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:

  • Observations: This technique details behavioral patterns that may occur within an observation group. These patterns could be the time spent in an activity, the type of activity or the method of communication employed.
  • Documents: Similar to the observation of patterns of behavior, in document technique, different types of documentation resources can be coded and also divided based on the type of material they consist.
  • Interviews: The interview approach allows for highly-focused data segmentation and is one of the best approaches that can be used for narrative data. The enquiry response can be grouped either by theme, topic or category.


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:

  • Regression analysis: This technique is used to test the nature of the relationship between a dependent variable and one/ more independent variables.
  • Cohort analysis: This technique investigates a specific group with a certain trait & observes over a duration.
  • Predictive and prescriptive analysis: Predictive analysis studies the data and predicts the future based questions. Whereas prescriptive analysis advice the possible outcomes and determines the possible solutions.


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.

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