In social science research, asking “Is there a relationship?” is often not enough. Sometimes we want to know “How much does it affect?”, “Which variable is stronger?”, or “Which one explains the outcome first?” This is where regression analysis comes in. In this article, we’ll explain the most commonly used types of regression in simple terms.

1. Types of Regression

Simple Linear Regression
The most basic type of regression. It examines the linear relationship between one dependent and one independent variable.

Example:
Does exam score increase as study time increases?

Model:
Y = β₀ + β₁X + ε

When to use:

• Only one predictor variable
• Relationship is linear

Multiple Linear Regression
Examines the effect of multiple independent variables on one dependent variable simultaneously.

Example:
Do study time, sleep duration, and social media use together affect exam performance?

Model:
Y = β₀ + β₁X₁ + β₂X₂ + β₃X₃ + … + ε

Advantage:
Allows comparison of the relative impact of each variable.

Logistic Regression
Used when the dependent variable is categorical (e.g., yes/no, pass/fail).

Example:
Can we predict whether a student passes the exam based on study time and attendance?

Model:
p = 1 / (1 + e^-(β₀ + β₁X₁ + β₂X₂ + …))

When to use:

• Binary dependent variable
• Probability estimation

Nonlinear Regression
Used when the relationship between variables is not linear.

Example:
If stress increases performance up to a point, then decreases (inverted U-shaped relationship).

Model:
Y = β₀ + β₁X + β₂X² + ε (polynomial example)

Tip:
Use scatter plots to visualize the relationship shape.

Hierarchical Regression
Independent variables are added to the model step by step. Each step tests how much the model improves.

Example:
First add demographic variables (age, gender), then psychological ones (self-esteem, anxiety) to see how they explain performance.

Advantage:
Allows evaluation of the contribution of variable groups separately.

2. Comparison Table of Regression Types

3. How to Report Regression in Your Thesis

“Study time and social media use significantly predicted exam performance, R² = 0.42, F(2, 97) = 8.56, p < 0.001. Study time had a positive effect, while social media use had a negative effect.”

4. Conclusion

Regression analysis reveals not just relationships, but also the direction and strength of effects. However, not every model fits every question. Choosing the right type of regression in your thesis improves both the quality of your analysis and your academic credibility.