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Skip to main content This website does not fully support Internet Explorer. For a better experience, consider using a modern browser like Chrome, Firefox, or Edge. If youre reading this, you probably already know the importance of data analysis. And you already know that it can be incredibly difficult. The easiest way to classify data is into two different categories: quantitative data and qualitative data. But what is the difference between them? And when should they be used? And how can you use them together? So lets clear up the complexities by carefully explaining the similarities and differences between qualitative and quantitative data and how both are essential to the success of any research and data analysis. Knowing both approaches will help you better understand your data and ultimately better understand your customers. KEY FINDINGS: Quantitative data refers to any information that can be quantified, counted, or measured and assigned a numerical value. Qualitative data is descriptive in nature and is expressed in language rather than numerical values. Quantitative research is based on numerical data. Qualitative research focuses on user characteristics - the why behind the numbers. Without qualitative and quantitative data, it is difficult to conduct successful data analysis. Both have their advantages and disadvantages and often complement each other. What is quantitative data? Qualitative and varied in approach and type of data collected. Quantitative data refers to any information that can be quantified, i.e. numbers. If it can be counted or measured and given a numerical value, it is quantitative in nature. Think of it as a benchmark. Quantitative variables can indicate how much, how much or how often. Some examples of quantitative data: How many people attended the webinar last week? What was the turnover of our company last year? How often does the customer click on this app? Analyzing these research questions and understanding this quantitative data usually requires some form of statistical analysis—collecting, evaluating, and presenting large amounts of data to uncover patterns and trends. Quantitative data lends itself to this type of analysis because it is easier to analyze numerically and mathematically. Statistical analysis is now performed by computers, although traditional methods have been used for years. But todays data volumes make statistics more valuable and useful than ever. When you think of statistical analysis now, think of the powerful computers and algorithms that power many of the software tools we use today. Popular methods of collecting quantitative data are surveys, experiments, polls and others. What is quantitative research? Its all about the numbers. Quantitative research is based on the collection and interpretation of numerical data. It focuses on measuring (using derived statistics) and generalizing the results.Skip to main content This site does not fully support Internet Explorer. For more convenience, consider using a modern browser such as Chrome, Firefox, or Edge. If you are reading this, you probably already know the importance of data analysis. And you already know that it can be extremely difficult. Simply put, data can be divided into two different categories: quantitative data and qualitative data. But what is the difference between them? And when should they be used? And how can they be used together? So, lets clear up the complexity by explaining in detail the similarities and differences between qualitative and quantitative data and how they are important to the success of any research and data analysis. Knowing both approaches will help you better understand your data and, ultimately, better understand your customers. Main conclusions. Quantitative data is any information that can be quantified, counted, or measured and quantified. Qualitative data is descriptive, expressed in language rather than numbers. Quantitative research is based on numerical data. Qualitative research focuses on user characteristics—the why behind the numbers. It is difficult to conduct a successful data analysis without qualitative and quantitative data. Both have their advantages and disadvantages and often complement each other. What is Quantitative Data? Qualitative and differ in the approach and type of data collected. Quantitative data refers to any information that can be quantified, i.e. numbers. If it can be counted or measured and assigned a numerical value, then it is quantitative. Think of it like a measuring cup. Quantitative variables can say how much, how much, or how often. Some examples of quantitative data: How many people attended the webinar last week? How much revenue did our company get last year? How often do customers click on this app? To analyze these research questions and make sense of quantitative data, a form of statistical analysis is commonly used—the collection, evaluation, and presentation of large amounts of data to identify patterns and trends. Quantitative data favors this type of analysis because it is numeric and easier to analyze mathematically. Although traditional methods have been used for years, statistical analysis is computer driven. However, todays volume of data makes statistics more valuable and useful than ever before. When you think of statistical analysis now, you think of the powerful computers and algorithms that underlie many of the software tools we use today. Popular methods for collecting quantitative data include surveys, experiments, surveys, and more. for numbers. Quantitative research is based on the collection and interpretation of numerical data. The focus is on measuring (using logical statistics) and summarizing the results. 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