Heterogeneous Mental Health Responses to the COVID-19 Pandemic in Germany: An Examination of Long-Term Trajectories, Risk Factors, and Vulnerable Groups.

Abundant studies have examined mental health in the early periods of the COVID-19 pandemic. However, empirical work examining the mental health impact of the pandemic's subsequent phases remains limited. In the present study, we investigated how mental vulnerability and resilience evolved over the various phases of the pandemic in 2020 and 2021 in Germany. Data were collected (n = 3522) across seven measurement occasions using validated and self-generated measures of vulnerability and resilience. We found evidence for an immediate increase in vulnerability during the first lockdown in Germany, a trend towards recovery when lockdown measures were eased, and an increase in vulnerability with each passing month of the second lockdown. Four different latent trajectories of resilience-vulnerability emerged, with the majority of participants displaying a rather resilient trajectory, but nearly 30% of the sample fell into the more vulnerable groups. Females, younger individuals, those with a history of psychiatric disorders, lower income groups, and those with high trait vulnerability and low trait social belonging were more likely to exhibit trajectories associated with poorer mental well-being. Our findings indicate that resilience-vulnerability responses in Germany during the COVID-19 pandemic may have been more complex than previously thought, identifying risk groups that could benefit from greater support.

Structural architecture and brain network efficiency link polygenic scores to intelligence.

Intelligence is highly heritable. Genome-wide association studies (GWAS) have shown that thousands of alleles contribute to variation in intelligence with small effect sizes. Polygenic scores (PGS), which combine these effects into one genetic summary measure, are increasingly used to investigate polygenic effects in independent samples. Whereas PGS explain a considerable amount of variance in intelligence, it is largely unknown how brain structure and function mediate this relationship. Here, we show that individuals with higher PGS for educational attainment and intelligence had higher scores on cognitive tests, larger surface area, and more efficient fiber connectivity derived by graph theory. Fiber network efficiency as well as the surface of brain areas partly located in parieto-frontal regions were found to mediate the relationship between PGS and cognitive performance. These findings are a crucial step forward in decoding the neurogenetic underpinnings of intelligence, as they identify specific regional networks that link polygenic predisposition to intelligence.

Regularized continuous time structural equation models: A network perspective.

Regularized continuous time structural equation models are proposed to address two recent challenges in longitudinal research: Unequally spaced measurement occasions and high model complexity. Unequally spaced measurement occasions are part of most longitudinal studies, sometimes intentionally (e.g., in experience sampling methods) sometimes unintentionally (e.g., due to missing data). Yet, prominent dynamic models, such as the autoregressive cross-lagged model, assume equally spaced measurement occasions. If this assumption is violated parameter estimates can be biased, potentially leading to false conclusions. Continuous time structural equation models (CTSEM) resolve this problem by taking the exact time point of a measurement into account. This allows for any arbitrary measurement scheme. We combine CTSEM with LASSO and adaptive LASSO regularization. Such regularization techniques are especially promising for the increasingly complex models in psychological research, the most prominent example being network models with often dozens or hundreds of parameters. Here, LASSO regularization can reduce the risk of overfitting and simplify the model interpretation. In this article we highlight unique challenges in regularizing continuous time dynamic models, such as standardization or the optimization of the objective function, and offer different solutions. Our approach is implemented in the R (R Core Team, 2022) package regCtsem. We demonstrate the use of regCtsem in a simulation study, showing that the proposed regularization improves the parameter estimates, especially in small samples. The approach correctly eliminates true-zero parameters while retaining true-nonzero parameters. We present two empirical examples and end with a discussion on current limitations and future research directions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A Dynamic Structural Equation Approach to Modeling Wage Dynamics and Cumulative Advantage across the Lifespan.

Wages and wage dynamics directly affect individuals' and families' daily lives. In this article, we show how major theoretical branches of research on wages and inequality-that is, cumulative advantage (CA), human capital theory, and the lifespan perspective-can be integrated into a coherent statistical framework and analyzed with multilevel dynamic structural equation modeling (DSEM). This opens up a new way to empirically investigate the mechanisms that drive growing inequality over time. We demonstrate the new approach by making use of longitudinal, representative U.S. data (NLSY-79). Analyses revealed fundamental between-person differences in both initial wages and autoregressive wage growth rates across the lifespan. Only 0.5% of the sample experienced a "strict" CA and unbounded wage growth, whereas most individuals revealed logarithmic wage growth over time. Adolescent intelligence and adult educational levels explained substantial heterogeneity in both parameters. We discuss how DSEM may help researchers study CA processes and related developmental dynamics, and we highlight the extensions and limitations of the DSEM framework.

The association between history of prenatal loss and maternal psychological state in a subsequent pregnancy: an ecological momentary assessment (EMA) study.

BACKGROUND: Prenatal loss which occurs in approximately 20% of pregnancies represents a well-established risk factor for anxiety and affective disorders. In the current study, we examined whether a history of prenatal loss is associated with a subsequent pregnancy with maternal psychological state using ecological momentary assessment (EMA)-based measures of pregnancy-specific distress and mood in everyday life. METHOD: This study was conducted in a cohort of N = 155 healthy pregnant women, of which N = 40 had a history of prenatal loss. An EMA protocol was used in early and late pregnancy to collect repeated measures of maternal stress and mood, on average eight times per day over a consecutive 4-day period. The association between a history of prenatal loss and psychological state was estimated using linear mixed models. RESULTS: Compared to women who had not experienced a prior prenatal loss, women with a history of prenatal loss reported higher levels of pregnancy-specific distress in early as well as late pregnancy and also were more nervous and tired. Furthermore, in the comparison group pregnancy-specific distress decreased and mood improved from early to late pregnancy, whereas these changes across pregnancy were not evident in women in the prenatal loss group. CONCLUSION: Our findings suggest that prenatal loss in a prior pregnancy is associated with a subsequent pregnancy with significantly higher stress and impaired mood levels in everyday life across gestation. These findings have important implications for designing EMA-based ambulatory, personalized interventions to reduce stress during pregnancy in this high-risk group.

Intra-Subject Variability, Intelligence, and ADHD Traits in a Community-Based Sample.

OBJECTIVE: The present study investigates the predictive validity of intra-subject variability (ISV) for ADHD traits in a community-based sample and the stability of the relationship between ISV and fluid intelligence (gf) across the continuum of ADHD traits. METHOD: Age-residualized data from 426 participants (8-18 years, 6% ADHD) was used to investigate whether ex-Gaussian and DDM parameters derived from simple choice-reaction-time tasks can predict continuously assessed ADHD traits. Multiple-Group-Analyses and Latent-Moderated-Structural-Equations were used to test whether ADHD traits moderate the relationship between ISV and gf. RESULTS: σ and µ of the ex-Gaussian model as well as DDM parameters drift rate (v) and boundary separation (a) significantly predicted general ADHD traits, while τ predicted attention difficulties specifically. Across the ADHD continuum, σ and v were significant predictors of gf. CONCLUSION: The results confirm the link between ISV and ADHD. The relationship between ISV and gf appears stable across the ADHD continuum.

Modeling dynamic personality theories in a continuous-time framework: An illustration.

OBJECTIVE: Personality psychology has traditionally focused on stable between-person differences. Yet, recent theoretical developments and empirical insights have led to a new conceptualization of personality as a dynamic system (e.g., Cybernetic Big Five Theory). Such dynamic systems comprise several components that need to be conceptually distinguished and mapped to a statistical model for estimation. METHOD: In the current work, we illustrate how common components from these new dynamic personality theories may be implemented in a continuous time-modeling framework. RESULTS: As an empirical example, we reanalyze experience sampling data with N = 180 persons (with on average T = 40 [SD = 8] measurement occasions) to investigate four different effects between momentary happiness, momentary extraverted behavior, and the perception of a situation as social: (1) between-person effects, (2) contemporaneous effects, (3) autoregressive effects, and (4) cross-lagged effects. CONCLUSION: We highlight that these four effects must not necessarily point in the same direction, which is in line with assumptions from dynamic personality theories.

Resource-Building Processes Across Life Domains: Father-Child Interactions as Starting Points for Resource Caravans.

In two studies, we examined preconditions of resource-building processes between family and work. Focusing on positive father-child interactions, we investigated positive mood states as links between the two life domains. Fathers employed in information technology (N 1 = 59) and the retail sector (N 2 = 75) participated in micro-longitudinal studies, both for eight consecutive workdays. Study 1 revealed that fathers with more positive interactions with a child also reported more positive mood states and fathers with more positive mood states perceived more social resources from their supervisor during the week. The indirect effect was small but significant. In Study 2, multilevel structural-equation models did not find indirect effects at the within-person level but did show that positive father-child interactions after work were related to fathers' positive mood states before going to bed and positive mood in the morning predicted perceived social resources from supervisors (but not from coworkers) in the forenoon. There were also positive effects of perceived social resources from supervisors on positive mood states, after work. But these did not translate into an increase in positive father-child interactions, in the evening. Hence, only single elements were supported but not the overall resource caravan. Supplementary Information: The online version contains supplementary material available at 10.1007/s10902-022-00523-4.

Exploring the Structure and Interrelations of Time-Stable Psychological Resilience, Psychological Vulnerability, and Social Cohesion.

The current study explores the relationship between three constructs of high relevance in the context of adversities which have, however, not yet been systematically linked on the level of psychological dispositions: psychological vulnerability, psychological resilience, and social cohesion. Based on previous theoretical and empirical frameworks, a collection of trait questionnaires was assessed in a Berlin sample of 3,522 subjects between 18 and 65 years of age. Using a confirmatory factor analytical approach, we found no support for a simple three-factor structure. Results from exploratory structural analyses suggest that instead of psychological resilience and psychological vulnerability constituting two separate factors, respective indicators load on one bipolar latent factor. Interestingly, some psychological resilience indicators contributed to an additional specific latent factor, which may be interpreted as adaptive capacities, that is, abilities to adapt to changes or adjust to consequences of adversities. Furthermore, instead of evidence for one single social cohesion factor on the psychological level, indicators of perceived social support and loneliness formed another specific factor of social belonging, while indicators of prosocial competencies were found to form yet another distinct factor, which was positively associated to the other social factors, adaptive capacities and social belonging. Our results suggest that social cohesion is composed of different independent psychological components, such as trust, social belonging, and social skills. Furthermore, our findings highlight the importance of social capacities and belonging for psychological resilience and suggest that decreasing loneliness and increasing social skills should therefore represent a valuable intervention strategy to foster adaptive capacities.

Immediate impact of child maltreatment on mental, developmental, and physical health trajectories.

OBJECTIVE: The immediate impact of child maltreatment on health and developmental trajectories over time is unknown. Longitudinal studies starting in the direct aftermath of exposure with repeated follow-up are needed. METHOD: We assessed health and developmental outcomes in 6-month intervals over 2 years in 173 children, aged 3-5 years at study entry, including 86 children with exposure to emotional and physical abuse or neglect within 6 months and 87 nonmaltreated children. Assessments included clinician-administered, self- and parent-report measures of psychiatric and behavioral symptoms, development, and physical health. Linear mixed models and latent growth curve analyses were used to contrast trajectories between groups and to investigate the impact of maltreatment features on trajectories. RESULTS: Maltreated children exhibited greater numbers of psychiatric diagnoses (b = 1.998, p < .001), externalizing (b = 13.29, p < .001) and internalizing (b = 11.70, p < .001) symptoms, impairments in cognitive (b = -11.586, p < .001), verbal (b = -10.687, p < .001), and motor development (b = -7.904, p = .006), and greater numbers of medical symptoms (b = 1.021, p < .001) compared to nonmaltreated children across all time-points. Lifetime maltreatment severity and/or age at earliest maltreatment exposure predicted adverse outcomes over time. CONCLUSION: The profound, immediate, and stable impact of maltreatment on health and developmental trajectories supports a biological embedding model and provides foundation to scrutinize the precise underlying mechanisms. Such knowledge will enable the development of early risk markers and mechanism-driven interventions that mitigate adverse trajectories in maltreated children.

Coping with the COVID-19 Pandemic: Perceived Changes in Psychological Vulnerability, Resilience and Social Cohesion before, during and after Lockdown.

The COVID-19 pandemic and associated lockdowns have posed unique and severe challenges to our global society. To gain an integrative understanding of pervasive social and mental health impacts in 3522 Berlin residents aged 18 to 65, we systematically investigated the structural and temporal relationship between a variety of psychological indicators of vulnerability, resilience and social cohesion before, during and after the first lockdown in Germany using a retrospective longitudinal study design. Factor analyses revealed that (a) vulnerability and resilience indicators converged on one general bipolar factor, (b) residual variance of resilience indicators formed a distinct factor of adaptive coping capacities and (c) social cohesion could be reliably measured with a hierarchical model including four first-order dimensions of trust, a sense of belonging, social interactions and social engagement, and one second-order social cohesion factor. In the second step, latent change score models revealed that overall psychological vulnerability increased during the first lockdown and decreased again during re-opening, although not to baseline levels. Levels of social cohesion, in contrast, first decreased and then increased again during re-opening. Furthermore, participants who increased in vulnerability simultaneously decreased in social cohesion and adaptive coping during lockdown. While higher pre-lockdown levels of social cohesion predicted a stronger lockdown effect on mental health, individuals with higher social cohesion during the lockdown and positive change in coping abilities and social cohesion during re-opening showed better mental health recovery, highlighting the important role of social capacities in both amplifying but also overcoming the multiple challenges of this collective crisis.

Psychopathological networks: Theory, methods and practice.

In recent years, network approaches to psychopathology have sparked much debate and have had a significant impact on how mental disorders are perceived in the field of clinical psychology. However, there are many important challenges in moving from theory to empirical research and clinical practice and vice versa. Therefore, in this article, we bring together different points of view on psychological networks by methodologists and clinicians to give a critical overview on these challenges, and to present an agenda for addressing these challenges. In contrast to previous reviews, we especially focus on methodological issues related to temporal networks. This includes topics such as selecting and assessing the quality of the nodes in the network, distinguishing between- and within-person effects in networks, relating items that are measured at different time scales, and dealing with changes in network structures. These issues are not only important for researchers using network models on empirical data, but also for clinicians, who are increasingly likely to encounter (person-specific) networks in the consulting room.

Beyond the Mean: A Flexible Framework for Studying Causal Effects Using Linear Models.

Graph-based causal models are a flexible tool for causal inference from observational data. In this paper, we develop a comprehensive framework to define, identify, and estimate a broad class of causal quantities in linearly parametrized graph-based models. The proposed method extends the literature, which mainly focuses on causal effects on the mean level and the variance of an outcome variable. For example, we show how to compute the probability that an outcome variable realizes within a target range of values given an intervention, a causal quantity we refer to as the probability of treatment success. We link graph-based causal quantities defined via the do-operator to parameters of the model implied distribution of the observed variables using so-called causal effect functions. Based on these causal effect functions, we propose estimators for causal quantities and show that these estimators are consistent and converge at a rate of [Formula: see text] under standard assumptions. Thus, causal quantities can be estimated based on sample sizes that are typically available in the social and behavioral sciences. In case of maximum likelihood estimation, the estimators are asymptotically efficient. We illustrate the proposed method with an example based on empirical data, placing special emphasis on the difference between the interventional and conditional distribution.

Investigating differential effects of socio-emotional and mindfulness-based online interventions on mental health, resilience and social capacities during the COVID-19 pandemic: The study protocol.

BACKGROUND: The SARS-CoV-2 pandemic has led to a mental health crisis on a global scale. Epidemiological studies have reported a drastic increase in mental health problems, such as depression and anxiety, increased loneliness and feelings of disconnectedness from others, while resilience levels have been negatively affected, indicating an urgent need for intervention. The current study is embedded within the larger CovSocial project which sought to evaluate longitudinal changes in vulnerability, resilience and social cohesion during the pandemic. The current second phase will investigate the efficacy of brief online mental training interventions in reducing mental health problems, and enhancing psychological resilience and social capacities. It further provides a unique opportunity for the prediction of intervention effects by individual biopsychosocial characteristics and preceding longitudinal change patterns during the pandemic in 2020/21. METHODS: We will examine the differential effects of a socio-emotional (including 'Affect Dyad') and a mindfulness-based (including 'Breathing Meditation') intervention, delivered through a web- and cellphone application. Participants will undergo 10 weeks of intervention, and will be compared to a retest control group. The effectiveness of the interventions will be evaluated in a community sample (N = 300), which is recruited from the original longitudinal CovSocial sample. The pre- to post-intervention changes, potential underlying mechanisms, and prediction thereof, will be assessed on a wide range of outcomes: levels of stress, loneliness, depression and anxiety, resilience, prosocial behavior, empathy, compassion, and the impact on neuroendocrine, immunological and epigenetic markers. The multi-method nature of the study will incorporate self-report questionnaires, behavioral tasks, ecological momentary assessment (EMA) approaches, and biological, hormonal and epigenetic markers assessed in saliva. DISCUSSION: Results will reveal the differential effectiveness of two brief online interventions in improving mental health outcomes, as well as enhancing social capacities and resilience. The present study will serve as a first step for future application of scalable, low-cost interventions at a broader level to reduce stress and loneliness, improve mental health and build resilience and social capacities in the face of global stressors. TRIAL REGISTRATION: This trial has been registered on May 17, 2020 with the ClinicalTrials.gov NCT04889508 registration number (clinicaltrials.gov/ct2/show/NCT04889508).

Lifetime post-traumatic stress disorder in older individuals with a history of institutional upbringing in childhood: the role of social acknowledgement and stressful life events.

Background: Child maltreatment (CM), particularly in institutional contexts, can affect the development of post-traumatic stress disorder (PTSD). Research suggests that factors during CM (e.g. severity, variety, duration) and in the aftermath of CM (e.g. stressful life events, and social acknowledgement, i.e. the degree to which an individual feels validated and supported following a traumatic event) can explain some of the heterogeneity in PTSD development. However, there is a lack of research on long-term correlates of CM and mitigating factors, with only a few studies having been conducted with older survivors of institutional upbringing. Such research is relevant, given the long-term associations between CM and the older age status of many survivors. Objective: The current study examined the link between CM and PTSD in older individuals with a history of institutional upbringing (risk group; RG) and a matched control group (CG). Differences in stressful life events and social acknowledgement were also investigated. Method: Participants were n = 116 RG (Mage = 70.25 years, 41% female) and n = 122 CG (Mage = 70.71 years, 51% female). Data was assessed using self-report questionnaires and a clinical interview. Results: The RG reported higher levels of exposure to CM. Lifetime PTSD showed a bigger association with the level of exposure to CM, compared to having an institutional upbringing. Participants with higher CM levels reported more stressful life events. High levels of social acknowledgement mediated the relationship between CM and PTSD in the CG. Conclusions: Exposure to CM had a stronger association with PTSD than a history of institutional upbringing. In the CG, the survivors' perception of social acknowledgement ameliorated lifetime PTSD to a small extent. A critical issue for policy makers should be to enhance safeguarding measures against CM exposure, not only in institutional contexts, but also more generally, given the link to PTSD.

Antecedentes: El maltrato infantil (MI), particularmente en contextos institucionales, puede incidir en el desarrollo del trastorno de estrés postraumático (TEPT). La investigación sugiere que los factores durante el MI (ej. gravedad, variedad, duración) y en el periodo posterior al MI (ej. eventos estresantes de la vida y reconocimiento social, es decir, el grado en que un individuo se siente validado y apoyado después de un evento traumático) pueden explicar en parte la heterogeneidad en el desarrollo del TEPT. Sin embargo, hay una falta de investigación sobre los correlatos a largo plazo del MI y los factores atenuantes, y solo se han realizado unos pocos estudios con personas mayores que han sobrevivido a la crianza institucional. Dicha investigación es relevante, dadas las asociaciones a largo plazo entre MI y el estado a mayor edad de muchos sobrevivientes.Objetivo: El presente estudio examinó el vínculo entre MI y TEPT en personas mayores con antecedentes de crianza institucional (grupo de riesgo; GR) y un grupo de control emparejado (GC). También se investigaron las diferencias en los eventos vitales estresantes y el reconocimiento social.Método: Los participantes fueron N = 116 en GR (edad promedio = 70,25 años, 41% mujeres) y N = 122 en GC (edad promedio = 70,71 años, 51% mujeres). Los datos se evaluaron mediante cuestionarios de auto-reporte y una entrevista clínica.Resultados: El GR reportó niveles más altos de exposición a MI. El TEPT durante la vida mostró una mayor asociación con el nivel de exposición a MI, en comparación con la crianza institucional. Los participantes con niveles más altos de MI reportaron más eventos vitales estresantes. Altos niveles de reconocimiento social mediaron la relación entre MI y TEPT en el GC.Conclusiones: La exposición a MI tuvo una asociación más fuerte con el TEPT que el historial de crianza institucional. En el GC, la percepción de reconocimiento social de los sobrevivientes mejoró en pequeña medida el TEPT durante la vida. Una cuestión fundamental para los responsables de la formulación de políticas debería ser mejorar las medidas de protección contra la exposición a MI, no solo en contextos institucionales, sino también de manera más general, dado el vínculo con el trastorno de estrés postraumático.

Polygenic Scores for Cognitive Abilities and Their Association with Different Aspects of General Intelligence-A Deep Phenotyping Approach.

Intelligence is a highly polygenic trait and genome-wide association studies (GWAS) have identified thousands of DNA variants contributing with small effects. Polygenic scores (PGS) can aggregate those effects for trait prediction in independent samples. As large-scale light-phenotyping GWAS operationalized intelligence as performance in rather superficial tests, the question arises which intelligence facets are actually captured. We used deep-phenotyping to investigate the molecular determinants of individual differences in cognitive ability. We, therefore, studied the association between PGS of intelligence (IQ-PGS), cognitive performance (CP-PGS), and educational attainment (EA-PGS) with a wide range of intelligence facets in a sample of 557 healthy adults. IQ-PGS, CP-PGS, and EA-PGS had the highest incremental R2s for general (2.71%; 4.27%; 2.06%), verbal (3.30%; 4.64%; 1.61%), and numerical intelligence (3.06%; 3.24%; 1.26%) and the weakest for non-verbal intelligence (0.89%; 1.47%; 0.70%) and memory (0.80%; 1.06%; 0.67%). These results indicate that PGS derived from light-phenotyping GWAS do not reflect different facets of intelligence equally well, and thus should not be interpreted as genetic indicators of intelligence per se. The findings refine our understanding of how PGS are related to other traits or life outcomes.

From Data to Causes III: Bayesian Priors for General Cross-Lagged Panel Models (GCLM).

This article describes some potential uses of Bayesian estimation for time-series and panel data models by incorporating information from prior probabilities (i.e., priors) in addition to observed data. Drawing on econometrics and other literatures we illustrate the use of informative "shrinkage" or "small variance" priors (including so-called "Minnesota priors") while extending prior work on the general cross-lagged panel model (GCLM). Using a panel dataset of national income and subjective well-being (SWB) we describe three key benefits of these priors. First, they shrink parameter estimates toward zero or toward each other for time-varying parameters, which lends additional support for an income → SWB effect that is not supported with maximum likelihood (ML). This is useful because, second, these priors increase model parsimony and the stability of estimates (keeping them within more reasonable bounds) and thus improve out-of-sample predictions and interpretability, which means estimated effect should also be more trustworthy than under ML. Third, these priors allow estimating otherwise under-identified models under ML, allowing higher-order lagged effects and time-varying parameters that are otherwise impossible to estimate using observed data alone. In conclusion we note some of the responsibilities that come with the use of priors which, departing from typical commentaries on their scientific applications, we describe as involving reflection on how best to apply modeling tools to address matters of worldly concern.

Forecasting Causal Effects of Interventions versus Predicting Future Outcomes.

The present article provides a didactic presentation and extension of selected features of Pearl's DAG-based approach to causal inference for researchers familiar with structural equation modeling. We illustrate key concepts using a cross-lagged panel design. We distinguish between (a) forecasts of the value of an outcome variable after an intervention and (b) predictions of future values of an outcome variable. We consider the mean level and variance of the outcome variable as well as the probability that the outcome will fall within an acceptable range. We extend this basic approach to include additive random effects, allowing us to distinguish between average effects of interventions and person-specific effects of interventions. We derive optimal person-specific treatment levels and show that optimal treatment levels may differ across individuals. We present worked examples using simulated data based on the results of a prior empirical study of the relationship between blood insulin and glucose levels.

Calibrating the experimental measurement of psychological attributes.

Behavioural researchers often seek to experimentally manipulate, measure and analyse latent psychological attributes, such as memory, confidence or attention. The best measurement strategy is often difficult to intuit. Classical psychometric theory, mostly focused on individual differences in stable attributes, offers little guidance. Hence, measurement methods in experimental research are often based on tradition and differ between communities. Here we propose a criterion, which we term 'retrodictive validity', that provides a relative numerical estimate of the accuracy of any given measurement approach. It is determined by performing calibration experiments to manipulate a latent attribute and assessing the correlation between intended and measured attribute values. Our approach facilitates optimising measurement strategies and quantifying uncertainty in the measurement. Thus, it allows power analyses to define minimally required sample sizes. Taken together, our approach provides a metrological perspective on measurement practice in experimental research that complements classical psychometrics.