Data types are important concepts in statistics, they enable us to apply statistical measurements correctly on data and assist in correctly concluding certain assumptions about it.
Having an adequate comprehension of the various data types is significantly essential for doing Exploratory Data Analysis or EDA since you can use certain factual measurements just for particular data types.
SImilarly, you need to know which data analysis and its type you are working to select the correct perception technique. You can consider data types as an approach to arrange various types of variables.
If you go into detail then there are only two classes of data in statistics, that is Qualitative and Quantitative data. But, after that, there is a subdivision and it breaks into 4 types of data. Data types are like a guide for doing the whole study of statistics correctly!
This blog gives you a glance over different types of data need to know for performing proper exploratory data analysis.
Qualitative and Quantitative Data
Qualitative data is a bunch of information that cannot be measured in the form of numbers. It is also known as categorical data. It normally comprises words, narratives, and we labelled them with names.
It delivers information about the qualities of things in data. The outcome of qualitative data analysis can come in the type of featuring key words, extracting data, and ideas elaboration.
Hair colour- black, brown, red
Opinion- agree, disagree, neutral
On the other side, Quantitative data is a bunch of information gathered from a group of individuals and includes statistical data analysis. Numerical data is another name for quantitative data. Simply, it gives information about quantities of items in the data and the items that can be estimated. And, we can formulate them in terms of numbers.
We can measure the height (1.70 meters), distance (1.35 miles) with the help of a ruler or tape.
We can measure water (1.5 litres) with a jug.
Under a subdivision, nominal data and ordinal data come under qualitative data. Interval data and ratio data come under quantitative data. Here we will read in detail about all these data types.
Different Types of Data
Nominal data are used to label variables where there is no quantitative value and has no order. So, if you change the order of the value then the meaning will remain the same.
Thus, nominal data are observed but not measured, are unordered but non-equidistant, and have no meaningful zero.
The only numerical activities you can perform on nominal data is to state that perception is (or isn't) equivalent to another (equity or inequity), and you can use this data to amass them.
You can't organize nominal data, so you can't sort them.
Neither would you be able to do any numerical tasks as they are saved for numerical data. With nominal data, you can calculate frequencies, proportions, percentages, and central points.
Examples of Nominal data:
You can clearly see that in these examples of nominal data the categories have no order.
Ordinal data is almost the same as nominal data but not in the case of order as their categories can be ordered like 1st, 2nd, etc. However, there is no continuity in the relative distances between adjacent categories.
Ordinal Data is observed but not measured, is ordered but non-equidistant, and has no meaningful zero. Ordinal scales are always used for measuring happiness, satisfaction, etc.
With ordinal data, likewise, with nominal data, you can amass the information by evaluating whether they are equivalent or extraordinary.
As ordinal data are ordered, they can be arranged by making basic comparisons between the categories, for example, greater or less than, higher or lower, and so on.
You can't do any numerical activities with ordinal data, however, as they are numerical data.
With ordinal data, you can calculate the same things as nominal data like frequencies, proportions, percentage, central point but there is one more point added in ordinal data that is summary statistics and similarly bayesian statistics.
Examples of Ordinal data:
- Mostly agree
- Mostly disagree
In these examples, there is an obvious order to the categories.
Interval Data are measured and ordered with the nearest items but have no meaningful zero.
The central point of an Interval scale is that the word 'Interval' signifies 'space in between', which is the significant thing to recall, interval scales not only educate us about the order but additionally about the value between every item.
Interval data can be negative, though ratio data can't.
Even though interval data can show up fundamentally the same as ratio data, the thing that matters is in their characterized zero-points. If the zero-point of the scale has been picked subjectively, at that point the data can't be ratio data and should be interval data.
Hence, with interval data you can easily correlate the degrees of the data and also you can add or subtract the values.
There are some descriptive statistics that you can calculate for interval data are central point (mean, median, mode), range (minimum, maximum), and spread (percentiles, interquartile range, and standard deviation).
In addition to that, similar other statistical data analysis techniques can be used for more analysis.
Examples of Interval data:
Temperature (°C or F, but not Kelvin)
Dates (1066, 1492, 1776, etc.)
Time interval on a 12-hour clock (6 am, 6 pm)
Ratio Data are measured and ordered with equidistant items and a meaningful zero and never be negative like interval data.
An outstanding example of ratio data is the measurement of heights. It could be measured in centimetres, inches, meters, or feet and it is not practicable to have a negative height.
Ratio data enlightens us regarding the order for variables, the contrasts among them, and they have absolutely zero. It permits a wide range of estimations and surmisings to be performed and drawn.
Ratio data is fundamentally the same as interval data, aside from zero means none.
The descriptive statistics which you can calculate for ratio data are the same as interval data which are central point (mean, median, mode), range (minimum, maximum), and spread (percentiles, interquartile range, and standard deviation).
Example of Ratio data:
Age (from 0 years to 100+)
Temperature (in Kelvin, but not °C or F)
Distance (measured with a ruler or any other assessing device)
Time interval (measured with a stop-watch or similar)
Therefore, for these examples of ratio data, there is an actual, meaningful zero-point like the age of a person, absolute zero, distance calculated from a specified point or time all have real zeros.
We hope you understood about 4 types of data in statistics and their importance, now you can learn how to handle data correctly, which statistical hypothesis tests you can use, and what you could calculate with them. Moreover,
Nominal data and ordinal data are the types of qualitative data or categorical data.
Interval data and ratio data are the types of quantitative data which are also known as numerical data.
Nominal Data are not measured but observed and they are unordered, non-equidistant, and also have no meaningful zero.
(Also check: Types of Statistical Analysis)
Ordinal Data is also not measured but observed and they are ordered however non-equidistant and have no meaningful zero.
Interval Data are measured and ordered with equidistant items yet have no meaningful zero.
Ratio Data are also measured and ordered with equidistant items and a meaningful zero.