6 types of variables

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



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.



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



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.



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.



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)



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



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



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



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



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.



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



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%)

6 types of variables

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