The importance of data cannot be underestimated. It helps organizations make informed decisions and measure the effectiveness of the implemented strategies. In addition to this, it helps in identifying problems and finding practical solutions to them. The data are categorized into types, which helps in applying statistical measurements. Broadly speaking, the data is classified into two types: qualitative and quantitative data. Furthermore, it is classified into various subtypes. In this discussion, we will look at different kinds of data and understand them with the help of examples. The data is classified so that it can be stored accordingly. It allows eliminating errors in data processing, which helps in getting better results. With that said, let’s look at each of the types of data in depth.
In qualitative data, the numbers cannot be used. Instead, the considered object is described. Since we cannot use numbers to count or measure these forms of data, we use words, symbols, labels, and narratives. It is also called categorical data, as the categories are used to sort the information. Qualitative data is further subdivided into two types: nominal and ordinal data.
In nominal data, we label the variables that do not have quantitative value or order. As the nominal data are not organized, they cannot be sorted. This means even after interchanging the value, the meaning will remain the same. A few examples of nominal data are gender, ethnicity, and languages known. You may know several languages such as English, Spanish, Arabic, and French. But, since they are nominal variables, you cannot put them in order.
In nominal data, we get the least amount of details, which we can present in the form of charts and tables. These forms of data are mostly collected from open and close-ended survey questions. If there are many possible labels for your selected variable, you can use open-ended survey questions. Whereas, if there are few labels for your selected variable, then close-ended survey questions such as “Yes” or “No” can be used. The nominal data can be grouped into categories, where the frequency or percentage of each category can be calculated. Statistical methods such as hypothesis testing can be used to analyze the nominal data.
The difference between ordinal and nominal data is that the former can be categorized into orders. It can be arranged into groups which can be further categorized into orders such as higher or lower. An example of ordinal data is satisfaction surveys, where respondents can select one option among several options such as agree, disagree, neutral, mostly agree, and mostly disagree.
What makes ordinal data favorable for carrying out questionnaires or surveys, is its ordered nature. Based on the responses of the participants, they can be put into different categories. The easy process of categorization makes it a suitable option for conducting research, personality tests, and customer service. The visualization tools such as pie charts, bar charts, and tables can be used to analyze ordinal data.
The quantitative data deals with numbers, making it easier to explain. It can be used for statistical analysis and mathematical calculations. With the help of quantitative data, questions such as “how many”, “how much”, and “how often” can be answered. It is further subdivided into two types: interval and ratio data.
The interval data is measured along the interval scale, where each point is placed at an equal distance. It introduces precise and continuous intervals. Here, the data can be added and subtracted, but not multiplied or divided. An example of interval data is time. The numbers on the clock are equidistant, which means the difference between 4 o’clock to 5 o’clock is the same as the difference between 7 o’clock to 8 o’clock. A few other examples of interval data are CGPA (Cumulative Grade Point Average), temperature data, and grading systems.
The collection technique for interval data are surveys, direct observation, or interviews. It is compatible with most statistical tests, which is why it is one of the most used kinds of data. It can be used for the calculation of frequency distribution, mean, median, mode, standard deviation, and variance. The interval data is analyzed using descriptive and inferential statistics and can be organized and distributed using tables and graphs. It is used to analyze trends and gain insights over specific time intervals.
In ratio data, absolute zero is treated as a point of origin, and there is an equal and definitive ratio between each data. This means the degree of difference between two variables can be easily calculated. The numerical value of the ratio data cannot be negative, as zero is the starting point of the ratio scale. Consider the example of ratio data that is height, where it cannot be negative. It can be analyzed statistically, as well as can be added, subtracted, multiplied, and divided. A few of the other examples of ratio data are temperature on a Kelvin scale, weight, and age.
Some techniques such as grouping and sorting can be used to calculate ratio data. In grouping, it is compared whether the ratio variables are equal or not. And, in sorting, the degree of ratio variables are compared to check if one value is greater or lesser than the other value. Other analysis techniques such as conjoint and contingency tables can be applied to ratio data. Using the ratio scale, the perception of users regarding the products or services can be analyzed and understood. It also helps to understand the relationship between multiple values.
Learning about different types of data is important for data management. Not every variety of data is created equally, so it is important to analyze and measure them correctly. The qualitative data needs to be observed subjectively, whereas quantitative data needs to be measured objectively. One can gain actionable insights from the data, only when they know how to use it and what techniques to apply. Knowing different types of data is the first step in gathering information and using it to solve problems.