Seasonal dynamic pattern analysis in service of climate change research. A methodical case-study - monitoring and simulation based on an aquatic insect community

Our aim was to approach an important and well-investigable phenomenon – connected to a relatively simple but real field situation – in such a way, that the results of field observations could be directly comparable with the predictions of a simulation model-system which uses a simple mathematical apparatus and to simultaneously gain such a hypothesis-system, which creates the theoretical opportunity for a later experimental series of studies. As a phenomenon of the study, we chose the seasonal coenological changes of aquatic and semiaquatic Heteroptera community. Based on the observed data, we developed such an ecological model-system, which is suitable for generating realistic patterns highly resembling to the observed temporal patterns, and by the help of which predictions can be given to alternative situations of climatic circumstances not experienced before (e.g. climate changes), and furthermore; which can simulate experimental circumstances. The stable coenological state-plane, which was constructed based on the principle of indirect ordination is suitable for unified handling of data series of monitoring and simulation, and also fits for their comparison. On the state-plane, such deviations of empirical and model-generated data can be observed and analysed, which could otherwise remain hidden.

Bizalom az üzleti kapcsolatokban. A diadikus adatelemzés egy alkalmazása = Trust in business relations - an application of dyadic data analysis

A tanulmány arra a feltevésre épül, hogy minél erősebb a bizalomra méltóság szintje egy adott üzleti kapcsolatban, annál inkább igaz, hogy nagy kockázatú tevékenységek mennek végbe benne. Ilyen esetekben a bizalomra méltóság a kapcsolatban zajló események, cselekvések irányítási eszközévé válik, és az üzleti kapcsolatban megjelenik a cselekvési hajlandóságként értelmezett bizalom. A tanulmány felhívja a figyelmet a bizalom és a bizalomra méltóság fogalmai közötti különbségre, szisztematikus különválasztásuk fontosságára. Bemutatja az úgynevezett diadikus adatelemzés gazdálkodástudományi alkalmazását. Empirikus eredményei is igazolják, hogy ezzel a módszerrel az üzleti kapcsolatok társas jellemzőinek (köztük a bizalomnak) és a közöttük lévő kapcsolatoknak mélyebb elemzésére nyílik lehetőség. ____ The paper rests on the behavioral interpretation of trust, making a clear distinction between trustworthiness (honesty) and trust interpreted as willingness to engage in risky situations with specific partners. The hypothesis tested is that in a business relation marked by high levels of trustworthiness as perceived by the opposite parties, willingness to be involved in risky situations is higher than it is in relations where actors do not believe their partners to be highly trustworthy. Testing this hypothesis clearly calls for dyadic operationalization, measurement, and analysis. The authors present the first economic application of a newly developed statistical technique called dyadic data analysis, which has already been applied in social psychology. It clearly overcomes the problem of single-ended research in business relations analysis and allows a deeper understanding of any dyadic phenomenon, including trust/trustworthiness as a governance mechanism.

Introduction to data analysis

With the latest development in computer science, multivariate data analysis methods became increasingly popular among economists. Pattern recognition in complex economic data and empirical model construction can be more straightforward with proper application of modern softwares. However, despite the appealing simplicity of some popular software packages, the interpretation of data analysis results requires strong theoretical knowledge. This book aims at combining the development of both theoretical and applicationrelated data analysis knowledge. The text is designed for advanced level studies and assumes acquaintance with elementary statistical terms. After a brief introduction to selected mathematical concepts, the highlighting of selected model features is followed by a practice-oriented introduction to the interpretation of SPSS1 outputs for the described data analysis methods. Learning of data analysis is usually time-consuming and requires efforts, but with tenacity the learning process can bring about a significant improvement of individual data analysis skills.