Reliability and Metric Multidimensional Scaling

Using Multidimensional Scaling for Assessment Economic Development of Regions

Addressing socio-economic development issues are strategic and most important for any country. Multidimensional statistical analysis methods, including comprehensive index assessment, have been successfully used to address this challenge, but they donchr('39')t cover all aspects of development, leaving some gap in the development of multidimensional metrics. The purpose of the study is to const...

Random Effects Diagonal Metric Muefidimensional Scaling Models

By assuming a distribution for the sul~ect weights in a diagonal metric (INDSCAL) multidimensional scaling model, the subiect weights become random effects. Including random effects in multidimensional scaling models offers several advantages over traditional diagonal metric models such as those fitted by the INDSCAL, ALSCAL, and other nmltidimensional scaling programs. Unlike traditional model...

Multi-Dimensional Scaling Assignment ‘Non-metric multidimensional scaling on data from the USA Beer Market’

A New Solution to the Additive Constant Problem in Metric Multidimensional Scaling*

A new solution to the additive constant problem in metric multidimensional scaling is developed. This solution determines, for a given dimensionality, the additive constant and the resulting stimulus projections on the dimensions of a Euclidean space which minimize the sum of squares of discrepancies between the formal model for metric multidimensional scaling and the original data. A modificat...

Applying Metric and Nonmetric Multidimensional Scaling to Ecological Studies: Some New Results'

Metric (eigenanalysis) and nonmetric multidimensional scaling strategies for ecological ordination were compared. The results, based on simulated coenoplane data showing varying degrees of species turnover on two independent environmental axes, suggested some strong differences between metric and nonmetric scaling methods in their ability to recover underlying nonlinear data structures. Prior d...