Psychological well-being in Europe after the outbreak of war in Ukraine.

The Russian invasion of Ukraine on February 24, 2022, has had devastating effects on the Ukrainian population and the global economy, environment, and political order. However, little is known about the psychological states surrounding the outbreak of war, particularly the mental well-being of individuals outside Ukraine. Here, we present a longitudinal experience-sampling study of a convenience sample from 17 European countries (total participants = 1,341, total assessments = 44,894, countries with >100 participants = 5) that allows us to track well-being levels across countries during the weeks surrounding the outbreak of war. Our data show a significant decline in well-being on the day of the Russian invasion. Recovery over the following weeks was associated with an individual's personality but was not statistically significantly associated with their age, gender, subjective social status, and political orientation. In general, well-being was lower on days when the war was more salient on social media. Our results demonstrate the need to consider the psychological implications of the Russo-Ukrainian war next to its humanitarian, economic, and ecological consequences.

Extraversion, social interactions, and well-being during the COVID-19 pandemic: Did extraverts really suffer more than introverts?

A large body of research suggests that extraversion is positively related to well-being. However, it is unclear whether this association can be explained by social participation (i.e., more extraverted individuals engage in social interactions more frequently) or social reactivity (i.e., more extraverted individuals profit more from social interactions) processes. Here, we examined the role of social interactions for the extraversion-well-being relationship during the COVID-19 pandemic, an unprecedented time of reduced social contact. We analyzed data from an international, longitudinal study (Study 1: 10,523 assessments provided by 4,622 participants) and two experience sampling studies (Study 2: 29,536 assessments provided by 293 participants; Study 3: 61,492 assessments provided by 1,381 participants). Preregistered multilevel structural equation models revealed that extraversion was robustly related to well-being, even when social restrictions were in place. Across data sets, we found some support for the social participation hypothesis (i.e., the relationship between extraversion and well-being is mediated by social interactions), but the social reactivity hypothesis (i.e., extraversion moderates the relationship between social interactions and well-being) was not consistently supported. Strikingly, however, exploratory analyses showed that the social reactivity hypothesis was supported for specific facets of extraversion (i.e., sociability) and well-being (i.e., activated positive affect). Moreover, changes in social interaction patterns during the COVID-19 pandemic (e.g., decreases in face-to-face interactions and interactions with friends) were unrelated to extraversion, and more extraverted individuals did not suffer more from these changes. Taken together, these findings underline the robustness of the effect of extraversion on well-being during a societal crisis. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Gradient Tree Boosting for Hierarchical Data.

Gradient tree boosting is a powerful machine learning technique that has shown good performance in predicting a variety of outcomes. However, when applied to hierarchical (e.g., longitudinal or clustered) data, the predictive performance of gradient tree boosting may be harmed by ignoring the hierarchical structure, and may be improved by accounting for it. Tree-based methods such as regression trees and random forests have already been extended to hierarchical data settings by combining them with the linear mixed effects model (MEM). In the present article, we add to this literature by proposing two algorithms to estimate a combination of the MEM and gradient tree boosting. We report on two simulation studies that (i) investigate the predictive performance of the two MEM boosting algorithms and (ii) compare them to standard gradient tree boosting, standard random forest, and other existing methods for hierarchical data (MEM, MEM random forests, model-based boosting, Bayesian additive regression trees [BART]). We found substantial improvements in the predictive performance of our MEM boosting algorithms over standard boosting when the random effects were non-negligible. MEM boosting as well as BART showed a predictive performance similar to the correctly specified MEM (i.e., the benchmark model), and overall outperformed the model-based boosting and random forest approaches.

The true role that suppressor effects play in condition-based regression analysis: None. A reply to Fiedler (2021).

Condition-based regression analysis (CRA) is a statistical method for testing self-enhancement effects. That is, CRA indicates whether, in a set of empirical data, people with higher values on the directed discrepancy self-view S minus reality criterion R (i.e., S-R) tend to have higher values on some outcome variable (e.g., happiness). In a critical comment, Fiedler (2021) claims that CRA yields inaccurate conclusions in data with a suppressor effect. Here, we show that Fiedler's critique is unwarranted. All data that are simulated in his comment show a positive association between S-R and H, which is accurately detected by CRA. By construction, CRA indicates an association between S-R and H only when it is present in the data. In contrast to Fiedler's claim, it also yields valid conclusions when the outcome variable is related only to the self-view or when there is a suppressor effect. Our clarifications provide guidance for evaluating Fiedler's comment, clear up with the common heuristic that suppressor effects are always problematic, and assist readers in fully understanding CRA. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Cubic response surface analysis: Investigating asymmetric and level-dependent congruence effects with third-order polynomial models.

Congruence hypotheses play a major role in many areas of psychology. They refer to, for example, the consequences of person-environment fit, similarity, or self-other agreement. For example, are people psychologically better adjusted when their self-view is in line with their reputation? A valid statistical approach that can be applied to investigate congruence hypotheses of this kind is quadratic Response Surface Analysis (RSA) in which a second-order polynomial model is fit to the data and appropriately interpreted. However, quadratic RSA does not allow researchers to investigate more precise expectations about a congruence effect. Do the data support an asymmetric congruence effect, in the sense that congruence leads to the highest (or lowest) outcome, but incongruence in one direction (e.g., self-view exceeds reputation) affects the outcome differently than incongruence in the other direction (e.g., self-view falls behind reputation)? Is there a level-dependent congruence effect, such that the amount of congruence is more strongly related to the outcome variable for some levels of the predictors (e.g., high self-view and reputation) than for others (e.g., low self-view and reputation)? Such complex congruence hypotheses have frequently been suggested in the literature, but they could not be investigated because an appropriate statistical approach has yet to be developed. Here, we present analytical strategies, based on third-order polynomial models, that enable users to investigate asymmetric and level-dependent congruence effects, respectively. To facilitate the correct application of the suggested approaches, we provide respective step-by-step guidelines, corresponding R syntax, and illustrative analyses using simulated and real data. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Response Surface Analysis with Missing Data.

Response Surface Analysis (RSA) is gaining popularity in psychological research as a tool for investigating congruence hypotheses (e.g., consequences of self-other agreement, person-job fit, dyadic similarity). RSA involves the estimation of a nonlinear polynomial regression model and the interpretation of the resulting response surface. However, little is known about how best to conduct RSA when the underlying data are incomplete. In this article, we compare different methods for handling missing data in RSA. This includes different strategies for multiple imputation (MI) and maximum-likelihood (ML) estimation. Specifically, we consider the "just another variable" (JAV) approach to MI and ML, an approach that is in regular use in applications of RSA, and the more novel "substantive-model-compatible" (SMC) approach. In a simulation study, we evaluate the impact of these methods on focal outcomes of RSA, including the accuracy of parameter estimates, the shape of the response surface, and the testing of congruence hypotheses. Our findings suggest that the JAV approach can sometimes distort parameter estimates and conclusions about the shape of the response surface, whereas the SMC approach performs well overall. We illustrate applications of the methods in a worked example with real data and provide recommendations for their application in practice.

A Lasso and a Regression Tree Mixed-Effect Model with Random Effects for the Level, the Residual Variance, and the Autocorrelation.

Research in psychology is experiencing a rapid increase in the availability of intensive longitudinal data. To use such data for predicting feelings, beliefs, and behavior, recent methodological work suggested combinations of the longitudinal mixed-effect model with Lasso regression or with regression trees. The present article adds to this literature by suggesting an extension of these models that-in addition to a random effect for the mean level-also includes a random effect for the within-subject variance and a random effect for the autocorrelation. After introducing the extended mixed-effect location scale (E-MELS), the extended mixed-effect location-scale Lasso model (Lasso E-MELS), and the extended mixed-effect location-scale tree model (E-MELS trees), we show how its parameters can be estimated using a marginal maximum likelihood approach. Using real and simulated example data, we illustrate how to use E-MELS, Lasso E-MELS, and E-MELS trees for building prediction models to forecast individuals' daily nervousness. The article is accompanied by an R package (called mels) and functions that support users in the application of the suggested models.

Understanding Loneliness in Adolescence: A Test of Competing Hypotheses on the Interplay of Extraversion and Neuroticism.

Given that adolescents often experience fundamental changes in social relationships, they are considered to be especially prone to loneliness. Meanwhile, theory and research highlight that both extraversion and neuroticism are closely intertwined with individual differences in loneliness. Extant research has explored the linear main effects of these personality traits, yet potential non-linear associations (e.g., exponential effects) and the potential interplay of extraversion and neuroticism (e.g., mutual reinforcement effects) remain unknown. We addressed these open questions using cross-sectional and one-year longitudinal data from two adolescent samples (overall N = 583, Mage = 17.57, 60.55% girls) and an information-theoretic approach combined with polynomial regression. Analyses showed little evidence for interaction effects but revealed non-linear effects in addition to the main effects of extraversion and neuroticism on loneliness. For example, the positive cross-sectional association between neuroticism and loneliness was stronger at higher neuroticism levels (i.e., exponential effect). Results differed across loneliness facets in that both traits predicted emotional loneliness, but only extraversion predicted social loneliness. Longitudinal analyses showed that loneliness changes were mainly related to neuroticism. We discuss results in the light of sample differences, elaborate on the importance to differentiate between emotional versus social aspects of loneliness, and outline implications for adolescent development.

Response surface analysis with multilevel data: Illustration for the case of congruence hypotheses.

Response surface analysis (RSA) is a statistical approach that enables researchers to test congruence hypotheses; the proposition that the degree of congruence between people's values in 2 psychological constructs should be positively or negatively related to their value in an outcome variable. This is done by estimating a polynomial regression model and using the graph of the model and several parameters as a guide to interpret the resulting regression coefficients in terms of the congruence hypothesis. One problem with using RSA in applied research is that the model and the interpretation of the model's parameters in terms of congruence effects have only been thoroughly developed for single-level data. Here, we present an extension of RSA to multilevel data. Among other things we show how the standard errors can be computed and how researchers can decide whether the occurrence of a congruence effect depends on a Level 2 covariate. We illustrate the suggested extension with 2 examples that guide readers through the test of congruence effects in the case of multilevel data. We also provide R scripts that researchers can adopt to conduct multilevel RSA. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Is accurate, positive, or inflated self-perception most advantageous for psychological adjustment? A competitive test of key hypotheses.

Empirical research on the (mal-)adaptiveness of favorable self-perceptions, self-enhancement, and self-knowledge has typically applied a classical null-hypothesis testing approach and provided mixed and even contradictory findings. Using data from 5 studies (laboratory and field, total N = 2,823), we used an information-theoretic approach combined with Response Surface Analysis to provide the first competitive test of 6 popular hypotheses: that more favorable self-perceptions are adaptive versus maladaptive (Hypotheses 1 and 2: Positivity of self-view hypotheses), that higher levels of self-enhancement (i.e., a higher discrepancy of self-viewed and objectively assessed ability) are adaptive versus maladaptive (Hypotheses 3 and 4: Self-enhancement hypotheses), that accurate self-perceptions are adaptive (Hypothesis 5: Self-knowledge hypothesis), and that a slight degree of self-enhancement is adaptive (Hypothesis 6: Optimal margin hypothesis). We considered self-perceptions and objective ability measures in two content domains (reasoning ability, vocabulary knowledge) and investigated 6 indicators of intra- and interpersonal psychological adjustment. Results showed that most adjustment indicators were best predicted by the positivity of self-perceptions. There were some specific self-enhancement effects, and evidence generally spoke against the self-knowledge and optimal margin hypotheses. Our results highlight the need for comprehensive and simultaneous tests of competing hypotheses. Implications for the understanding of underlying processes are discussed. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Enhanced versus simply positive: A new condition-based regression analysis to disentangle effects of self-enhancement from effects of positivity of self-view.

Despite a large body of literature and ongoing refinements of analytical techniques, research on the consequences of self-enhancement (SE) is still vague about how to define SE effects, and empirical results are inconsistent. In this paper, we point out that part of this confusion is due to a lack of conceptual and methodological differentiation between effects of individual differences in how much people enhance themselves (SE) and in how positively they view themselves (positivity of self-view; PSV). We show that methods commonly used to analyze SE effects are biased because they cannot differentiate between the effects of PSV and the effects of SE. We provide a new condition-based regression analysis (CRA) that unequivocally identifies effects of SE by testing intuitive and mathematically derived conditions on the coefficients in a bivariate linear regression. Using data from 3 studies on intellectual SE (total N = 566), we then illustrate that the CRA provides novel results as compared with traditional methods. Results suggest that many previously identified SE effects are in fact effects of PSV alone. The new CRA approach thus provides a clear and unbiased understanding of the consequences of SE. It can be applied to all conceptualizations of SE and, more generally, to every context in which the effects of the discrepancy between 2 variables on a third variable are examined. (PsycINFO Database Record