If the quality level research work across any academic domain is being looked at, then it can be seen that most of the quality piece of works have been carried out by means of analyzing the real world data on several parameters, and that is the reason behind significance of those studies. At any point of time, building up of a theory is never considered to be completed, unless and until that theory is substantiated using the analysis of real world empirical or qualitative data. For analyzing the real world data, there are several methodologies available. However, all of those methodologies differ from each other to a great extent and one all kinds of data can never be analyzed by making use of only a single methodology, as they are very specific in nature. Therefore, based on the available set of data and the research question, a researcher has to choose appropriate methodology for analyzing the data.
For example, if a researcher wants to analyze the nature and behavior of financial market of any nation, then the data can be mostly time series in nature. Now, in order to analyze the behavior of the market, the market return data has to be analyzed against several indicative parameters. In order to carry this work out, one of the best possible research methodology is regression analysis, which can be used to explain the variance of the dependent variable with respect to the variances of other independent variables. However, plugging time series data directly into regression model will undoubtedly increase the regression coefficient value, but the coefficients of independent variables will suffer from heteroscedasticity and multicolinearity related errors, which will make the entire model insignificant. Therefore, lagged values of those variables should be used in developing the model, and in this process, those errors will not appear. For more information about various aspects of deciding upon a proper research methodology, kindly browse through the pages of www.ithesisedit.com.