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.
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Notes
Please note that, with reference to the hierarchy of global CFA base models (see von Eye, & Wiedermann, 2022), the P-CFA base model discussed here could be considered a first order base model. A second order P-CFA base model could be specified by taking all first order interactions among predictors and criterion variables into account. Accordingly, higher order P-CFA base models can be considered. In the present article, we focus on first order P-CFA base models.
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AvE drafted the main manuscript. AvE, WW, and EYM analyzed and interpreted the data; WW and EYM reviewed and edited the manuscript. All authors have read and approved the final manuscript.
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von Eye, A., Wiedermann, W. & Mun, EY. Log-Linear and Configural Analysis of Intra-Individual Time Series under Consideration of Serial Dependence. Integr. psych. behav. 58, 759–770 (2024). https://doi.org/10.1007/s12124-023-09815-7
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DOI: https://doi.org/10.1007/s12124-023-09815-7