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Types of Variables

Types of Variables

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Types of Variables

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  1. Types of Variables Objective: • Students should be able to identify the different types of variables, and know the characteristics of each type

  2. Types of Variables • Categorical (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.

  3. Types of Variables: Categorical • Nominal • 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

  4. Nominal • In medicine, nominal variables are often used to describe 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.

  5. Ordinal • Variables that have an inherent order to the relationship • among 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 same for different individuals Note: The scale of measurement for most ordinal variables is called a Likert scale.

  6. Ordinal • In medicine, ordinal variables often describe the patient’s characteristics, 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)

  7. Types of Variables: Quantitative/Numerical • Interval • 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

  8. Ratio • Variables have equal intervals between values, the zero point 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

  9. 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

  10. Discrete Data • Quantitative 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

  11. Continuous Data Quantitative 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.

  12. 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………… or 59.892345678…………

  13. Classification of variables in The Bypass Angioplasty Revascularization Investigation