1-2008 A Review of the Literature on the Empathy Construct Using Cluster Analysis

s are a reasonable proxy for paragraphs. Generally, an abstract consists of one or more sentences that are discussing a single thought, idea, or topic, similar to a typical paragraph within the body of a research paper. Also, like a paragraph, an abstract typically consists of one cohesive textual unit (i.e., is not separated by any line breaks). A review of the abstracts in the other disciplines did not reveal any abstracts that were composed of multiple paragraphs. Additionally, abstracts and paragraphs are similar in length. The abstracts in this study were typically composed of three to eight lines of 12-point font text, which is comparable to most paragraphs within a body of a research paper. The primary difference between an abstract and a paragraph is that an abstract possesses a focus different than that of a typical paragraph in the body of a research paper because an abstract summarizes the contents of an entire article (e.g., the objective of the study, a summary of the results, and the contributions). However, such variability could also be observed when comparing two paragraphs within the body of a paper. For example, paragraphs in the introduction of the paper have a broader focus because they are providing an overview of the rest of the study, whereas paragraphs in the literature review or background section are typically describing a specific research area or concept. In summary, even though some issues exist with equating abstracts to paragraphs, these units of text do share clear similarities, and the paragraph seems to be an appropriate unit of analysis for this research. Data Preparation After the 1,721 paragraphs were collected, the stop words were removed from the analysis by using SAS TMTM. Stop words are words that have little or no meaning (e.g., prepositions) when taken out of a sentence and simply provide structure to it. Also, field-specific words such as psychology, teacher, doctor, and nurse were removed from the subsequent analysis. Next, the remaining words were reduced to their roots to remove the morphological variance in the corpus. For instance, words such as “running” and “runs” were reduced to their root form, “run. ” This process reduces the dispersion created by the various forms of a word. After the words were reduced to their roots, the m terms and n documents were arranged in a very sparse m x n term-by-document matrix, i.e., a term-frequency matrix (TF). In a TF, the ij cell represents the number of times that term i occurred in document j. This matrix was then transformed using both local (log) and global (entropy) weighing. Then the fully transformed matrix was decomposed into three matrices using singular value decomposition (SVD). One matrix is related to the terms, one to the columns, and another contains the weights of the dimensions extracted by the decomposition. Only the most important dimensions, as measured by their weights, are retained for subsequent analysis. The determination of the number of dimensions to retain is an ongoing research question (e.g., [Berry and Browne 2005; Belew 2000]). For this study, SAS TMTM chose 75 SVD dimensions for the analysis. Cluster Analysis Cluster analysis separates data into groups that are represented by clusters, which are meaningful, useful, or both [Tan et al. 2006]. The clusters are constructed to be as internally homogenous as possible while also being as externally heterogeneous as possible. Numerous clustering algorithms have been used for the analysis of quantitative and qualitative data, and interested readers are encouraged to read the review of data clustering by Jain et al. [1999]. For this analysis, the Expectation-Maximization (EM) clustering algorithm (see [Dempster et al. 1977]) implemented within SAS TMTM was used. Document Projection The process of projection related the IS empathy research to the clustering that resulted from the analysis of the empathy research in other disciplines. To project a new text unit (e.g., a paragraph) into a k-dimensional space obtained from a SVD of a corpus, perform the following steps: