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8/11/2019 6 Types of Variables
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Types of VariablesObjective:
Students should be able to identify the different types of
variables, and know the characteristics of each type
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Types of VariablesCategorical (data that are counted)
• Nominal
• Ordinal
Quantitative or Numerical (data that are measured)
• Interval
• Ratio
Why is the type of variable important?
The methods used to display, summarize, and analyze data depend on whether the
variables are categorical or quantitative.
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Types of Variables:
CategoricalNominal
Variables that are “named”, i.e. classified into one or more
qualitative categories that describe the characteristic of interest
• no ordering of the different categories
• no measure of distance between values• categories can be listed in any order without affecting
the relationship between them
Nominal variables are the simplest type of variable
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Nominal
In medicine, nominal variables are often used todescribe the patient. Examples of nominal variables
might include:
Gender (male, female) Eye color (blue, brown, green, hazel)
Surgical outcome (dead, alive)
Blood type (A, B, AB, O)
Note: When only two possible categories exist, the variable is sometimes called
dichotomous, binary, or binomial.
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Ordinal
Variables that have an inherent order to the relationshipamong the different categories
•an implied ordering of the categories (levels)
•quantitative distance between levels is unknown
•distances between the levels may not be the same
•meaning of different levels may not be the samefor different individuals
Note: The scale of measurement for most ordinal variables is called a Likert scale.
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Ordinal
In medicine, ordinal variables often describe the patient’scharacteristics, attitude, behavior, or status. Examples of
ordinal variables might include:
Stage of cancer (stage I, II, III, IV)
Education level (elementary, secondary, college)
Pain level (mild, moderate, severe)
Satisfaction level (very dissatisfied, dissatisfied, neutral,satisfied, very satisfied)
Agreement level (strongly disagree, disagree, neutral, agree,
strongly agree)
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Types of Variables:
Quantitative/NumericalInterval
Variables that have constant, equal distances between
values, but the zero point is arbitrary.
Examples of interval variables:
Intelligence (IQ test score of 100, 110, 120, etc.)
Pain level (1-10 scale)
Body length in infant
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Ratio
Variables have equal intervals between values, the zeropoint is meaningful, and the numerical relationships
between numbers is meaningful.
Examples of ratio variables:
Weight (50 kilos, 100 kilos, 150 kilos, etc.)
Pulse rate
Respiratory rate
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Levels of Measurement Higher level variables can always be expressed at a lower level,
but the reverse is not true.
For example, Body Mass Index (BMI) is typically measured at an
interval-level such as 23.4.
BMI can be collapsed into lower-level Ordinal categories such as:
• >30: Obese
• 25-29.9: Overweight
• <25: Underweight
or Nominal categories such as:
• Overweight
• Not overweight
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Discrete DataQuantitative or Numerical variables that are measured
in each individual in a data set, but can only be whole
numbers.
Examples are counts of objects or occurrences:
Number of children in household
Number of relapses
Number of admissions to a hospital
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Continuous DataQuantitative or Numerical variables that are measured in
each individual in a data set.
Continuous variables can theoretically take on an infinite
number of values - the accuracy of the measurement is
limited only by the measuring instrument.
Note: Continuous data often include decimals or fractions of numbers.
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Continuous Data
Examples of continuous variables:
Height, weight, heart rate, blood pressure,
serum cholesterol, age, temperature
A person’s height may be measured and recorded as 60
cm, but in theory the true height could be an infinite
number of values:
height may be 60.123456789…………..cm
or 59.892345678…………..cm
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Classification of variables in The Bypass Angioplasty Revascularization Investigation
Variable
CABG
(n=914)
PTCA
(n=915)
Type of
VariableAge (years, mean SD) 61.1 3.2 61.8 3.7
Weight (kg, mean SD) 80.9 5.6 78.8 6.0
Gender [#, (%)]
Males
Females
676 (74%)
238 (26%)
668 (73%)
247 (27%)
Education [#, (%)]
Grade School
High School
Some College
College Graduate or >
192 (21%)
457 (50%)
165 (18%)
100 (11%)
192 (21%)
458 (50%)
165 (18%)
100 (11%)
Prior Hospitalizations (mean SD) 4.0 0.6 3.8 0.6
Post Treatment Mortality
Alive at 5 Years
Dead at 5 Years
902 (98.7%)
12 (1.3%)
898 (98.1%)
17 (1.9%)