis of co-occurrence matrices in SPSS
Author: Zhou, QiuJu(周秋菊)1; Leng, FuHai(冷伏海)1; LEYDESDORFF Loet2
Source: Chinese Journal of Library and Information Science
Issued Date: 2015-06-27
Volume: 8, Issue:2, Pages:11-24
Keyword: Co-occurrence matrices ; Hierarchical cluster analysis ; SPSS ; Similarity algorithm ; The syntax editor
Subject: 新闻学与传播学 ; 图书馆、情报与文献学
Indexed Type: 其他
Corresponding Author: Fuhai Leng (E-mail: lengfh@mail.las.ac.cn).
DOC Type: Research Papers
Abstract: Purpose: To discuss the problems arising from hierarchical cluster analysis of co-occurrence matrices in SPSS, and the corresponding solutions.

Design/methodology/approach: We design different methods of using the SPSS hierarchical clustering module for co-occurrence matrices in order to compare these methods. We offer the correct syntax to deactivate the similarity algorithm for clustering analysis within the hierarchical clustering module of SPSS.

Findings: When one inputs co-occurrence matrices into the data editor of the SPSS hierarchical clustering module without deactivating the embedded similarity algorithm, the program calculates similarity twice, and thus distorts and overestimates the degree of similarity.

Practical implications: We offer the correct syntax to block the similarity algorithm for clustering analysis in the SPSS hierarchical clustering module in the case of co-occurrence matrices. This syntax enables researchers to avoid obtaining incorrect results.
 
Originality/value: This paper presents a method of editing syntax to prevent the default use of a similarity algorithm for SPSS's hierarchical clustering module. This will help researchers, especially those from China, to properly implement the co-occurrence matrix when using SPSS for hierarchical cluster analysis, in order to provide more scientific and rational results.
English Abstract: Purpose: To discuss the problems arising from hierarchical cluster analysis of co-occurrence matrices in SPSS, and the corresponding solutions.

Design/methodology/approach: We design different methods of using the SPSS hierarchical clustering module for co-occurrence matrices in order to compare these methods. We offer the correct syntax to deactivate the similarity algorithm for clustering analysis within the hierarchical clustering module of SPSS.

Findings: When one inputs co-occurrence matrices into the data editor of the SPSS hierarchical clustering module without deactivating the embedded similarity algorithm, the program calculates similarity twice, and thus distorts and overestimates the degree of similarity.

Practical implications: We offer the correct syntax to block the similarity algorithm for clustering analysis in the SPSS hierarchical clustering module in the case of co-occurrence matrices. This syntax enables researchers to avoid obtaining incorrect results.
 
Originality/value: This paper presents a method of editing syntax to prevent the default use of a similarity algorithm for SPSS's hierarchical clustering module. This will help researchers, especially those from China, to properly implement the co-occurrence matrix when using SPSS for hierarchical cluster analysis, in order to provide more scientific and rational results.
Related URLs: 查看原文
Language: 英语
Content Type: 期刊论文
URI: http://ir.las.ac.cn/handle/12502/7807
Appears in Collections:Chinese Journal of Library and Information Science-2015_期刊论文

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description.institution: 1.National Science Library, Chinese Academy of Sciences, 100190 Beijing, China
2.University of Amsterdam, Amsterdam School of Communication Research (ASCoR), PO Box 15793, 1001 NG Amsterdam, the Netherlands

Recommended Citation:
ZHOU Qiuju,LENG Fuhai,LEYDESDORFF Loet. The reflection of hierarchical cluster analysis of co-occurrence matrices in SPSS[J]. Chinese Journal of Library and Information Science,2015,8(2):11-24.
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