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Open AccessArticle

New Unidimensional Indexes for China

Warrington College of Business, University of Florida, P.O. Box 117150, Gainesville, FL 32611‐7150, USA
Academic Editor: Fazal M. Mahomed
Math. Comput. Appl. 2017, 22(1), 13; https://doi.org/10.3390/mca22010013
Received: 18 December 2016 / Revised: 4 January 2017 / Accepted: 4 January 2017 / Published: 24 January 2017
A first principal component combines several indicators so as to maximize their internal consistency for measuring a construct. First principal components are extracted here from Swiss Economic Institute and World Bank datasets containing yearly societal indicators for China. These indicators are input to population-weighted regressions without recourse to survey sampling or probabilistic inference. The results demonstrate Chomskyan globalization and domestic credit as strong exogenous and endogenous predictors of Chinese per capita GDP. These encouraging findings, easily extendable to other nations, are brought by two new societal indexes with assured unidimensionality. View Full-Text
Keywords: Chomskyan globalization; domestic credit; latent principal‐components and regression; per capita GDP; population-weighted indexes and regression slopes; quality assurance; ɷ‐homogeneity Chomskyan globalization; domestic credit; latent principal‐components and regression; per capita GDP; population-weighted indexes and regression slopes; quality assurance; ɷ‐homogeneity
MDPI and ACS Style

Bechtel , G. New Unidimensional Indexes for China. Math. Comput. Appl. 2017, 22, 13.

AMA Style

Bechtel  G. New Unidimensional Indexes for China. Mathematical and Computational Applications. 2017; 22(1):13.

Chicago/Turabian Style

Bechtel , Gordon. 2017. "New Unidimensional Indexes for China" Math. Comput. Appl. 22, no. 1: 13.

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