Nature and quality of life: the case of Kolindros, prefecture of Pieria, Greece: an application of multidimensional data analysis methods
New Medit, vol 7, n. 2, (June 2008), pp. 57-63
Language: EN
Jel classification: O180, Q180
In the framework of the Multifor.RD project (Multifunctional Forestry as a Means to Rural Development), residents from specific areas under research in the nine participating countries (Greece, France, Denmark, Hungary, Ireland, The Netherlands, Austria, Germany, and Spain) were interviewed in relation to: a) the quality of life in rural societies and b) the contribution of forests to this quality. As regards the first aim, in the case of one of the two Greek areas – the Municipality of Kolindros – it was considered necessary to construct a suitable measurement scale. The methodology that was applied for this construction was a combination of applied methodological research techniques, mainly from the field of Social Sciences. Collected data were analyzed using applied methods of multidimensional data analysis. More specifically, at a first level, the Cronbachs a coefficient was estimated and Principal Component Analysis was applied in order to determine the internal consistency (reliability) and the factorial validity of the measurement scale respectively. At a second level, resulting factors were used for the development of a typology of the area’s residents through the use of Cluster Analysis. The clusters profile was examined in relation to other variables of research with the assistance of Correspondence Analysis. The use of the resulting three residents groups (clusters), with a first and second-degree profile, is necessary at any level of decision-making in Greece, linked to the regional and particularly local development and to the improvement of the local residents quality of life. Such analysis is also essential in order to provide the European Commission with the relevant information required for the development of region-specific forest policies.
quality of life, rural societies, forests, multidimensional data analysis
