A Network Analysis of Executive Functions in Children and Adolescents With and Without Attention-Deficit/Hyperactivity Disorder.

This study applied network analysis to executive function test performances to examine differences in network parameters between demographically matched children and adolescents with and without attention-deficit/hyperactivity disorder (ADHD) (n = 141 per group; M = 12.7 ± 2.9 years-old; 72.3% boys, 66.7% White, 65.2% ≥ 12 years maternal education). All participants completed the NIH Toolbox Cognition Battery, including the Flanker, measuring inhibition, Dimensional Change Card Sort, measuring shifting, and List Sorting test, measuring working memory. Children with and without ADHD had comparable mean test performances (d range: .05-0.11) but presented with differences in network parameters. Among participants with ADHD, shifting was less central, had a weaker relationship with inhibition, and did not mediate the relationship between inhibition and working memory. These network characteristics were consistent with the executive function network structure of younger ages in prior research and may reflect an immature executive function network among children and adolescents with ADHD, aligning with the delayed maturation hypothesis.

Putting the individual into reliability: Bayesian testing of homogeneous within-person variance in hierarchical models.

Measurement reliability is a fundamental concept in psychology. It is traditionally considered a stable property of a questionnaire, measurement device, or experimental task. Although intraclass correlation coefficients (ICC) are often used to assess reliability in repeated measure designs, their descriptive nature depends upon the assumption of a common within-person variance. This work focuses on the presumption that each individual is adequately described by the average within-person variance in hierarchical models. And thus whether reliability generalizes to the individual level, which leads directly into the notion of individually varying ICCs. In particular, we introduce a novel approach, using the Bayes factor, wherein a researcher can directly test for homogeneous within-person variance in hierarchical models. Additionally, we introduce a membership model that allows for classifying which (and how many) individuals belong to the common variance model. The utility of our methodology is demonstrated on cognitive inhibition tasks. We find that heterogeneous within-person variance is a defining feature of these tasks, and in one case, the ratio between the largest to smallest within-person variance exceeded 20. This translates into a tenfold difference in person-specific reliability! We also find that few individuals belong to the common variance model, and thus traditional reliability indices are potentially masking important individual variation. We discuss the implications of our findings and possible future directions. The methods are implemented in the R package vICC.

Optimizing Detection of True Within-Person Effects for Intensive Measurement Designs: A Comparison of Multilevel SEM and Unit-Weighted Scale Scores.

Intensive repeated measurement designs are frequently used to investigate within-person variation over relatively brief intervals of time. The majority of research utilizing these designs relies on unit-weighted scale scores, which assume that the constructs are measured without error. An alternative approach makes use of multilevel structural equation models (MSEM), which permit the specification of latent variables at both within-person and between-person levels. These models disaggregate measurement error from systematic variance, which should result in less biased within-person estimates and larger effect sizes. Differences in power, precision, and bias between multilevel unit-weighted models and MSEMs were compared through a series of Monte Carlo simulations. Results based on simulated data revealed that precision was consistently poorer in the MSEMs than the unit-weighted models, particularly when reliability was low. However, the degree of bias was considerably greater in the unit-weighted model than the latent variable model. Although the unit-weighted model consistently underestimated the effect of a covariate, it generally had similar power relative to the MSEM model due to the greater precision. Considerations for scale development and the impact of within-person reliability are highlighted.

Bayesian Multivariate Mixed-Effects Location Scale Modeling of Longitudinal Relations Among Affective Traits, States, and Physical Activity.

Intensive longitudinal studies and experience sampling methods are becoming more common in psychology. While they provide a unique opportunity to ask novel questions about within-person processes relating to personality, there is a lack of methods specifically built to characterize the interplay between traits and states. We thus introduce a Bayesian multivariate mixed-effects location scale model (M-MELSM). The formulation can simultaneously model both personality traits (the location) and states (the scale) for multivariate data common to personality research. Variables can be included to predict either (or both) the traits and states, in addition to estimating random effects therein. This provides correlations between location and scale random effects, both across and within each outcome, which allows for characterizing relations between any number of personality traits and the corresponding states. We take a fully Bayesian approach, not only to make estimation possible, but also because it provides the necessary information for use in psychological applications such as hypothesis testing. To illustrate the model we use data from 194 individuals that provided daily ratings of negative and positive affect, as well as their physical activity in the form of step counts over 100 consecutive days. We describe the fitted model, where we emphasize, with visualization, the richness of information provided by the M-MELSM. We demonstrate Bayesian hypothesis testing for the correlations between the random effects. We conclude by discussing limitations of the MELSM in general and extensions to the M-MELSM specifically for personality research.

APOEε4 Genotype and Hypertension Modify 8-year Cortical Thinning: Five Occasion Evidence from the Seattle Longitudinal Study.

We investigated individual differences in longitudinal trajectories of brain aging in cognitively normal healthy adults from the Seattle Longitudinal Study covering 8 years of longitudinal change (across 5 occasions) in cortical thickness in 249 midlife and older adults (52-95 years old). We aimed to understand true brain change; examine the influence of salient risk factors that modify an individual's rate of cortical thinning; and compare cross-sectional age-related differences in cortical thickness to longitudinal within-person cortical thinning. We used Multivariate Multilevel Modeling to simultaneously model dependencies among 5 lobar composites (Frontal, Parietal, Temporal, Occipital, and Cingulate [CING]) and account for the longitudinal nature of the data. Results indicate (1) all 5 lobar composites significantly atrophied across 8 years, showing nonlinear longitudinal rate of cortical thinning decelerated over time, (2) longitudinal thinning was significantly altered by hypertension and Apolipoprotein-E ε4 (APOEε4), varying by location: Frontal and CING thinned more rapidly in APOEε4 carriers. Notably, thinning of parietal and occipital cortex showed synergistic effect of combined risk factors, where individuals who were both APOEε4 carriers and hypertensive had significantly greater 8-year thinning than those with either risk factor alone or neither risk factor, (3) longitudinal thinning was 3 times greater than cross-sectional estimates of age-related differences in thickness in parietal and occipital cortices.

On Nonregularized Estimation of Psychological Networks.

An important goal for psychological science is developing methods to characterize relationships between variables. Customary approaches use structural equation models to connect latent factors to a number of observed measurements, or test causal hypotheses between observed variables. More recently, regularized partial correlation networks have been proposed as an alternative approach for characterizing relationships among variables through off-diagonal elements in the precision matrix. While the graphical Lasso (glasso) has emerged as the default network estimation method, it was optimized in fields outside of psychology with very different needs, such as high dimensional data where the number of variables (p) exceeds the number of observations (n). In this article, we describe the glasso method in the context of the fields where it was developed, and then we demonstrate that the advantages of regularization diminish in settings where psychological networks are often fitted ( p≪n ). We first show that improved properties of the precision matrix, such as eigenvalue estimation, and predictive accuracy with cross-validation are not always appreciable. We then introduce nonregularized methods based on multiple regression and a nonparametric bootstrap strategy, after which we characterize performance with extensive simulations. Our results demonstrate that the nonregularized methods can be used to reduce the false-positive rate, compared to glasso, and they appear to provide consistent performance across sparsity levels, sample composition (p/n), and partial correlation size. We end by reviewing recent findings in the statistics literature that suggest alternative methods often have superior performance than glasso, as well as suggesting areas for future research in psychology. The nonregularized methods have been implemented in the R package GGMnonreg.

A Bayesian nonlinear mixed-effects location scale model for learning.

We present a Bayesian nonlinear mixed-effects location scale model (NL-MELSM). The NL-MELSM allows for fitting nonlinear functions to the location, or individual means, and the scale, or within-person variance. Specifically, in the context of learning, this model allows the within-person variance to follow a nonlinear trajectory, where it can be determined whether variability reduces during learning. It incorporates a sub-model that can predict nonlinear parameters for both the location and scale. This specification estimates random effects for all nonlinear location and scale parameters that are drawn from a common multivariate distribution. This allows estimation of covariances among the random effects, within and across the location and the scale. These covariances offer new insights into the interplay between individual mean structures and intra-individual variability in nonlinear parameters. We take a fully Bayesian approach, not only for ease of estimation but also for inference because it provides the necessary and consistent information for use in psychological applications, such as model selection and hypothesis testing. To illustrate the model, we use data from 333 individuals, consisting of three age groups, who participated in five learning trials that assessed verbal memory. In an exploratory context, we demonstrate that fitting a nonlinear function to the within-person variance, and allowing for individual variation therein, improves predictive accuracy compared to customary modeling techniques (e.g., assuming constant variance). We conclude by discussing the usefulness, limitations, and future directions of the NL-MELSM.

A Mixed-Effects Location Scale Model for Dyadic Interactions.

We present a mixed-effects location scale model (MELSM) for examining the daily dynamics of affect in dyads. The MELSM includes person and time-varying variables to predict the location, or individual means, and the scale, or within-person variances. It also incorporates a submodel to account for between-person variances. The dyadic specification can accommodate individual and partner effects in both the location and the scale components, and allows random effects for all location and scale parameters. All covariances among the random effects, within and across the location and the scale are also estimated. These covariances offer new insights into the interplay of individual mean structures, intra-individual variability, and the influence of partner effects on such factors. To illustrate the model, we use data from 274 couples who provided daily ratings on their positive and negative emotions toward their relationship - up to 90 consecutive days. The model is fit using Hamiltonian Monte Carlo methods, and includes subsets of predictors in order to demonstrate the flexibility of this approach. We conclude with a discussion on the usefulness and the limitations of the MELSM for dyadic research.

Detecting short-term change and variation in health-related quality of life: within- and between-person factor structure of the SF-36 health survey.

BACKGROUND: A major goal of much aging-related research and geriatric medicine is to identify early changes in health and functioning before serious limitations develop. To this end, regular collection of patient-reported outcome measure (PROMs) in a clinical setting may be useful to identify and monitor these changes. However, existing PROMs were not designed for repeated administration and are more commonly used as one-time screening tools; as such, their ability to detect variation and measurement properties when administered repeatedly remain unknown. In this study we evaluated the potential of the RAND SF-36 Health Survey as a repeated-use PROM by examining its measurement properties when modified for administration over multiple occasions. METHODS: To distinguish between-person (i.e., average) from within-person (i.e., occasion) levels, the SF-36 Health Survey was completed by a sample of older adults (N = 122, M age = 66.28 years) daily for seven consecutive days. Multilevel confirmatory factor analysis (CFA) was employed to investigate the factor structure at both levels for two- and eight-factor solutions. RESULTS: Multilevel CFA models revealed that the correlated eight-factor solution provided better model fit than the two-factor solution at both the between-person and within-person levels. Overall model fit for the SF-36 Health Survey administered daily was not substantially different from standard survey administration, though both were below optimal levels as reported in the literature. However, individual subscales did demonstrate good reliability. CONCLUSIONS: Many of the subscales of the modified SF-36 for repeated daily assessment were found to be sufficiently reliable for use in repeated measurement designs incorporating PROMs, though the overall scale may not be optimal. We encourage future work to investigate the utility of the subscales in specific contexts, as well as the measurement properties of other existing PROMs when administered in a repeated measures design. The development and integration of new measures for this purpose may ultimately be necessary.

The identification of regions of significance in the effect of multimorbidity on depressive symptoms using longitudinal data: an application of the Johnson-Neyman technique.

BACKGROUND: The investigation of multimorbidity and aging is complex and highly intertwined with aging-related changes in physical and cognitive capabilities, and mental health and is known to affect psychological distress and quality of life. Under these circumstances it is important to understand how the effects of chronic conditions evolve over time relative to aging-related and end-of-life changes. The identification of periods in time where multimorbidity impacts particular outcomes such as depressive symptoms, versus periods of time where this is not the case, reduces the complexity of the phenomenon. OBJECTIVE: We present the Johnson-Neyman (J-N) technique in the context of a curvilinear longitudinal model with higher-order terms to probe moderators and to identify regions of statistical significance. In essence, the J-N technique allows one to identify conditions under which moderators impact an outcome from conditions where these effects are not significant. METHODS: To illustrate the use of the J-N technique in a longitudinal sample, we used data from the Health and Retirement Study. Analyses were based on time-to-death models including participants who died within the study duration of 12 years. RESULTS: Multimorbidity differentially affects rates of change in depression. For some periods in time the effects are statistically significant while in other periods the same effects are not statistically different from zero. CONCLUSION: The J-N technique is useful to continuously probe moderating effects and to identify particular interactions with the model for time when certain effects are or are not statistically significant. In the context of multimorbidity this method is particularly useful for interpreting the complex interactions with differential change over time.

The Reliability Factor: Modeling Individual Reliability with Multiple Items from a Single Assessment.

Reliability is a crucial concept in psychometrics. Although it is typically estimated as a single fixed quantity, previous work suggests that reliability can vary across persons, groups, and covariates. We propose a novel method for estimating and modeling case-specific reliability without repeated measurements or parallel tests. The proposed method employs a "Reliability Factor" that models the error variance of each case across multiple indicators, thereby producing case-specific reliability estimates. Additionally, we use Gaussian process modeling to estimate a nonlinear, non-monotonic function between the latent factor itself and the reliability of the measure, providing an analogue to test information functions in item response theory. The reliability factor model is a new tool for examining latent regions with poor conditional reliability, and correlates thereof, in a classical test theory framework.

Who is and is not "average"? Random effects selection with spike-and-slab priors.

Mixed-effects models are often employed to study individual differences in psychological science. Such analyses commonly entail testing whether between-subjects variability exists and including covariates to explain that variability. We argue that researchers have much to gain by explicitly focusing on the individual in individual differences research. To this end, we propose the spike-and-slab prior distribution for random effect selection in (generalized) mixed-effects models as a means to gain a more nuanced perspective of individual differences. The prior for each random effect is a two-component mixture consisting of a point-mass "spike" centered at zero and a diffuse "slab" capturing nonzero values. Effectively, such an approach allows researchers to answer questions about particular individuals; specifically, "Who is average?", in the sense of deviating from an average effect, such as the population-averaged slope. We begin with an illustrative example, where the spike-and-slab formulation is used to select random intercepts in logistic regression. This demonstrates the utility of the proposed methodology in a simple setting while also highlighting its flexibility in fitting different kinds of models. We then extend the approach to random slopes that capture experimental effects. In two cognitive tasks, we show that despite there being little variability in the slopes, there were many individual differences in performance. In two simulation studies, we assess the ability of the proposed method to correctly identify (non)average individuals without compromising the mixed-effects estimates. We conclude with future directions for the presented methodology. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

A new frontier for studying within-person variability: Bayesian multivariate generalized autoregressive conditional heteroskedasticity models.

Research on individual variation has received increased attention. The bulk of the models discussed in psychological research so far, focus mainly on the temporal development of the mean structure. We expand the view on within-person residual variability and present a new model parameterization derived from classic multivariate GARCH models used to predict and forecast volatility in financial time-series. We propose a new pdBEKK and a modified dynamic conditional correlation (DCC) model that accommodate external time-varying predictors for the within-person variance. The main goal of this work is to evaluate the potential usefulness of MGARCH models for research in within-person variability. MGARCH models partition the within-person variance into, at least, 3 components: An overall constant and unconditional baseline variance, a process that introduces variance conditional on previous innovations, or random shocks, and a process that governs the carry-over effects of previous conditional variance, similar to an AR model. These models allow for variance spillover effects from one time-series to another. We illustrate the pdBEKK- and the DCC-MGARCH on two individuals who have rated their daily positive and negative affect over 100 consecutive days. The full models comprised a multivariate ARMA(1,1) model for the means and included physical activity as moderator of the overall baseline variance. Overall, the pdBEKK seems to result in a more straightforward psychological interpretation, but the DCC is generally easier to estimate and can accommodate more simultaneous time-series. Both models require rather large amounts of datapoints to detect nonzero parameters. We provide an R-package bmgarch that facilitates the estimation of these types of models. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The unity and diversity of executive functions: A network approach to life span development.

As a novel approach to conceptualizing executive functions, this study applied network analysis to a common battery of executive function tests administered to a sample covering the life span. Participants (N = 3,944; age: M = 20.8 years, SD = 19.6, range: 3-85; maternal/self education: M = 12.9 years, SD = 2.6; 53.3% girls/women, 46.7% boys/men; 61.1% White, 18.2% African American, 14.0% Latinx, 6.8% other races/ethnicities) completed tests of inhibition, shifting, and updating/working memory. Zero-order and partial correlation network models were calculated for divided age groups, with network parameters compared between groups: edge weights, corresponding to zero-order or partial correlations between two executive functions; expected influence, quantifying centrality; and global strength, quantifying differentiation. Executive functions differentiated from childhood to adolescence and dedifferentiated during young adulthood, with further dedifferentiation at older adulthood. Shifting emerged as more central than other abilities in adolescence and adulthood versus childhood, with a mediational role of shifting between inhibition and updating/working memory. A network approach can appropriately capture the unity and diversity of executive functions, by which unity reflects the reciprocal engagement between diverse abilities to produce goal-directed behavior. The engagement between abilities, and a mediational role of shifting between inhibition and updating/working memory, may be necessary for the emergence of effective goal-directed behavior. Through a network approach, the unity of executive functions represents an emergent property of the dynamics between multiple abilities (e.g., inhibition, shifting, and updating/working memory) that, when working effectively in tandem, lead to integrative processes (e.g., problem solving) that contribute to successful executive behavior and goal attainment. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Understanding the Error in Psychopathology: Notable Intraindividual Differences in Neural Variability of Performance Monitoring.

BACKGROUND: Abnormal performance monitoring is a possible transdiagnostic marker of psychopathology. Research on neural indices of performance monitoring, including the error-related negativity (ERN), typically examines group and interindividual (between-person) differences in mean/average scores. Intraindividual (within-person) variability in activity captures the capacity to dynamically adjust from moment to moment, and excessive variability appears maladaptive. Intraindividual variability in ERN represents a unique and largely unexamined dimension that might impact functioning. We tested whether psychopathology group differences (major depressive disorder, generalized anxiety disorder, obsessive-compulsive disorder) or corresponding psychiatric symptoms account for intraindividual variability in single-trial ERN scores. METHODS: High-density electroencephalogram was recorded during a semantic flanker task in 51 participants with major depressive disorder, 44 participants with generalized anxiety disorder, 31 participants with obsessive-compulsive disorder, and 56 psychiatrically healthy participants. Time-window mean ERN amplitude was scored 0-125 ms following participant response across four frontocentral sites. Multilevel location-scale models were used to simultaneously examine interindividual and intraindividual differences in ERN. RESULTS: Analyses indicated considerable intraindividual variability in ERN that was common across groups. However, we did not find strong evidence to support relationships between ERN and psychopathology groups or transdiagnostic symptoms. CONCLUSIONS: These findings point to important methodological implications for studies of performance monitoring in healthy and clinical populations-the common assumption of fixed intraindividual variability (i.e., residual variance) may be inappropriate for ERN studies. Implementation of multilevel location-scale models in future research can leverage between-person differences in intraindividual variability in performance monitoring to gain a rich understanding of trial-to-trial performance monitoring dynamics.

Beneath the surface: Unearthing within-person variability and mean relations with Bayesian mixed models.

Mixed-effects models are becoming common in psychological science. Although they have many desirable features, there is still untapped potential. It is customary to view homogeneous variance as an assumption to satisfy. We argue to move beyond that perspective, and to view modeling within-person variance as an opportunity to gain a richer understanding of psychological processes. The technique to do so is based on the mixed-effects location scale model that can simultaneously estimate mixed-effects submodels to both the mean (location) and within-person variance (scale). We develop a framework that goes beyond assessing the submodels in isolation of one another and introduce a novel Bayesian hypothesis test for mean-variance correlations in the distribution of random effects. We first present a motivating example, which makes clear how the model can characterize mean-variance relations. We then apply the method to reaction times (RTs) gathered from 2 cognitive inhibition tasks. We find there are more individual differences in the within-person variance than the mean structure, as well as a complex web of structural mean-variance relations. This stands in contrast to the dominant view of within-person variance (i.e., "noise"). The results also point toward paradoxical within-person, as opposed to between-person, effects: several people had slower and less variable incongruent responses. This contradicts the typical pattern, wherein larger means tend to be associated with more variability. We conclude with future directions, spanning from methodological to theoretical inquires, that can be answered with the presented methodology. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Back to the basics: Rethinking partial correlation network methodology.

The Gaussian graphical model (GGM) is an increasingly popular technique used in psychology to characterize relationships among observed variables. These relationships are represented as elements in the precision matrix. Standardizing the precision matrix and reversing the sign yields corresponding partial correlations that imply pairwise dependencies in which the effects of all other variables have been controlled for. The graphical lasso (glasso) has emerged as the default estimation method, which uses ℓ1 -based regularization. The glasso was developed and optimized for high-dimensional settings where the number of variables (p) exceeds the number of observations (n), which is uncommon in psychological applications. Here we propose to go 'back to the basics', wherein the precision matrix is first estimated with non-regularized maximum likelihood and then Fisher Z transformed confidence intervals are used to determine non-zero relationships. We first show the exact correspondence between the confidence level and specificity, which is due to 1 minus specificity denoting the false positive rate (i.e., α). With simulations in low-dimensional settings (p â‰ª n), we then demonstrate superior performance compared to the glasso for detecting the non-zero effects. Further, our results indicate that the glasso is inconsistent for the purpose of model selection and does not control the false discovery rate, whereas the proposed method converges on the true model and directly controls error rates. We end by discussing implications for estimating GGMs in psychology.

Comparing Gaussian graphical models with the posterior predictive distribution and Bayesian model selection.

Gaussian graphical models are commonly used to characterize conditional (in)dependence structures (i.e., partial correlation networks) of psychological constructs. Recently attention has shifted from estimating single networks to those from various subpopulations. The focus is primarily to detect differences or demonstrate replicability. We introduce two novel Bayesian methods for comparing networks that explicitly address these aims. The first is based on the posterior predictive distribution, with a symmetric version of Kullback-Leibler divergence as the discrepancy measure, that tests differences between two (or more) multivariate normal distributions. The second approach makes use of Bayesian model comparison, with the Bayes factor, and allows for gaining evidence for invariant network structures. This overcomes limitations of current approaches in the literature that use classical hypothesis testing, where it is only possible to determine whether groups are significantly different from each other. With simulation we show the posterior predictive method is approximately calibrated under the null hypothesis (α = .05) and has more power to detect differences than alternative approaches. We then examine the necessary sample sizes for detecting invariant network structures with Bayesian hypothesis testing, in addition to how this is influenced by the choice of prior distribution. The methods are applied to posttraumatic stress disorder symptoms that were measured in 4 groups. We end by summarizing our major contribution, that is proposing 2 novel methods for comparing Gaussian graphical models (GGMs), which extends beyond the social-behavioral sciences. The methods have been implemented in the R package BGGM. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Age differences in the underconfidence-with-practice effect.

In two verbal learning experiments, the authors examined the accuracy of memory monitoring and the underconfidence-with-practice (UWP) effect in younger and older adults. Memory monitoring was operationalized as judgements of learning (JOL). An open issue is whether UWP can also be found in older adults. In the first experiment, both younger and older adults overestimated their memory performance in the first trial, but the older group differed from the young group in the second trial. The JOLs given by older participants matched, on average, their recall performance. In fact, the UWP effect was not observed in any of several conditions in older participants. In the second experiment involving five study-test cycles and two age groups, the same basic pattern of results was present: Older adults did not show an UWP effect. These findings appear to fit into a framework of dual factors affecting JOLs, which posits that the magnitude of JOLs derives both from an anchoring point and from on-line monitoring of items.