Residual Variance Research Articles

Dealing with clustered samples for assessing map accuracy by cross-validation

Mapping of environmental variables often relies on map accuracy assessment through cross-validation with the data used for calibrating the underlying mapping model. When the data points are spatially clustered, conventional cross-validation leads to optimistically biased estimates of map accuracy. Several papers have promoted spatial cross-validation as a means to tackle this over-optimism. Many of these papers blame spatial autocorrelation as the cause of the bias and propagate the widespread misconception that spatial proximity of calibration points to validation points invalidates classical statistical validation of maps. We present and evaluate alternative cross-validation approaches for assessing map accuracy from clustered sample data. The first method uses inverse sampling-intensity weighting to correct for selection bias. Sampling-intensity is estimated by a two-dimensional kernel approach. The two other approaches are model-based methods rooted in geostatistics, where the first assumes homogeneity of residual variance over the study area whilst the second accounts for heteroscedasticity as a function of the sampling intensity. The methods were tested and compared against conventional k-fold cross-validation and blocked spatial cross-validation to estimate map accuracy metrics of above-ground biomass and soil organic carbon stock maps covering western Europe. Results acquired over 100 realizations of five sampling designs ranging from non-clustered to strongly clustered confirmed that inverse sampling-intensity weighting and the heteroscedastic model-based method had smaller bias than conventional and spatial cross-validation for all but the most strongly clustered design. For the strongly clustered design where large portions of the maps were predicted by extrapolation, blocked spatial cross-validation was closest to the reference map accuracy metrics, but still biased. For such cases, extrapolation is best avoided by additional sampling or limitation of the prediction area. Weighted cross-validation is recommended for moderately clustered samples, while conventional random cross-validation suits fairly regularly spread samples.

Residual variance and asset pricing in the art market

AbstractIn this paper, we compute residual variance of art prices to examine asset pricing in contemporary art market. Our empirical work shows a few interesting results. First, we discover that the residual variance is significantly and positively related to the average price level achieved by an artist. Second, the residual variance has additional explanatory power in terms of how often the artist’s works are cited and exhibited, even after we control for artist fixed (reputation) effects. Third, collectors tend to value more those artworks with higher residual variance. Artworks by those artists with high residual variance tend to outperform the market within and out of sample. One possible explanation of our results is that residual variance could be a proxy for creative risk taking by contemporary artists. The most creative artists dare to take more risks, which results in higher residual price volatility of their artworks.

Exploring polygenic-environment and residual-environment interactions for depressive symptoms within the UK Biobank.

Substantial advances have been made in identifying genetic contributions to depression, but little is known about how the effect of genes can be modulated by the environment, creating a gene–environment interaction. Using multivariate reaction norm models (MRNMs) within the UK Biobank (N = 61294–91644), we investigate whether the polygenic and residual variance components of depressive symptoms are modulated by 17 a priori selected covariate traits—12 environmental variables and 5 biomarkers. MRNMs, a mixed‐effects modelling approach, provide unbiased polygenic–covariate interaction estimates for a quantitative trait by controlling for outcome‐covariate correlations and residual–covariate interactions. A continuous depressive symptom variable was the outcome in 17 MRNMs—one for each covariate trait. Each MRNM had a fixed‐effects model (fixed effects included the covariate trait, demographic variables, and principal components) and a random effects model (where polygenic–covariate and residual–covariate interactions are modelled). Of the 17 selected covariates, 11 significantly modulate deviations in depressive symptoms through the modelled interactions, but no single interaction explains a large proportion of phenotypic variation. Results are dominated by residual–covariate interactions, suggesting that covariate traits (including neuroticism, childhood trauma, and BMI) typically interact with unmodelled variables, rather than a genome‐wide polygenic component, to influence depressive symptoms. Only average sleep duration has a polygenic–covariate interaction explaining a demonstrably nonzero proportion of the variability in depressive symptoms. This effect is small, accounting for only 1.22% (95% confidence interval: [0.54, 1.89]) of variation. The presence of an interaction highlights a specific focus for intervention, but the negative results here indicate a limited contribution from polygenic–environment interactions.

Exploratory and Confirmatory Factor Analysis for Disposition Levels of Computational Thinking Instrument Among Secondary School Students

<p style="text-align:justify">Computational thinking (CT) is a method for solving complex problems, but also gives people an inventive inspiration to adapt to our smart and changing society. Globally it has been considered as vital abilities for solving genuine issues successfully and efficiently in the 21st century. Recent studies have revealed that the nurture of CT mainly centered on measuring the technical skill. There is a lack of conceptualization and instruments that cogitate on CT disposition and attitudes. This study attends to these limitations by developing an instrument to measure CT concerning dispositions and attitudes. The instruments' validity and reliability testing were performed with the participation from secondary school students in Malaysia. The internal consistency reliability, standardized residual variance, construct validity and composite reliability were examined. The result revealed that the instrument validity was confirmed after removing items. The reliability and validity of the instrument have been verified. The findings established that all constructs are useful for assessing the disposition of computer science students. The implications for psychometric assessment were evident in terms of giving empirical evidence to corroborate theory-based constructs and also validating items' quality to appropriately represent the measurement.</p>

Multivariate explanation of the establishment of soybean initial growth pattern via biostimulant seed treatment

Given the search for answers that improve soybean plant development, this study aimed to analyze the multivariate explanation about the establishment of the initial soybean growth pattern through seed treatment. The study was conducted at Luiz Eduardo de Oliveira Sales Experimental Farm, in the municipality of Mineiros-GO. The soil of the experimental area was classified as NEOSSOLO Quartzarenico. The experimental design was randomized blocks in factorial 10x5 corresponding to 10 soybean genotypes (Flecha, Bonus, TEC7548, M7739, 36B31, W791, M7198, M6210, Power and 48B32) and 5 seed treatments (Water, Sprint-Alga, Booster, Acorda and Stimulate), in 4 repetitions. The obtained data was submitted to the assumptions of the statistical model, verifying the normality and homogeneity of the residual variances, as well as the additivity of the model. Afterwards, the analysis of variance was performed in order to identify the interaction between soybean genotypes x seed treatment, applying uni and multivariate tests. The summary analysis of variance revealed significant interaction between cultivar x seed treatment. The seed treatment influenced the morphological components of soybean seedlings, showing their correlation with the fresh aerial and root mass, as well as different patterns that were observed according to the genetic variation.

Fitting random regression models with Legendre polynomial and B-spline to model the lactation curve for Indian dairy goat of semi-arid tropic.

The present investigation aimed at genetic evaluation of tropical Indian dairy Jamunapari goat using random regression models (RRM) for the estimation of genetic parameters in the first three lactations across test days (TD) and also to come out with a pragmatic breeding plan in the nucleus. Variations in the lactation curves were modelled using 67,172 TD milk yield (TDMY) records. To obtain adequate and parsimonious models for the estimation of genetic parameters, orthogonal Legendre Polynomials (LP) and B-splines (BS) were compared. The analysis was carried out using a single-trait RRM approach. Average TDMY was 0.72, 0.81 and 0.79 kg in 1st to 3rd parities that also had 4th TD peak yield in common. BS function resulted in robust genetic parameters and a smoother curve for lactation as compared to LP. Maternal effects were evaluated and then dropped from the final model, owing to no significant contribution to the genetic variance. The best RRM was a quadratic BS function with six knots for the mean trend, curves of additive genetic, animal permanent environmental (c2 ) and 22 classes of residual variance. Additive variances and heritability (h2 ) estimates were higher in the early lactation. For first parity, the estimates of h2 varied between 0.19 to 0.35 across TD. Moderate h2 estimate suggests further scope for selection using desirable combinations of TD over the lactation. We observed a very high variance due to c2 across TD in three lactations. Genetic correlations were positive and larger between adjacent TDMY and weakened for distant TDMY. Looking into the robust estimates of genetic parameters and better fitting of lactation curve, we suggest the use of B-spline function for regular genetic evaluation of Jamunapari goat.

Understanding the Gold-Inflation Nexusi In Malaysia: Hedge and Safe Haven Perspectives

The main objective of this study is to examine the roles of gold as a hedge or a safe haven against inflation in Malaysia. We propose the standard and quantile techniques in the volatility models, with a time-varying conditional variance of regression residuals based on TGARCH specifications. We found that gold only plays a minor role as a hedge and safe haven against inflation since their returns do not evolve at the same pace as inflation. On the other hand, the rolling regression results reveal that shelter incidents against purchasing power loss only occasionally occur at different times and not consistently across holding periods. We conclude that gold does not have the ability to secure Malaysian investment during high inflationary periods and at all times. Thus, Malaysian investors should hold a well-diversified portfolio to earn sustainable returns and protection from purchasing power loss.

Population density and spreading of COVID-19 in England and Wales.

We investigated daily COVID-19 cases and deaths in the 337 lower tier local authority regions in England and Wales to better understand how the disease propagated over a 15-month period. Population density scaling models revealed residual variance and skewness to be sensitive indicators of the dynamics of propagation. Lockdowns and schools reopening coincided with increased variance indicative of conditions with local impact and country scale heterogeneity. University reopening and December holidays reduced variance indicative of country scale homogenisation which reached a minimum in mid-January 2021. Homogeneous propagation was associated with better correspondence with normally distributed residuals while heterogeneous propagation was more consistent with skewed models. Skewness varied from strongly negative to strongly positive revealing an unappreciated feature of community propagation. Hot spots and super-spreading events are well understood descriptors of regional disease dynamics that would be expected to be associated with positively skewed distributions. Positively skewed behaviour was observed; however, negative skewness indicative of “cold-spots” and “super-isolation” dominated for approximately 8 months during the period of study. In contrast, death metrics showed near constant behaviour in scaling, variance, and skewness metrics over the full period with rural regions preferentially affected, an observation consistent with regional age demographics in England and Wales. Regional positions relative to density scaling laws were remarkably persistent after the first 5–9 days of the available data set. The determinants of this persistent behaviour probably precede the pandemic and remain unchanged.

Inclusion of unexposed clusters improves the precision of fixed effects analysis of stepped-wedge cluster randomized trials.

Stepped-wedge cluster randomized trials (SW-CRTs) are typically analyzed using mixed effects models. The fixed effects model is a useful alternative that controls for all time-invariant cluster-level confounders and has proper control of type I error when the number of clusters is small. In principle, all clusters in SW-CRTs are designed to eventually receive the intervention, but in real-world research, some trials can end with unexposed clusters (clusters that never received the intervention), such as when a trial is terminated early based on interim analysis results. Typically, unexposed clusters are expected to contribute no information to the fixed effects intervention effect estimator and are excluded from fixed effects analyses. In this article we mathematically prove that inclusion of unexposed clusters improves the precision of the fixed effects least squares dummy variable (LSDV) intervention effect estimator, re-analyze data from a recent SW-CRT of a novel palliative care intervention containing an unexposed cluster, and evaluate the methods by simulation. We found that including unexposed clusters improves the precision of the fixed effects LSDV intervention effect estimator in both real and simulated datasets. Our simulations also reveal an increase in power and decrease in root mean square error. These improvements are present even if the assumptions of constant residual variance and period effects are violated. In the case that a SW-CRT concludes with unexposed clusters, these unexposed clusters can be included in the fixed effects LSDV analysis to improve precision, power, and root mean square error.

Variation in first-generation L1 deictic systems: Language attrition and bilingualism effects

Aims and Objectives: This study explored the extent to which bilingual language exposure and practice might alter the way in which bilingual first-generation adult speakers use deictic demonstratives in their first language (Spanish) after immersion in a new language environment (Norwegian). Fully developed L1 systems are expected to be stable and less susceptible to change or restructuring than child systems. In addition, core domains of a language such as deictic demonstrative reference are hypothesized to be more robust. Design: Participants were tested with the Spanish version of the memory game. They completed an ethnolinguistic background questionnaire with questions targeting demographic data, experience with language, and daily routines in language use. Data and analyses: Demonstrative use was analysed using binomial multilevel modelling, allowing residual variance to be partitioned into a between-participant component and a within-participant component. Findings: Results demonstrate a shift in the demonstrative system of Spanish native speakers who have resided in Norway for a median of 6.5 years. This shift is reflected in extensive use of the semantically underspecified item ese at the expense of the form aquel. The latter form is less frequent and highly context-dependent in corpora of the modern language. It can be hypothesized that first-generation speakers are faster in converging on a simplified system of deictic reference than the native speaker group tested in Spain, but this development parallels tendencies observed in the monolingual variety of the language. This faster shift may well be influenced and catalysed by bilingual language practice. Originality: This article addresses a gap in research on deictic terms under conditions of language attrition. It documents a restructuring of the deictic system in first-generation speakers of Spanish residing in another country. The results suggest that marking peri-personal space is a core feature of deictic systems across languages, also preserved under deictic system shift.

Genetic parameters prediction for Khuzestani buffaloes milk characteristics by random regression method

In this research, Data of milk yield, fat and protein percentage of Khuzestani buffalo were used. The daily records of milk yield were collected in the south-west of Iran where buffalo experienced a hot climate. The data set was constructed with 8,123 records of 1,430 first lactation buffalo. A univariate random regression model (RR/CF) was applied to data. Regression of additive genetic effect based on Legendre polynomials from the day of lactation was considered in the model. The results showed that maximum residual variance for the milk and fat production was estimated at the beginning of the lactation period. The minimum amount of additive genetic variation of milk and fat traits was seen in the early lactation period and the maximum amount of the component was estimated at the end of the lactation period. The lowest heritability of the mentioned traits was at the beginning of the lactation period. The level of this parameter increased to mid-lactation and was at its maximum during the late months of lactation, then decreased to the end of lactation. The estimated additive genetic correlations between close test-days were higher than faraway test-day records for each milk yield and milk fat content. Based on the results of this study, a random regression model with fitting orders 3 and 4, seems to be suitable for additive covariance functions in order to analyze the milk test-day records of buffaloes.

Predation risk can modify the foraging behaviour of frugivorous carnivores: Implications of rewilding apex predators for plant-animal mutualisms.

Apex predators play key roles in food webs and their recovery can trigger trophic cascades in some ecosystems. Intra‐guild competition can reduce the abundances of smaller predators and perceived predation risk can alter their foraging behaviour thereby limiting seed dispersal by frugivorous carnivores. However, little is known about how plant–frugivore mutualisms could be disturbed in the presence of larger predators.We evaluated the top‐down effect of the regional superpredator, the Iberian lynx Lynx pardinus, on the number of visits and fruits consumed by medium‐sized frugivorous carnivores, as well as the foraging behaviour of identified individuals, by examining the consumption likelihood and the foraging time.We carried out a field experiment in which we placed Iberian pear Pyrus bourgaeana fruits beneath fruiting trees and monitored pear removal by frugivorous carnivores, both inside and outside lynx ranges. Using camera traps, we recorded the presence of the red fox Vulpes vulpes, the Eurasian badger Meles meles and the stone marten Martes foina, as well as the number of fruits they consumed and their time spent foraging.Red fox was the most frequent fruit consumer carnivore. We found there were fewer visits and less fruit consumed by foxes inside lynx ranges, but lynx presence did not seem to affect badgers. We did not observe any stone marten visits inside lynx territories. The foraging behaviour of red foxes was also altered inside lynx ranges whereby foxes were less efficient, consuming less fruit per unit of time and having shorter visits. Local availability of fruit resources, forest coverage and individual personality also were important variables to understand visitation and foraging in a landscape of fear.Our results show a potential trophic cascade from apex predators to primary producers. The presence of lynx can reduce frugivorous carnivore numbers and induce shifts in their feeding behaviour that may modify the seed dispersal patterns with likely consequences for the demography of many fleshy‐fruited plant species. We conclude that knowledge of the ecological interactions making up trophic webs is an asset to design effective conservation strategies, particularly in rewilding programs.

Genomic Predictions for Common Bunt, FHB, Stripe Rust, Leaf Rust, and Leaf Spotting Resistance in Spring Wheat.

Some studies have investigated the potential of genomic selection (GS) on stripe rust, leaf rust, Fusarium head blight (FHB), and leaf spot in wheat, but none of them have assessed the effect of the reaction norm model that incorporated GE interactions. In addition, the prediction accuracy on common bunt has not previously been studied. Here, we investigated within-population prediction accuracies using the baseline M1 model and two reaction norm models (M2 and M3) with three random cross-validation (CV1, CV2, and CV0) schemes. Three Canadian spring wheat populations were evaluated in up to eight field environments and genotyped with 3158, 5732, and 23,795 polymorphic markers. The M3 model that incorporated GE interactions reduced residual variance by an average of 10.2% as compared with the main effect M2 model and increased prediction accuracies on average by 2–6%. In some traits, the M3 model increased prediction accuracies up to 54% as compared with the M2 model. The average prediction accuracies of the M3 model with CV1, CV2, and CV0 schemes varied from 0.02 to 0.48, from 0.25 to 0.84, and from 0.14 to 0.87, respectively. In both CV2 and CV0 schemes, stripe rust in all three populations, common bunt and leaf rust in two populations, as well as FHB severity, FHB index, and leaf spot in one population had high to very high (0.54–0.87) prediction accuracies. This is the first comprehensive genomic selection study on five major diseases in spring wheat.

Bayesian GGE model for heteroscedastic multienvironmental trials

AbstractThe dissection of genotype × environment interaction (GEI) is a crucial aspect of the final stages of plant breeding pipelines and recommendation of cultivars. Linear‐bilinear models used to analyze this interaction, such as the additive main effects and multiplicative interaction (AMMI) and genotype plus GEI (GGE), often assume homogeneity of the residual variances across environments which affects the estimates and therefore, interpretations and conclusions. Our main objective was to propose a GGE model that considers heteroscedasticity across environments using Bayesian inference and to evaluate its implications in the interpretation of real and simulated data. The GGE model assuming common variance was also fitted for comparison purposes. The great flexibility of the Bayesian inference is transferred to the biplots, allowing the construction of credible regions for genotypic and environmental scores. The inference on the stability and adaptability of genotypes might change when heteroscedasticity is ignored. When real data are used, different patterns of correlations between environments also affect the representativeness and discrimination of the target environment. The modeling of heteroscedasticity allowed the clustering of environments into subgroups, with similar effects for GEI. The proposed GGE model was more adequate and realistic to deal with scenarios of heterogeneous variance in multienvironment trials, which can be useful for exploiting the GEI.

Nutritional management in soybean crop for high yields using organomineral fertilizers

The objective of this work was to evaluate the performance of soybean cultivars as a function of the application of organomineral fertilizer via foliar, to obtain high yields. The study was conducted at the municipality of Mineiros-GO, Brazil. The soil is classified as neosol quartzeneic ortic typical, with medium texture. The experimental design was a 5x3 factorial, totaling 15 treatments, corresponding to five doses of Potamol Plus® (0, 0.25, 0.5, 0.75 and 1 L ha-1) and 3 cultivars of (M7739, M8372 and TMG7062) in 4 replications. The data obtained was submitted to the assumptions of the statistical model, verifying the normality and homogeneity of the residual variances, as well as the additivity of the model, for these were performed single and multivariate analyzes. The analysis of variance showed that the cultivar factor was significant for all variables. Significance was also observed in the dose factor for APR, LUG, LQG, REN, LDG, GTP and GCP. Interaction between factors (C x D) occurred in APR, LUG, LDG, LQG, LTG, GTP and GCP. Potamol Plus® organomineral fertilizer applied foliar at 30 (50%) and 45 (50%) days after sowing did not increase the productive performance of soybean cultivars M7739, M8372 and TMG7062. The cultivars M7739 and TMG7062 presented high yields (yield ≥ 70 bag ha-1).

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

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

Genotype selection based on multiple traits in cotton crops: The application of genotype by yield*trait biplot

In cotton crops, the cotton seed yield significantly contributes with the success of any cultivar. However, other traits are considered when an ideotype is pointed out in the selection, such as the fiber quality traits. The aim of this study was to applied genotype by yield*trait (GYT) biplot to a multi-environment trial data of cotton genotypes and selected the best genotypes. For this end, thirteen genotypes from nineteen trials were assessed. Seven traits were evaluated [cotton seed yield (SY), fiber percentage (FP), fiber length (FL), fiber uniformity (FU), short fiber index (SFI), fiber strength (FS), and elongation (EL)] and residual error variances structures [identity variance (IDV) and diagonal (Diag)] were tested by bayesian information criterion. After, the REML/BLUP approach was applied to predict the genetic values of each trait and the selective accuracy were measured from the prediction. Then, the GYT-biplot were applied to the data. For SP and SFI traits, the model with Diag residual variance was indicated, whereas for SY FL, FU, FS, and EL traits the model with IDV residual variance demonstrated the best fit to the data. Values of accuracy were higher than 0.9 for all traits analyzed. In the GYT-biplot acute angles were find for all traits relations, which means high correlation between the yield*traits combination. Besides that, the correlation still can be seen in the GYT-biplot, as shown by the magnitudes of the angles between the pairs Yield*FU-Yield*FS and Yield*FS-Yield*EL. Also, the GYT-biplot indicates the genotype G4 with the best performance for Yield*FS, Yield*SFI, Yield*FU, Yield*FL, and Yield*FP combined. The genotypes G4, G1, G13, G8, and G9 represent those genotypes with yield advantage over the other cultivars. Then, the genotype G4 combines all desirable characteristics and demonstrate have large potential in the cotton breeding. The GYT approach were valuable and were highly recommended in cotton breeding programs for selection purpose in a multivariate scenario.

Investigating the Extent of Coronaphobia and some Related Psycho-Social Variables in Iran

Investigating the Extent of Coronaphobia and some Related Psycho-Social Variables in Iran

The validity of the residuals approach to measuring resilience to adverse childhood experiences

BackgroundResilience is broadly defined as the ability to maintain or regain functioning in the face of adversity. Recent work to harmonise the quantification and definition of resilience quantifies resilience as the residual variance in psychosocial functioning that remains after accounting for adversity exposure. However, there have been no published studies that have formally investigated the validity of this approach. Considering this, we examine the construct and predictive validity of the residuals approach using participants from the Avon Longitudinal Study of Parents and Children (ALSPAC), a multigenerational, longitudinal cohort study.MethodsWe regressed exposures of adolescent adversity on adolescent psychopathology scores using the Strength and Difficulties Questionnaire and obtained the residual variance. We investigated construct validity by analysing whether previously identified demographic and resilience factors significantly predicted resilience. Predictive validity of resilience was investigated by comparing the predictive power of resilience with other determinants of psychosocial functioning on two developmental outcomes: depressive symptoms at 18 years, measured by the Short Moods and Feelings Questionnaire, and NEET (Not in Employment, Education or Training) status at 17 and 23 years. The associations between depressive symptoms at 18, resilience, ACEs and covariates were tested using multiple linear regression. NEET status at 17 and 23 were run as separate binary multiple logistic regression models to test associations with resilience and known demographics previously associated with NEET status.ResultsSeven previously identified protective factors, including self-esteem, positive sibling relationship, temperament, and positive perception of school, significantly predicted resilience to adolescent psychopathology, thus providing strong construct validity. Resilience significantly predicted a reduction in depressive symptoms at 18 years, and significantly decreased the likelihood of having NEET status at both 17 years and 23 years, even after taking into account early childhood adversity and other risk factors. None of the socioeconomic factors were significantly associated with resilience.ConclusionsOur study demonstrates that the residuals method of operationalising resilience has good construct and predictive validity yet recommend replication studies. It has the potential to advance research into the mechanisms and modifiability of resilience.Trial Registration: Not applicable.

Validating the Group-Based Medical Mistrust Scale with English and Spanish Speaking Latino Parents of Adolescents.

Medical mistrust can be a barrier to health care utilization. While the Group-Based Medical Mistrust Scale (GBMMS) has been validated among diverse populations, we know little about its psychometric performance among English-Speaking (ES) and Spanish-Speaking (SS) Latinos. We aim to examine the factor structure of GBMMS among Latino parents and explore whether scale latent factor structures would be different across preferred languages. Parents of adolescents participating in an urban academic enrichment program for low-income students completed an online survey as part of a cross-sectional study about Human Papillomavirus (HPV) prevention. We tested the validity of the GBMMS in ES and SS respondents and performed exploratory factor analysis to identify latent factors. We examined scale scores, item means, item endorsement, and residual variance across language groups. 2-factor latent structure was identified for both ES and SS groups; factors were labeled as 'Suspicion' and 'System-wide discrimination.' Scale validity was better among SS (α = 0.76). Medical mistrust scores did not vary across language groups, but variation emerged in item endorsement by language. Our results suggest that the factor structures of the medical mistrust construct, measured by the GBMMS, are similar across ES and SS Latinos and that the GBMMS is valid for both populations. High factor pattern loading reflects a shared mistrust of how health care systems view and treat people of their racial/ethnic background, independent of their preferred languages. Measurement of medical mistrust and identifying its underlying causes are needed within diverse populations to fully address structural- and community-level influences on health inequities.