Data types are crucial narratives in statistics because they let us apply statistical measurements appropriately and make correct conclusions about data.
A good understanding of the various data kinds is necessary for exploratory data analysis or EDA because you can utilize certain factual measurements for specific types of data alone.
This article offers an overview of the numerous facts you need to know to analyze the exploratory data properly.
To think about data types as a mechanism for organizing different types of variables, consider the following:
When it comes to statistics, there are only two types of data: qualitative and quantitative data. However, there is a subdivision following that, and it is divided into four distinct categories of data. Data types are like a roadmap to appropriately conduct the entire statistical survey!
This blog gives an overview of the numerous kinds of data you need to know in order to perform a good analysis of exploratory data.
Qualitative and Quantitative Data
Qualitative data is a collection of information that cannot be quantified numerically. Category data is another word used to describe this type of information. It is typically composed of words and narratives, which we have labeled with labels to distinguish them.
It is used to transmit information about the attributes of items that are present within data sets. The results of qualitative or statistical data analysis can include keyword selection, data extraction, and concept development.
- Consider the following examples:
- Hair colours include black, brown, and red.
- Opinion – accept, disagree, or remain neutral
Comparatively, quantitative data is a collection of information acquired from a group of persons, which may involve types of data in statistics and other methods. Quantitative data is sometimes called numeric data. Simply said, it tells you how many of each item in the data is estimated. We can express them numerically as well.
Nominal Data
For variables with no quantitative value or order, nominal data are used. Consequently, changing the sequence of the values does not affect the meaning.
Consequently, nominal data are observed but not measured, they are unordered but not equally spaced, and there is no meaningful zero in the data set.
When dealing with nominal data, the only numerical actions you can engage in are stating that one perception is (or isn’t) similar to another (equity or inequity), and you can use this data to accumulate more perceptions.
Because nominal data cannot be organized, it is impossible to sort them.
You would also not be able to perform any numerical tasks because they are reserved for numerical data alone. Nominal information can be employed in the calculation of frequencies, proportions and central points.
Nominal data examples:
What languages are you fluent in?
- English
- German
- French
- Punjabi
What country are you from?
American \Indian \Japanese \German
You can readily observe that the categories in these samples of nominal data are not in any particular order.
Data that is ordinal
Unlike nominal data, ordinal data is essentially identical to nominal data, with the exception of the order of its categories, which can be sorted into first, second, and so on categories. The relative distances between adjacent categories, on the other hand, are not consistent.
Ordinal data is observed but not quantified; it is ordered but not equidistant; and it lacks any meaningful zero. Ordinal scales are often used to measure happiness and contentment.
As with nominal data, you can accumulate information about ordinal data by deciding whether or not they are equivalent or outstanding in comparison to one another.
Ordinal data can be sorted by comparing essential categories, for example, higher or lower, and so on, as they are ordered.
Because ordinal data are numerical, you can’t execute any numerical operations on them.
If you have ordinal data, you can compute the same things that you do with nominal data, including frequencies, proportions, percentages, and the central point. However, ordinal data has one additional point, which is summary statistics, which is comparable to bayesian statistics.
Examples of Ordinal data:
Opinion
- Agree
- Disagree
- Mostly agree
- Neutral
- Mostly disagree
Time of day:
- Morning
- Noon
- Evening
In these examples, the categories are clearly arranged.
Data on Intervals
Consequently, interval data makes it simple to correlate degrees of data as well as to add and remove numbers from a set of data.
The key element of an interval scale is that the word ‘interval’ means’ space in between,’ which is important to remember because interval scales teach us not only about the order but also about the worth of each type of data.
Interval data may be negative, but ratio data cannot be negative.
Although interval data can essentially display the same as proportion data, its described zero points are important. If the scale’s zero points is chosen subjectively, the data cannot be ratio data and must instead be interval data.
As a result, interval data allows you to simply correlate the degrees of the data as well as add and subtract numbers.
Some descriptive statistics, especially in types of data in statistics can be calculated by the center point (mean, median, mode), range (minimum, max) and dispersion for the interval data (percentiles, interquartile range, and standard deviation).
Data are measured and arranged in the proportion format using equidistant elements and significant null and can never, as interval dates, be negative.
Interval data examples:
- Temperature (in degrees Celsius or Fahrenheit, not Kelvin)
- Timetables (1066, 1492, 1776, etc.)
- Time interval at 12 hours (6 am, 6 pm)
Ratio Information
Data in the ratio format is measured and arranged with equidistant elements and a meaningful zero, and it can never be negative, as can be the case with interval data.
The measuring of heights is a particularly good illustration of ratio data in action. It can be measured in centimeters, inches, meters, or feet, and a negative height is not possible.
Ratio data provide us the order, the contrast and the zero-order of the variables. It makes possible the making and drawing of a wide range of estimations and conjectures.
Ratio data is almost identical to interval data, except for the fact that zero = none.
The descriptive types of data in statistics for ratio data are the same as the central (mean, median, mode), range (at least, max) and spread interval data (percentiles, interquartile range, and standard deviation).
Ratio data example:
- Age (from 0 to 100 years)
- Temperature (in Kelvin, not in degrees Celsius or Fahrenheit)
- A certain distance (measured with a ruler or any other assessing device)
- Interval of time (measured with a stop-watch or similar)
So, for example, age, absolute zero, a distance determined from a specific location, and time all have real zeros.
Conclusion
We hope you have gained a better understanding of the four forms of data in statistics and their significance; now you can learn how to manage data appropriately, which statistical hypothesis tests you can perform, and what you can compute using these data. Moreover,
Nominal data and ordinal data are the sorts of qualitative data or categorical data that are distinguished from one another. Interval and ratio data are both types of data quantitative.
Nominal Data are observed rather than measured, and they are unsorted, non-equidistant, and lack a meaningful zero.
Ordinal data isn’t measured, but rather observed, and it’s arranged in a non-equidistant manner with no meaningful zero.
Despite the fact that interval data is measured and arranged with equidistant pieces, there is no meaningful zero.
Additionally, ratio types of data are quantified and organized using equidistant pieces and a meaningful zero.
