Thomas J. Webster

A principal component analysis of the U.S. News & World Report tier rankings of colleges and universities

This paper utilizes principal component regression analysis to examine the relative contributions of 11 ranking criteria used to construct the U.S. News & World Report(USNWR) tier rankings of national universities. The main finding of this study is that the actual contributions of the 11 ranking criteria examined differ substantially from the explicit USNWR weighting scheme because of severe and pervasive multicollinearity among the ranking criteria. USNWR assigns the greatest weight to academic reputation. However, generated first principal component eigenvalues of tier rankings indicate that the most significant ranking criterion is the average SAT scores of enrolled students. This result is significant since admission requirements are policy variables that indirectly affect, for example, admission applications, yields, enrollment, retention, tuition-based revenues, and alumni contributions.

MALAYSIAN ECONOMIC DEVELOPMENT, LEADING INDUSTRIES AND INDUSTRIAL CLUSTERS

This paper briefly reviews six decades of Malaysias economic development strategy, which may be described as bounded industrial policy that favors export-led growth. The objective of the current Tenth Malaysia Plan (2011–2015) is to achieve high-income status by 2020 by promoting high-value-added production through increased investments in human capital, adopting new technologies, promoting entrepreneurship to drive innovation and creativity, and elevating domestic demand as an engine of economic growth. Principal components analysis (PCA) and medoid partitioning applied to inflation-adjusted industrial production suggests that Malaysia satisfies the necessary, although not necessarily the sufficient, conditions to achieve this goal.

Economic development strategies and methods for identifying leading industries and industrial clusters

This paper reviews several popular economic development strategies and discusses the practical problem of identifying leading industries and innovation clusters for government regulatory and financial support. Although there is no substitute for in-depth industry-by-industry analysis, several statistical techniques are available that can assist in the identification process, including principal component analysis, k-means clustering, hierarchical clustering, medoid partitioning and fuzzy clustering. The Republic of Indonesia is used as a case study to illustrate the strengths and weakness of each of these procedures.