A Review of the Literature on the Empathy Construct Using Cluster Analysis <s> Limitations <s> We present an algorithm for large-scale document clustering of biological text, obtained from Medline abstracts. The algorithm is based on statistical treatment of terms, stemming, the idea of a ‘go-list’, unsupervised machine learning and graph layout optimization. The method is flexible and robust, controlled by a small number of parameter values. Experiments show that the resulting document clusters are meaningful as assessed by cluster-specific terms. Despite the statistical nature of the approach, with minimal semantic analysis, the terms provide a shallow description of the document corpus and support concept discovery. <s> BIB001 </s> A Review of the Literature on the Empathy Construct Using Cluster Analysis <s> Limitations <s> As engineers gain experience and become experts in their domain, the structure and content of their knowledge changes. Two studies are presented that examine differences in knowledge representation among freshman and senior engineering students. The first study examines recall of mechanical devices and chunking of components, and the second examines whether seniors represent devices in a more abstract functional manner than do freshmen. The most prominent differences between these 2 groups involve their representation of the functioning of groups of electromechanical components and how these groups of components interact to produce device behavior. Seniors are better able to construct coherent representations of devices by focusing on the function of sets of components in the device. The findings from these studies highlight some ways in which the structure and content of mental representations of design knowledge differ during the early stages of expertise acquisition. <s> BIB002
This study has several limitations that need to be addressed. First, the limitation of using the full-text versions of articles for this analysis are: (1) not all online databases contain full-text journal search features, and (2) some fulltext versions of articles could not be accessed due to university licensing agreements. Both of these limitations were encountered in this research (see Appendix A) and therefore a complete analysis of the IS empathy research could not be performed. Second, the clustering of empathy research from other disciplines was performed without human judgment, but the researchers created the cluster descriptions by interpreting the descriptive terms and abstracts that were included in the cluster analysis. Consequently, the cluster naming procedure was not completely free from human judgment. Cluster naming techniques different from the one implemented within SAS TM™ exist (e.g., BIB001  ), but all of these naming techniques still only provide the researcher(s) with a set of words that describe a cluster. The researcher(s) must interpret these words to define a specific cluster. Third, a limitation of the document projection approach used in this study is that the similarities were interpreted using the cosine measure, and we are not aware of any research that explicitly defines what a high versus a low cosine measure is and what a particular cosine means. Many research papers have used cosines as a measure of similarity (e.g., BIB002 Dong 2005; Wolfe et al. 1998 ]) for research topics such as grading of handwritten essays and studying of team communication. However, there is no universal interpretation of cosine measures. For instance, Britt et al. [2004] define a high cosine as 0.70 or greater and a low cosine as 0.20 or smaller, but do not provide the reasoning behind their interpretation of cosine measures. An interpretation of the results of this research seem to indicate that a cosine of greater than 0.50 is "high" (i.e., represents significant similarities), a cosine lower than about 0.15 is "low" (i.e., does not contain similarities), and all the cosines in between represent varying degrees of moderate similarities. Future research needs to define what a particular cosine measure means (i.e., if it represents semantic similarities), and how to best interpret cosine results (i.e., a definition of a high cosine). Finally, a possible weakness of the document projection approach used in this study is that the cosine measure was calculated between the centroid (or center) of the clusters and the IS paragraphs. Therefore, if the IS paragraph was closer to points on the perimeter of the cluster, then the cosine measure would have been higher. As a result, if the cosine was calculated between different points, some of the cosines that were reported in Table 4 could have been higher or lower. However, the results reported in Table 4 are encouraging because 6 paragraphs had cosines greater than .60 and 22 paragraphs had cosines between .50 and .60. Hence, even though the centroid of the cluster was used for the calculation, the clusters of empathy research and the IS paragraphs still shared strong similarities.