Multivariate psychological processes have recently been studied, visualized, and analyzed as networks. In this network approach, psychological constructs are represented as complex systems of interacting components. In addition to insightful visualization of dynamics, a network perspective leads to a new way of thinking about the nature of psychological phenomena by offering new tools for studying dynamical processes in psychology. In this article, we explain the rationale of the network approach, the associated methods and visualization, and illustrate it using an empirical example focusing on the relation between the daily fluctuations of emotions and neuroticism. The results suggest that individuals with high levels of neuroticism had a denser emotion network compared with their less neurotic peers. This effect is especially pronounced for the negative emotion network, which is in line with previous studies that found a denser network in depressed subjects than in healthy subjects. In sum, we show how the network approach may offer new tools for studying dynamical processes in psychology.

Previous research has shown that individual differences in negative emotion differentiation may play a prominent role in well-being. Yet, many basic questions about negative emotion differentiation remain unanswered, including how it relates and overlaps with related and known dimensions of individual differences and what its possible underlying processes are. To answer these questions, in the current article we present three correlational studies that chart the nomological network of individual differences in negative emotion differentiation in terms of personality, difficulties in identifying and describing feelings, and several indicators of well-being, propose a novel paradigm to assess it in the lab, and explore relationships with a possible underlying mechanism in terms of the motivation to approach or avoid emotions. The results affirm consistent relations between negative emotion differentiation and indicators of adjustment like negative affect, self-esteem, neuroticism, depression and meta-knowledge about one's emotions, and show how it is related to the motivation to experience affective states.

Emotion differentiation, which involves experiencing and labeling emotions in a granular way, has been linked with well-being. It has been theorized that differentiating between emotions facilitates effective emotion regulation, but this link has yet to be comprehensively tested. In two experience-sampling studies, we examined how negative emotion differentiation was related to (a) the selection of emotion-regulation strategies and (b) the effectiveness of these strategies in downregulating negative emotion ( Ns = 200 and 101 participants and 34,660 and 6,282 measurements, respectively). Unexpectedly, we found few relationships between differentiation and the selection of putatively adaptive or maladaptive strategies. Instead, we found interactions between differentiation and strategies in predicting negative emotion. Among low differentiators, all strategies (Study 1) and four of six strategies (Study 2) were more strongly associated with increased negative emotion than they were among high differentiators. This suggests that low differentiation may hinder successful emotion regulation, which in turn supports the idea that effective regulation may underlie differentiation benefits.

Although parenting styles constitute a well-known concept in parenting research, two issues have largely been overlooked in existing studies. In particular, the psychological control dimension has rarely been explicitly modelled and there is limited insight into joint parenting styles that simultaneously characterize maternal and paternal practices and their impact on child development. Using data from a sample of 600 Flemish families raising an 8-to-10 year old child, we identified naturally occurring joint parenting styles. A cluster analysis based on two parenting dimensions (parental support and behavioral control) revealed four congruent parenting styles: an authoritative, positive authoritative, authoritarian and uninvolved parenting style. A subsequent cluster analysis comprising three parenting dimensions (parental support, behavioral and psychological control) yielded similar cluster profiles for the congruent (positive) authoritative and authoritarian parenting styles, while the fourth parenting style was relabeled as a congruent intrusive parenting style. ANOVAs demonstrated that having (positive) authoritative parents associated with the most favorable outcomes, while having authoritarian parents coincided with the least favorable outcomes. Although less pronounced than for the authoritarian style, having intrusive parents also associated with poorer child outcomes. Results demonstrated that accounting for parental psychological control did not yield additional parenting styles, but enhanced our understanding of the pattern among the three parenting dimensions within each parenting style and their association with child outcomes. More similarities than dissimilarities in the parenting of both parents emerged, although adding psychological control slightly enlarged the differences between the scores of mothers and fathers.

In psychology, modeling multivariate dynamical processes within a person is gaining ground. A popular model is the lag-one vector autoregressive or VAR(1) model and its variants, in which each variable is regressed on all variables (including itself) at the previous time point. Many parameters have to be estimated in the VAR(1) model, however. The question thus rises whether the VAR(1) model is not too complex and overfits the data. If the latter is the case, the estimated model will not properly predict new unseen data. As a consequence, it cannot be trusted that the estimated parameters adequately characterize the individual from which the data at hand were sampled. In this article, we evaluate for current psychological applications whether the VAR(1) model outpredicts simpler models, using cross-validation (CV) techniques to determine the predictive accuracy. As it is unclear whether one should use standard CV techniques (leave-one-out CV or K-fold CV) or variants that take time dependence into account (blocked CV, hv-block CV, or accumulated prediction errors), we first compare the relative performance of these five CV techniques in a simulation study. The simulation settings mimic the data characteristics of current psychological VAR(1) applications and show that blocked CV has the best performance in general. Subsequently, we use blocked CV to assess to what extent the VAR(1) models predict unseen data for three recent psychological applications. We show that the VAR(1) based models do not outperform the AR(1) based ones for the three presented psychological applications. (PsycINFO Database Record (c) 2018 APA, all rights reserved).