Methods of quantitative Data Analysis: When and Where Appropriate

Data analysis is a crucial part of empirical research process yet many researcher do not have unhindered access to materials on quantitative methods for data analysis. This paper is an attempt at placing at the doorsteps of researchers (particularly nonexperts and early career academics) a broad range of quantitative methods for data analysis. The paper discusses some of the several methods for analysing quantitative data and when it is appropriate to use them. A researcher needs to understand when and why a method is appropriate to be used because employing a wrong method would yield wrong results and unreliable conclusions. The methods discussed were broadly grouped by their purpose, namely: descriptive statistics to understand pattern; ANOVA analysis to compare groups; correlation coefficients for measuring relationship; regression for measuring impact/effect; algebraic techniques for optimization and for evaluating the level of efficiency/effectiveness. The choice of technique of analysis from this robust list of methods is shown to depend on the unit of analysis, the way variables are measured and objective of study.