Data analysis is the process of analyzing all the information and evaluating the relevant information that can be helpful in better decision making (Sivia & Skilling, 2006). Data analysis is an important part of your dissertation. This post would be helpful while you do your dissertation. Analysis can be done by using various tools and methods. Data analysis helps in deriving the conclusion out of the gathered information.
There are various methods that have been adopted by an organization to analyze the accuracy of the data collected.
Statistical Test: Data that has been collected can be analyzed by doing various statistical tests. Organization can select the type of statistical test on the basis of data collected (Ott & Longnecker, 2008). For example researcher has collected the data through observation then organization should select the correlation statistical method.
Mechanical Techniques: There are various mechanical techniques that can be adopted by an organization. Large amount of data can get scanned through computers and other mechanical devices (Ramsay & Silverman, 2002). This method helps in quick and easy analysis of the collected data and also provides more accurate data.
Interpretations of interviews and case studies: Analysis can be done through the interpretation of the interviews that has been conducted during the data collection. Interpretation of the case studies is also a method of analyzing the data. This will help in acquiring relevant employees for the organization.
Descriptive method: In this method, collected data are organized in such a way that it will describe the nature and type of data collected. This can be done by using various diagrams and tables (Lindlof & Taylor, 2010). It will be helpful in making better decisions. And through the diagram it will be easy to understand and analyze the data.
Data Presentation: All the numerical data that has been collected must be presented in graphical or matrix form so that it will be easy to analyze the data. Some of the ways are histogram, bar chart etc. All the numerical data are put into graphs or matrix and through this calculation can be done easily (Weerahandi, 2003). This will further help in forecasting the future requirements and also help in framing the budgets.
Moving Average Method: In this method, average of the entire variable data is taken out. This method can be used with the time series and it will further help in smoothing the fluctuations that are short term in nature (Raedt & Siebes, 2001). Moving average method also interpret the longer term trends that will also help in adopting some controlling techniques in advance.
Lindlof, T.R., & Taylor, B.C. (2010). Qualitative Communication Research Methods (3rd ed.). California: SAGE.
Ott, L., & Longnecker, M. (2008). An introduction to statistical methods and data analysis (5th ed.). USA: Cengage Learning.
Raedt, L.D., & Siebes, A. (2001). Principles of data mining and knowledge discovery. New York: Springer.
Ramsay, J.O., & Silverman, B.W. (2002). Applied Functional Data Analysis: methods and case studies. New York: Springer.
Sivia, D.S., & Skilling, J. (2006). Data analysis: a Bayesian tutorial (2nd ed.). US: Oxford University Press.
Weerahandi, S. (2003). Exact statistical methods for data analysis. New York: Springer.