Dissertation Data Analysis
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Every dissertation/thesis writer has to submit the outcomes and results of their performed investigation. In this chapter of your dissertation, you are required to explain & analyze the data collected after extensive research. Data analysis is an ongoing activity that not only answers your question but also provide you the direction for future data collection. Dissertation data analysis is the process of systematically using statistical/ analytical techniques to evaluate the collected data.
The researcher must be aware of the issues that arise in dissertation analysis. These are following:-
ANOVA: - The one-way analysis of variance (ANOVA) is applied to determine whether there are any major differences between the means of three or more independent (unrelated) groups.
Cluster Analysis: - Cluster analysis is a data analysis tool that aims at sorting different objects into groups in a way that the degree of association between two objects is maximal if they belong to the same group and minimal otherwise.
Discriminant Analysis: - Statistical analysis using a discriminant function to assign data to one of two or more groups.
Factor Analysis: - it is a process in which the values of observed data are expressed as functions of many possible causes to find which are the most significant.
Multidimensional Scaling Overview: -The purpose of multidimensional scaling (MDS) is to provide a visual representation of the pattern of proximities (i.e., similarities or distances) among a set of objects.
Regression Analysis: - Regression analysis is a statistical process for evaluating the relationships among variables. It includes many techniques for modeling and analyzing several variables when the focus is on the relationship between a dependent variable and one or more independent variables.
Sampling: - A process used in statistical analysis in which a predetermined number of observations will be taken from a larger population.
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