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Crucial Dissertation Data Analyses

By the current time databases of considerable volumes containing the information from various problem areas are saved up. The specialized technologies solving problems of accumulation, storage, statistical processing, administration are developed. However, the backlog in working out of methods and programs intended for the data analysis is observed. These data are capable to find out potentially useful, but the implicit information. Dissertation data analysis deals with the extraction of this information which can give a critical push in scientific researches and other areas.

It is based on the very accurate methodologies and consists of all the data that the dissertation contains and the interpretation of that data. If the inappropriate methodologies are used, the data is gathered incorrectly and, consequently, the dissertation data analyses are invalid. The choice of the best method leans mostly against heuristic reasons. It is caused by requirements of some sciences.

What can you start with? Which methods can be appropriate for data collection? What are the basic components of conducting the statistical data? How should your work be structured?
• contact your supervisor to discuss with him the details and methods of the data analysis;
• attend the libraries and the web-sites, you should be competent in conducting data analysis;
• make notes.

Remember, dissertation data analysis is based on the three methodological principles:
1) The first principle – a variation of the preconditions underlying chosen methods. Any method leans against certain model of the studied phenomenon, i.e. certain system of preconditions and postulates.
2) The second principle – the system approach. In the course of the data analysis the different methods are found for the most complete use of the data describing studied object, and the data describing an inhabitancy of the object. The system approach makes increased demands to the researcher.
3) The third principle – refusal of the viewpoint, that any research has the beginning and the end, i. e. readiness for constant return to the same data. In the course of the data analysis the ruptures are provided, which allow to take the saved up information and to make the decisions connected with data processing management, with a choice of the further steps of the data analysis. Formal operations alternate with informal procedures of decision-making. With the advent of new data there are new ideas, approaches, methods, the understanding of occurring processes etc. is specified

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