A screen-time-based mixture model for identifying and monitoring careless and insufficient effort responding in ecological momentary assessment data.

Ecological momentary assessment (EMA) involves repeated real-time sampling of respondents' current behaviors and experiences. The intensive repeated assessment imposes an increased burden on respondents, rendering EMAs vulnerable to respondent noncompliance and/or careless and insufficient effort responding (C/IER). We developed a mixture modeling approach that equips researchers with a tool for (a) gauging the degree of C/IER contamination of their EMA data and (b) studying the trajectory of C/IER across the study. For separating attentive from C/IER behavior, the approach leverages collateral information from screen times, which are routinely recorded in electronically administered EMAs, and translates theoretical considerations on respondents' behavior into component models for attentive and careless screen times as well as for the functional form of C/IER trajectories. We show how a sensible choice of component models (a) allows disentangling short screen times due to C/IER from familiarity effects due to repeated exposure to the same measures, (b) aids in gaining a fine-grained understanding of C/IER trajectories by distinguishing within-day from between-day effects, and (c) allows investigating interindividual differences in attentiveness. The approach shows good parameter recovery when attentive and C/IER screen time distributions exhibit sufficient separation and yields valid conclusions even in scenarios of uncontaminated data. The approach is illustrated on EMA data from the German Socio-Economic Panel innovation sample. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A Mixed-Effects Model in Which the Parameters of the Autocorrelated Error Structure Can Differ between Individuals.

Research in psychology has seen a rapid increase in the usage of experience sampling methods and daily diary methods. The data that result from using these methods are typically analyzed with a mixed-effects or a multilevel model because it allows testing hypotheses about the time course of the longitudinally assessed variable or the influence of time-varying predictors in a simple way. Here, we describe an extension of this model that does not only allow to include random effects for the mean structure but also for the residual variance, for the parameter of an autoregressive process of order 1 and/or the parameter of a moving average process of order 1. After we have introduced this extension, we show how to estimate the parameters with maximum likelihood. Because the likelihood function contains complex integrals, we suggest using adaptive Gauss-Hermite quadrature and Quasi-Monte Carlo integration to approximate it. We illustrate the models using a real data example and also report the results of a small simulation study in which the two integral approximation methods are compared.

Random Effects Multinomial Processing Tree Models: A Maximum Likelihood Approach.

The present article proposes and evaluates marginal maximum likelihood (ML) estimation methods for hierarchical multinomial processing tree (MPT) models with random and fixed effects. We assume that an identifiable MPT model with S parameters holds for each participant. Of these S parameters, R parameters are assumed to vary randomly between participants, and the remaining [Formula: see text] parameters are assumed to be fixed. We also propose an extended version of the model that includes effects of covariates on MPT model parameters. Because the likelihood functions of both versions of the model are too complex to be tractable, we propose three numerical methods to approximate the integrals that occur in the likelihood function, namely, the Laplace approximation (LA), adaptive Gauss-Hermite quadrature (AGHQ), and Quasi Monte Carlo (QMC) integration. We compare these three methods in a simulation study and show that AGHQ performs well in terms of both bias and coverage rate. QMC also performs well but the number of responses per participant must be sufficiently large. In contrast, LA fails quite often due to undefined standard errors. We also suggest ML-based methods to test the goodness of fit and to compare models taking model complexity into account. The article closes with an illustrative empirical application and an outlook on possible extensions and future applications of the proposed ML approach.

Temporal dynamics of insight in body dysmorphic disorder: An ecological momentary assessment study.

Individuals with body dysmorphic disorder (BDD) often lack insight into the nature of their perceived appearance flaws that are slight, if they exist at all. Despite the generally held assumption that insight fluctuates over time within individuals with BDD, its temporal instability and associated features remain unstudied. We examined insight as a multidimensional, contextually embedded, and dynamic factor using ecological momentary assessment (EMA), further assessing affect and self-esteem as potentially associated states. Thirty individuals with BDD and 30 mentally healthy controls (HCs) completed 6 days of EMA (M = 8.54 assessments per day, N = 3075 assessments in total). Multilevel analyses revealed substantial intraindividual fluctuations of insight dimensions (across 30 min to hours) that were more pronounced for individuals with BDD than for HCs. Poorer insight correlated with higher negative affect, lower positive affect, and lower self-esteem at concurrent time points in the BDD group. Considering all predictors at the same time point, poorer insight was characterized by lower self-esteem and, partly, negative affect in the BDD group. Across time, self-esteem and insight influenced each other reciprocally, with comparatively stronger effects for the prediction of poorer insight by previously lower self-esteem. These findings demonstrate that insight is a highly variable phenomenon in BDD, further suggesting a clinically and etiologically relevant link between self-esteem and insight in symptom maintenance. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Principles behind variance misallocation in temporal exploratory factor analysis for ERP data: Insights from an inter-factor covariance decomposition.

Temporal exploratory factor analysis (EFA) is commonly applied to ERP data sets to reduce their dimensionality and the ambiguity with respect to the underlying components. However, the risk of variance misallocation (i.e., the incorrect allocation of condition effects) has raised concerns with regard to EFA usage. Here, we show that variance misallocation occurs because of biased factor covariance estimates and the temporal overlap between the underlying components. We also highlight the consequences of our findings for the analysis of ERP data with EFA. For example, a direct consequence of our expositions is that researchers should use oblique rather than orthogonal rotations, especially when the factors have a substantial topographic overlap. A Monte Carlo simulation confirms our results by showing, for instance, that characteristic biases occur only for orthogonal Varimax rotation but not for oblique rotation methods such as Geomin or Promax. We discuss the practical implications of our results and outline some questions for future research.

What IAPT CBT High-Intensity Trainees Do After Training.

BACKGROUND: The UK Department of Health Improving Access to Psychological Therapies (IAPT) initiative set out to train a large number of therapists in cognitive behaviour therapies (CBT) for depression and anxiety disorders. Little is currently known about the retention of IAPT CBT trainees, or the use of CBT skills acquired on the course in the workplace after training has finished. AIMS: This study set out to conduct a follow-up survey of past CBT trainees on the IAPT High Intensity CBT Course at the Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London (KCL), one of the largest IAPT High Intensity courses in the UK. METHOD: Past trainees (n = 212) across 6 cohorts (2008-2014 intakes) were contacted and invited to participate in a follow-up survey. A response rate of 92.5% (n = 196) was achieved. RESULTS: The vast majority of IAPT trainees continue to work in IAPT services posttraining (79%) and to practise CBT as their main therapy modality (94%); 61% have become CBT supervisors. A minority (23%) have progressed to other senior roles in the services. Shortcomings are reported in the use of out-of-office CBT interventions, the use of disorder-specific outcome measures and therapy recordings to inform therapy and supervision. CONCLUSIONS: Past trainees stay working in IAPT services and continue to use CBT methods taught on the course. Some NICE recommended treatment procedures that are likely to facilitate patients' recovery are not being routinely implemented across IAPT services. The results have implications for the continued roll out of the IAPT programme, and other future large scale training initiatives.

Metacognitions, worry and sleep in everyday life: Studying bidirectional pathways using Ecological Momentary Assessment in GAD patients.

BACKGROUND: The metacognitive model of generalized anxiety disorder proposes that negative metacognitive beliefs are crucial in the maintenance of excessive worry. Furthermore, according to the cognitive model of insomnia, worry leads to problems falling or staying asleep and poor sleep quality. In order to test the assumed causal relationships, the present study examined the time-dependent course of negative metacognition and worry as well as worry and sleep quality, using Ecological Momentary Assessment (EMA). METHOD: Negative metacognitions, worry and sleep were assessed by self-report questionnaires as well as EMA in 56 GAD patients who carried a portable device for 1 week and logged sleep quality, negative metacognition and worry processes four times a day. RESULTS: Metacognitions, worry and sleep were significantly correlated. Structural equation modeling using multilevel analyses showed a unidirectional relationship of negative metacognitions leading to prolonged worry processes and a bidirectional relationship of worry and sleep quality. CONCLUSIONS: These findings support the theoretically derived assumptions on the relationship between negative metacognitions, worry and sleep. Implications for further research as well as clinical implications are discussed.

How the 2SLS/IV estimator can handle equality constraints in structural equation models: a system-of-equations approach.

Parameters in structural equation models are typically estimated using the maximum likelihood (ML) approach. Bollen (1996) proposed an alternative non-iterative, equation-by-equation estimator that uses instrumental variables. Although this two-stage least squares/instrumental variables (2SLS/IV) estimator has good statistical properties, one problem with its application is that parameter equality constraints cannot be imposed. This paper presents a mathematical solution to this problem that is based on an extension of the 2SLS/IV approach to a system of equations. We present an example in which our approach was used to examine strong longitudinal measurement invariance. We also investigated the new approach in a simulation study that compared it with ML in the examination of the equality of two latent regression coefficients and strong measurement invariance. Overall, the results show that the suggested approach is a useful extension of the original 2SLS/IV estimator and allows for the effective handling of equality constraints in structural equation models.

A Monte Carlo study comparing PIV, ULS and DWLS in the estimation of dichotomous confirmatory factor analysis.

We conducted a Monte Carlo study to investigate the performance of the polychoric instrumental variable estimator (PIV) in comparison to unweighted least squares (ULS) and diagonally weighted least squares (DWLS) in the estimation of a confirmatory factor analysis model with dichotomous indicators. The simulation involved 144 conditions (1,000 replications per condition) that were defined by a combination of (a) two types of latent factor models, (b) four sample sizes (100, 250, 500, 1,000), (c) three factor loadings (low, moderate, strong), (d) three levels of non-normality (normal, moderately, and extremely non-normal), and (e) whether the factor model was correctly specified or misspecified. The results showed that when the model was correctly specified, PIV produced estimates that were as accurate as ULS and DWLS. Furthermore, the simulation showed that PIV was more robust to structural misspecifications than ULS and DWLS.

Perceiving events as both inevitable and unforeseeable in hindsight: the Leipzig candidacy for the Olympics.

We present a new conceptualization of hindsight bias in terms of three separate hindsight components (foreseeability impressions, perceptions of necessity and memory distortions) and report three kinds of supporting evidence from an internet study (N = 101) of the unsuccessful application of the City of Leipzig to host the Olympic Games: (1) strongly diverging hindsight effects, (2) low intercorrelations between the components, and (3) dissociative effects of third variables on them. Specifically, experiencing the failure of the application as personally negative (due to a pro-application attitude and previous commitment), led to perceiving it as inevitable but also as unforeseeable. This surprising result helps to resolve seeming contradictions between previous findings (Louie, 1999; Mark et al., 2003; Tykocinski, 2001) by relating the opposite hindsight effects to differences in the nature and functions (dissonance reduction vs. coping with disappointment) of the foreseeability and necessity components.