In complex survey data, each sampled observation has assigned a sampling weight, indicating the number of units that it represents in the population. Whether sampling weights should or not be considered in the estimation process of model parameters is a question that still continues to generate much discussion among researchers in different fields. We aim to contribute to this debate by means of a real data based simulation study in the framework of logistic regression models. In order to study their performance, three methods have been considered for estimating the coefficients of the logistic regression model: a) the unweighted model, b) the weighted model, and c) the unweighted mixed model. The results suggest the use of the weighted logistic regression model, showing the importance of using sampling weights in the estimation of the model parameters.

翻译：在复杂调查数据中，每个被抽样的观测值都被赋予一个抽样权重，表示其在总体中所代表的单位数量。在模型参数的估计过程中是否应考虑抽样权重，这一问题仍在不同领域的研究人员中引发广泛讨论。我们旨在通过一项基于真实数据的模拟研究，在逻辑回归模型的框架下为这一辩论做出贡献。为评估其表现，我们考虑了三种方法来估计逻辑回归模型的系数：a）未加权模型，b）加权模型，以及c）未加权混合模型。结果表明应使用加权逻辑回归模型，显示了在模型参数估计中采用抽样权重的重要性。