当一个调查涉及到违法或违背社会道德行为时(例如逃税避税、滥用药物、非法堕胎等)，由于受访者认为他们的隐私受到侵犯，调查人员通常很难直接从受访者处获得准确有效的信息。为了提高受访者隐私被保护的程度、改进敏感参数估计的准确性和精确性并拓宽调查设计方案的应用范围，在本项目中，我们主要研究以下方面的内容：首先，通过建立基于非随机化的平行模型的logistic回归函数来研究敏感变量与其它非敏感协变量之间的依存关系从而对敏感数据进行深入分析；其次，我们将固定效应模型推广到随机效应模型以增强模型的灵活性以及实用性；更进一步，我们还将考虑logistic回归中混淆变量的识别以及有效变量的筛选，提高模型的解释能力和预测的准确性。

When an investigation deals with phenomena that are considered as illegal or violation of social morality activities (such as tax evasion, drug abuse, illegal abortion and so on), it is often difficult to obtain reliable information from interviewees directly because they may feel their personal information is infringed. In order to collect reliable sensitive information, increase the degree of privacy protection of the survey, improve the accuracy and precision of the estimation of the sensitive parameters and broaden the application scope of our approaches, in this topic, we will focus on the following aspects: first, to study the relationship between the sensitive variable and other non-sensitive covariates via constructing logistic regression models based on the non-randomized parallel model is the first objective, from which we could know how the covariates would affect the sensitive variable of interest; in addition, we extend the fixed effect model to the random effect model aiming at increase the flexibility and practicability of such models; furthermore, the determination of the confounding variable(s) and the selection of valid explanatory variables will also be taken into account in order to enhance the explanatory ability of the logistic models.