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MANAGEMENT IN THE HUMANISTIC PERSPECTIVE (Zarzadzanie w perspektywie humanistycznej)

100%
Gorski P.
Organization and Management
|
2009
|
issue 4(138)
107-121
EN
The paper is focused on the characteristic of 'humanistic perspective' in a research of organization and management. This perspective can be described by three features: communication, values and historic. In the second part the author presents some examples of researches whose subjects are communication, organization's values and organizational change.
2
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Proč užívat hierarchické lineární modely?

100%
Soukup P.
Sociologický časopis. Czech Sociological Review
|
2006
|
vol. 42
|
issue 5
987-1012
EN
The article briefly describes multilevel models and presents their simplest applications. After the methodological and statistical need for this procedure is explained, real data are used to demonstrate how a hierarchical linear model is constructed. The article presents models with a random intercept, models with random slopes, and models with explanatory variables measured at higher levels. In the conclusion, other possible applications of multilevel analysis are discussed, and the basic readings on multilevel analysis are presented.
3
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Prostorová autokorelace – všudypřítomný jev při analýze prostorových dat?

88%
Spurna P.
Sociologický časopis. Czech Sociological Review
|
2008
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vol. 44
|
issue 4
767-788
EN
The article deals with one of the big analytical issues in spatial data analysis: spatial autocorrelation. This phenomenon is described as a key concept and potential problem of spatial analysis, but especially as a method of spatial analysis. The method is introduced first by reviewing the basic methodological framework including some related issues (the choice of spatial weighting system etc.) and second by presenting empirical examples of its application. Spatial autocorrelation statistics detect the presence of interdependence between the values of data at neighbouring locations. In addition to global spatial autocorrelation, measuring the overall degree of clustering, and calculated, for example, by Moran's I, emphasis is placed on the local analysis of spatial autocorrelation. This local form of spatial autocorrelation is based on the premise that the presence of spatial autocorrelation can vary across the study area, and it is fully in line with contemporary developments in spatial analysis. The results of LISA (local indicators of spatial association, local Moran) can be mapped for the purpose of identifying clusters. The empirical examples based on aggregate statistical data at the municipal level highlight the relevance and usefulness of analysis of spatial autocorrelation and show how these analyses can be used in social research and can improve our understanding of spatial processes.
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