The purpose of this article was to explore how family chaos, parenting processes, parent–child relationship qualities, and sibling relationship qualities changed before versus the early months of the COVID-19 pandemic. Participants included one parent and two adolescent-aged children from 682 families (2,046 participants). Parents and youth participating in an ongoing longitudinal study in five Midwestern states in the United States completed an additional web-based assessment of family processes and family relationship qualities during the May–June 2020 pandemic-related shutdowns. A series of two-wave latent change score models indicated that family chaos increased with the onset of pandemic-related shutdowns and that the level of chaos within a family during the shutdowns had implications for changes in several parenting processes and family relationship qualities. Specifically, higher levels of family chaos during the pandemic mitigated observed increases in parental knowledge and were associated with declines in parental autonomy granting. Family chaos during pandemic-related shutdowns also was associated with increases in maternal–child conflict, paternal–child conflict, and sibling conflict as well as decreases in paternal–child intimacy, sibling intimacy, and sibling disclosure. Overall, consistent with a family stress perspective, the onset of the COVID-19 pandemic was associated with increased strain and commotion within many households, and these changes had implications for multiple family relationships.

Exploratory mediation analysis refers to a class of methods used to identify a set of potential mediators of a process of interest. Despite its exploratory nature, conventional approaches are rooted in confirmatory traditions, and as such have limitations in exploratory contexts. We propose a two-stage approach called exploratory mediation analysis via regularization (XMed) to better address these concerns. We demonstrate that this approach is able to correctly identify mediators more often than conventional approaches and that its estimates are unbiased. Finally, this approach is illustrated through an empirical example examining the relationship between college acceptance and enrollment.

Structural equation model trees (SEM Trees) allow for the construction of decision trees with structural equation models fit in each of the nodes (Brandmaier, von Oertzen, McArdle, & Lindenberger, 2013). Based on covariate information, SEM Trees can be used to create distinct subgroups containing individuals with similar parameter estimates. Currently, the structural equation modeling component of SEM Trees is implemented in the R packages OpenMx and lavaan. We extend SEM Trees so that the models can be fit in Mplus, in the hopes that its efficiency and accessibility allow a broader group of researchers to fit a wider range of models.We discuss the Mplus Trees algorithm, its implementation, and its position among the growing number of tree-based methods in psychological research. We also provide several examples using publicly available data to illustrate how Mplus Trees can be implemented in practice with the R package MplusTrees.

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