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Multinomial Logistic Regression

Multinomial Logistic Regression. 3 or more groups. Students in Engineering at ECU Persisters – still in the program after 2 years Left in Good Standing (GPA  2.00) Left in Poor Standing (GPA < 2.00). Predictor Variables. SAT Scores (Verbal and Quantitative)

eugenia-weber
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Multinomial Logistic Regression

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  1. Multinomial Logistic Regression

  2. 3 or more groups • Students in Engineering at ECU • Persisters – still in the program after 2 years • Left in Good Standing (GPA  2.00) • Left in Poor Standing (GPA < 2.00)

  3. Predictor Variables • SAT Scores (Verbal and Quantitative) • ALEKS Scores – calculus readiness • High School GPA • NEO Five-Factor Inventory (5 variables) • Nowicki–Duke Locus of Control (high = external)

  4. Standardize Predictors

  5. Analyze, Regression, Multinomial Logistic

  6. Ask for a classification table

  7. Output

  8. k-1 sets of coefficients • Earlier we designated the reference group to be that with the highest code, the persisters. • Each of the other two groups will be contrasted with that group.

  9. Those who left in poor standing versus those who persisted.

  10. REGWQ A Posteriori Pairwise Comparisons Between Group Means.

  11. Presenting the Results • Please see the associated document.

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