Leisure-time sedentary behaviors are differentially associated with all-cause dementia regardless of engagement in physical activity.

Sedentary behavior (SB) is associated with cardiometabolic disease and mortality, but its association with dementia is currently unclear. This study investigates whether SB is associated with incident dementia regardless of engagement in physical activity (PA). A total of 146,651 participants from the UK Biobank who were 60 years or older and did not have a diagnosis of dementia (mean [SD] age: 64.59 [2.84] years) were included. Self-reported leisure-time SBs were divided into two domains: time spent watching television (TV) or time spent using a computer. A total of 3,507 individuals were diagnosed with all-cause dementia over a mean follow-up of 11.87 (±1.17) years. In models adjusted for a wide range of covariates, including time spent in PA, time spent watching TV was associated with increased risk of incident dementia (HR [95% CI] = 1.24 [1.15 to 1.32]) and time spent using a computer was associated with decreased risk of incident dementia (HR [95% CI] = 0.85 [0.81 to 0.90]). In joint associations with PA, TV time and computer time remained significantly associated with dementia risk at all PA levels. Reducing time spent in cognitively passive SB (i.e., TV time) and increasing time spent in cognitively active SB (i.e., computer time) may be effective behavioral modification targets for reducing risk of dementia regardless of engagement in PA.

Correcting for Sampling Error in between-Cluster Effects: An Empirical Bayes Cluster-Mean Approach with Finite Population Corrections.

With clustered data, such as where students are nested within schools or employees are nested within organizations, it is often of interest to estimate and compare associations among variables separately for each level. While researchers routinely estimate between-cluster effects using the sample cluster means of a predictor, previous research has shown that such practice leads to biased estimates of coefficients at the between level, and recent research has recommended the use of latent cluster means with the multilevel structural equation modeling framework. However, the latent cluster mean approach may not always be the best choice as it (a) relies on the assumption that the population cluster sizes are close to infinite, (b) requires a relatively large number of clusters, and (c) is currently only implemented in specialized software such as Mplus. In this paper, we show how using empirical Bayes estimates of the cluster means can also lead to consistent estimates of between-level coefficients, and illustrate how the empirical Bayes estimate can incorporate finite population corrections when information on population cluster sizes is available. Through a series of Monte Carlo simulation studies, we show that the empirical Bayes cluster-mean approach performs similarly to the latent cluster mean approach for estimating the between-cluster coefficients in most conditions when the infinite-population assumption holds, and applying the finite population correction provides reasonable point and interval estimates when the population is finite. The performance of EBM can be further improved with restricted maximum likelihood estimation and likelihood-based confidence intervals. We also provide an R function that implements the empirical Bayes cluster-mean approach, and illustrate it using data from the classic High School and Beyond Study.

Evaluating two small-sample corrections for fixed-effects standard errors and inferences in multilevel models with heteroscedastic, unbalanced, clustered data.

Multilevel modeling (MLM) is commonly used in psychological research to model clustered data. However, data in applied research usually violate one of the essential assumptions of MLM-homogeneity of variance. While the fixed-effect estimates produced by the maximum likelihood method remain unbiased, the standard errors for the fixed effects are misestimated, resulting in inaccurate inferences and inflated or deflated type I error rates. To correct the bias in fixed effects standard errors and provide valid inferences, small-sample corrections such as the Kenward-Roger (KR) adjustment and the adjusted cluster-robust standard errors (CR-SEs) with the Satterthwaite approximation for t tests have been used. The current study compares KR with random slope (RS) models and the adjusted CR-SEs with ordinary least squares (OLS), random intercept (RI) and RS models to analyze small, heteroscedastic, clustered data using a Monte Carlo simulation. Results show the KR procedure with RS models has large biases and inflated type I error rates for between-cluster effects in the presence of level 2 heteroscedasticity. In contrast, the adjusted CR-SEs generally yield results with acceptable biases and maintain type I error rates close to the nominal level for all examined models. Thus, when the interest is only in within-cluster effect, any model with the adjusted CR-SEs could be used. However, when the interest is to make accurate inferences of the between-cluster effect, researchers should use the adjusted CR-SEs with RS to have higher power and guard against unmodeled heterogeneity. We reanalyzed an example in Snijders & Bosker (2012) to demonstrate the use of the adjusted CR-SEs with different models.

Associations between public and self-stigma of help-seeking with help-seeking attitudes and intention: A meta-analytic structural equation modeling approach.

The present study examined the association between help-seeking public stigma and help-seeking self-stigma (i.e., internalization of stigma) and the relative association of both types of stigma with help-seeking attitude and intention using a full-information meta-analytic structural equation modeling approach. We also investigated the moderating effect of gender, age, collectivism, and social group in the internalization process. Results from 115 independent samples containing data from 54,793 individuals showed that public stigma of help-seeking was strongly and positively associated with self-stigma of help-seeking. Moreover, after controlling for the effect of each other, self-stigma, but not public stigma, remained significantly associated with help-seeking attitude and help-seeking intention. Gender, age, collectivism, and social group did not significantly moderate the association between public and self-stigma. The findings highlight that people who perceive more stigma of help-seeking from others tend to have higher levels of self-stigma. Compared with help-seeking public stigma, help-seeking self-stigma might have a larger impact on one's help-seeking attitude and intention. Help-seeking promotion campaigns should be devised to tackle both types of stigma to foster positive help-seeking attitude and intention. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Adjusting for Measurement Noninvariance with Alignment in Growth Modeling.

Longitudinal measurement invariance-the consistency of measurement in data collected over time-is a prerequisite for any meaningful inferences of growth patterns. When one or more items measuring the construct of interest show noninvariant measurement properties over time, it leads to biased parameter estimates and inferences on the growth parameters. In this paper, I extend the recently developed alignment-within-confirmatory factor analysis (AwC) technique to adjust for measurement biases for growth models. The proposed AwC method does not require a priori knowledge of noninvariant items and the iterative searching of noninvariant items in typical longitudinal measurement invariance research. Results of a Monte Carlo simulation study comparing AwC with the partial invariance modeling method show that AwC largely reduces biases in growth parameter estimates and gives good control of Type I error rates, especially when the sample size is at least 1,000. It also outperforms the partial invariance method in conditions when all items are noninvariant. However, all methods give biased growth parameter estimates when the proportion of noninvariant parameters is over 25%. Based on the simulation results, I conclude that AO is a viable alternative to the partial invariance method in growth modeling when it is not clear whether longitudinal measurement invariance holds. The current paper also demonstrates AwC in an example modeling neuroticism over three time points using a public data set, which shows how researchers can compute effect size indices for noninvariance in AwC to assess to what degree invariance holds and whether AwC results are trustworthy.

Sedentary Behavior and Incident Dementia Among Older Adults.

Importance: Sedentary behavior is associated with cardiometabolic disease and mortality, but its association with dementia is unclear. Objective: To investigate whether accelerometer-assessed sedentary behavior is associated with incident dementia. Design, Setting, and Participants: A retrospective study of prospectively collected data from the UK Biobank including 49 841 adults aged 60 years or older without a diagnosis of dementia at the time of wearing the wrist accelerometer and living in England, Scotland, or Wales. Follow-up began at the time of wearing the accelerometer (February 2013 to December 2015) and continued until September 2021 in England, July 2021 in Scotland, and February 2018 in Wales. Exposures: Mean daily sedentary behavior time (included in the primary analysis) and mean daily sedentary bout length, maximum daily sedentary bout length, and mean number of daily sedentary bouts (included in the secondary analyses) were derived from a machine learning-based analysis of 1 week of wrist-worn accelerometer data. Main Outcome and Measures: Incident all-cause dementia diagnosis from inpatient hospital records and death registry data. Cox proportional hazard models with linear and cubic spline terms were used to assess associations. Results: A total of 49 841 older adults (mean age, 67.19 [SD, 4.29] years; 54.7% were female) were followed up for a mean of 6.72 years (SD, 0.95 years). During this time, 414 individuals were diagnosed with incident all-cause dementia. In the fully adjusted models, there was a significant nonlinear association between time spent in sedentary behavior and incident dementia. Relative to a median of 9.27 hours/d for sedentary behavior, the hazard ratios (HRs) for dementia were 1.08 (95% CI, 1.04-1.12, P < .001) for 10 hours/d, 1.63 (95% CI, 1.35-1.97, P < .001) for 12 hours/d, and 3.21 (95% CI, 2.05-5.04, P < .001) for 15 hours/d. The adjusted incidence rate of dementia per 1000 person-years was 7.49 (95% CI, 7.48-7.49) for 9.27 hours/d of sedentary behavior, 8.06 (95% CI, 7.76-8.36) for 10 hours/d, 12.00 (95% CI, 10.00-14.36) for 12 hours/d, and 22.74 (95% CI, 14.92-34.11) for 15 hours/d. Mean daily sedentary bout length (HR, 1.53 [95% CI, 1.03-2.27], P = .04 and 0.65 [95% CI, 0.04-1.57] more dementia cases per 1000 person-years for a 1-hour increase from the mean of 0.48 hours) and maximum daily sedentary bout length (HR, 1.15 [95% CI, 1.02-1.31], P = .02 and 0.19 [95% CI, 0.02-0.38] more dementia cases per 1000 person-years for a 1-hour increase from the mean of 1.95 hours) were significantly associated with higher risk of incident dementia. The number of sedentary bouts per day was not associated with higher risk of incident dementia (HR, 1.00 [95% CI, 0.99-1.01], P = .89). In the sensitivity analyses, after adjustment for time spent in sedentary behavior, the mean daily sedentary bout length and the maximum daily sedentary bout length were no longer significantly associated with incident dementia. Conclusions and Relevance: Among older adults, more time spent in sedentary behaviors was significantly associated with higher incidence of all-cause dementia. Future research is needed to determine whether the association between sedentary behavior and risk of dementia is causal.

Does strict invariance matter? Valid group mean comparisons with ordered-categorical items.

Measurement invariance (MI) of a psychometric scale is a prerequisite for valid group comparisons of the measured construct. While the invariance of loadings and intercepts (i.e., scalar invariance) supports comparisons of factor means and observed means with continuous items, a general belief is that the same holds with ordered-categorical (i.e., ordered-polytomous and dichotomous) items. However, as this paper shows, this belief is only partially true-factor mean comparison is permissible in the correctly specified scalar invariance model with ordered-polytomous items but not with dichotomous items. Furthermore, rather than scalar invariance, full strict invariance-invariance of loadings, thresholds, intercepts, and unique factor variances in all items-is needed when comparing observed means with both ordered-polytomous and dichotomous items. In a Monte Carlo simulation study, we found that unique factor noninvariance led to biased estimations and inferences (e.g., with inflated type I error rates of 19.52%) of (a) the observed mean difference for both ordered-polytomous and dichotomous items and (b) the factor mean difference for dichotomous items in the scalar invariance model. We provide a tutorial on invariance testing with ordered-categorical items as well as suggestions on mean comparisons when strict invariance is violated. In general, we recommend testing strict invariance prior to comparing observed means with ordered-categorical items and adjusting for partial invariance to compare factor means if strict invariance fails.

Adjusting for partial invariance in latent parameter estimation: Comparing forward specification search and approximate invariance methods.

Measurement invariance is the condition that an instrument measures a target construct in the same way across subgroups, settings, and time. In psychological measurement, usually only partial, but not full, invariance is achieved, which potentially biases subsequent parameter estimations and statistical inferences. Although existing literature shows that a correctly specified partial invariance model can remove such biases, it ignores the model uncertainty in the specification search step: flagging the wrong items may lead to additional bias and variability in subsequent inferences. On the other hand, several new approaches, including Bayesian approximate invariance and alignment optimization methods, have been proposed; these methods use an approximate invariance model to adjust for partial measurement invariance without the need to directly identify noninvariant items. However, there has been limited research on these methods in situations with a small number of groups. In this paper, we conducted three systematic simulation studies to compare five methods for adjusting partial invariance. While specification search performed reasonably well when the proportion of noninvariant parameters was no more than one-third, alignment optimization overall performed best across conditions in terms of efficiency of parameter estimates, confidence interval coverage, and type I error rates. In addition, the Bayesian version of alignment optimization performed best for estimating latent means and variances in small-sample and low-reliability conditions. We thus recommend the use of the alignment optimization methods for adjusting partial invariance when comparing latent constructs across a few groups.

The Immigrant Paradox in the Problem Behaviors of Youth in the United States: A Meta-analysis.

This meta-analysis synthesizes the empirical data on problem behaviors among foreign- (G1) and U.S-born (G2+) youth and explores the effects of immigrant status on youth internalizing and externalizing problems. A random effects meta-regression with robust variance estimates summarized effect sizes for internalizing and externalizing problems across 91 studies (N = 179,315, Mage  = 13.98). Results indicated that G1 youth reported significantly more internalizing problems (g = .06), and fewer externalizing problems than G2+ youth (g = -.06). Gender and sample type moderated the effects. The findings provide a first-step toward reconciling mixed support for the immigrant paradox by identifying for whom and under what conditions the immigrant experience serves as a risk or protective factor for youth.

Bootstrap Confidence Intervals for Multilevel Standardized Effect Size.

Although many methodologists and professional organizations have urged applied researchers to compute and report effect size measures accompanying tests of statistical significance, discussions on obtaining confidence intervals (CIs) for effect size with clustered/multilevel data have been scarce. In this paper, I explore the bootstrap as a viable and accessible alternative for obtaining CIs for multilevel standardized mean difference effect size for cluster-randomized trials. A simulation was carried out to compare 17 analytic and bootstrap procedures for constructing CIs for multilevel effect size, in terms of empirical coverage rate and width, for both normal and nonnormal data. Results showed that, overall, the residual bootstrap with studentized CI had the best coverage rates (94.75% on average), whereas the residual bootstrap with basic CI had better coverage in small samples. These two procedures for constructing CIs showed better coverage than using analytic methods for both normal and nonnormal data. In addition, I provide an illustrative example showing how bootstrap CIs for multilevel effect size can be easily obtained using the statistical software R and the R package bootmlm. I strongly encourage applied researchers to report CIs to adequately convey the uncertainty of their effect size estimates.

Sex differences in moral judgements across 67 countries.

Most of the empirical research on sex differences and cultural variations in morality has relied on within-culture analyses or small-scale cross-cultural data. To further broaden the scientific understanding of sex differences in morality, the current research relies on two international samples to provide the first large-scale examination of sex differences in moral judgements nested within cultures. Using a sample from 67 countries (Study 1; n = 336 691), we found culturally variable sex differences in moral judgements, as conceptualized by Moral Foundations Theory. Women consistently scored higher than men on Care, Fairness, and Purity. By contrast, sex differences in Loyalty and Authority were negligible and highly variable across cultures. Country-level sex differences in moral judgements were also examined in relation to cultural, socioeconomic, and gender-equality indicators revealing that sex differences in moral judgements are larger in individualist, Western, and gender-equal societies. In Study 2 (19 countries; n = 11 969), these results were largely replicated using Bayesian multi-level modelling in a distinct sample. The findings were robust when incorporating cultural non-independence of countries into the models. Specifically, women consistently showed higher concerns for Care, Fairness, and Purity in their moral judgements than did men. Sex differences in moral judgements were larger in individualist and gender-equal societies with more flexible social norms. We discuss the implications of these findings for the ongoing debate about the origin of sex differences and cultural variations in moral judgements as well as theoretical and pragmatic implications for moral and evolutionary psychology.

Confidence is sexy and it can be trained: Examining male social confidence in initial, opposite-sex interactions.

OBJECTIVE: We investigated whether men's social confidence in an initial, opposite-sex chatting context can be improved through a video tutorial and the extent to which being perceived as socially confident results in being seen as more romantically desirable and worthy of future contact. METHOD: Women chatted with men who had received or not received a tutorial on how to handle speed-dating chats (Study 1: N = 129; Study 2: N = 60) or with male targets selected for having high versus moderate confidence in handling initial, opposite-sex encounters (Study 3: N = 46). RESULTS: Tutorial-trained men felt more confident going into the chats and they, as well as male targets selected for their confidence, were perceived by female chat partners to be higher in social confidence, status, and dominance. However, only perceptions of social confidence were further associated with being perceived as more romantically desirable (as a short-term mate) and worthy of future contact. CONCLUSIONS: Findings indicate that social confidence is trainable and that other-perceived social confidence can impact the outcomes of social interactions.

Estimating Standardized Effect Sizes for Two- and Three-Level Partially Nested Data.

Although previous research has discussed an effect size estimator for partially nested cluster randomized designs, the existing estimator (a) is not efficient when used with primary data, (b) can be biased when the homogeneity of variance assumption is violated, and (c) has not yet been empirically evaluated for its finite sample properties. The present paper addresses these limitations by proposing an alternative maximum likelihood estimator for obtaining standardized mean difference effect size and the corresponding sampling variance for partially nested data, as well as the variants that do not make an assumption of homogeneity of variance. The typical estimator, denoted as d (dW with pooled SD and dC with control arm SD), requires input of summary statistics such as observed means, variances, and the intraclass correlation, and is useful for meta-analyses and secondary data analyses; the newly proposed estimator [Formula: see text] ([Formula: see text] and [Formula: see text]) takes parameter estimates from a correctly specified multilevel model as input and is mainly of interest to researchers doing primary research. The simulation results showed that the two methods (d and [Formula: see text]) produced unbiased point and variance estimates for effect size. As expected, in general, [Formula: see text] was more efficient than d with unequal cluster sizes, especially with large average cluster size and large intraclass correlation. Furthermore, under heterogeneous variances, [Formula: see text] demonstrated a greater relative efficiency with small sample size for the unclustered control arm. Real data examples, one from a youth preventive program and one from an eating disorder intervention, were used to demonstrate the methods presented. In addition, we extend the discussion to a scenario with a three-level treatment arm and an unclustered control arm, and illustrate the procedures for effect size estimation using a hypothetical example of multiple therapy groups of clients clustered within therapists.

Internet Gambling Among Community Adults and University Students in Macao.

Despite the high availability of offline gambling in Macao, China, Internet gambling may remain attractive to many gamblers due to its anonymity and convenience. Given the scarcity of relevant research, this study aims to not only investigate the public attitude and prevalence of Internet gambling but also identify the demographic and psychological characteristics of Internet gamblers in Macao. We recruited 952 community adults with the random residential number dialing method and 427 university students through convenience sampling. Only 5.4% of the community adult respondents preferred online gambling compared to offline gambling, and the past-year prevalence of online gambling was about 1%. As hypothesized, Internet gambling was found to be positively associated with pathological gambling in both community and student samples. It was also associated with casino employment across samples. Moreover, we found that male gender, casino employment, materialism, and life dissatisfaction were significant risk factors of pathological gambling among Chinese gamblers. The findings provide insights on future designs of preventive measures and research direction for Internet gambling and pathological gambling in Chinese communities.

Validation of the Gambling Refusal Self-Efficacy Questionnaire for Chinese undergraduate students.

Although research on self-efficacy in the gambling literature took place more than 25 years ago, only in the recent decade did researchers attempt to develop valid and reliable measures of gambling-related self-efficacy. Recently Casey et al. (J Gambl Stud 24:229-246, 2008) developed the Gambling Refusal Self-Efficacy Questionnaire (GRSEQ) in an Australian sample, which is a valuable tool for gambling research. The first objective of this study is to validate the measure in a new sample. Given that previous research on Chinese's gamblers' self-efficacy is lacking, and that related research often used ad-hoc measures of the construct, a second objective of this study is to evaluate whether the GRSEQ is suitable for Chinese people. A sample of 427 university students (56.4 % females, 50.7 % gamblers) answered a questionnaire with measures including the GRSEQ, subjective norms, intentions toward gambling, general self-efficacy, impulsiveness, and pathological gambling symptoms. Evidence was found for the four-factor structure, internal consistency, criterion-related validity, and discriminant validity of the Chinese version of the GRSEQ among this young Chinese group.

Are factor scores measurement invariant?

There has been increased interest in practical methods for integrative analysis of data from multiple studies or samples, and using factor scores to represent constructs has become a popular and practical alternative to latent variable models with all individual items. Although researchers are aware that scores representing the same construct should be on a similar metric across samples-namely they should be measurement invariant-for integrative data analysis, the methodological literature is unclear whether factor scores would satisfy such a requirement. In this note, we show that even when researchers successfully calibrate the latent factors to the same metric across samples, factor scores-which are estimates of the latent factors but not the factors themselves-may not be measurement invariant. Specifically, we prove that factor scores computed based on the popular regression method are generally not measurement invariant. Surprisingly, such scores can be noninvariant even when the items are invariant. We also demonstrate that our conclusions generalize to similar shrinkage scores in item response models for discrete items, namely the expected a posteriori scores and the maximum a posteriori scores. Researchers should be cautious in directly using factor scores for cross-sample analyses, even when such scores are obtained from measurement models that account for noninvariance. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Validation of the Gambling Refusal Self-Efficacy Questionnaire for Chinese Undergraduate Students.

Although research on self-efficacy in the gambling literature took place more than 25 years ago, only in the recent decade did researchers attempt to develop valid and reliable measures of gambling-related self-efficacy. Recently Casey et al. (J Gambl Stud 24:229-246, 2008) developed the Gambling Refusal Self-Efficacy Questionnaire (GRSEQ) in an Australian sample, which is a valuable tool for gambling research. The first objective of this study is to validate the measure in a new sample. Given that previous research on Chinese's gamblers' self-efficacy is lacking, and that related research often used ad-hoc measures of the construct, a second objective of this study is to evaluate whether the GRSEQ is suitable for Chinese people. A sample of 427 university students (56.4 % females, 50.7 % gamblers) answered a questionnaire with measures including the GRSEQ, subjective norms, intentions toward gambling, general self-efficacy, impulsiveness, and pathological gambling symptoms. Evidence was found for the four-factor structure, internal consistency, criterion-related validity, and discriminant validity of the Chinese version of the GRSEQ among this young Chinese group.

Chinese attitudes, norms, behavioral control and gambling involvement in Macao.

This study tested the applicability of the theory of planned behavior (TPB) on gambling intention and involvement in a representative Chinese sample in Macao, recruited by a random residential number dialing method. We successfully interviewed 685 respondents, with about one-third of them reporting that they had participated in gambling activities during the 12 months prior to the interview. The results of structural equation modeling suggested that favorable attitudes toward superstition and techniques, poor sense of perceived behavioral control over gambling refusal, and high gambling intention increased vulnerability to excessive gambling involvement. These findings generally support the efficacy of the TPB in explaining gambling intention and involvement among both Chinese gamblers and non-gamblers. Some practical implications of the findings are discussed within the Chinese context.

A Bayesian region of measurement equivalence (ROME) approach for establishing measurement invariance.

Measurement invariance research has focused on identifying biases in test indicators measuring a latent trait across two or more groups. However, relatively little attention has been devoted to the practical implications of noninvariance. An important question is whether noninvariance in indicators or items results in differences in observed composite scores across groups. The current study introduces the Bayesian region of measurement equivalence (ROME) as a framework for visualizing and testing the combined impact of partial invariance on the group difference in observed scores. Under the proposed framework, researchers first compute the highest posterior density intervals (HPDIs)-which contain the most plausible values-for the expected group difference in observed test scores over a range of latent trait levels. By comparing the HPDIs with a predetermined range of values that is practically equivalent to zero (i.e., region of measurement equivalence), researchers can determine whether a test instrument is practically invariant. The proposed ROME method can be used for both continuous indicators and ordinal items. We illustrated ROME using five items measuring mathematics-specific self-efficacy from a nationally representative sample of 10th graders. Whereas conventional invariance testing identifies a partial strict invariance model across gender, the statistically significant noninvariant items were found to have a negligible impact on the comparison of the observed scores. This empirical example demonstrates the utility of the ROME method for assessing practical significance when statistically significant item noninvariance is found. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The Impacts of the COVID-19 Pandemic on Therapy Utilization Among Racially/Ethnically and Socio-Economically Diverse Autistic Children.

PURPOSE: The purpose of current study was to evaluate change in hours of Applied Behavior Analysis (ABA) therapy utilization for autistic children during the year prior to the COVID-19 pandemic, the first three months of the pandemic (crisis phase), and the following 9 months of the pandemic (mitigation phase). Additionally, this study aimed to evaluate if change in therapy utilization differed based on child race, ethnicity, and primary payer of services. Finally, we aimed to identify potential mechanisms of ABA therapy disruption by interpreting findings using an extended version of Donabedian's structure-process-outcome model. METHODS: Retrospective clinical data on client demographics and therapy utilization (n = 283) were collected from ABA clinics in California and analyzed with four piecewise growth multi-level models. RESULTS: We found that therapy utilization dropped during the first three months of the pandemic (-10.65 h/month; p < .001) and increased during the following 9 months (2.39 h/month; p < .001). Moderator analyses revelated that Asian, Non-Latinx and school-district funded children had significantly different trajectories of change in therapy utilization compared to white, non-Latinx participants and private insurance funded participants, respectively. CONCLUSION: Findings suggest that utilization of ABA therapy was disrupted for a full year following the onset of the COVID-19 pandemic and that child race/ethnicity and primary payer influenced the degree to which autistic children were impacted by service disruption. These findings have implications for autistic children who lost therapy access during key developmental periods and for the ABA care delivery system.