Data interpretation refers to the application of processes by which data are reviewed in order to reach an informed conclusion. Data interpretation assigns meaning to the analyzed information and determines its meaning and implications.
According to Ellingson (2007), the process of data interpretation is often cumbersome and should naturally be made more difficult with the increased amount of data being produced on a daily basis. However, with the accessibility of data analysis tools and machine learning techniques, analysts are gradually finding it easier to interpret the data.
Interpretation of data is very important as it helps to derive useful information from an irrelevant data set and to make informed decisions. It is useful for individuals, companies and researchers.
What are data interpretation methods?
Data interpretation methods are how analysts help people make sense of numerical data that has been collected, analyzed, and presented. Data, when collected raw, can be difficult for laymen to understand, so analysts have to break down the collected information so that others can make sense of it.
For example, when founders target potential investors, they need to interpret the data (eg, market size, growth rate, etc.) to better understand them. There are two main methods to do this: quantitative methods and qualitative methods.
Importance of Data Interpretation
The importance of interpreting the data is evident and that is why it must be done correctly. It is very likely that the data will come from multiple sources and have a tendency to enter the analysis process in a random order. According to Patten (2004), data analysis tends to be extremely subjective. That is, the nature and purpose of the interpretation will vary from company to company, probably in correlation with the type of data being analyzed. Although there are several different types of processes that are applied depending on the nature of the data, the two broadest and most common categories are “quantitative analysis” and “qualitative analysis.”
However, before any serious data interpretation research can begin, it must be understood that visual presentations of data results are irrelevant unless a sound decision is made regarding measurement scales. Before any serious data analysis can begin, the scale of data measurement must be decided, as this will have a long-term impact on the ROI of data interpretation.
Scales in Data Measurement
The different scales include:
Non-numerical categories that cannot be ranked or compared quantitatively. Variables are exclusive and exhaustive.
Exclusive and exhaustive categories but with a logical order. Quality indices and agreement indices are examples of ordinal scales (eg, good, very good, fair, etc., or agree, strongly agree, disagree, etc.).
Measurement scale in which the data are grouped into categories with ordered and equal distances between the categories. There is always an arbitrary zero point.
It contains characteristics of all three.
How to interpret the data?
Once the measurement scales have been selected, it is time to choose which of the two general interpretation processes will best suit your data needs. Let’s take a closer look at those specific data interpretation methods and potential data interpretation issues.
Illustration of data interpretation on blackboard
When interpreting the data, an analyst must try to discern the differences between correlation, causality, and coincidence, as well as many other biases, but must also consider all the factors that may have led to a result. There are several methods of data interpretation that can be used.
Data interpretation is intended to help people make sense of the numerical data that has been collected, analyzed and presented. Having a reference method (or methods) for interpreting data will provide your analyst teams with structure and a consistent foundation.
In fact, if you have different approaches to interpret the same data, even if they share the same objectives, some mismatches can occur. Disparate methods will lead to duplication of effort, inconsistent solutions, wasted energy and, inevitably, time and money.
Qualitative interpretation of the data
Qualitative data analysis can be summed up in one word: categorical. With qualitative analysis, the data is not described by numerical values or patterns, but by using a descriptive context (ie, a text). Typically, narrative data is collected using a wide variety of person-to-person techniques. These techniques include:
Detail the behavior patterns that occur within an observation group. These patterns can be the amount of time spent on an activity, the type of activity, and the method of communication used.
Just as behavioral patterns can be observed, different types of documentary resources can be coded and divided according to the type of material they contain.
A key difference between qualitative and quantitative analysis becomes clear at the interpretation stage. Qualitative data, being widely open to interpretation, must be “coded” to facilitate the grouping and labeling of data into identifiable themes. Since person-to-person data collection techniques can often lead to disputes about the appropriate analysis, qualitative data analysis is often summed up in three basic principles: notice things, pick things up, think about things.
Interpretation of quantitative data
If the interpretation of quantitative data could be summed up in one word (and it really can’t) that word would be ‘numerical’. There are few certainties when it comes to data analysis, but you can be sure that if the research you’re involved in doesn’t have numbers, it’s not quantitative research. Quantitative analysis refers to a set of processes by which numerical data is analyzed. In most cases, it involves the use of statistical models such as the standard deviation, the mean, and the median. Let’s quickly review the most common statistical terms:
The mean represents a numerical average for a set of responses. When dealing with a data set (or multiple data sets), a mean will represent a central value of a specific set of numbers. It is the sum of the values divided by the number of values within the data set. Other terms that can be used to describe the concept are arithmetic mean, average, and mathematical expectation.
It is another statistical term that often appears in quantitative analysis. The standard deviation reveals the distribution of responses around the mean. Describes the degree of consistency of responses; together with the mean, it allows knowing the data sets.
It is a measure that measures the rate of occurrence of a response within a data set. When using a survey, for example, the frequency distribution has the ability to determine the number of times a specific ordinal scale response appears (ie, agree, strongly agree, disagree, etc.). The frequency distribution is very useful in determining the degree of consensus between the data points.
Typically, quantitative data is measured by visually presenting evidence of correlation between two or more significant variables. Different processes can be used together or separately, and comparisons can be made to finally reach a conclusion. Other quantitative data interpretation processes are regression, cohort, predictive and prescriptive analyses.
Qualitative Data Interpretation
The qualitative data interpretation method is used to analyze qualitative data, which is also known as categorical data. This method uses text, rather than numbers or patterns, to describe the data.
According to Creswell, (1997), qualitative data is often collected using a wide variety of person-to-person techniques, which can be difficult to analyze compared to the quantitative research method.
Unlike quantitative data, which can be directly analyzed once collected and classified, qualitative data must first be encoded into numbers before it can be analyzed. This is because texts are often cumbersome, and will take longer and lead to many errors if parsed in their original state. The coding done by the analyst should also be documented so that it can be reused by others and analyzed as well.
There are two main types of qualitative data: nominal and ordinal data. These two types of data are interpreted in the same way, but ordinal data is much easier to interpret than nominal data.
In most cases, ordinal data is usually labeled with numbers during the data collection process and may not need to be coded. This is different from nominal data, which still needs to be encoded for correct interpretation.