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Understanding characteristics that contribute to psychology students' academic success is important to better support them during their studies. Referring to person-environment fit theory, we examined effects of study-relevant characteristics (self-efficacy beliefs, self-assessed level of information about the study program) on subjective criteria of success (persistence with the choice of study subject, later study satisfaction) and controlled for effects of grade point average (GPA) and mathematical skills. We present a longitudinal survey study including five cohorts of first-year students (total N = 854). Mediation models (N = 254) revealed that self-efficacy and level of information at study entry predicted students' persistence at the end of the first semester, which predicted satisfaction at the end of the second semester. In the presumed overall model we found total effects of self-efficacy and level of information, with direct and indirect effects (via persistence) on satisfaction, and no total or direct effects of GPA and mathematical skills, but an indirect effect of GPA on satisfaction. Thus, psychology students' satisfaction substantially depends on study-relevant characteristics and less on skills. An enhancement of students' self-efficacy beliefs and comprehensive information for those who are interested in the subject might help to increase satisfaction and thus success.

During the Covid-19 pandemic, the global public has relied on their political leaders to guide them through the crisis. The current study investigated if and how political leader’s rhetoric would be associated with collective emotional responses. We used text analytical methods to investigate association between political leader speech and daily aggregates of expressed emotions on Twitter. We collected posts concerning Covid-19 and all speeches by the highest executive power from the USA, UK, Germany, and Switzerland. We applied cross-lagged time series analyses. Political leaders whose communication was more analytic and communal corresponded to increased positivity on Twitter. Collective communal focus, in turn, increased after speeches which were more analytic and negative. Processes of socio-affective dynamics between political leaders and the general public are apparent. Our findings demonstrate that political leaders who present public crises competently and with a sense of community are associated with more positive responses on Twitter.

This study addresses the high level of academic demands and workload in Chinese high schools and aims to identify protective factors against students' negative emotional responses. Using the well-established Job Demands-Resources Model as a guiding framework, we investigated the relationship between workload, perceived academic demands, teacher-student relationships, personal resources (self-efficacy, self-esteem, and optimism), and school burnout symptoms (emotional exhaustion and cynicism). Data analyses were based on self-reports from 1,083 Chinese students. The results of structural equation modelling showed that workload and academic demands were positively related to burnout. Among the resources, optimism was negatively related to emotional exhaustion and cynicism, whereas self-efficacy was only negatively related to cynicism. Surprisingly, self-esteem was positively related to cynicism after controlling for the other two personal resources. Our findings underline the potentially protective effect of students' self-efficacy, dispositional optimism, and self-esteem (at least at the bivariate level) against school burnout.

The present contribution provides a tutorial for the estimation of the social relations model (SRM) by means of structural equation modeling (SEM). In the overarching SEM-framework, the SRM without roles (with interchangeable dyads) is derived as a more restrictive form of the SRM with roles (with noninterchangeable dyads). Starting with the simplest type of the SRM for one latent construct assessed by one manifest round-robin indicator, we show how the model can be extended to multiple constructs each measured by multiple indicators. We illustrate a multiple constructs multiple indicators SEM SRM both with and without roles with simulated data and explain the parameter interpretations. We present how testing the substantial model assumptions can be disentangled from testing the interchangeability of dyads. Additionally, we point out modeling strategies that adhere to cases in which only some members of a group can be differentiated with regards to their roles (i.e., only some group members are noninterchangeable). In the online supplemental materials , we provide concrete examples of specific modeling problems and their implementation into statistical software (Mplus, lavaan, and OpenMx). Advantages, caveats, possible extensions, and limitations in comparison with alternative modeling options are discussed. The present contribution provides a tutorial for the estimation of the social relations model (SRM) by means of structural equation modeling (SEM). In the overarching SEM-framework, the SRM without roles (with interchangeable dyads) is derived as a more restrictive form of the SRM with roles (with noninterchangeable dyads). Starting with the simplest type of the SRM for one latent construct assessed by one manifest round-robin indicator, we show how the model can be extended to multiple constructs each measured by multiple indicators. We illustrate a multiple constructs multiple indicators SEM SRM both with and without roles with simulated data and explain the parameter interpretations. We present how testing the substantial model assumptions can be disentangled from testing the interchangeability of dyads. Additionally, we point out modeling strategies that adhere to cases in which only some members of a group can be differentiated with regards to their roles (i.e., only some group members are noninterchangeable). In the online supplemental materials , we provide concrete examples of specific modeling problems and their implementation into statistical software (Mplus, lavaan, and OpenMx). Advantages, caveats, possible extensions, and limitations in comparison to alternative modeling options are discussed. [ABSTRACT FROM AUTHOR]

Smartphones are omnipresent and serve as a tool for various purposes. In Switzerland, more than 90% of the population owns a smartphone and uses it on average around 2.0 to 5 h daily. Smartphone app use is linked with both higher and lower levels of well-being. However, it remains unclear whether smartphone app use is the antecedent or the consequence of well-being. Based on the stimulation (online communication increases well-being) and the displacement (online communication decreases well-being) hypotheses, this study explores how daily smartphone app use and well-being are associated over time. A total of N  =  130 employees (M = 35.61; SD = 10.62) participated in a three-week intensive longitudinal study. Daily minutes of different types of smartphone app use (e.g. email apps and chat apps) were tracked by a mobile application. Furthermore, daily self-reports of positive and negative affect were assessed in a morning and end-of day diary. Positive morning affect was negatively associated with the use of email apps. Furthermore, a negative between-person relationship of chat app use with end-of day positive affect was observed. The results are in favour of the displacement hypothesis. Future studies should investigate causal relationships between specific forms of smartphone usage and the user’s well-being. [ABSTRACT FROM AUTHOR]

Introduction: While the effectiveness of cognitive-behavioral stress management trainings (SMTs) is well-documented, the underlying mechanisms, especially in an occupational context, are not fully understood. We tested whether SMT-induced improvements in stress management skills, particularly in the mastery of changing cognitions, may explain beneficial SMT effects. Methods: Our non-randomized controlled trial comprised 108 employees of a German health insurance company, with 65 of them participating in a cognitive-behavioral SMT and 43 participating in an alternative control training (AT). As outcome variables, we repeatedly assessed stress-related (functional stress management skills, relaxation, stress reactivity, exhaustion), work-related (job dissatisfaction), and specific-context-related (social support, trait anger) measures at baseline, 2 weeks, and 3 months after the trainings. Functional stress management skills and, in particular, a subscale assessing perceived mastery of changing cognitions ("cognitive-strategies-and-problem-solving") were tested as mediators of change. Results: Repeated measures (M)AN(C)OVAs and complementary multigroup latent difference models confirmed improvements in all outcomes in the SMT-group compared to the AT-group (p's ≤ 0.015). Multivariate mediation path analyses revealed that, regarding mechanisms of change, the subscale cognitive-strategies-and-problem-solving was identified as the most important mediator for all outcomes (95% CIs for expected increases in SMT- vs. AT-group = [lower limits (LLs) ≥ 0.004]; 95% CIs for expected decreases in the SMT- vs. AT-group = [upper limits(ULs) ≤ -0.078]) except for job dissatisfaction. Discussion: Our findings confirm that employees can effectively learn to master stress reduction techniques and consequently lower the resulting burden. Moreover, beneficial SMT effects seem to result from improvements in functional stress management skills, particularly in the ability to change cognitions. This points to the importance of training cognitive techniques. [ABSTRACT FROM AUTHOR]

AbstractThe social relations model (SRM) is the standard approach for analyzing dyadic data stemming from round-robin designs. The model can be used to estimate correlation-coefficients that reflect the overall reciprocity or accuracy of judgements for individual and dyads on the sample- or population level. Within the social relations structural equation modeling framework and on the statistical grounding of stochastic measurement and classical test theory, we show how the multiple indicator SRM can be modified to capture inter-individual and inter-dyadic differences in reciprocal engagement or inter-individual differences in reciprocal accuracy. All models are illustrated on an open-access round-robin data set containing measures of mimicry, liking, and meta-liking (the belief to be liked). Results suggest that people who engage more strongly in reciprocal mimicry are liked more after an interaction with someone and that overestimating one’s own popularity is strongly associated with being liked less. Further applications, advantages and limitations of the models are discussed. [ABSTRACT FROM AUTHOR]

We didactically derive a correlated traits correlated (methods – 1) [CTC(M – 1)] multitrait‐multimethod (MTMM) model for dyadic round‐robin data augmented by self‐reports. The model is an extension of the CTC(M – 1) model for cross‐classified data and can handle dependencies between raters and targets by including reciprocity covariance parameters that are inherent in augmented round‐robin designs. It can be specified as a traditional structural equation model. We present the variance decomposition as well as consistency and reliability coefficients. Moreover, we explain how to evaluate fit of a CTC(M – 1) model for augmented round‐robin data. In a simulation study, we explore the properties of the full information maximum likelihood estimation of the model. Model (mis)fit can be quite accurately detected with the test of not close fit and dynamic root mean square errors of approximation. Even with few small round‐robin groups, relative parameter estimation bias and coverage rates are satisfactory, but several larger round‐robin groups are needed to minimize relative parameter estimation inaccuracy. Further, neglecting the reciprocity covariance‐structure of the augmented round‐robin data does not severely bias the remaining parameter estimates. All analyses (including data, R scripts, and results) and the simulation study are provided in the Supporting Information. Implications and limitations are discussed. [ABSTRACT FROM AUTHOR]

Lecturers teaching statistics are faced with several challenges supporting students' learning in appropriate ways. A variety of methods and tools exist to facilitate students' learning on statistics courses. The online questionnaires presented in this report are a new, slightly different computer-based tool: the central aim was to support students to master the course content and to provide the lecturer with continuous feedback about the students' difficulties and deficits. The aims of online questionnaires supporting teaching and learning of statistics are compared to aims of other computer-based and non-computerized methods and tools. Additionally, data from a first evaluation of the questionnaires is presented. In a first year psychology student's statistics course (bachelor program), students were offered the possibility of filling in an online questionnaire every time a specific theme had been taught. Out of the 126 first year students there were 28 who answered the online questionnaires regularly and 12 answered them sometimes. Those who answered the online questionnaires assessed them as helpful for their understanding of the course's content. The lecturer judged the online questionnaires as particularly helpful for improving teaching and assessing performance deficits.

Both parental psychological well-being (e.g., depressive symptoms) and parental relationship functioning (e.g., negative communication) are common parental risk factors for dysfunctional parenting. The spillover process from these parental characteristics to dysfunctional parenting is assumed to be amplified by parental stress, which is particularly common among mothers and fathers of young children. However, few studies have examined dyadic spillover processes from parental risk factors and parental stress on parenting in early childhood. In the current study, we first examined direct actor and partner effects of parents' depressive symptoms and negative communication at 10 months postpartum on dysfunctional parenting at 48 months postpartum in 168 primiparous mixed-gender couples. Second, we analyzed indirect effects via one's own and the partner's parental stress at 36 months postpartum using Actor-Partner Interdependence Mediation Models (APIMeM). We found direct actor effects for mothers' depressive symptoms and negative communication on their dysfunctional parenting. Additionally, indirect actor effects were found for depressive symptoms and negative communication among mothers and fathers. Specifically, mediating effects of depressive symptoms and negative communication on one's dysfunctional parenting through one's parental stress were found. There were no indirect partner effects through parental stress. These findings highlight the important role of parental stress in early childhood as a mediator between both individual and relationship parental risk factors and dysfunctional parenting. These results further underscore the importance of longitudinal dyadic analyses in providing early and tailored interventions for both mothers and fathers of young children. [ABSTRACT FROM AUTHOR]

Multimethod data analysis is a complex procedure that is often used to examine the degree to which different measures of the same construct converge in the assessment of this construct. Several authors have called for a greater understanding of the definition and meaning of method effects in different models for multimethod data. In this article, we compare 2 recently proposed approaches for modeling data with structurally different methods with regard to the definition and meaning of method effects, the restricted CT-C(M - 1) model (Geiser, Eid, & Nussbeck, 2008) and the latent difference model (Lischetzke, Eid, & Nussbeck, 2002). We also introduce the concepts of individual, conditional, and general method bias and show how these types of biases are represented in the models. An application to a multirater data set (N = 199) as well as recommendations for the application and interpretation of each model are provided. (Contains 5 tables, 4 figures and 4 footnotes.)

The authors show how structural equation modeling can be applied to analyze change in longitudinal multitrait-multimethod (MTMM) studies. For this purpose, an extension of latent difference models (McArdle, 1988; Steyer, Eid, & Schwenkmezger, 1997) to multiple constructs and multiple methods is presented. The model allows investigators to separate true change from measurement error and to analyze change simultaneously for different methods. The authors also show how Campbell and Fiske's (1959) guidelines for analyzing convergent and discriminant validity can be applied to the measurement of latent change. The practical application of the multimethod change model is illustrated in a reanalysis of child depression and anxiety scores (N = 906 American children) that were assessed by self- and parent reports on three measurement occasions. The analyses revealed that (a) the convergent validity of change was low for both constructs and (b) sex was a significant predictor of self-reported, but not of parent reported, anxiety states. Finally, the authors discuss advantages and limitations and compare the model with other approaches for analyzing longitudinal MTMM data. (Contains 3 tables, 2 figures and 6 footnotes.)

The question as to which structural equation model should be selected when multitrait-multimethod (MTMM) data are analyzed is of interest to many researchers. In the past, attempts to find a well-fitting model have often been data-driven and highly arbitrary. In the present article, the authors argue that the measurement design (type of methods used) should guide the choice of the statistical model to analyze the data. In this respect, the authors distinguish between (a) interchangeable methods, (b) structurally different methods, and (c) the combination of both kinds of methods. The authors present an appropriate model for each type of method. All models allow separating measurement error from trait influences and trait-specific method effects. With respect to "interchangeable methods," a multilevel confirmatory factor model is presented. For "structurally different methods," the correlated trait-correlated (method-1) model is recommended. Finally, the authors demonstrate how to appropriately analyze data from MTMM designs that simultaneously use "interchangeable" and "structurally different methods." All models are applied to empirical data to illustrate their proper use. Some implications and guidelines for modeling MTMM data are discussed. (Contains 5 figures, 7 tables, and 2 footnotes.)

The analysis of convergent and discriminant validity is an integral part of the construct validation process. Models for analyzing the convergent and discriminant validity have typically been developed for cross-sectional data. There exist, however, only a few approaches for longitudinal data that can be applied for analyzing the construct validity of fluctuating states. In this article, the authors show how models of latent state-trait theory can be combined with models of multitrait-multimethod analysis to develop a model that allows for analyzing convergent and discriminant validity in time: the multimethod latent state-trait model. The model allows for identifying different sources of variance (trait consistency, trait-method specificity, occasion-specific consistency, occasion-specific method specificity, and unreliability). It is applied to the repeated measurement of depression and anxiety in children, which was assessed by self and teacher reports (N = 375). The application shows that the proposed models fit the data well and allow a deeper understanding of method effects in clinical assessment. (Contains 1 figure, 3 tables, and 2 footnotes.)

In a recent article, A. Maydeu-Olivares and D. L. Coffman (2006, see EJ751121) presented a random intercept factor approach for modeling idiosyncratic response styles in questionnaire data and compared this approach with competing confirmatory factor analysis models. Among the competing models was the CT-C(M-1) model (M. Eid, 2000). In an application to the Life Orientation Test (M. F. Scheier & C. S. Carver, 1985), Maydeu-Olivares and Coffman found that results obtained from the CT-C(M-1) model were difficult to interpret. In particular, Maydeu-Olivares and Coffman challenged the asymmetry of the CT-C(M-1) model. In the present article, the authors show that the difficulties faced by Maydeu-Olivares and Coffman rest upon an improper interpretation of the meaning of the latent factors. The authors' aim is to clarify the meaning of the latent variables in the CT-C(M-1) model. The authors explain how to properly interpret the results from this model and introduce an alternative restricted model that is conceptually similar to the CT-C(M-1) model and nested within it. The fit of this model is invariant across different reference methods. Finally, the authors provide guidelines as to which model should be used in which research context.

Extensions of latent state-trait models for continuous observed variables to mixture latent state-trait models with and without covariates of change are presented that can separate individuals differing in their occasion-specific variability. An empirical application to the repeated measurement of mood states (N = 501) revealed that a model with 2 latent classes fits the data well. The larger class (76%) consists of individuals whose mood is highly variable, whose general well-being is comparatively lower, and whose mood variability is influenced by daily hassles and uplifts. The smaller class (24%) represents individuals who are rather stable and happier and whose mood is influenced only by daily uplifts but not by daily hassles. A simulation study on the model without covariates with 5 sets of sample sizes and 5 sets of number of occasions revealed that the appropriateness of the parameter estimates of this model depends on number of observations (the higher the better) and number of occasions (the higher the better). Another simulation study estimated Type I and II errors of the Lo-Mendell-Rubin test.

In multilevel analysis, Level-1 predictors that also explain variance at a higher level are called contextual predictors. In the multilevel manifest covariate model, the Level-2 component is modeled as the average of the Level-1 predictor scores within a cluster. In the multilevel latent covariate model, the predictor is decomposed into two latent variables at Level-1 and Level-2. Performance conditions of these modeling approaches for three-level models are largely unexplored. We investigate the two approaches' performance with respect to bias, coverage, and power in a three-level random intercept model. Results reveal differences in estimation quality and required sample sizes. We provide sampling recommendations for both approaches. [ABSTRACT FROM AUTHOR]