Learning The  Art Of Data Interpretation

The interpretation of data helps researchers to categorize, manipulate, and summarize the information in order to answer critical questions.

Data interpretation refers to the process of using diverse analytical methods to review data and arrive at relevant conclusions.

While there are several types of processes that are implemented based on individual data nature, the two broadest and most common categories are “quantitative and qualitative analysis”.

The interpretation of data is designed to help people make sense of numerical data that has been collected, analyzed, and presented. Having a baseline method for interpreting data will provide your analyst teams with a structure and consistent foundation. Indeed, if several departments have different approaches to interpreting the same data while sharing the same goals, some mismatched objectives can result.  In this post, we will look at the two main methods of interpretation of data: qualitative and quantitative analysis.

Qualitative Data Interpretation

Qualitative data analysis can be summed up in one word – categorical. 

With this type of analysis, data is not described through numerical values or patterns, but through the use of descriptive context. Typically, narrative data is gathered by employing a wide variety of person-to-person techniques. These techniques include:

• Observations: detailing behavioral patterns that occur within an observation group. These patterns could be the amount of time spent in an activity, the type of activity, and the method of communication employed.
• Focus groups: Group people and ask them relevant questions to generate a collaborative discussion about a research topic.
• Secondary Research: much like how patterns of behavior can be observed, various types of documentation resources can be coded and divided based on the type of material they contain.
• Interviews: one of the best collection methods for narrative data. Inquiry responses can be grouped by theme, topic, or category. The interview approach allows for highly-focused data segmentation.

A key difference between qualitative and quantitative analysis is clearly noticeable in the interpretation stage. The first one is widely open to interpretation and must be “coded” so as to facilitate the grouping and labeling of data into identifiable themes. 

After qualitative data has been collected through transcripts, questionnaires, audio and video recordings, or the researcher’s notes, it is time to interpret it. For that purpose, there are some common methods used by researchers and analysts.

• Content analysis: As its name suggests, this is a research method used to identify frequencies and recurring words, subjects and concepts in image, video, or audio content. 

• Thematic analysis: This method focuses on analyzing qualitative data such as interview transcripts, survey questions, and others, to identify common patterns and separate the data into different groups according to found similarities or themes. 

• Narrative analysis: A bit more specific and complicated than the two previous methods, narrative analysis is used to analyze stories and discover the meaning behind them. 

• Discourse analysis: This method is used to draw the meaning of any type of visual, written, or symbolic language in relation to a social, political, cultural, or historical context. It is used to understand how context can affect the way language is carried out and understood.

• Grounded theory analysis: The grounded theory approach aims at creating or discovering a new theory by carefully testing and evaluating the data available. Unlike all other qualitative approaches on this list, grounded theory analysis helps in extracting conclusions and hypotheses from the data, instead of going into the analysis with a defined hypothesis. This method is very popular amongst researchers, analysts, and marketers as the results are completely data-backed, providing a factual explanation of any scenario. It is often used when researching a completely new topic or with little knowledge as this space to start from the ground up. 

Quantitative Data Interpretation

This analysis refers to a set of processes by which numerical data is analyzed. More often than not, it involves the use of statistical modeling such as standard deviation, mean and median. Typically, quantitative data is measured by visually presenting correlation tests between two or more variables of significance. Different processes can be used together or separately, and comparisons can be made to ultimately arrive at a conclusion. Other signature interpretation processes of quantitative data include:

• Regression analysis: Essentially, it uses historical data to understand the relationship between a dependent variable and one or more independent variables. Knowing which variables are related and how they developed in the past allows you to anticipate possible outcomes and make better decisions going forward. 

• Cohort analysis: This method identifies groups of users who share common characteristics during a particular time period. 

• Predictive analysis: As its name suggests, the predictive method aims to predict future developments by analyzing historical and current data. 

• Prescriptive analysis: Also powered by predictions, the prescriptive method uses techniques such as graph analysis, complex event processing, and neural networks, among others, to try to unravel the effect that future decisions will have in order to adjust them before they are actually made. 

• Conjoint analysis: Typically applied to survey analysis, the conjoint approach is used to analyze how individuals value different attributes of a product or service. This helps researchers and businesses to define pricing, product features, packaging, and many other attributes. 

• Cluster analysis: Cluster is a method used to group objects into categories. Since there is no target variable when using cluster analysis, it is a useful method to find hidden trends and patterns in the data. 

In conclusion, Data interpretation help improve processes and identify problems. It is difficult to grow and make dependable improvements without, at the very least, minimal data collection and interpretation.