(1) Background: Psychological burnout (BO) is one of the most significant challenges faced by university students. This study aimed to explore the perceived levels of BO, psychological resilience (PR), and psychological hardiness among university students in Jordan. It also investigated the factors that may contribute to psychological BO. (2) Methods: Using a cross-sectional design, a total of 1219 university students (female = 933 (76.5%), male = 286 (23.5%)) were conveniently selected from four universities (two public and two private) to represent the study sample. (3) Results: Results found that 40.39% of university students had severe psychological BO levels, although most of them had acceptable PR (92.74%) and hardiness (83.7%) levels. Unmarried students at public universities, medical schools, and fourth-year students or beyond showed higher levels of psychological BO than any other category. A negative correlation (p < 0.05) was observed among psychological BO and both perceived PR and hardiness variables. Additionally, a positive correlation was found between perceived PR and psychological hardiness. Significant contributions of hardiness, university type, and academic year to BO were detected, with a variance of 29.7%. (4) Conclusions: Academic teachers and policymakers should tackle this problem among university students to help alleviate the different sources of stressors in university life.

Statistical models are essential tools in data analysis. However, missing data plays a pivotal role in impacting the assumptions and effectiveness of statistical models, especially when there is a significant amount of missing data. This study addresses one of the core assumptions supporting many statistical models, the assumption of unidimensionality. It examines the impact of missing data rates and imputation methods on fulfilling this assumption. The study employs three imputation methods: Corrected Item Mean, multiple imputation, and expectation maximization, assessing their performance across nineteen levels of missing data rates, and examining their impact on the assumption of unidimensionality using several indicators (Cronbach’s alpha, corrected correlation coefficients, factor analysis (Eigenvalues ( λ1 , λ1/λ2 , and λ1−λ2λ2−λ3), cumulative variance, and communalities). The study concluded that all imputation methods used effectively provided data that maintained the unidimensionality assumption, regardless of missing data rates. Additionally, it was found that most of the unidimensionality indicators increased in value as missing data rates rose.