Abstract
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.