1. Meaning of Variables

• A variable is any characteristic, attribute, or phenomenon that can take different values or vary from one entity to another or within the same entity over time.
• In research, variables are the building blocks — they are what researchers observe, measure, and analyze to study relationships.

Example: Age, income, gender, intelligence, motivation, social media use, etc.

2. Definitions

• Kerlinger (1986): “A variable is a property that takes on different values.”
• Best & Kahn: “A variable is a condition or characteristic that can take on different values or categories.”
• Goode & Hatt: “A variable is a symbol to which numerals or values are assigned.”

3. Types of Variables

(a) Based on Role in Research

• Independent Variable (IV): The variable that is manipulated or considered as the cause. Example: Hours of study, type of teaching method.
• Dependent Variable (DV): The variable that is observed/measured, the effect or outcome. Example: Student achievement, exam score.
• Intervening/Extraneous Variable: Variables that interfere with or influence the relationship between IV and DV. Example: IQ, motivation, or home environment may influence the effect of study hours on achievement.
• Moderator Variable A variable that affects the strength or direction of the relationship between IV and DV. Example: Gender moderating the effect of teaching method on achievement.
• Control Variable: Variables the researcher keeps constant to prevent them from affecting the results. Example: Keeping the classroom environment same while testing two teaching methods.

(b) Based on Measurement

• Continuous Variable: Can be measured in fine gradations. Example: Height, weight, income, intelligence.
• Discrete Variable: Values determined by counting; whole numbers. Example: Number of books, children in family.
• Qualitative Variables: Cannot be measured in numbers; categorized by qualities. Example: Gender, religion, occupation.
• Quantitative Variables: Measured in numbers; show magnitude. Example: Age, income, weight.

(c) Based on Measurement Scale (S.S. Stevens, 1946)

• Nominal Scale: Variables are categories without order. Example: Religion (Hindu/Muslim/Christian), Gender (Male/Female).
• Ordinal Scale: Variables are ranked but differences are not equal. Example: Rank in class (1st, 2nd, 3rd).
• Interval Scale: Variables measured on a scale with equal intervals but no true zero. Example: Temperature in Celsius.
• Ratio Scale: Variables with equal intervals and a true zero. Example: Weight, height, income.

4) Controlling Extraneous Variables

To ensure the results reflect the true IV → DV relationship:

• Randomization → assigning subjects randomly.
• Elimination → removing the effect of irrelevant variables.
• Matching → pairing subjects with similar characteristics.
• Statistical Control → using ANCOVA, regression, etc.
• Including as additional independent variable → model the extraneous variable.