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Levels of Measurement
ï‚· Data are collected and coded into numbers
ï‚· Levels define what the numbers assigned to variables represent
ï‚· There are 4 levels or scales
o Nominal: numbers represent assigned or named categories (e.g., 1 = male, 2 =
female)
o Ordinal: numbers represent ranked or ordered categories that are not necessarily
equal in size (e.g., 1 = low, 2 = medium, 3 = high)
o Interval: numbers represent values of equal units (e.g., temperature, age in years).
Note: temperature of 0°C is not the same as 0°F and 0° on both scales does not mean there is
no temperature; age in years is not ratio because 0 years old is anywhere between birth and
the day before the first year.
o Ratio: numbers represent values of equal units and there is a set zero point
(height, weight, blood pressure)
Note: with ratio data ONLY, values can be compared as ratios
(e.g., a risk factor doubled the odds of disease, a diet supplement resulted in a 5% weight
loss)
Rules:
ï‚· Higher level data can be converted to a lower level
Examples:
o ratio data for weight loss can be arranged in 5 lb. interval groups;
o interval age in years can be converted to ordinal age groups such as 5-17, 18-44,
45-64, >65 years;
o ordinal ranks can be combined in nominal categories as in 1 = agree and strongly
agree, 2 = disagree and strongly disagree, 3 = no opinion, (where there is no
specific order)
ï‚· Lower level data cannot be converted to a higher level
Data types:
ï‚· There are 3 data types
o Categorical: nominal and ordinal data; numbers represent categories
(examples: gender, race/ethnicity)
o Ordinal: categorical data that are ranked; not everyone makes this distinction
(examples: low, medium, high)
o Continuous: interval and ratio data; numbers represent numerical values
(examples: age, height, weight)
Rules (extension of the rules for levels of measurement):
o Continuous data can be transformed to categorical data
o Categorical data cannot be transformed to continuous data
Descriptive Statistics
ï‚· Descriptive statistics describe the d
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