Qualitative data can be analysed both manually and through Computer Assisted Qualitative Data Analysis Software (CAQDAS). But, given the vastness of data and systematic & rigorous preparation needed, the latter is overpowering the former.
As pointed out in the Auto-Coding article, the process has its own set of issues and limitations, which might create certain obstacles in the nodes created and therefore needs to be taken care of.
R software has plenty of packages and is unique in handling big data. Therefore it can handle both the structured and unstructured data. This makes it suitable for big data analysis also.
Data envelopment analysis (DEA) is the most commonly used program for this purpose (Coelli, 2008). Tim Coelli from of University of New England (Centre for Efficiency and Productivity Analysis, Department of Econometrics, Australia) had uploaded the program in DOS based command which is in a zipped format along with guidelines on how to use it.
The normal linear regression analysis and the ANOVA test are only able to take one dependent variable at a time. So one cannot measure the true effect if there are multiple dependent variables. In such cases multivariate analysis can be used.
In the previous article on Linear Regression using STATA, a simple linear regression model was used to test the hypothesis. However the linear regression will not be effective if the relation between the dependent and independent variable is non linear.