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

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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.

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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

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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.

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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.

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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)

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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

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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

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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 BMI can be collapsed into lower-level Ordinal categories such as: >30: Obese : Overweight <25: Underweight or Nominal categories such as: Overweight Not overweight

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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

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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.

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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 …………..cm or …………..cm

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Classification of variables in The Bypass Angioplasty Revascularization Investigation

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