Tanzania: A Hierarchical Cluster Analysis Approach

It is common for researchers and rural development policy stakeholders to describe smallholder farmers as a homogeneous group in terms of their demand for farm credit and farm investment behaviour. Given the diversity of factors such as farm credit products (input credit in cash, input credit in kind), farming systems (extensive Vs intensive farming, food crop Vs traditional cash crop production, crop production Vs livestock keeping), asset endowment, income sources and experience in farm credit borrowing, it is obvious that the demand for farm credit and use with which it is put are also diverse among farmers. Using survey data from Kibondo district, west Tanzania, we use hierarchical cluster analysis to classify borrower farmers according to their borrowing behaviour into four distinctive clusters. The appreciation of the existence of heterogeneous farmer clusters is vital in forging credit delivery policies that are not only appropriate for particular categories of farmers but also that do provide potential for reducing supply side transaction risks and costs.Key words: Smallholder farmers, hierarchical cluster analysis, farm credit supply

Ranking and Clustering Australian University Research Performance, 1998-2002

This paper clusters and ranks the research performance of thirty-seven Australian universities over the period 1998-2002. Research performance is measured according to audited numbers of PhD completions, publications and grants (in accordance with rules established by the Department of Education, Science and Training) and analysed in both total and per academic staff terms. Hierarchical cluster analysis supports a binary division between fifteen higher and twenty-two lower-performing universities, with the specification in per academic staff terms identifying the self-designated research intensive "Group of Eight" (Go8) universities, plus several others in the better-performing group. Factor analysis indicates that the top-three research performers are the Universities of Melbourne, Sydney and Queensland in terms of total research performance and the Universities of Melbourne, Adelaide and Western Australia in per academic staff terms.Higher education, hierarchical cluster analysis, research performance, factor analysis

Pattern aggregation of wind energy conversion technologies using clustering analysis

The main objective of this research is the identification of homogeneous groups within a set of wind farms of a major wind energy promoter in Portugal, based on two multivariate analyses: Hierarchical Cluster Analysis and K-means Clustering, using two independent variables, capacity factor and net production, both per year. K-means Clustering output provides the same results as the Hierarchical Cluster Analysis. Outputs allowed the identification of three homogenous groups of wind farms: (1) medium installed capacity and asynchronous generator based technologies, (2) high installed capacity and direct driven synchronous generator based technology and (3) low installed capacity with no differentiation on the technology concept, but including the wind farms with the higher capacity factors.info:eu-repo/semantics/publishedVersio

Commonalities and Disparities among the EU Candidate

One of the important challenges of the European Union (EU) at the beginning of the 21st century is its enlargement. After the integration of the 12 countries in 2005 and 2007, the EU continues its strategy for stability, security and prosperity in Europe. The new candidate countries, at different levels of development, are Western Balkan countries and Turkey. The objective of the paper is to investigate the differences among the EU candidate countries according to the current measures of welfare/sustainability and to find their similarities and differences. This analysis of the differences and the similitude between candidate countries is done by using multidimensional scaling method (MDS) and hierarchical cluster analysis of sustainability, which takes into account, at the same time, economic, health, standard of living, people and environmental variables, as part of the multivariate statistical analysis technique - one of the basic methods of multidimensional scaling. Furthermore, MDS method allows a standardized (transformed) analysis of the data collected in different scales. This study is based on the data standardized by means of Z score transformation. The main conclusions of the analysis light up the differences between candidate countries and could be an important tool for the policy makers to focus their efforts on the difficult goal to join the European Union.multidimensional scaling, hierarchical cluster analysis, statistical analysis

Quality of life indicators in selected European countries: Statistical hierarchical cluster analysis approach

The average expected duration of human life is rising because of different reasons. On the other hand, not only the duration, but the quality of life level is important, too. The higher the quality of life level, the citizens’ happiness and satisfaction levels are higher, which has positive impact on the development and operating of an economy. The goal of this paper is to identify groups of European countries, using statistical hierarchical cluster analysis, by using the quality of life indicators, and to recognise differences in quality of life levels. The quality of life is measured by using seven different indicators. The conducted statistical hierarchical cluster analysis is based on the Ward’s clustering method, and squared Euclidean distances. The results of conducted statistical hierarchical cluster analysis enabled recognizing of three different groups of European countries: old European Union member states, new European Union members, and non-European Union member states. The analysis has revealed that the old European Union member states seem to have in average higher quality of life level than the new European Union member states. Furthermore, the European Union member states have in average higher quality of live level than non-European Union members do. The results indicate that quality of life levels and economic development levels are connected