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International Journal of STEM Education

Table 6 Logistic regression model for active-learning cluster

From: Predicting implementation of active learning by tenure-track teaching faculty using robust cluster analysis

 

Estimated

95% confidence

Test

p-value

Odds

Interval

Statistic

Intercept

7.27

(1.15, 45.95)

2.11

0.04*

Faculty type

 RG: Teaching faculty

    

 Research faculty

0.28

(0.08, 0.93)

− 2.08

0.04*

 Lecturers

0.46

(0.13, 1.60)

− 1.22

0.22

Faculty rank

 RG: Assistant

    

 Associate

0.53

(0.13, 2.12)

− 0.90

0.37

 Full

0.72

(0.14, 3.78)

− 0.39

0.69

 Years of teaching

1.03

(0.95, 1.13)

0.74

0.46

Gender

 RG: non-female

    

 Female

1.67

(0.60, 4.61)

0.98

0.33

Campus

 RG: Campus 3

    

 Campus 2

0.19

(0.04, 0.98)

− 1.98

0.05*

 Campus 1

2.21

(0.39, 12.60)

0.89

0.37

Discipline

 RG: Biological Sciences

    

 Engineering

0.33

(0.07, 1.54)

− 1.41

0.16

 I &C Sciences

0.59

(0.14, 2.50)

− 0.72

0.47

 Physical sciences

0.12

(0.03, 0.52)

− 2.86

\(<0.001\)*

Class size

 RG: Small (0–99)

    

 Medium (100–199)

0.15

(0.04, 0.57)

− 2.80

0.01*

 Large (200 +)

0.25

(0.08, 0.80)

− 2.33

0.02*

 AIC \(=\) 149.58

    
  1. The coefficients represent the increase/decrease in the odds of being in the active-learning cluster (based on the final cluster assignment) for each of the variables of interest (while holding the other variables in the model constant). The reference group (RG) are labeled for each of the categorical variables
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