All you wanted to know about Dissertation Data Analysis

After having collected a huge pile of data related to the research topic, it is quite essential for you to ensure the selection of the right kind of data analysis process and strategy. If you have collected words, much reading, sorting and cross-referencing lies ahead. If you have collected numbers, lots of figures need calculating, cross-tabulating and testing. Both; words and numbers must be analysed carefully. Statistics do not guarantee the rigour of your research, nor does naturalistic inquiry guarantee its meaning. The distinction between qualitative and quantitative data is ultimately a false one. Even if we plan a research project that completely avoids numbers, we still have to understand basic measurement principles. Dissertation writers across the globe ensure that the data collected by them is being analysed in the best and the most professional manner.

In research, measure has a particular meaning derived from measurement scales, which are technically defined methods for classifying or categorising. All information is data (whether represented by words or numbers) that can be categorised as qualities and, therefore, measured as quantities. Chanakya Research has emerged as one of the most successful firms indulged in offering professional assistance to the research students who are unable to cope up with the issues that arise during the data analysis process undertaken for the huge chunk of data collected as per the chosen research theme. In research, measurement scales order data: a) informally to explore patterns arising from the data as we analyse it; and b) formally to test hypothesised relationships between variables. We cannot prove that the hypothesised relationship exists, so operationally we test for its non-existence using the null hypothesis, which is a prediction that no difference will be found. Rejection of the null hypothesis gives a difference predicted by the research hypothesis and the theory from which it derives, which are supported (technically, the research failed to reject them). If the null hypothesis is supported, the difference predicted by the research hypothesis did not occur. In this case, the research hypothesis and maybe the theory are rejected as false.

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