Nominal measurements organizes data into categories that cannot necessarily be arranged in any particular order.  Data at this level tends to be qualitative. Nominal measurements are considered to be the lowest level of measurement and describe data that has no order. Nominal data can be thought of descriptive categories or qualities that can only be counted or classified.  One thing to note is that categories typically must be mutually exclusive meaning the the description or data can only fall into one category. Examples include biological gender, race, hometown, primary language at home, fabric type,  car brand, college, and color IE car colors.

Ordinal measurements - considered to be the next "level of data" in the hierarchy-  organizes data in some order, this order or ranking tends to be relative and the differences in the data values cannot always be determined, nor is it necessarily worthwhile. When you hear ordinal think order. Data categories or ordinal data have some logical order, where the order is based on perceived importance or meaning. The aforementioned categories are mutually exclusive and exhaustive. Examples include rating something as big, bigger, and biggest or A/B/C/D or F grading scales or cancer stages. This type of measurement is also great for questionnaires that ask questions about habits, ie how likely are you to purchase xx again unlikely,  moderately likely, or highly likely.

Interval measurements are similar to ordinal in that you can ascertain  some sort of order but now the differences between data is meaningful, and can always be determined. Interval level data includes all the characteristics of the ordinal level, and the difference between values is a constant size. One thing to note is that with interval measurements there is also no natural zero point or "true zero" - zero doesn't exist in nature. Examples include ring size, year an event happened, pH level, dress size, temperature and shoe size or number of stars in a yelp review. If you wear size 0 clothing it doesn't mean your naked, or that you don't wear clothes.

Ratio measurements, unlike intervals, have a zero starting point, and  differences are always meaningful. Ratio level data includes all the characteristic of the interval level and there is a meaningful zero point. Differences and ratios comprising of ratio level measurements are meaningful and can be used for analysis. Examples  include height, heart beats per minute, amount of money, volume of fluid, number of miles driven, and years of education.