Quantitative Research Methods

Regression Analysis in
Education Research

Understand how to predict student satisfaction using statistical modeling, interpret coefficients, and report findings in APA style.

Application

See how regression analysis is practically applied in real-world education research scenarios.

Interpretation

Master the art of interpreting complex statistical results in a social science context.

Reporting

Practice writing precise, academic results sections following APA style guidelines.

1

Research Context

Imagine a study investigating the drivers of student happiness at a university. We want to know which factors best predict satisfaction and how much variance we can explain.

Variables

  • Dependent (Y): Student Satisfaction Score
  • Independent (X): Teaching Support, Workload, Motivation
Teaching Support
+
Workload
-
Student Satisfaction
Motivation
+
2

Key Results Dashboard

Model Fit (R²)

52% Variance Explained

Adjusted R² = 0.50

Explains about half the variance in satisfaction.

ANOVA Significance

F(3, 120) = 43.6
p < .001

The model is statistically significant overall.

Diagnostics Check

  • Independence (D-W)
    1.92
  • Multicollinearity (VIF)
    < 2.0
  • Residuals
    Normal

Coefficients (Impact Strength)

Teaching Support (B = 0.30, p < .001) Strong Positive
Motivation (B = 0.22, p < .01) Moderate Positive
Workload (B = -0.18, p < .05) Negative Impact

Interpretation

Teaching support is the strongest predictor of satisfaction. Interestingly, while motivation helps, a heavier workload actively decreases satisfaction scores.

3

APA Write-Up Example

A multiple regression examined predictors of student satisfaction. The model was significant, F(3, 120) = 43.6, p < .001, explaining 50% of the variance in satisfaction (Adjusted R² = .50). Teaching support (B = .30, p < .001) and motivation (B = .22, p < .01) were positive predictors, while workload (B = –.18, p < .05) negatively predicted satisfaction.

4

Quick Check-in

1. Which factor was the strongest positive predictor?

Why is Adjusted R² useful?

Click to reveal answer

It penalizes the model for adding useless variables, giving a more realistic estimate of how well the model generalizes to the population.

Discuss: The Negative Workload Effect

"How would you explain the negative coefficient for workload to a non-specialist?"

Show Suggested Explanation
"Think of it like a seesaw: When the workload gets heavier, student satisfaction goes down, assuming everything else (like support and motivation) stays the same."
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