Which Of The Following Data Is Used To Determine Credit Scores?

Levels of measurement: nominal, ordinal, interval and ratio

In statistics, we use data to respond interesting questions. Simply non all data is created equal. At that place are actually four differentdata measurement scales that are used to categorize unlike types of data:

1. Nominal

2. Ordinal

iii. Interval

4. Ratio

In this post, we define each measurement scale and provide examples of variables that can be used with each calibration.

Nominal

The simplest measurement calibration we can use to label variables is anominal scale.

Nominal scale: A calibration used to label variables that have no quantitative values.

Some examples of variables that can be measured on a nominal scale include:

Gender: Male person, female
Middle colour: Blueish, light-green, chocolate-brown
Hair color: Blonde, blackness, brown, grayness, other
Claret type: O-, O+, A-, A+, B-, B+, AB-, AB+
Political Preference:Republican, Democrat, Independent
Identify you alive: City, suburbs, rural

Variables that tin be measured on a nominal scale have the post-obit properties:

They have no natural order. For example, nosotros tin can't arrange eye colors in gild of worst to best or lowest to highest.
Categories are mutually sectional. For example, an private tin't havebothblue and brown optics. Similarly, an individual can't livebothin the metropolis and in a rural area.
The just number we can summate for these variables arecounts. For example, we can count how many individuals have blonde hair, how many have black hair, how many have brownish hair, etc.
The just measure of central tendency we can calculate for these variables is the fashion. The mode tells usa which category had the most counts. For example, we could notice which eye color occurred most frequently.

The most common way that nominal calibration data is collected is through a survey. For instance, a researcher might survey 100 people and inquire each of them what blazon of place they live in.

Question: What type of area do you live in?

Possible Answers: City, Suburbs, Rural.

Using this data, the researcher can discover out how many people live in each area, as well as which area is the most common to alive in.

Ordinal

The next blazon of measurement scale that we can use to label variables is anordinal calibration.

Ordinal scale: A scale used to characterization variables that have a naturalorder, but no quantifiable difference between values.

Some examples of variables that can be measured on an ordinal scale include:

Satisfaction: Very unsatisfied, unsatisfied, neutral, satisfied, very satisfied
Socioeconomic status: Low income, medium income, high income
Workplace condition: Entry Annotator, Analyst I, Analyst Ii, Lead Annotator
Degree of pain: Small-scale amount of hurting, medium amount of pain, high corporeality of pain

Variables that can be measured on an ordinal scale take the following properties:

They take a natural gild. For case, "very satisfied" is improve than "satisfied," which is better than "neutral," etc.
The difference betwixt values tin can't be evaluated.For example, we can't exactly say that the difference betwixt "very satisfied and "satisfied" is the same as the departure between "satisfied" and "neutral."
The two measures of key tendency we can calculate for these variables arethe modeandthe median. The mode tells us which category had the most counts and the median tells us the "middle" value.

Ordinal scale data is oftentimes collected past companies through surveys who are looking for feedback about their product or service. For case, a grocery store might survey 100 recent customers and enquire them about their overall experience.

Question: How satisfied were you with your most recent visit to our store?

Possible Answers: Very unsatisfied, unsatisfied, neutral, satisfied, very satisfied.

Using this data, the grocery shop tin analyze the total number of responses for each category, identify which response was most common, and identify the median response.

Interval

The adjacent type of measurement calibration that we can use to label variables is aninterval scale.

Interval scale: A scale used to label variables that have a natural order and a quantifiable deviation between values,but no "true zero" value.

Some examples of variables that tin be measured on an interval scale include:

Temperature: Measured in Fahrenheit or Celsius
Credit Scores: Measured from 300 to 850
Sat Scores: Measured from 400 to 1,600

Variables that can be measured on an interval scale have the following properties:

These variables have a natural order.
We tin can measure the hateful, median, mode, and standard deviation of these variables.
These variables have an verbal divergence betwixt values.Recall that ordinal variables have no verbal difference between variables – we don't know if the deviation betwixt "very satisfied" and "satisfied" is the aforementioned as the difference between "satisfied" and "neutral." For variables on an interval scale, though, we know that the divergence between a credit score of 850 and 800 is the exact aforementioned as the difference between 800 and 750.
These variables take no "true zero" value.For instance, it'due south impossible to accept a credit score of nothing. It's also impossible to accept an SAT score of zippo. And for temperatures, it'due south possible to have negative values (e.thousand. -ten° F) which means there isn't a true aught value that values tin can't go below.

The nice affair virtually interval calibration data is that information technology can be analyzed in more than ways than nominal or ordinal data. For example, researchers could get together data on the credit scores of residents in a certain county and calculate the following metrics:

Median credit score (the "center" credit score value)
Mean credit score (the average credit score)
Mode credit score (the credit score that occurs most frequently)
Standard departure of credit scores (a way to measure how spread out credit scores are)

Ratio

The concluding blazon of measurement scale that we tin can use to label variables is a ratio calibration.

Ratio scale: A scale used to label variables that have a natural order, a quantifiable divergence between values, and a "true nothing" value.

Some examples of variables that tin exist measured on a ratio scale include:

Height: Tin can be measured in centimeters, inches, feet, etc. and cannot have a value beneath zero.
Weight:Can be measured in kilograms, pounds, etc. and cannot accept a value below zero.
Length:Can be measured in centimeters, inches, anxiety, etc. and cannot have a value below zero.

Variables that can exist measured on a ratio calibration accept the following properties:

These variables accept a natural order.
We tin can summate the mean, median, mode, standard deviation, and a variety of other descriptive statistics for these variables.
These variables have an exact difference betwixt values.
These variables have a "true zip" value.For example, length, weight, and height all take a minimum value (naught) that tin't be exceeded. It's not possible for ratio variables to have on negative values. For this reason, the ratiobetween values can be calculated. For example, someone who weighs 200 lbs. can be said to counterbalancetwo timesas much as someone who weights 100 lbs. Likewise someone who is 6 feet tall is ane.5 times taller than someone who is 4 feet tall.

Data that can be measured on a ratio scale can be analyzed in a variety of means. For example, researchers could gather information near the summit of individuals in a sure school and calculate the following metrics:

Median tiptop
Mean acme
Mode elevation
Standard deviation of heights
Ratio of tallest top to smallest height

Summary

The following table provides a summary of the variables in each measurement calibration:

Property	Nominal	Ordinal	Interval	Ratio
Has a natural "order"	Yes	YES	YES	Yes
Mode can exist calculated	YES	Yeah	YES	YES
Median can be calculated		Yes	Yes	Yeah
Hateful can be calculated			Yes	Yep
Exact difference betwixt values			Yep	Yeah
Has a "true zero" value				YES