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143 Figure 1: Data View Window of SPSS Analysing Quantitative Data Quantitative methods of data analysis principally refer to objective measurements with primary emphasis on statistical, mathematical, or numerical analysis of data collected. Quantitative Data Analysis (QDA) involves mathematical calculations that produce statistics about the tabulated data. Examples of collected data may include among others, questionnaires and surveys. QDA focuses on inferential statistics with various tests used to help assess the confidence of research and project findings (Pell & Fogelman, 2002). Important in QDA are descriptive and exploratory analyses, which are usually the first step in the analysis. These (descriptive and exploratory analyses) produce the first results in form of simple distributions and or summary statistics like averages and measures of dispersion in the data. After these initial analyses have been performed, complex statistical techniques can follow that will enable the researcher to make inferences from the data. The exact techniques to implore are usually based on the project aims, objectives and questions asked. In addition, the nature of the data in quantity and completeness also determines the kind of statistical techniques to be used. If the data does not fit the planned assumptions, objectives and questions, the evaluators may have to regroup and decide on what to do with the presented data. In cases where observations in the data have huge variations, perhaps a