Data types are crucial ideas in statistics because they allow us to apply statistical tests to data in the right ways and help us draw the right conclusions from them.
Exploratory Data Analysis, or EDA, requires a thorough understanding of the various data kinds because you can utilize certain factual measurements only for specific data categories.
Similar to this, choosing the appropriate perception approach requires that you are aware of the type of data analysis on which you are focusing. You can think of data types as a way to organize different kinds of variables.
When it comes to statistics, there are only two types of data: qualitative and quantitative data. But after that, it is divided into 4 different categories of data. For properly conducting the entire study of statistics, data types act as a guide.
Qualitative and Quantitative Data
A collection of information that cannot be quantified in numerical terms is known as qualitative data. It also goes by the name of categorical data. It typically consists of words, stories, and names that humans gave to them.
It provides details about the characteristics of things in data. The results of a qualitative data analysis may include key word inclusion, data extraction, or concept elaboration.
- Red, brown, or black hair
- Opinion: concur, contra, or neutral
Comparatively, quantitative data is a collection of information that incorporates statistical data analysis and was acquired from a variety of people. Quantitative data is often referred to as numerical data. Simply put, it provides information on the estimated quantities of the items and the amounts of the items in the data. Additionally, we can express them in numerical form.
- With the aid of a ruler or tape, we may measure the height (1.70 meters) and distance (1.35 miles).
- A jug can be used to measure water (1.5 litres).
Nominal and ordinal data fall under the category of qualitative data in a subdivision. Quantitative data includes interval and ratio data. We shall read about each of these data kinds in detail here.
When a variable has neither an order nor a quantitative value, nominal data are employed to label it. Therefore, even if you rearrange the values, the meaning will still be clear.
Nominal data are therefore unordered but not equally spaced, observed but not measured, and lack a meaningful zero.
The only numerical operations you can carry out on nominal data are to assert that one perception (equity or inequity) is (or is not) identical to another, and you can utilize this data to accumulate them.
Nominal data cannot be sorted since they cannot be organized.
You wouldn’t be able to perform any numerical tasks either because those are just for numerical data. You may compute frequencies, proportions, percentages, and central points using nominal data.
What languages do you speak?
What’s your nationality?
Ordinal data is virtually identical to nominal data, except that its categories can differ in terms of order, such as first, second, etc. The relative distances between adjacent categories, however, are not continuous.
Ordinal data lacks a meaningful zero, is ordered but not evenly spaced, and is observed but not measured. Ordinal scales are always used to measure things like contentment and happiness.
You can gather information by determining whether ordinal data and nominal data are equivalent or exceptional.
Ordinal data can be sorted by comparing the categories in a simple way, such as greater or less than, higher or lower, and so on.
However, since ordinal data are numerical data, you cannot do any numerical operations on them.
You may calculate the same things with ordinal data as you can with nominal data, such as frequencies, proportions, percentages, and the central point, but ordinal data also includes summary statistics and bayesian statistics.
- Mostly agree
- Mostly disagree
Time of day
The nearest elements are used to measure and sort interval data, but there is no meaningful zero.
The key concept of an interval scale is that the word “interval” denotes “space in between,” which is an important concept to keep in mind because interval scales teach us not just about the order but also about the value between each item.
Ratio data cannot be negative, but interval data can.
Even though interval data and ratio data can appear to be fundamentally identical, what really matters is how their zero-points are defined. The data cannot be ratio data and must instead be interval data if the scale’s zero point was chosen arbitrarily.
As a result, you can quickly add or remove values from interval data as well as correlate the degrees of the data.
You may determine the center point (mean, median, and mode), range (minimum, maximum), and spread (percentiles, interquartile range, and standard deviation) for interval data.
In addition, similar methods from other statistical data analysis might be applied for additional analysis.
- Temperature (°C or F, but not Kelvin)
- Dates (1066, 1492, 1776, etc.)
- Time interval on a 12-hour clock (6 am, 6 pm)
Like interval data, ratio data are measured, ordered, and have equidistant elements and a meaningful zero. However, ratio data can never be negative.
The measuring of heights is a superb example of ratio data. It can be measured in centimeters, inches, meters, or feet, and a negative height is not practical.
The order of the variables, how they compare to one another, and the fact that they have zero are all revealed by ratio data. It enables a variety of calculations and inferences to be made and estimated.
With the exception of zero meaning none, ratio data and interval data are fundamentally the same
Calculating the center point (mean, median, mode), range (minimum, maximum), and dispersion (percentiles, interquartile range, and standard deviation) for ratio data is the same as doing so for interval 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)