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Regional development assessment: A structural equation approach
Affiliation:1. Department of Statistics, London School of Economics, Houghton Street, WC2A 2AE London, UK;2. Faculty of Economics, University of Ljubljana, Kardeljeve Ploščad 17, Ljubljana 1101, Slovenia;3. Macroeconomics Research Department, IMO 2 Farkaša Vukotinovića, Zagreb 10000, Croatia;1. Division of Vascular Surgery, University of Arizona College of Medicine, Tucson, Ariz;2. Division of Vascular and Endovascular Surgery, Boston University School of Medicine, Boston, Mass;3. Division of Vascular Surgery, Department of Surgery, University of Utah Health, Salt Lake City, Utah;4. Division of Vascular Surgery, Department of Surgery, David Geffen School of Medicine at UCLA, Los Angeles, Calif;5. Department of Biostatistics, Boston University School of Public Health, Boston, Mass;1. Norwegian University of Science and Technology, Dept. of Mechanical and Industrial Engineering, N-7491 Trondheim, Norway;2. SINTEF Technology and Society, Postboks 4760 Sluppen, N-7465 Trondheim, Norway;1. Department of Computer Science and Engineering, Netaji Subhash Engineering College, Kolkata 700152, India;2. Department of Information Technology, Jadavpur University, Kolkata, India;3. Machine Intelligence Unit, Indian Statistical Institute, Kolkata 700108, India;1. Department of General, Transplant and Liver Surgery, Medical University of Warsaw, Warsaw, Poland;2. Department of Epidemiology, Medical University of Warsaw, Warsaw, Poland;3. Department of Pathology, Medical University of Warsaw, Warsaw, Poland;4. 2nd Department of Clinical Radiology, Medical University of Warsaw, Warsaw, Poland;5. Department of Laboratory Diagnostics, Medical University of Warsaw, Warsaw, Poland
Abstract:We propose a multivariate statistical framework for regional development assessment based on structural equation modelling with latent variables and show how such methods can be combined with non-parametric classification methods such as cluster analysis to obtain development grouping of territorial units. This approach is advantageous over the current approaches in the literature in that it takes account of distributional issues such as departures from normality in turn enabling application of more powerful inferential techniques; it enables modelling of structural relationships among latent development dimensions and subsequently formal statistical testing of model specification and testing of various hypothesis on the estimated parameters; it allows for complex structure of the factor loadings in the measurement models for the latent variables which can also be formally tested in the confirmatory framework; and enables computation of latent variable scores that take into account structural or causal relationships among latent variables and complex structure of the factor loadings in the measurement models. We apply these methods to regional development classification of Slovenia and Croatia.
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