A Comparison of the Accuracy of Four Methods for Clustering Jobs

Abstract

Four methods of cluster analysis were examined for their accuracy in clustering simulated job analytic data. The methods included hierarchical mode analysis, Ward's method, k-means method from a random start, and k-means based on the results of Ward's method. Thirty data sets, which differed according to number of jobs, number of population clusters, number of job dimensions, degree of cluster separation, and size of population clusters, were generated using a monte carlo technique. The results from each of the four methods were then compared to actual classifications. The performance of hierarchical mode analysis was significantly poorer than that of the other three methods. Correlations were computed to determine the effects of the five data set variables on the accuracy of each method. From an applied perspective, these relationships indicate which method is most appropriate for a given data set. These results are discussed in the context of certain limitations of this investigation. Suggestions are also made regarding future directions for cluster analysis research.