Moderated Nonlinear Factor Analysis Research Articles

Does your job shape your experience or interpretation of workplace telepressure? Exploring measurement invariance across occupational characteristics

Workplace telepressure is a psychological experience related to work-related messages. Research on measurement invariance for telepressure is scarce, especially with respect to occupations. This paper used a moderated nonlinear factor analysis technique to examine how occupational characteristics predict telepressure experiences and differential ratings across two studies with full-time workers. Email-related behaviors and demands predicted factor means and variances of telepressure, but there were no consistent results for job control and time pressure. Invariance testing at the item-level showed that occupational characteristics did not moderate most item parameter estimates; that is, employees interpreted workplace telepressure items equivalently regardless of occupational context.

Assessing measurement invariance with moderated nonlinear factor analysis using the R package OpenMx.

Assessing measurement invariance is an important step in establishing a meaningful comparison of measurements of a latent construct across individuals or groups. Most recently, moderated nonlinear factor analysis (MNLFA) has been proposed as a method to assess measurement invariance. In MNLFA models, measurement invariance is examined in a single-group confirmatory factor analysis model by means of parameter moderation. The advantages of MNLFA over other methods is that it (a) accommodates the assessment of measurement invariance across multiple continuous and categorical background variables and (b) accounts for heteroskedasticity by allowing the factor and residual variances to differ as a function of the background variables. In this article, we aim to make MNLFA more accessible to researchers without access to commercial structural equation modeling software by demonstrating how this method can be applied with the open-source R package OpenMx. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A Bayesian Moderated Nonlinear Factor Analysis Approach for DIF Detection under Violation of the Equal Variance Assumption

AbstractResearch has shown that multiple‐indicator multiple‐cause (MIMIC) models can result in inflated Type I error rates in detecting differential item functioning (DIF) when the assumption of equal latent variance is violated. This study explains how the violation of the equal variance assumption adversely impacts the detection of nonuniform DIF and how it can be addressed through moderated nonlinear factor analysis (MNLFA) model via Bayesian estimation approach to overcome limitations from the restrictive assumption. The Bayesian MNLFA approach suggested in this study better control Type I errors by freely estimating latent factor variances across different groups. Our experimentation with simulated data demonstrates that the BMNFA models outperform the existing MIMIC models, in terms of Type I error control as well as parameter recovery. The results suggest that the MNLFA models have the potential to be a superior choice to the existing MIMIC models, especially in situations where the assumption of equal latent variance assumption is not likely to hold.

How is inflammation biology truly associated with depression in patients with stable coronary heart disease?: Insights from the heart and Soul study

Depression is known to be associated with inflammation among patients with established coronary heart disease (CHD), but it is unclear whether this is due to individual depression symptoms or to the broader construct of depression. We addressed this gap by using moderated non-linear factor analysis (MNLFA) to determine the extent that inflammation is associated with latent depression and/or individual symptoms in this patient group. We evaluated 1,024 outpatients with stable CHD from the baseline cross-sectional data of the Heart and Soul Study. Depression was assessed using the 9-item Patient Health Questionnaire, while inflammation was evaluated via C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), and monocyte chemoattractant protein-1 (MCP-1) levels. MNLFA is based on the concept of model parameter moderation with regard to individual characteristics. Using the MNLFA approach, we simultaneously tested for differences in (1) latent depression, (2) individual depression items, and (3) the factor loading of the item on latent depression as a function of inflammatory markers, with and without covariate adjustment. Higher TNF-α levels were associated with both higher levels of a latent depression factor and greater endorsement of an individual symptom (appetite changes). Increased CRP levels were significantly associated with greater appetite changes, lower concentration difficulty, and greater fatigue. Elevated IL-6 levels were only related to greater fatigue, while increased MCP-1 levels were linked to greater sleep disturbance. After adjusting for covariates, some associations became insignificant. Inflammatory markers were not consistent predictors of factor loadings. This study represents the initial step to discussing how inflammation biology is truly related to depression among patients with established CHD.

Using Regularization to Identify Measurement Bias Across Multiple Background Characteristics: A Penalized Expectation–Maximization Algorithm

Testing for differential item functioning (DIF) has undergone rapid statistical developments recently. Moderated nonlinear factor analysis (MNLFA) allows for simultaneous testing of DIF among multiple categorical and continuous covariates (e.g., sex, age, ethnicity, etc.), and regularization has shown promising results for identifying DIF among many covariates. However, computationally inefficient estimation methods have hampered practical use of the regularized MNFLA method. We develop a penalized expectation–maximization (EM) algorithm with soft- and firm-thresholding to more efficiently estimate regularized MNLFA parameters. Simulation and empirical results show that, compared to previous implementations of regularized MNFLA, the penalized EM algorithm is faster, more flexible, and more statistically principled. This method also yields similar recovery of DIF relative to previous implementations, suggesting that regularized DIF detection remains a preferred approach over traditional methods of identifying DIF.

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).

Measurement invariance of maternal depressive symptoms across the first 2 years since birth and across racial group, education, income, primiparity, and age.

Up to 19% of postpartum mothers experience depressive symptoms, which are associated with infant development. Thus, research examining postpartum depression has implications for mothers' and infants' well-being. However, this research relies on the often-untested assumption of measurement invariance-that measures capture the same construct across time and sociodemographic characteristics. In the absence of invariance, measurement bias may confound differences across time and group, contributing to invalid inferences. In a sociodemographically diverse (40.7% African American, 58.9% White; 67.9% below two times the federal poverty line; 19.4% with less than high school education), rural, longitudinal sample (N = 1,275) of mothers, we used moderated nonlinear factor analysis (MNLFA) to examine measurement invariance of the Brief Symptom Inventory-18 (BSI-18) Depressive Symptoms subscale across time since birth, racial group, education, income, primiparity, and maternal age at childbirth. We identified evidence of differential item functioning (DIF; i.e., measurement noninvariance) as a function of racial group and education. Subsequent analyses indicated, however, that the DIF-induced bias had minimal impacts on substantive comparisons examining change over time since birth and group differences. Thus, the presence of measurement noninvariance does not appear to bias substantive comparisons using the BSI-18 Depressive Symptoms subscale across the first 2 years since birth in a sample comprising primarily African American and White mothers living in predominately rural, low-income communities. This study demonstrates the importance of assessing measurement invariance and highlights MNLFA for evaluating the impact of noninvariance as a preliminary step that increases confidence in the validity of substantive inferences. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Gratitude Moments as Predictors of Self- and Other-Orientation Indicators in a Racially/Ethnically Diverse Sample of US Young Adolescents during the COVID-19 Pandemic

Scholars posit that gratitude may enhance other-oriented beliefs and behaviors and dampen self-oriented ones through a cycle of upward generativity. We examined associations between gratitude as an indicator of self-orientation (i.e., materialism and entitlement) and other orientation (i.e., connection to nature; attitudes, beliefs, and conversations about social justice; prosocial behavior) in the US youth across six months as moderated by race/ethnicity and gender. Specifically, Study 1 evaluated the psychometric performance of the gratitude moments scale in a more racially and ethnically diverse sample than that included in the original scale development. In study 2, we evaluated pre-registered hypotheses among the youth who completed surveys in October 2020 and again in January 2021 (n = 812). These hypotheses tested (1) whether there were ethnic/racial differences in the measure of children’s gratitude; (2) whether there were reciprocal associations over time between children’s gratitude and indices of self and other orientation; and (3) whether these reciprocal associations varied as a function of youth race/ethnicity and gender. The results of study 1 found that the gratitude moments scale demonstrated high reliability and validity in racially/ethnically diverse young adolescents (n = 89). Using moderated nonlinear factor analysis in study 2, we found only one difference in how the gratitude moments scale performed as a function of race/ethnicity or gender. In cross-lagged panel models, gratitude moments did not predict subsequent self- and other-orientation indices, though youth with lower social justice attitudes and greater prosocial behavior showed increases in later gratitude moments. Prosocial behavior was more strongly and consistently related to self- and other-orientation indices than gratitude. These findings are consistent with models of prosocial behavior as a catalyst for the development of additional forms of other-oriented beliefs, attitudes, and actions that may underlie the development of an other-oriented purpose.

Differential symptom weighting in estimating empirical thresholds for underlying PTSD severity: Toward a "platinum" standard for diagnosis?

Symptom counts as the basis for Post-Traumatic Stress Disorder(PTSD) diagnoses in the DSM presume each symptom is equally reflective of underlying disorder severity. However, the "equal weight" assumption fails to fit PTSD symptom data when tested. The present study developed an enhanced PTSD diagnosis based on (a) a conventional PTSD diagnosis from a clinical interview and (b) an empirical classification of full PTSD that reflected the relative clinical weights of each symptom. Baseline structured interview data from Project Harmony (N=2658) was used. An enhanced diagnosis for full PTSD was estimated using an empirical threshold from moderated nonlinear factor analysis (MNLFA) latent PTSD scale scores, in combination with a full conventional PTSD diagnosis based on interview data. One in 4 patients in the sample had a PTSD diagnosis that was inconsistent with their empirical PTSD grouping, such that the enhanced diagnostic standard reduced the diagnostic discrepancy rate by 20%. Veterans, and in particular female Veterans, were at greatest odds for discrepancy between their underlying PTSD severity and DSM diagnosis. Psychometric methodologies that differentially weight symptoms can complement DSM criteria and may serve as a platform for symptom prioritization for diagnoses in future editions of DSM.

Informing Harmonization Decisions in Integrative Data Analysis: Exploring the Measurement Multiverse.

Combining datasets in an integrative data analysis (IDA) requires researchers to make a number of decisions about how best to harmonize item responses across datasets. This entails two sets of steps: logical harmonization, which involves combining items which appear similar across datasets, and analytic harmonization, which involves using psychometric models to find and account for cross-study differences in measurement. Embedded in logical and analytic harmonization are many decisions, from deciding whether items can be combined prima facie to how best to find covariate effects on specific items. Researchers may not have specific hypotheses about these decisions, and each individual choice may seem arbitrary, but the cumulative effects of these decisions are unknown. In the current study, we conducted an IDA of the relationship between alcohol use and delinquency using three datasets (total N = 2245). For analytic harmonization, we used moderated nonlinear factor analysis (MNLFA) to generate factor scores for delinquency. We conducted both logical and analytic harmonization 72 times, each time making a different set of decisions. We assessed the cumulative influence of these decisions on MNLFA parameter estimates, factor scores, and estimates of the relationship between delinquency and alcohol use. There were differences across paths in MNLFA parameter estimates, but fewer differences in estimates of factor scores and regression parameters linking delinquency to alcohol use. These results suggest that factor scores may be relatively robust to subtly different decisions in data harmonization, and measurement model parameters are less so.

Quantifying latent social motivation and its associations with joint attention and language in infants at high and low likelihood for autism spectrum disorder.

Social motivation-the psychobiological predisposition for social orienting, seeking social contact, and maintaining social interaction-manifests in early infancy and is hypothesized to be foundational for social communication development in typical and atypical populations. However, the lack of infant social-motivation measures has hindered delineation of associations between infant social motivation, other early-arising social abilities such as joint attention, and language outcomes. To investigate how infant social motivation contributes to joint attention and language, this study utilizes a mixed longitudinal sample of 741 infants at high (HL=515) and low (LL=226) likelihood for ASD. Using moderated nonlinear factor analysis (MNLFA), we incorporated items from parent-report measures to establish a novel latent factor model of infant social motivation that exhibits measurement invariance by age, sex, and familial ASD likelihood. We then examined developmental associations between 6- and 12-month social motivation, joint attention at 12-15 months, and language at 24 months of age. On average, greater social-motivation growth from 6-12 months was associated with greater initiating joint attention (IJA) and trend-level increases in sophistication of responding to joint attention (RJA). IJA and RJA were both positively associated with 24-month language abilities. There were no additional associations between social motivation and future language in our path model. These findings substantiate a novel, theoretically driven approach to modeling social motivation and suggest a developmental cascade through which social motivation impacts other foundational skills. These findings have implications for the timing and nature of intervention targets to support social communication development in infancy. HIGHLIGHTS: We describe a novel, theoretically based model of infant social motivation wherein multiple parent-reported indicators contribute to a unitary latent social-motivation factor. Analyses revealed social-motivation factor scores exhibited measurement invariance for a longitudinal sample of infants at high and low familial ASD likelihood. Social-motivation growth from ages 6-12 months is associated with better 12-15-month joint attention abilities, which in turn are associated with greater 24-month language skills. Findings inform timing and targets of potential interventions to support healthy social communication in the first year of life.

Developmental Models of Depression, Externalizing Problems, and Self-regulatory Processes: Integrated Data Analysis Across Four Longitudinal Studies of Youth.

Integrative data analysis (IDA) was used to derive developmental models of depression, externalizing problems, and self-regulatory processes in three prevention trials of the Family Check-Up and one longitudinal, community-based study of girls over a 10-year span covering early to late adolescence (N = 4,773; 74.9% female, 41.7% white). We used moderated nonlinear factor analysis to create harmonized scores based on all available items for a given participant in the pooled dataset while accounting for potential differences in both the latent factor and the individual items as a function of observed covariates. We also conducted latent growth model analyses to examine developmental trajectories of risk. Results indicated a bidirectional relationship between depression and externalizing problems, with greater baseline externalizing problems and depression predicting growth in inhibitory control difficulties. Furthermore, initial level of inhibitory control difficulties was associated with growth in depression. We did not, however, find a relationship between early inhibitory control difficulties and growth in externalizing problems. This work illustrates the utility of IDA techniques to harmonize data across multiple studies to identify risk factors for the development of depression and externalizing problems that can be targeted by prevention efforts.

Hierarchical Inflammatory Phenotypes of Depression: A Novel Approach Across Five Independent Samples and 27,730 Adults

BackgroundAlthough characterizing associations between inflammation and depression may prove critical for informing theory, research, and treatment decisions, extant research has been limited by ignoring the possibility that inflammation may be simultaneously associated with depression broadly and with a subset of symptoms. This lack of direct comparison has hampered attempts to understand inflammatory phenotypes of depression and critically fails to consider that inflammation might be uniquely associated with both depression broadly and individual symptoms. MethodsWe used moderated nonlinear factor analysis in 5 NHANES (National Health and Nutrition Examination Survey) cohorts (N = 27,730, 51% female, mean age = 46 years). ResultsC-reactive protein (CRP) is simultaneously associated with latent depression, appetite, and fatigue. Specifically, CRP was associated with latent depression in all 5 samples (rs: 0.044–0.089; ps: < .001–.002) and was associated with both appetite (significant rs: 0.031–0.049, significant ps: .001–.007) and fatigue (significant rs: 0.030–0.054, significant ps: < .001–.029) in 4 samples. These results were largely robust to covariates. ConclusionsMethodologically, these models indicate that the Patient Health Questionnaire-9 is scalar noninvariant as a function of CRP (i.e., identical Patient Health Questionnaire-9 scores may represent different constructs in those with high vs. low CRP levels). Therefore, mean comparisons of depression total scores and CRP might be misleading without accounting for symptom-specific associations. Conceptually, these findings indicate that studies investigating inflammatory phenotypes of depression should examine how inflammation is simultaneously related both to depression broadly and to specific symptoms, and whether these relations function via different mechanisms. This has the potential to yield new theoretical insights and may lead to the development of novel therapies for reducing inflammation-related symptoms of depression.

Bias in measurement of autism symptoms by spoken language level and non-verbal mental age in minimally verbal children with neurodevelopmental disorders.

Increasing numbers of children with known genetic conditions and/or intellectual disability are referred for evaluation of autism spectrum disorder (ASD), highlighting the need to refine autism symptom measures to facilitate differential diagnoses in children with cognitive and language impairments. Previous studies have reported decreased specificity of ASD screening and diagnostic measures in children with intellectual disability. However, little is known about how cognitive and language abilities impact the measurement of specific ASD symptoms in this group. We aggregated a large sample of young children (N = 1196; aged 31–119 months) to examine measurement invariance of ASD symptoms among minimally verbal children within the context of the Autism Diagnostic Observation Schedule (ADOS) Module 1. Using confirmatory factor analysis (CFA) and moderated non-linear factor analysis (MNLFA), we examined how discrete behaviors were differentially associated with the latent symptom domains of social communication impairments (SCI) and restricted and repetitive behaviors (RRB) across spoken language levels and non-verbal mental age groupings. While the two-factor structure of SCI and RRB held consistently across language and cognitive levels, only partial invariance was observed for both ASD symptom domains of SCI and RRB. Specifically, four out of the 15 SCI items and one out of the three RRB items examined showed differential item functioning between children with “Few to No Words” and those with “Some Words”; and one SCI item and one RRB item showed differential item functioning across non-verbal mental age groups. Moreover, even after adjusting for the differential item functioning to reduce measurement bias across groups, there were still differences in ASD symptom domain scores across spoken language levels. These findings further underscore the influence of spoken language level on measurement of ASD symptoms and the importance of measuring ASD symptoms within refined spoken language levels, even among those with minimal verbal abilities.

Harmonizing Depression Measures Across Studies: a Tutorial for Data Harmonization.

There has been increasing interest in applying integrative data analysis (IDA) to analyze data across multiple studies to increase sample size and statistical power. Measures of a construct are frequently not consistent across studies. This article provides a tutorial on the complex decisions that occur when conducting harmonization of measures for an IDA, including item selection, response coding, and modeling decisions. We analyzed caregivers' self-reported data from the ADHD Teen Integrative Data Analysis Longitudinal (ADHD TIDAL) dataset; data from 621 of 854 caregivers were available. We used moderated nonlinear factor analysis (MNLFA) to harmonize items reflecting depressive symptoms. Items were drawn from the Symptom Checklist 90-Revised, the Patient Health Questionnaire-9, and the World Health Organization Quality of Life questionnaire. Conducting IDA often requires more programming skills (e.g., Mplus), statistical knowledge (e.g., IRT framework), and complex decision-making processes than single-study analyses and meta-analyses. Through this paper, we described how we evaluated item characteristics, determined differences across studies, and created a single harmonized factor score that can be used to analyze data across all four studies. We also presented our questions, challenges, and decision-making processes; for example, we explained the thought process and course of actions when models did not converge. This tutorial provides a resource to support prevention scientists to generate harmonized variables accounting for sample and study differences.

Utilizing Moderated Non-linear Factor Analysis Models for Integrative Data Analysis: A Tutorial

Integrative data analysis (IDA) is an analytic tool that allows researchers to combine raw data across multiple, independent studies, providing an improved measurement of latent constructs as compared to single study analysis or meta-analyses. This is often achieved through the implementation of moderated non-linear factor analysis (MNLFA), an advanced modeling approach that allows for covariate moderation of item and factor parameters. The current paper provides an overview of this modeling technique, highlighting distinct advantages most apt for IDA. We further illustrate the complex model building process involved in MNLFA by providing a tutorial using empirical data from five separate prevention trials. The code and data used for analyses are also provided.

Long-term Effects of the Family Check-Up on Suicidality in Childhood and Adolescence: Integrative Data Analysis of Three Randomized Trials.

This study employed integrative data analysis techniques to examine the long-term effects of the family check-up (FCU) on changes in youth suicide risk using three randomized prevention trials, including one trial initiated in early childhood and two initiated in early adolescence. Data were harmonized across studies using moderated nonlinear factor analysis, and intervention effects were tested using an autoregressive latent trajectory model examining changes in suicide risk across long-term follow-up. Across trials, significant long-term effects of the FCU on reductions in suicide risk were observed, although differences between intervention and control group trajectories declined over time. No moderation of intervention effects was observed by youth gender or race/ethnicity or across samples. While results offer further support for the benefits of the FCU for suicide risk reduction, they also suggest that such effects may wane over time, underscoring the need for continued development of the FCU to enhance longer-term durability of effects on suicide-related behaviors.

Measurement Error-Corrected Estimation of Clinically Significant Change Trajectories for Interventions Targeting Comorbid PTSD and Substance Use Disorders in OEF/OIF Veterans

In randomized control trials (RCTs), a focus on average differences between treatment arms often limits our understanding of whether individuals show clinically significant improvement or deterioration. The present study examined differences in individual-level clinical significance trajectories between Concurrent Treatment of PTSD and Substance Use Disorders Using Prolonged Exposure (COPE) and Relapse Prevention (RP). Eighty-one treatment-seeking veterans with a comorbid PTSD/SUD diagnosis were randomized to COPE or RP; data from an additional n = 48 patients who did not meet criteria for both disorders was used to establish a normative threshold. A newly developed, modernized approach to the Jacobson and Truax (1991) clinically significant change framework, using (a) moderated nonlinear factor analysis (MNLFA) scale scoring and (b) measurement error-corrected multilevel modeling (MEC-MLM) was used; this approach was compared to other approaches using conventional total scores and/or assuming no measurement error. Using a conventional approach to estimating the Reliable Change Index (RCI) yielded no differences between COPE and RP in the percentage of patients achieving statistically significant improvement (SSI; 88.9% for both groups). However, under MNLFA/MEC-MLM, higher percentages of patients receiving COPE (75.0%) achieved SSI compared to RP (40.7%). Findings suggest that, even though COPE and RP appear to reduce the same number of PTSD symptoms, MNLFA scoring of outcome measures gives greater weight to interventions that target and reduce “hallmark” PTSD symptoms.

Estimating posttraumatic stress disorder severity in the presence of differential item functioning across populations, comorbidities, and interview measures: Introduction to Project Harmony.

Multiple factor analytic and item response theory studies have shown that items/symptoms vary in their relative clinical weights in structured interview measures for posttraumatic stress disorder (PTSD). Despite these findings, the use of total scores, which treat symptoms as though they are equally weighted, predominates in practice, with the consequence of undermining the precision of clinical decision-making. We conducted an integrative data analysis (IDA) study to harmonize PTSD structured interview data (i.e., recoding of items to a common symptom metric) from 25 studies (total N = 2,568). We aimed to identify (a) measurement noninvariance/differential item functioning (MNI/DIF) across multiple populations, psychiatric comorbidities, and interview measures simultaneously and (b) differences in inferences regarding underlying PTSD severity between scale scores estimated using moderated nonlinear factor analysis (MNLFA) and a total score analog model (TSA). Several predictors of MNI/DIF impacted effect size differences in underlying severity across scale scoring methods. Notably, we observed MNI/DIF substantial enough to bias inferences on underlying PTSD severity for two groups: African Americans and incarcerated women. The findings highlight two issues raised elsewhere in the PTSD psychometrics literature: (a) bias in characterizing underlying PTSD severity and individual-level treatment outcomes when the psychometric model underlying total scores fails to fit the data and (b) higher latent severity scores, on average, when using DSM-5 (net of MNI/DIF) criteria, by which multiple factors (e.g., Criterion A discordance across DSM editions, changes to the number/type of symptom clusters, changes to the symptoms themselves) may have impacted severity scoring for some patients.

Measurement matters: An individual differences examination of family socioeconomic factors, latent dimensions of children’s experiences, and resting state functional brain connectivity in the ABCD sample

The variation in experiences between high and low-socioeconomic status contexts are posited to play a crucial role in shaping the developing brain and may explain differences in child outcomes. Yet, examinations of SES and brain development have largely been limited to distal proxies of these experiences (e.g., income comparisons). The current study sought to disentangle the effects of multiple socioeconomic indices and dimensions of more proximal experiences on resting-state functional connectivity (rsFC) in a sample of 7834 youth (aged 9–10 years) from the Adolescent Brain Cognitive Development (ABCD) study. We applied moderated nonlinear factor analysis (MNLFA) to establish measurement invariance among three latent environmental dimensions of experience (material/economic deprivation, caregiver social support, and psychosocial threat). Results revealed measurement biases as a function of child age, sex, racial group, family income, and parental education, which were statistically adjusted in the final MNLFA scores. Mixed-effects models demonstrated that socioeconomic indices and psychosocial threat differentially predicted variation in frontolimbic networks, and threat statistically moderated the association between income and connectivity between the dorsal and ventral attention networks. Findings illuminate the importance of reducing measurement biases to gain a more socioculturally-valid understanding of the complex and nuanced links between socioeconomic context, children’s experiences, and neurodevelopment.