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“What is the difference between the two?” “Which type of data will give the best result for my research?” These are the questions that come to mind whenever you undertake a research project. Especially when you are involved in the data science or statistics field, it becomes essential to understand the difference between qualitative and quantitative data to perform data analysis to make informed business decisions. Knowing both data analysis approaches can help you collect customer feedback to understand them better and make decisions responsible for your business’s success. Read this blog to learn about what makes them different, the different methods for collecting qualitative and quantitative data, and different methods of analyzing data. Let’s Begin. What Is the Difference Between Qualitative and Quantitative Data?In a nutshell, qualitative data is the numerical data that produces information that can be converted into numbers. On the other hand, qualitative data is expressed in words and used to understand thoughts, opinions, and experiences. Now that we have covered the fundamental difference between the two, let’s dive deep into quantitative data vs. qualitative data and their types with examples.
What Is Qualitative Data?Qualitative Data is non-statistical data that describes the information that cannot be measured in numbers. It is descriptive in nature. Mainly expressed in words, this type of data collects people’s thoughts, opinions, and experiences about a particular statement or subject. The data collection techniques of qualitative data range from unstructured to semi-structured techniques. Researchers use this data to ask questions like “why” and “how.” It is often used to analyze open-ended studies, allowing respondents to show their true feelings and actions or dig deeper into the problem. Example of Qualitative data: Suppose qualitative research tells you that 3 out 5 employees in your company are not satisfied with their job. To determine the reasons behind your employees’ unhappiness, you will conduct a quantitative study to provide insight into how an employee feels about working in your company. In this case, you are not just looking at “numbers”; you are asking employees to express their feelings in “words.” Qualitative Data Types With ExamplesWhen considering the difference between quantitative vs. qualitative data, it becomes crucial to understand data types with examples. Let’s first look into the quantitative data. It can be divided into two types: nominal and ordinal
What Is Quantitative Data?Contrary to qualitative data, quantitative data describes the information that is quantified – easily measured and counted and can be given a numerical value. Researchers use quantitative data to ask “how much,” “how many,” and “how often” questions. To analyze and examine quantitative data, one must conduct statistical analysis. Compared with qualitative data, quantitative data is more structured and concise. Qualitative data describes conclusive information that is not open for exploration. Using different statistical methods, it establishes the cause and effect relationship between two variables. Examples of quantitative data include:
Types of Quantitative Data and ExamplesQuantitative data can be further divided into discrete and continuous data.
Qualitative vs Quantitative Research
One of the key differences between qualitative vs. quantitative data lies in their research methods or how they are collected. Let’s look at some of the qualitative vs. quantitative data collection methods in detail: Types of Qualitative Research MethodsQualitative data is gathered through interviews, surveys, focus groups, case studies, and observations. Let’s take a look at these methods in more detail:
Types of Quantitative Research MethodsQuantitative data is collected using structured interviews, polls, close-ended surveys, and analytics tools. Let’s take a look at these methods in more detail:
Qualitative vs Quantitative Data: AnalysisQualitative or quantitative data do not provide any insights unless they are analyzed. So, it becomes crucial to understand the difference between qualitative vs. quantitative data analysis. Let’s have a look at how qualitative and quantitative data are analyzed: Quantitative Data AnalysisBecause quantitative data is based on numbers, some form of mathematical analysis is required to derive patterns in data, and the results are reported through tables and graphs. The methods range from simple mathematical formulas like calculating means and median to more advanced statistical analysis like calculating the statistical significance of your results. Tools like Excel and SPSS are used to calculate:
Qualitative Data AnalysisBecause qualitative data consists of words, photos, or videos rather than statistics, it is more difficult to analyze than quantitative data. Some of the most prevalent ways to analyze qualitative data are as follows:
Which Type Is Better for Data Analysis?
Now that we have discussed qualitative vs. quantitative data in detail, it is time to determine which data type is better for data analysis. If we talk about qualitative data, it is usually unstructured in nature and is known for being subjective. It helps the researcher to gather in-depth insights into particular topics. Since it is expressed in words, it becomes difficult to collect and analyze qualitative data using conventional methods. However, it is still considered valuable data for obtaining qualitative insights. On the other hand, quantitative data is structured data that includes numerical information that can be easily collected and analyzed using statistical methods. Since this data is more concrete, it is usually preferred for data analysis. However, the use of quantitative or qualitative data entirely depends on your research and what you are trying to measure. Using the combination of both qualitative as well as quantitative data also yields the best result for statistical analysis. Hence, mixing qualitative and quantitative methods and using different data collection techniques is recommended as it offers a more holistic understanding of the subject. It will also provide researchers with more coherent, reliable, and valuable business conclusions. |