Statistics Chapter 2 - Classification of Variables

Classification of Variables

• Variables are factors or properties measured, controlled, or manipulated by researchers.
• Types of variable classifications include: Numeric, Categorical, Experimental, Non-Experimental, and based on the number of variables studied.

Numeric Variables

• Numeric variables represent measurable numerical quantities, answering "how many" or "how much."
• Comprised of Continuous and Discrete variables.

Continuous Variables

• Can take any value within a specified range of real numbers, also referred to as Interval Variables.
• Examples include time, age, temperature, height, and weight.

Discrete Variables

• Only take whole values within the defined limits.
• Examples include the number of registered cars, business locations, children in a family, student population, and faculty numbers.

Categorical Variables

• Describe qualities or characteristics and answer questions like "what type" or which category.
• Further divided into Ordinal, Nominal, Dichotomous, and Polychotomous variables.

Ordinal Variables

• Can be logically ordered or ranked.
• Examples include academic grades, clothing sizes, and attitude measures like "strongly agree."

Nominal Variables

• Values cannot be logically sequenced.
• Examples encompass business types, eye colors, religions, and types of learners.

Dichotomous Variables

• Represent only two categories.
• Examples are male/female, yes/no, and true/false.

Polychotomous Variables

• Have multiple categories.
• Examples include educational attainment and levels of performance.

Experimental Variables

• Include Independent, Dependent, and Extraneous variables.

Independent Variables

• Typically manipulated in experiments, also known as explanatory variables.

Dependent Variables

• Affected by the manipulation of independent variables, also referred to as response or predicted variables.

Extraneous Variables

• Also called mediating or intervening variables; can affect the relationship between independent and dependent variables.

Non-Experimental Variables

• Include Predictor and Criterion variables.

Predictor Variables

• Change one or more other variables in non-experimental studies.

Criterion Variables

• Influenced by predictor variables.

Study Types by Number of Variables

• Univariate study: focuses on one variable.
• Bivariate study: examines two variables.
• Polyvariate study: investigates more than two variables.