3.8 Summary

In Quantitative data analysis the procedures consist of measuring of numerical values, (Kombo and Tromp, 2006) where measures of central tendency, measures of variability are worked out and various inferences done. These numerical data can be either discrete or continuous, meaning either countable data or parametric (variables), measurable and expressed on a continuous scale, for example, the height of a person. Quantitative data analysis varies from simple to complex due to differences in , especially experiments done for the purpose of data collection.

In correlation research studies data is mainly analyzed using some kind of coefficient of correlation. This implies the degree of relationship between variables, usually two variables. The degree of relationship varies from (positive 1), perfect correlation; no correlation (zero) to (negative 1), perfect opposite correlation.

Although there are still more types of correlation analysis such as reliability and validity studies that require our mention here, we may not be able to go through them now until later when we discuss exhaustively the concepts of validity and reliability of data, what constitutes validity and reliability in research studies.

3.8 Summary

The methods used in data analysis are influenced by whether the research is qualitative or quantitative or both. But whether both or not, the methods of analysis do not vary significantly. To explain raw data is difficult or impossible. It requires expertise in data manipulations which includes interpretation in order to arrive at the meaning. After interpretation of analyzed data then answers to problem questions are arrived at. The two major methods of statistical data analysis are exploratory and confirmatory. Exploratory is mostly used in qualitative research while Confirmatory methods use the theory of probability.