Most research studies utilize both qualitative and quantitative research designs. The qualitative data analysis approach is most suitable for a dataset that is largely numerical in nature rather than objective which is best analyzed by a qualitative research study. Quantitative research design usually involves the use of statistical programs such as SPSS, Excel, and access in the analysis of data, this is because a major characteristic of this type of data is that they are numerical. Once the data has been entered and organized it becomes easy to analyze it by performing various statistical tests needed for various reasons. Among the most often performed statistical tests in quantitative research include the investigation of whether there is a positive causal association between two variables of interest.
Some examples of relevant statistical tests that are effectively done using quantitative research approaches are the Pearson Correlation Coefficient which is a measure of linear dependence, referred to as the correlation between two variables of interest that occurs in a continuous format. Others are Variance and Standard Deviation used to measure the variability of a dataset i.e. how widely the data is dispersed and Coefficient of Determination used to measure the accuracy of a regression equation through comparison of variation between two values among others.
Because the approach used by quantitative research designs in conducting research is more suitable in the investigation of particular research studies than others. In general quantitative research is best suited to studies that do not intend to investigate opinions, perceptions, and other objective variables. Instead, it is more suited to studies that have numerical variables such as research on the prevalence rates of diseases or criminal activities among a specific group of persons among others.