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Accessing Predictors of Burnout Among University Professors Using SPSS

Let’s delve into the intricate factors contributing to burnout among university professors. Employing hierarchical logistic regression, we systematically examine the impact of perceived control, coping ability, stress from teaching, and stress from research on the likelihood of experiencing burnout. The journey unfolds in three blocks, each unveiling crucial insights into the predictors of burnout. From establishing a baseline model to introducing key variables and observing their nuanced effects, SPSS becomes our lens to illuminate the intricate landscape of burnout prediction among academia. Join us as we decipher the statistical intricacies and unravel the stories hidden within the numbers, empowering us to understand and address burnout among university professors.
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Question 1: Perceived Value of Statistical Science and Study Time Analysis

Problem Description:

The SPSS assignment aims to confirm a hypothesis regarding the relationship between students' perceived value of statistical science and their study time, with an expectation that higher perceived value correlates with increased study time and, consequently, higher test scores.

Solution:

Upon analysis using the PROCESS module in SPSS, the direct effect between the perceived value of statistical science and test scores was found to be significant (P-value = 0.000). Additionally, both perceived value and study time had significant effects on test scores. The indirect effect of perceived value on test scores through study time was also significant, accounting for 22% of the total effect.

Selected Output:

  • Perceived value → Test scores: b = 0.43, SE = 0.09, t = 4.97, p-value = 0.00
  • Indirect effect: b = 0.32, SE = 0.08, 95% CI [0.17, 0.48], 22% of total effect.

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Question 2: Association between Eye Colors of Men and Women

Problem Description:

This assignment investigates whether there's a significant association between the eye colours of men and their female partners, utilizing the chi-squared test of independence.

Solution:

The chi-square test revealed a statistically significant relationship between the eye colours of men and women (X^2(4, N = 204) = 33.702, p-value = 0.000), indicating a correlation between the eye colours of men and their female partners.

Selected Output:

  • Chi-Square: X^2(4, N = 204) = 33.702, p-value = 0.000.

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Question 3: Predictors of Burnout among University Professors

Problem Description:

The assignment involves conducting hierarchical logistic regression to identify predictors of burnout among university professors, considering factors like perceived control, coping style, stress from teaching, and stress from research.

Solution:

The overall model was statistically significant, and individual predictors showed varied relationships with burnout. Perceived control and coping ability positively predicted burnout, while stress from teaching negatively predicted burnout. Stress from research did not significantly predict burnout.

Selected Output:

  • Perceived Control: OR = 1.100, 95% CI = [1.071, 1.130]
  • Coping Ability: OR = 1.146, 95% CI = [1.112, 1.181]
  • Stress from Teaching: OR = 0.920, 95% CI = [0.890, 0.951]
  • Stress from Research: Not a significant predictor (p-value > 0.05).

Question 3: Predictors of Burnout among University Professors (Output Analysis)

Problem Description:

The task involves hierarchical logistic regression to identify predictors of burnout among university professors. The analysis considers perceived control, coping ability, stress from teaching, and stress from research.

Solution:

Block 0: Beginning Block

  • Model Fit:
  • Total Cases: 467
  • Burnout Prediction Accuracy: 74.5%
  • Variables in the Equation:
  • Constant: Exp(B) = 0.342
  • Variables not in the Equation:
  • Perceived Control, Coping Ability
  • Overall Statistics: Chi-square = 162.861, df = 2, Sig. = .000

Block 1: Method = Enter

  • Model Fit:
  • Chi-square for Model Coefficients: 165.928, df = 2, Sig. = .000
  • Overall Model Chi-square: 165.928, df = 2, Sig. = .000
  • Model Summary: -2 Log likelihood = 364.179, Cox & Snell R Square = 0.299, Nagelkerke R Square = 0.441
  • Burnout Prediction Accuracy: 80.5%
  • Variables in the Equation:
  • Perceived Control: Exp(B) = 1.063
  • Coping Ability: Exp(B) = 1.086
  • Constant: Exp(B) = 0.011
  • Variables not in the Equation:
  • None

Block 2: Method = Enter

  • Model Fit:
  • Chi-square for Model Coefficients: 30.468, df = 2, Sig. = .000
  • Overall Model Chi-square: 196.396, df = 4, Sig. = .000
  • Model Summary: -2 Log likelihood = 333.711, Cox & Snell R Square = 0.343, Nagelkerke R Square = 0.506
  • Burnout Prediction Accuracy: 83.3%
  • Variables in the Equation:
  • Perceived Control: Exp(B) = 1.100
  • Coping Ability: Exp(B) = 1.146
  • Stress from Teaching: Exp(B) = 0.920
  • Stress from Research: Exp(B) = 1.018
  • Constant: Exp(B) = 0.052
  • Variables not in the Equation:
  • None

Summary:

The hierarchical logistic regression analysis indicates a significant prediction of burnout among university professors. Perceived control and coping ability play essential roles, while stress from teaching and research also contribute. The model shows improvement with the inclusion of stress factors.