All examples for SPSS Statistical Output Interpreter

Multiple Regression: Predictors of Employee Performance

Interprets R regression output with mixed significance predictors, explaining which variables matter, calculating adjusted R², and providing APA-formatted results with appropriate causal language caveats.

Input

What you provide to the skill

Multiple regression performance prediction (n=100): experience B=1.35 SE=0.39 p=.0009, education B=-0.23 p=.709, absenteeism B=-2.89 SE=0.71 p=.0001, salary B=0.0045 p=.035. R²=.5623, Adj-R²=.5421, F(4,95)=27.84 p<.001. Interpret predictors, provide APA format

Output

What the skill generates for you

Interpretation

Test Type: Multiple Linear Regression
Purpose: Predicting employee performance from experience, education years, absenteeism, and salary
Sample Size: n = 100 (95 residual df + 4 predictors + 1)

Overall Model Fit

  • F(4, 95) = 27.84, p < .001
  • Model is significant: predictors collectively explain performance
  • Adjusted R² = .54 (report this, not multiple R²)
  • Interpretation: 54% of variance explained (large effect)

Individual Predictors

Experience: B = 1.35, SE = 0.39, t(95) = 3.46, p < .001

  • Significant positive predictor
  • Each additional year of experience → 1.35 point increase in performance

Education Years: B = -0.23, SE = 0.62, t(95) = -0.38, p = .709

  • Not significant (p > .05)
  • No relationship with performance after controlling for other variables

Absenteeism: B = -2.89, SE = 0.71, t(95) = -4.06, p < .001

  • Significant negative predictor
  • Each additional absence → 2.89 point decrease in performance

Salary: B = 0.0045, SE = 0.0021, t(95) = 2.14, p = .035

  • Significant positive predictor
  • Each $1,000 increase in salary → 4.5 point increase in performance

APA-Formatted Results

“A multiple linear regression was conducted to predict employee performance from experience, education years, absenteeism, and salary. The overall model was significant, F(4, 95) = 27.84, p < .001, adjusted R² = .54. Experience (B = 1.35, SE = 0.39, t = 3.46, p < .001), absenteeism (B = -2.89, SE = 0.71, t = -4.06, p < .001), and salary (B = 0.0045, SE = 0.0021, t = 2.14, p = .035) were significant predictors. Experience and salary positively predicted performance, while absenteeism negatively predicted performance. Education years was not a significant predictor (B = -0.23, SE = 0.62, t = -0.38, p = .709).”

What NOT to Report

  • Residuals (diagnostic information)
  • Residual standard error
  • Significance stars (report exact p-values)
  • Multiple R-squared (use adjusted)
  • Intercept interpretation (35.21 has no meaningful context)

Common Mistakes to Avoid

  1. Confusing R² with correlation coefficient r
  2. Interpreting non-significant predictors (education) as meaningful
  3. Claiming causation from regression (association only)
  4. Forgetting standard errors when reporting coefficients
  5. Reporting multiple R² instead of adjusted R²
  6. Omitting direction of relationships (positive/negative)
View all examples for SPSS Statistical Output Interpreter

About This Skill

Interpret statistical output from SPSS, R, Python, or Stata. Explains each statistic in plain language, generates APA-formatted results, and calculates missing effect sizes.

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