There are four scales of measurement: Nominal, Ordinal, Interval, Ratio. Show These are considered under qualitative and quantitative data as under: Qualitative data:
In this scale, categories are nominated names (hence “nominal”). There is no inherent order between categories. Put simply, one cannot say that a particular category is superior/ better than another. Examples:
The various categories can be logically arranged in a meaningful order. However, the difference between the categories is not “meaningful”. Examples:
Quantitative data:
The values (not categories) can be ordered and have a meaningful difference, but doubling is not meaningful. This is because of the absence of an “absolute zero”. Example: The Celsius scale: The difference between 40 C and 50 C is the same as that between 20 C and 30 C (meaningful difference = equidistant). Besides, 50 C is hotter than 40 C (order). However, 20 C is not half as hot as 40 C and vice versa (doubling is not meaningful). Meaningful difference: In the Celsius scale, the difference between each unit is the same anywhere on the scale- the difference between 49 C and 50 C is the same as the difference between any two consecutive values on the scale ( 1 unit).[Thus, (2-1)= (23-22)= (40-39)=(99-98)= 1].
The values can be ordered, have a meaningful difference, and doubling is also meaningful. There is an “absolute zero”. Examples:
In addition, quantitative data may also be classified as being either Discrete or Continuous. Discrete: The values can be specific numbers only. Fractions are meaningless. In some situations, mathematical functions are not possible, too. Examples:
Continuous: Any numerical value (including fractions) is possible and meaningful. Examples:
Most of the numerical data we use is continuous. As you might have noticed by now, the Ratio scale often involves continuous data [Temperature is an exception, unless the Kelvin scale is being used]. http://en.wikibooks.org/wiki/Statistics/Different_Types_of_Data/Quantitative_and_Qualitative_Data http://www.cimt.plymouth.ac.uk/projects/mepres/book7/bk7i11/bk7_11i1.htm Click to access 03a_continuous_descriptive.slides.pdf Which level of measurement Cannot have ordered categories?With nominal level of measurement, no meaningful order is implied. This means we can re-order our list of variables without affecting how we look at the relationship among these variables. Here are some examples of nominal level data: The number on an athlete's uniform.
What level of measurement involves data that may be arranged in some order?Data are at the ordinal level of measurement if they can be arranged in some order, but differences between values either cannot be determined or are meaningless.
What level of measurement classifies data into categories with no order or ranking?Nominal. A nominal scale describes a variable with categories that do not have a natural order or ranking. You can code nominal variables with numbers if you want, but the order is arbitrary and any calculations, such as computing a mean, median, or standard deviation, would be meaningless.
What level of measurement is categories?There are 4 levels of measurement, which can be ranked from low to high: Nominal: the data can only be categorized. Ordinal: the data can be categorized and ranked. Interval: the data can be categorized and ranked, and evenly spaced.
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