Lecture on Types of Variables

Introduction

• Scenario: Identifying factors influencing low academic performance.
  • Possible factors: misunderstanding instructions, sleeping, time management issues, lack of focus.
• Definition of a Variable:
  • An entity that can take on different values.
  • Anything that can vary can be considered a variable.

Classifications of Variables

1. Numeric Variables

• Definition: Variables with measurable numerical values (quantitative data).
• Types:
  • Continuous/Interval Variables:
    • Can assume any value within a range.
    • Examples: time, age, weight, height.
    • Recognizes values between whole numbers.
  • Discrete Variables:
    • Whole number values only.
    • Examples: class attendance, number of establishments, number of children.

2. Categorical Variables

• Definition: Variables that describe a quality or characteristic (qualitative data).
• Types:
  • Ordinal Variables:
    • Logically ordered or ranked values.
    • Examples: clothing size, academic ranking, levels of satisfaction.
  • Nominal Variables:
    • Values cannot be logically ordered.
    • Examples: blood type, learning styles, language spoken.
  • Dichotomous Variables:
    • Two categories only.
    • Examples: yes/no, true/false, gender.
  • Polychotomous Variables:
    • Multiple categories.
    • Examples: performance level, educational attainment.

3. Experimental Variables

• Classification:
  • Independent Variables: Cause changes; manipulated in experiments (causal variable).
  • Dependent Variables: Affected by independent variables (effect variable).
• Examples:
  • Effect of studying on academic performance (studying = independent; performance = dependent).
  • Effect of diet/exercise on fitness (diet/exercise = independent; fitness = dependent).
• Control Variables:
  • Held constant to identify outcome differences.
  • Example: class duration.
• Moderator Variables:
  • Influence relationship changes under different conditions.
  • Example: music genre in a study on music's effect on academic performance.
• Extraneous Variables:
  • Pre-existing variables that could influence study results.
  • Example: noise, ventilation, and lighting in a music study.

4. Non-experimental Variables

• Definition: Variables not manipulated by researchers.
• Types:
  • Predictor Variables: Affect other variables in non-experimental studies.
  • Criterion Variables: Influenced by predictor variables.
• Examples:
  • Management styles (predictor) affecting employee satisfaction (criterion).
  • Guidance counseling programs (predictor) affecting absenteeism and dropout rate (criterion).

Conclusion

• Understanding variables and their classifications helps researchers analyze their interactions and effects, contributing to meaningful study outcomes.