Data interpretation refers to the application of processes by which data are reviewed in order to reach an informed conclusion. The interpretation of the data assigns a meaning to the analyzed information and determines its meaning and implications.
It is an important aspect of working with datasets in any field or research and statistics. Both go hand in hand, since the process of interpreting data involves the analysis of the same.
According to Ellingson (2007), the process of interpreting data is often cumbersome and should naturally become more difficult with the more data that is produced on a daily basis. However, with the accessibility of data analysis tools and machine learning techniques, analysts gradually find it easier to interpret the data.
The interpretation of data is very important, as it helps to obtain useful information from an irrelevant data set and to make informed decisions. It is useful for individuals, businesses and researchers.
What are data interpretation methods?
Data interpretation methods are how analysts help people make sense of the numerical data that has been collected, analyzed, and presented. The data, when collected raw, can be difficult for laymen to understand, so analysts have to break down the information collected so that others can make sense of it.
For example, when founders target potential investors, they should interpret the data (e.g., market size, growth rate, etc.) to better understand it. There are two main methods of doing this: quantitative methods and qualitative methods.
Importance of Data Interpretation
The importance of interpreting the data is obvious and must therefore 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 wise decision is made regarding measurement scales. Before any serious analysis of the data can begin, the scale of measurement of the data must be decided, as this will have a long-term impact on the ROI of the interpretation of the data.
Scales in Data Measurement
The different scales include:
Nominal scale
Non-numerical categories that cannot be classified or compared quantitatively. The variables are unique and exhaustive.
Ordinal scale
Exclusive and exhaustive categories but in a logical order. Quality indexes and agreement indexes are examples of ordinal scales (e.g., good, very good, regular, etc., OR agree, very agree, disagree, etc.).
Interval
The measurement scale at which the data is grouped into categories with ordered and equal distances between the categories. There is always an arbitrary zero point.
Ratio
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 the board
When interpreting the data, an analyst should try to discern the differences between correlation, causation, and coincidences, as well as many other biases, but he should also consider all the factors that may have led to an outcome. There are several methods of data interpretation that can be used.
The interpretation of the data 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 the data will provide your teams of analysts with a coherent structure and basis.
In fact, if you have different approaches to interpreting the same data, even if they share the same objectives, some mismatches can occur. Disparate methods will result in duplication of effort, inconsistent solutions, loss of energy and, inevitably, of time and money.
Qualitative interpretation of the data
The qualitative analysis of the data can be summarized in one word: categorical. With qualitative analysis, data are not described by numeric values or patterns, but by the use of a descriptive context (that is, a text). Typically, narrative data is collected using a wide variety of person-to-person techniques. These techniques include:
Observations
Detail the patterns of behavior 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.
Documents
Just as patterns of behavior can be observed, different types of documentary resources can be encoded and divided based on the type of material they contain.
Interviews
It is one of the best methods of collecting narrative data. Interview responses can be grouped by topic, topic, or category. The interview approach allows you to segment the data very precisely.
A key difference between qualitative and quantitative analysis is clearly seen in the interpretation phase. Qualitative data, being widely open to interpretation, should be “codified” to facilitate the grouping and labeling of data on identifiable topics. Since person-to-person data collection techniques can often lead to disputes about proper analysis, qualitative data analysis is often summed up in three basic principles: noticing things, collecting things, thinking 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 participate in doesn’t have numbers, it’s not quantitative research. Quantitative analysis refers to a set of processes by which numerical data are analyzed. In most cases, it involves the use of statistical models such as standard deviation, mean, and median. Let’s quickly review the most common statistical terms:
Medium
The mean represents a numeric average for a set of responses. When it comes to a dataset (or multiple data sets), a mean will represent a core value of a specific set of numbers. Is the sum of the values divided by the number of values within the dataset. Other terms that can be used to describe the concept are arithmetic mean, average, and mathematical expectation.
Standard deviation
It is another statistical term that usually appears in quantitative analysis. The standard deviation reveals the distribution of responses around the mean. Describes the degree of consistency of responses; along with the mean, it allows you to know the data sets.
Frequency distribution
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 (i.e., agree, very much agree, disagree, etc.). Frequency distribution is very useful for determining the degree of consensus between data points.
Typically, quantitative data are 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 eventually reach a conclusion. Other processes of interpretation of quantitative data are regression, cohort, predictive and prescriptive analyses.
Interpretation of Qualitative Data
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 are 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 analyzed directly once collected and classified, qualitative data must first be encoded in numbers before they can be analyzed. This is because texts are often cumbersome, and will take longer and lead to many errors if analyzed in their original state. The coding performed by the analyst must also be documented so that it can be reused by others and also analyzed.
There are two main types of qualitative data: nominal and ordinal data. These two data types are interpreted using the same method, but the interpretation of ordinal data is much easier than that of nominal data.
In most cases, ordinal data is often labeled with numbers during the data collection process, and may not need to be encoded. This is different from nominal data, which still needs to be encoded for proper interpretation.
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Bibliographic References
Patten, Mildred L. 2004. Understanding research methods: An overview of the essentials. 4th ed. Glendale, CA: Pyrczak Publishing. 170p. ISBN 1884585523 (pbk.) 5th ed.: 183p.
Ellingson, L. L. 2007. Review of Qualitative research methods for the social sciences, 6th ed, by B. L. Berg. Communication Research Trends 26.1: 24.
Creswell, John W. 1997. Qualitative inquiry and research design: Choosing among five traditions. Thousand Oaks, CA: Sage Publications. 403p. ISBN: 0761901442 (pbk.), US
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