Data interpretation assigns a definite meaning to the information analyzed and determines its significance and implications. The interpretation of data helps scholars make sense of numerical data that has been gathered, analyzed and finally presented.
When interpreting data, an analyst must discern the differences between correlation, causation and coincidences, and the other factors involved that have lead to a result. Use of disparate methods may lead to duplicated efforts, inconsistent solutions, waste in energy and time. Hence a suitable approach needs to be used while interpreting the data. The two popular types of techniques are qualitative data interpretation and quantitative data interpretation.
Qualitative data interpretation cannot be described through numerical values or patterns. Rather it is defined through the use of descriptive context. Typically, narrative data is collected by employing a variety of person-to-person techniques. Some of these techniques include:
Quantitative data interpretation can be easily summed up in just one word that is “numerical.” Typically, quantitative data can be interpreted by visually presenting correlation tests between two or more variables of significance. Various processes can be used together or separately, and comparisons can be made to arrive at a conclusion. The techniques used in quantitative data interpretation includes:
Why is it important to interpret the data?
Through data interpretation the researcher can understand the abstract principle that works beneath the findings. Interpretation leads to explanatory concepts that serve as a guide for future research studies. The interpretation of the outcomes of exploratory research study results into hypotheses for experimental research.
Importance of data interpretation cannot be denied as it helps to develop sound conclusion of your study. Hence make sure you use the appropriate technique to interpret the data.