ABSTRACT
This chapter focuses on quantitative data analysis. It discusses student an overview of the ways in which their can analyse the data that they have spent so much time and energy collecting. Using real-life data and examples, the chapter provides a guide to essential statistical techniques commonly used in undergraduate dissertation. Quantitative data analysis is generally divided into two categories – descriptive and inferential statistics. Descriptive statistics enable students to explore and understand their data before carrying out any further or detailed statistical analysis that may be required. Coding is an important stage in data processing; hence care should be taken while assigning codes to students' questionnaire information to make their analysis meaningful. Visual aids are an important part of data exploration and can help students make sense of their data. One of the objectives of simple linear regression analysis is to help us determine the best line through the data points in a scatterplot.
