Serial dependence often prevents researchers from obtaining unbiased parameter estimates. In this article, we propose taking serial dependence into account, and exploiting the information that comes with serial dependence. This can be done in the form of shifted variables that are included in addition to the original variables, when models are specified. This way, models become more complex but relations can be considered that, otherwise, cannot be analyzed. Two fields of application are discussed. The first is log-linear modeling. This method is variable-oriented, but it has found applications in person-oriented research. The gain from including shifted variables in log-linear models is that new, specific variable relations can be analyzed. The second field is that of Configural Frequency Analysis. This method is person-oriented, and it allows researchers to detect local relations that, without consideration of shifted variables, cannot be detected. Application examples are given in the context of single-case analysis.

序列依赖性常常阻止研究人员获得无偏参数估计。在本文中，我们建议考虑串行依赖性，并利用串行依赖性附带的信息。这可以通过在指定模型时除了原始变量之外还包括移位变量的形式来完成。这样，模型变得更加复杂，但可以认为关系，否则无法分析。讨论了两个应用领域。第一个是对数线性建模。这种方法是面向变量的，但它在面向人的研究中得到了应用。在对数线性模型中包含移位变量的好处是可以分析新的特定变量关系。第二个领域是配置频率分析。这种方法是以人为本的，它允许研究人员检测局部关系，而如果不考虑移位变量，则无法检测到这些局部关系。在单例分析的背景下给出了应用示例。