In recent years, entrepreneurship has become an important issue due to national economic development and the contribution of society. Data with a hierarchical structure received more attention and occur frequently in social science, public health and epidemiological researches. In such research, binary outcomes are common. Multilevel logistic regression models allow one to account for the clustering of subjects within clusters of higher-level units when estimating the effect of subject and cluster characteristics on subject outcomes. Traditional logistic regression is inappropriate when data are hierarchically structured. Therefore, this study presents multi-level Bayesian logistic analysis for entrepreneurial intention of students using classical and Bayesian approach. The descriptive result revealed that about 57.4% of the students had an entrepreneurial intention while 42.6% do not have an intention. The results also showed that entrepreneurial education/training and entrepreneurial attitudes significantly predicts students’ entrepreneurial intention at 5% level of significance. The model results indicate that the effects of the selected variable on entrepreneurial intention vary across university. By failing to take into account the clustering within university (level 2), Bayesian multilevel effects are not taken into consideration in modeling, the β coefficients in multilevel logistic model using classical approach are distorted somewhat in both directions either in over or under direction. This study also evaluates and compares the behavior of maximum likelihood and Bayesian estimators to investigate the relationship between covariates and the response. Both point and interval estimation performances were investigated. The results revealed that lower standard errors of the estimated coefficients in the Bayesian logistic regression approach as compared to classical approach. Moreover, the results revealed that the length of the Bayesian credible interval is smaller than the length of the maximum likelihood confidence interval for all factors. In order to identify the most plausible method between Bayesian method and maximum likelihood estimation of the data, AIC, BIC and DIC are adopted in this paper. The result of the study depicts that the Bayesian method performs better and more efficient than maximum likelihood estimation. The study recommends that the government as well as the universities should design programs that facilitate entrepreneurship to change the mindset, attitude, and intention of those students who do not have knowhow about entrepreneurship as a future career.

近年来，由于国民经济的发展和社会的贡献，企业家精神已成为重要问题。具有层次结构的数据越来越受到关注，并且在社会科学，公共卫生和流行病学研究中经常发生。在此类研究中，二元结果很常见。多级逻辑回归模型允许在评估主题和集群特征对主题结局的影响时考虑主题在较高级别单元集群中的集群。当数据采用层次结构时，传统的逻辑回归是不合适的。因此，本研究采用经典和贝叶斯方法对学生的创业意图提出了多层次的贝叶斯逻辑分析。描述性结果显示大约有57个。4％的学生有创业意向，而42.6％的学生没有创业意向。研究结果还表明，创业教育/培训和创业态度可以显着地预测学生的创业意图在5％的显着性水平。模型结果表明，所选变量对创业意向的影响因大学而异。由于没有考虑大学内的聚类（第2级），建模时未考虑贝叶斯多级效应，使用经典方法的多级逻辑模型中的β系数在向上或向下两个方向上都有些失真。这项研究还评估和比较了最大似然行为和贝叶斯估计量，以研究协变量与响应之间的关系。研究了点和区间估计性能。结果表明，与经典方法相比，贝叶斯逻辑回归方法的估计系数的标准误较低。此外，结果表明，对于所有因素，贝叶斯可信区间的长度都小于最大似然置信区间的长度。为了确定贝叶斯方法和数据的最大似然估计之间最合理的方法，本文采用了AIC，BIC和DIC。研究结果表明，贝叶斯方法比最大似然估计更好，更有效。该研究建议，政府和大学应设计促进创业的方案，以改变思维方式，态度，