# Dissertation Data Analysis with descriptive statistical measures Using statistics properly in a research work is a skill. Even prepackaged statistical programs, like SPSS, Strata, Eviews and Excel need to be used thoughtfully. No matter how much practice you have had with numerical analysis, never be shy to ask for advice from researchers with more experience. A Dissertation Statistics Consultant, with knowledge of the various tools and packages, will be the perfect person to help you when you are working on the statistical chapter of the dissertation. However, such consultants must be contacted at the initial level of the project, in order to avoid mistakes.

When numeric data pour out from the research, it is wise to reduce the mass to a few characteristic numbers, which will form descriptive statistics. These numbers are more than just summaries of the numerical data points. Descriptive statistics characterize the whole pile of data; they view a data pile as a thing, an entity with its own size, shape and texture. For more insight about descriptive statistics, look here.

Some descriptive statistics that give thorough information about the whole data pile are:

•   Size: the total number of data points in the pile. It is often represented by the alphabet ‘n’.
•   Range: the distance or difference between the smallest and the largest data values. It can be said to be the width of the data pile.
•   Mean: the centre of the mass or the data pile. As the average data point, mean can be considered as the balancing point between two extremes.
•   Mode: the data value that occurs most often. It will be the highest point of the data pile, showing the value with the greatest frequency.
•   Median: the centre point of the data pile, if you arrange the values in an ascending or descending order. This means that half the values are less than the median, and half are more than it.
•   Standard deviation: a commonly used measure of the spread of the data or the variation of the numbers. The standard deviation constitutes of the deviation of each point from the mean. A small standard deviation denotes that the pile is compact while a large one shows that the data is spread out. This statistical characteristic can be easily calculated using most programs and tools.
•   Central 50%: a more intuitive measure of the spread of a pile of numbers. The central 50% shows the limits of the central half of the data pile.

While Dissertation Data Analysis for some studies is descriptive, confirmatory or explanatory data analysis may also be used to solve problems. Whatever be the nature of the statistical analysis, the focus must be on accuracy and validity of the tests. If you can manage the data and apply the tests properly, only then will the results be reliable.