Modeling Heterogeneity in Temporal Dynamics: Extending Latent State-Trait Autoregressive and Cross-lagged Panel Models to Mixture Distribution Models.

Longitudinal models suited for the analysis of panel data, such as cross-lagged panel or autoregressive latent-state trait models, assume population homogeneity with respect to the temporal dynamics of the variables under investigation. This assumption is likely to be too restrictive in a myriad of research areas. We propose an extension of autoregressive and cross-lagged latent state-trait models to mixture distribution models. The models allow researchers to model unobserved person heterogeneity and qualitative differences in longitudinal dynamics based on comparatively few observations per person, while taking into account temporal dependencies between observations as well as measurement error in the variables. The models are extended to include categorical covariates, to investigate the distribution of encountered latent classes across observed groups. The potential of the models is illustrated with an application to self-esteem and affect data in patients with borderline personality disorder, an anxiety disorder, and healthy control participants. Requirements for the models' applicability are investigated in an extensive simulation study and recommendations for model applications are derived.

A Comparison of ML, WLSMV, and Bayesian Methods for Multilevel Structural Equation Models in Small Samples: A Simulation Study.

Multilevel structural equation models are increasingly applied in psychological research. With increasing model complexity, estimation becomes computationally demanding, and small sample sizes pose further challenges on estimation methods relying on asymptotic theory. Recent developments of Bayesian estimation techniques may help to overcome the shortcomings of classical estimation techniques. The use of potentially inaccurate prior information may, however, have detrimental effects, especially in small samples. The present Monte Carlo simulation study compares the statistical performance of classical estimation techniques with Bayesian estimation using different prior specifications for a two-level SEM with either continuous or ordinal indicators. Using two software programs (Mplus and Stan), differential effects of between- and within-level sample sizes on estimation accuracy were investigated. Moreover, it was tested to which extent inaccurate priors may have detrimental effects on parameter estimates in categorical indicator models. For continuous indicators, Bayesian estimation did not show performance advantages over ML. For categorical indicators, Bayesian estimation outperformed WLSMV solely in case of strongly informative accurate priors. Weakly informative inaccurate priors did not deteriorate performance of the Bayesian approach, while strong informative inaccurate priors led to severely biased estimates even with large sample sizes. With diffuse priors, Stan yielded better results than Mplus in terms of parameter estimates.

A general framework for the inclusion of time-varying and time-invariant covariates in latent state-trait models.

Latent state-trait (LST) models are increasingly applied in psychology. Although existing LST models offer many possibilities for analyzing variability and change, they do not allow researchers to relate time-varying or time-invariant covariates, or a combination of both, to loading, intercept, and factor variance parameters in LST models. We present a general framework for the inclusion of nominal and/or continuous time-varying and time-invariant covariates in LST models. The new framework builds on modern LST theory and Bayesian moderated nonlinear factor analysis and is termed moderated nonlinear LST (MN-LST) framework. The MN-LST framework offers new modeling possibilities and allows for a fine-grained analysis of trait change, person-by-situation interaction effects, as well as inter- or intraindividual variability. The new MN-LST approach is compared to alternative modeling strategies. The advantages of the MN-LST approach are illustrated in an empirical application examining dyadic coping in romantic relationships. Finally, the advantages and limitations of the approach are discussed, and practical recommendations are provided. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Great ape cognition is structured by stable cognitive abilities and predicted by developmental conditions.

Great ape cognition is used as a reference point to specify the evolutionary origins of complex cognitive abilities, including in humans. This research often assumes that great ape cognition consists of cognitive abilities (traits) that account for stable differences between individuals, which change and develop in response to experience. Here, we test the validity of these assumptions by assessing repeatability of cognitive performance among captive great apes (Gorilla gorilla, Pongo abelii, Pan paniscus, Pan troglodytes) in five tasks covering a range of cognitive domains. We examine whether individual characteristics (age, group, test experience) or transient situational factors (life events, testing arrangements or sociality) influence cognitive performance. Our results show that task-level performance is generally stable over time; four of the five tasks were reliable measurement tools. Performance in the tasks was best explained by stable differences in cognitive abilities (traits) between individuals. Cognitive abilities were further correlated, suggesting shared cognitive processes. Finally, when predicting cognitive performance, we found stable individual characteristics to be more important than variables capturing transient experience. Taken together, this study shows that great ape cognition is structured by stable cognitive abilities that respond to different developmental conditions.

Telephone counseling for patients with chronic heart failure: results of an evaluation study.

BACKGROUND: The effectiveness of a secondary prevention program for patients suffering from chronic heart failure (CHF) was evaluated. PURPOSE: The program aimed at improving participants' perceived health and actual physical symptoms. Insurants of a German health insurance company participated in a telephone counseling program with four modules focusing on dietary habits, physical activity, fluid intake, and medication compliance. METHOD: Multilevel analyses were conducted to analyze changes in health related outcome variables over time in N = 259 participants who completed the program in about 6 months. RESULTS: The results showed an improvement of perceived health status, physical symptoms, and somatic impairment. Furthermore, differential change was found when comparing "finishers" compared to "non-finishers" of specific modules indicating specific module effects. CONCLUSION: The results are auspicious and, if sustained, are expected to bring about long-term health benefits for our study's participants. The program proved to be applicable and well accepted in the sample of older, severely impaired CHF patients and effective in changing perceived health.

Analyzing Stability and Change in Dyadic Attachment: The Multi-Rater Latent State-Trait Model With Autoregressive Effects.

Previous research suggests that parental attachment is stable throughout emerging adulthood. However, the relationships between the mutual attachments in the dyads of emerging adults and their parents are still unclear. Our study examines the stability and change in dyadic attachment. We asked 574 emerging adults and 463 parents at four occasions over 1 year about their mutual attachments. We used a latent state-trait model with autoregressive effects to estimate the time consistency of the attachments. Attachment was very stable, and earlier measurement occasions could explain more than 60% of the reliable variance. Changes of attachment over time showed an accumulation of situational effects for emerging adults but not for their parents. We estimated the correlations of the mutual attachments over time using a novel multi-rater latent state-trait model with autoregressive effects. This model showed that the mutual attachments of parents and emerging adults were moderately to highly correlated. Our model allows to separate the stable attachment from the changing attachment. The correlations between the mutual attachments were higher for the stable elements of attachment than for the changing elements of attachment. Emerging adults and their parents share a stable mutual attachment, but they do not share the changes in their respective attachments.

Skew t Mixture Latent State-Trait Analysis: A Monte Carlo Simulation Study on Statistical Performance.

This simulation study assessed the statistical performance of a skew t mixture latent state-trait (LST) model for the analysis of longitudinal data. The model aims to identify interpretable latent classes with class-specific LST model parameters. A skew t-distribution within classes is allowed to account for non-normal outcomes. This flexible function covers heavy tails and may reduce the risk of identifying spurious classes, e.g., in case of outliers. Sample size, number of occasions and skewness of the trait variable were varied. Generally, parameter estimation accuracy increases with increasing numbers of observations and occasions. Larger bias compared to other parameters occurs for parameters referring to the skew t-distribution and variances of the latent trait variables. Standard error estimation accuracy shows diffuse patterns across conditions and parameters. Overall model performance is acceptable for large conditions, even though none of the models is free from bias. The application of the skew t mixture model in case of large numbers of occasions and observations may be possible, but results should be treated with caution. Moreover, the skew t approach may be useful for other mixture models.

Explaining general and specific factors in longitudinal, multimethod, and bifactor models: Some caveats and recommendations.

An increasing number of psychological studies are devoted to the analysis of g-factor structures. One key purpose of applying g-factor models is to identify predictors or potential causes of the general and specific effects. Typically, researchers relate predictor variables directly to the general and specific factors using a classical mimic approach. However, this procedure bears some methodological challenges, which often lead to model misspecification and biased parameter estimates. We propose 2 possible modeling strategies to circumvent these problems: the multiconstruct bifactor and the residual approach. We illustrate both modeling approaches for the application of g-factor models to longitudinal and multitrait-multimethod data. Practical guidelines are provided for choosing an appropriate method in empirical applications, and the implications of this investigation for multimethod and longitudinal research are discussed. (PsycINFO Database Record

Bayesian analysis of longitudinal multitrait-multimethod data with ordinal response variables.

A new multilevel latent state graded response model for longitudinal multitrait-multimethod (MTMM) measurement designs combining structurally different and interchangeable methods is proposed. The model allows researchers to examine construct validity over time and to study the change and stability of constructs and method effects based on ordinal response variables. We show how Bayesian estimation techniques can address a number of important issues that typically arise in longitudinal multilevel MTMM studies and facilitates the estimation of the model presented. Estimation accuracy and the impact of between- and within-level sample sizes as well as different prior specifications on parameter recovery were investigated in a Monte Carlo simulation study. Findings indicate that the parameters of the model presented can be accurately estimated with Bayesian estimation methods in the case of low convergent validity with as few as 250 clusters and more than two observations within each cluster. The model was applied to well-being data from a longitudinal MTMM study, assessing the change and stability of life satisfaction and subjective happiness in young adults after high-school graduation. Guidelines for empirical applications are provided and advantages and limitations of a Bayesian approach to estimating longitudinal multilevel MTMM models are discussed.

A Multimethod Latent State-Trait Model for Structurally Different And Interchangeable Methods.

A new multiple indicator multilevel latent state-trait (LST) model for the analysis of multitrait-multimethod-multioccasion (MTMM-MO) data is proposed. The LST-COM model combines current CFA-MTMM modeling approaches of interchangeable and structurally different methods and LST modeling approaches. The model enables researchers to specify construct and method factors on the level of time-stable (trait) as well as time-variable (occasion-specific) latent variables and analyze the convergent and discriminant validity among different rater groups across time. The statistical performance of the model is scrutinized by a simulation study and guidelines for empirical applications are provided.

The temporal interplay of self-esteem instability and affective instability in borderline personality disorder patients' everyday lives.

Borderline personality disorder (BPD) is defined by a pervasive pattern of instability. Although there is ample empirical evidence that unstable self-esteem is associated with a myriad of BPD-like symptoms, self-esteem instability and its temporal dynamics have received little empirical attention in patients with BPD. Even worse, the temporal interplay of affective instability and self-esteem instability has been neglected completely, although it has been hypothesized recently that the lack of specificity of affective instability in association with BPD might be explained by the highly intertwined temporal relationship between affective and self-esteem instability. To investigate self-esteem instability, its temporal interplay with affective instability, and its association with psychopathology, 60 patients with BPD and 60 healthy controls (HCs) completed electronic diaries for 4 consecutive days during their everyday lives. Participants reported their current self-esteem, valence, and tense arousal levels 12 times a day in approximately one-hr intervals. We used multiple state-of-the-art statistical techniques and graphical approaches to reveal patterns of instability, clarify group differences, and examine the temporal interplay of self-esteem instability and affective instability. As hypothesized, instability in both self-esteem and affect was clearly elevated in the patients with BPD. In addition, self-esteem instability and affective instability were highly correlated. Both types of instability were related to general psychopathology. Because self-esteem instability could not fully explain affective instability and vice versa and neither affective instability nor self-esteem instability was able to explain psychopathology completely, our findings suggest that these types of instability represent unique facets of BPD. (PsycINFO Database Record

Trait anxiety modulates fronto-limbic processing of emotional interference in borderline personality disorder.

Previous studies of cognitive alterations in borderline personality disorder (BPD) have yielded conflicting results. Given that a core feature of BPD is affective instability, which is characterized by emotional hyperreactivity and deficits in emotion regulation, it seems conceivable that short-lasting emotional distress might exert temporary detrimental effects on cognitive performance. Here we used functional magnetic resonance imaging (fMRI) to investigate how task-irrelevant emotional stimuli (fearful faces) affect performance and fronto-limbic neural activity patterns during attention-demanding cognitive processing in 16 female, unmedicated BPD patients relative to 24 age-matched healthy controls. In a modified flanker task, emotionally negative, socially salient pictures (fearful vs. neutral faces) were presented as distracters in the background. Patients, but not controls, showed an atypical response pattern of the right amygdala with increased activation during emotional interference in the (difficult) incongruent flanker condition, but emotion-related amygdala deactivation in the congruent condition. A direct comparison of the emotional conditions between the two groups revealed that the strongest diagnosis-related differences could be observed in the dorsal and, to a lesser extent, also in the rostral anterior cingulate cortex (dACC, rACC) where patients exhibited an increased neural response to emotional relative to neutral distracters. Moreover, in the incongruent condition, both the dACC and rACC fMRI responses during emotional interference were negatively correlated with trait anxiety in the patients, but not in the healthy controls. As higher trait anxiety was also associated with longer reaction times (RTs) in the BPD patients, we suggest that in BPD patients the ACC might mediate compensatory cognitive processes during emotional interference and that such neurocognitive compensation that can be adversely affected by high levels of anxiety.