scholarly journals ADHD: Restricted Phenotypes Prevalence, Comorbidity, and Polygenic Risk Sensitivity in ABCD Baseline Cohort

2021 ◽  
Author(s):  
Michaela Maria Cordova ◽  
Dylan Matthew Antovich ◽  
Peter Ryabinin ◽  
Christopher Neighbor ◽  
Michael A. Mooney ◽  
...  
Keyword(s):  
Cognitive Functioning ◽  
Latent Variables ◽  
Latent Variable ◽  
Present Report ◽  
Sampling Bias ◽  
The United States ◽  
Operational Definition ◽  
Polygenic Risk ◽  
Phenotype Definition ◽  
Composite Scores

Introduction. Estimates of prevalence and comorbidity of ADHD in the United States require additional national, multi-informant data. Further, it is unclear whether the polygenic, neurodevelopmental model of ADHD in DSM-5 is best modeled with a broad or restrictive phenotype definition. Method: In the Adolescent Behavior Cognition Development (ABCD) study baseline data on 9-10 year old children, ADHD prevalence, comorbidity, and association with cognitive functioning and polygenic risk were calculated at four thresholds of definition of ADHD phenotype restrictiveness using multiple measures and informants. Multi-indicator latent variable and composite scores were created and cross validated for ADHD symptoms and for irritability. Missing data, sample nesting, and sampling bias were corrected statistically. Results: Multi-informant estimate of ADHD prevalence by the most restrictive definition was 3.53% when restricted to children in which parent ratings and teacher ratings both converged with KSAD report of current ADHD. As stringency of the phenotype was increased, total comorbidity increased slightly, and associations with cognitive functioning and polygenic risk strengthened. Inclusion of children with past ADHD but now treated increased prevalence estimate without weakening detection of polygenic risk. Irritability and ADHD dimensional composite scores and latent variables achieved satisfactory model fit and expected external correlations. Conclusion: The present report strengthens estimates of ADHD prevalence and comorbidity. Research on polygenic and other correlates of ADHD as a clinical category in the ABCD sample may benefit from using a restrictive, multi-informant operational definition.

scholarly journals Comparing multiple statistical software for multiple-indicator, multiple-cause modeling: an application of gender disparity in adult cognitive functioning using MIDUS II dataset

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Chi Chang ◽  
Joseph Gardiner ◽  
Richard Houang ◽  
Yan-Liang Yu
Keyword(s):  
Cognitive Functioning ◽  
Latent Variable ◽  
The United States ◽  
Equation Modeling ◽  
Statistical Software ◽  
Mimic Model ◽  
Empirical Application ◽  
Data Manipulation ◽  
Software Packages ◽  
Multiple Indicator

Abstract Background The multiple-indicator, multiple-cause model (MIMIC) incorporates covariates of interest in the factor analysis. It is a special case of structural equation modeling (SEM), which is modeled under latent variable framework. The MIMIC model provides rigorous results and becomes broadly available in multiple statistical software. The current study introduces the MIMIC model and how it can be implemented using statistical software packages SAS CALIS procedure, R lavaan package, and Mplus version 8.0. Methods In this paper, we first discussed the formulation of the MIMIC model with regard to model specification and identification. We then demonstrated the empirical application of the MIMIC model with the Midlife in the United States II (MIDUS II) Study (N = 4109) using SAS CALIS procedure, R lavaan package and Mplus version 8.0 to examine gender disparities in cognitive functioning. The input, output, and diagram syntaxes of the three statistical software packages were also presented. Results In terms of data structure, all three statistical programs can be conducted using both raw data and empirical covariance matrix. SAS and R are comprehensive statistical analytic packages and encompass numerous data manipulation capacities. Mplus is designed primarily for latent variable modeling and has far more modeling flexibility compared to SAS and R, but limited in data manipulation. Differences in model results from the three statistical programs are trivial. Overall, the results show that while men show better performance in executive function than women, women demonstrate better episodic memory than men. Conclusions Our study demonstrates the utility of the MIMIC model in its empirical application, fitted with three popular statistical software packages. Results from our models align with empirical findings from previous research. We provide coding procedures and examples with detailed explanations in the hopes of providing a concise tutorial for researchers and methodologists interested in incorporating latent constructs with multiple indicators and multiple covariates in their research projects. Future researchers are encouraged to adopt this flexible and rigorous modeling approach.

scholarly journals Comparing Multiple Statistical Software for Multiple-Indicator, Multiple-Cause Modeling: An Application of Gender Disparity in Adult Cognitive Functioning Using MIDUS II Dataset

2020 ◽  
Author(s):  
Chi Chang ◽  
Joseph Gardiner ◽  
Richard T Houang ◽  
Yan-Liang Yu
Keyword(s):  
Cognitive Functioning ◽  
Latent Variable ◽  
The United States ◽  
Equation Modeling ◽  
Statistical Software ◽  
Mimic Model ◽  
Empirical Application ◽  
Data Manipulation ◽  
Software Packages ◽  
Multiple Indicator

Abstract Background The Multiple-indicator, multiple-cause model (MIMIC) incorporates covariates of interest in the factor analysis is a special case of structural equation modeling (SEM), which is modeled under latent variable framework. The MIMIC model provides rigorous results and becomes broadly available in multiple statistical software. The current study introduces the MIMIC model and how it can be implemented using statistical software packages SAS CALIS procedure, R lavaan package, and Mplus version 8.0. Methods In this paper, we first discussed the formulation of the MIMIC model with regard to model specification and identification. We then demonstrated the empirical application of the MIMIC model with the Midlife in the United States II (MIDUS II) Study (N=4,109) using SAS CALIS procedure, R lavaan package and Mplus version 8.0 to examine gender disparities in cognitive functioning. The input, output, and diagram syntaxes of the three statistical software packages were also presented.Results In terms of data structure, all three statistical programs can be conducted using both raw data and empirical covariance matrix. SAS and R are comprehensive statistical analytic packages and encompass numerous data manipulation capacities. Mplus is designed primarily for latent variable modeling and has far more modeling flexibility compared to SAS and R, but limited in data manipulation. Differences in model results from the three statistical programs are trivial. Overall, the results show that while men show better performance in executive function than women, women demonstrate better episodic memory than men.Conclusions Our study demonstrates the utility of the MIMIC model in its empirical application, fitted with three popular statistical software packages. Results from our models align with empirical findings from previous research. We provide coding procedures and examples with detailed explanations in the hopes of providing a concise tutorial for researchers and methodologists interested in incorporating latent constructs with multiple indicators and multiple covariates in their research projects. Future researchers are encouraged to adopt this flexible and rigorous modeling approach.

Invariance of the Measurement Model Underlying the Wechsler Adult Intelligence Scale—III in the United States and Canada

2008 ◽  
Vol 68 (6) ◽  
pp. 1024-1040 ◽  
Author(s):  
Stephen C. Bowden ◽  
Rael T. Lange ◽  
Lawrence G. Weiss ◽  
Donald H. Saklofske
Keyword(s):  
Test Scores ◽  
Latent Variables ◽  
Latent Variable ◽  
Measurement Model ◽  
The United States ◽  
Adult Intelligence Scale ◽  
Intelligence Scale ◽  
Wechsler Adult Intelligence Scale ◽  
Adult Intelligence ◽  
Wechsler Adult Intelligence

A measurement model is invoked whenever a psychological interpretation is placed on test scores. When stated in detail, a measurement model provides a description of the numerical and theoretical relationship between observed scores and the corresponding latent variables or constructs. In this way, the hypothesis that similar meaning can be derived from a set of test scores can be tested by examination of a measurement model across groups. This study examines the invariance of a measurement model underlying Wechsler Adult Intelligence Scale—Third Edition scores in the U.S. and the Canadian standardization samples. The measurement model, involving four latent variables, satisfies the assumption of invariance across samples. Subtest scores also show similar reliability in both samples. However, slightly higher latent variable means are found in the Canadian normative sample.

scholarly journals Comparing Multiple Statistical Software for Multiple-Indicator, Multiple-Cause Modeling: An Application of Gender Disparity in Adult Cognitive Functioning Using MIDUS II Dataset

2020 ◽  
Author(s):  
Chi Chang ◽  
Joseph Gardiner ◽  
Richard T Houang ◽  
Yan-Liang Yu
Keyword(s):  
Cognitive Functioning ◽  
Latent Variable ◽  
The United States ◽  
Gender Disparity ◽  
Model Specification ◽  
Statistical Software ◽  
Mimic Model ◽  
Empirical Application ◽  
Data Manipulation ◽  
Multiple Indicator

Abstract Background The Multiple-indicator, multiple-cause model (MIMIC) incorporates covariates of interest in the factor analysis using latent variable framework, of which classical structural equation model is a special case. The MIMIC model provides rigorous results and becomes broadly available in multiple statistical software. The current study introduces the MIMIC model and how it can be implemented using statistical software SAS CALIS procedure, R lavaan package, and Mplus version 8.0. Methods In this paper, we first discussed the formulation of the MIMIC model with regard to model specification and identification. We then demonstrated the empirical application of the MIMIC model with the Midlife in the United States II (MIDUS II) Study (N=4,109) using SAS CALIS procedure, R lavaan package and Mplus version 8.0 to examine gender disparities in cognitive functioning. The input, output, and diagram syntaxes of the three statistical software programs were also presented.Results In terms of data structure, all three statistical programs can be conducted using both raw data and empirical covariance matrix. SAS and R are comprehensive statistical analytic packages and encompass numerous data manipulation capacities. Mplus is designed primarily for latent variable modeling and has far more modeling flexibility compared to SAS and R, but limited in data manipulation. Differences in model results from the three statistical programs are trivial. Overall, the results show that while men show better performance in executive function than women, women demonstrate better episodic memory than men.Conclusions Our study demonstrates the utility of the MIMIC model in its empirical application, fitted with three popular statistical software packages. Results from our models align with empirical findings from previous research. We provide coding procedures and examples with detailed explanations in the hopes of providing a concise tutorial for researchers and methodologists interested in incorporating latent constructs with multiple indicators and multiple covariates in their research projects. Future researchers are encouraged to adopt this flexible and rigorous modeling approach.

scholarly journals Comparing Multiple Statistical Software for Multiple-Indicator, Multiple-Cause Modeling: An Application of Gender Disparity in Adult Cognitive Functioning Using MIDUS II Dataset

2020 ◽  
Author(s):  
Chi Chang ◽  
Joseph Gardiner ◽  
Richard T Houang ◽  
Yan-Liang Yu
Keyword(s):  
Cognitive Functioning ◽  
Latent Variable ◽  
The United States ◽  
Gender Disparity ◽  
Statistical Software ◽  
Mimic Model ◽  
Empirical Application ◽  
Data Manipulation ◽  
Software Packages ◽  
Multiple Indicator

Abstract Background The Multiple-indicator, multiple-cause model (MIMIC) incorporates covariates of interest in the factor analysis using latent variable framework, of which classical structural equation model is a special case. The MIMIC model provides rigorous results and becomes broadly available in multiple statistical software. The current study introduces the MIMIC model and how it can be implemented using statistical software packages SAS CALIS procedure, R lavaan package, and Mplus version 8.0. Methods In this paper, we first discussed the formulation of the MIMIC model with regard to model specification and identification. We then demonstrated the empirical application of the MIMIC model with the Midlife in the United States II (MIDUS II) Study (N=4,109) using SAS CALIS procedure, R lavaan package and Mplus version 8.0 to examine gender disparities in cognitive functioning. The input, output, and diagram syntaxes of the three statistical software packages were also presented.Results In terms of data structure, all three statistical programs can be conducted using both raw data and empirical covariance matrix. SAS and R are comprehensive statistical analytic packages and encompass numerous data manipulation capacities. Mplus is designed primarily for latent variable modeling and has far more modeling flexibility compared to SAS and R, but limited in data manipulation. Differences in model results from the three statistical programs are trivial. Overall, the results show that while men show better performance in executive function than women, women demonstrate better episodic memory than men.Conclusions Our study demonstrates the utility of the MIMIC model in its empirical application, fitted with three popular statistical software packages. Results from our models align with empirical findings from previous research. We provide coding procedures and examples with detailed explanations in the hopes of providing a concise tutorial for researchers and methodologists interested in incorporating latent constructs with multiple indicators and multiple covariates in their research projects. Future researchers are encouraged to adopt this flexible and rigorous modeling approach.

Probability-Based and Measurement-Related Hypotheses With Full Restriction for Investigations by Means of Confirmatory Factor Analysis

Methodology ◽  
2011 ◽  
Vol 7 (4) ◽  
pp. 157-164
Author(s):  
Karl Schweizer
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Cognitive Processing ◽  
Latent Variables ◽  
Repeated Measures ◽  
Latent Variable ◽  
Model Fit ◽  
Repeated Measures Data ◽  
Confirmatory Factor ◽  
And Performance

Probability-based and measurement-related hypotheses for confirmatory factor analysis of repeated-measures data are investigated. Such hypotheses comprise precise assumptions concerning the relationships among the true components associated with the levels of the design or the items of the measure. Measurement-related hypotheses concentrate on the assumed processes, as, for example, transformation and memory processes, and represent treatment-dependent differences in processing. In contrast, probability-based hypotheses provide the opportunity to consider probabilities as outcome predictions that summarize the effects of various influences. The prediction of performance guided by inexact cues serves as an example. In the empirical part of this paper probability-based and measurement-related hypotheses are applied to working-memory data. Latent variables according to both hypotheses contribute to a good model fit. The best model fit is achieved for the model including latent variables that represented serial cognitive processing and performance according to inexact cues in combination with a latent variable for subsidiary processes.

Conceptualizing Protective Family Context and Its effect on Substance Use: Comparisons Across Diverse Ethnic-Racial Youth

2019 ◽  
Author(s):  
Kevin Constante ◽  
Edward Huntley ◽  
Emma Schillinger ◽  
Christine Wagner ◽  
Daniel Keating
Keyword(s):  
Substance Use ◽  
Measurement Invariance ◽  
Latent Variables ◽  
Latent Variable ◽  
Path Model ◽  
Family Context ◽  
Partial Metric ◽  
Racial Groups ◽  
Protective Methods ◽  
Family Variables

Background: Although family behaviors are known to be important for buffering youth against substance use, research in this area often evaluates a particular type of family interaction and how it shapes adolescents’ behaviors, when it is likely that youth experience the co-occurrence of multiple types of family behaviors that may be protective. Methods: The current study (N = 1716, 10th and 12th graders, 55% female) examined associations between protective family context, a latent variable comprised of five different measures of family behaviors, and past 12 months substance use: alcohol, cigarettes, marijuana, and e-cigarettes. Results: A multi-group measurement invariance assessment supported protective family context as a coherent latent construct with partial (metric) measurement invariance among Black, Latinx, and White youth. A multi-group path model indicated that protective family context was significantly associated with less substance use for all youth, but of varying magnitudes across ethnic-racial groups. Conclusion: These results emphasize the importance of evaluating psychometric properties of family-relevant latent variables on the basis of group membership in order to draw appropriate inferences on how such family variables relate to substance use among diverse samples.

scholarly journals Interpretable Variational Graph Autoencoder with Noninformative Prior

2021 ◽  
Vol 13 (2) ◽  
pp. 51
Author(s):  
Lili Sun ◽  
Xueyan Liu ◽  
Min Zhao ◽  
Bo Yang
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Expert Knowledge ◽  
Structural Information ◽  
Standard Normal Distribution ◽  
Noninformative Prior ◽  
Latent Space ◽  
Distribution Parameters ◽  
Standard Normal ◽  
Low Dimensional

Variational graph autoencoder, which can encode structural information and attribute information in the graph into low-dimensional representations, has become a powerful method for studying graph-structured data. However, most existing methods based on variational (graph) autoencoder assume that the prior of latent variables obeys the standard normal distribution which encourages all nodes to gather around 0. That leads to the inability to fully utilize the latent space. Therefore, it becomes a challenge on how to choose a suitable prior without incorporating additional expert knowledge. Given this, we propose a novel noninformative prior-based interpretable variational graph autoencoder (NPIVGAE). Specifically, we exploit the noninformative prior as the prior distribution of latent variables. This prior enables the posterior distribution parameters to be almost learned from the sample data. Furthermore, we regard each dimension of a latent variable as the probability that the node belongs to each block, thereby improving the interpretability of the model. The correlation within and between blocks is described by a block–block correlation matrix. We compare our model with state-of-the-art methods on three real datasets, verifying its effectiveness and superiority.

Manual Therapy in Preadolescent Children: A Delphi Investigation of Physical Therapists in the United States

2021 ◽  
Author(s):  
Jenifer L Dice ◽  
Doug Dendy ◽  
Phillip S Sizer ◽  
Chad E Cook ◽  
Sara Feuling ◽  
...  
Keyword(s):  
United States ◽  
Manual Therapy ◽  
Physical Therapists ◽  
Initial Step ◽  
The United States ◽  
Group Differences ◽  
Electronic Survey ◽  
Craniosacral Therapy ◽  
Barriers To Use ◽  
Composite Scores

ABSTRACT Objective Limited research has investigated the use of manual therapy to treat the preadolescent (0–12 years of age) population with musculoskeletal and neurological impairments. The purpose of this study was to identify the following among physical therapists holding advanced credentials in pediatrics, neurodevelopmental treatment, or manual therapy: (1) consensus regarding effective techniques in the preadolescent population, (2) differences in opinion, and (3) perceived decision-making barriers and factors regarding use of manual therapy techniques. Methods Credentialed physical therapists in the United States were recruited for a 3-round Delphi investigation. An electronic survey in Round 1 identified musculoskeletal and neurological impairments and the manual techniques considered effective to treat such conditions, in addition to factors and barriers. Responses were used to create the second round, during which a 4-point Likert scale was used to score each survey item. A third round of scoring established consensus. Descriptive statistics and composite scores were calculated for each manual technique by impairment. Between-group differences were calculated using Mann–Whitney U with Bonferroni correction. Results Consensus was determined for several concepts. First, neuromuscular techniques were considered effective across all impairments, and joint mobilizations (grades I-IV) were believed to be effective to treat joint and muscle and myofascial impairments. Second, visceral manipulation and craniosacral therapy were considered ineffective in treating most impairments. There was lack of consensus and clear differences of opinion regarding the use of grade V mobilizations and dry needling. Significant barriers to use of manual therapy were: lack of knowledge, lack of evidence, and fear of litigation and harming patients. Conclusion This study is an initial step for developing manual therapy guidelines, research, and educational opportunities regarding manual therapy in pediatric physical therapy.

scholarly journals Spousal Loss and Cognitive Functioning: Do Pre-Loss Marital Quality and Gender Matter?

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 366-366
Author(s):  
Joohong Min ◽  
Jieun Song
Keyword(s):  
Cognitive Decline ◽  
Cognitive Functioning ◽  
Marital Quality ◽  
The United States ◽  
Cognitive Change ◽  
Life Transitions ◽  
Cognitive Changes ◽  
Spousal Loss ◽  
And Gender ◽  
The Impact

Abstract Prior research has found that the risk of cognitive decline increases after the death of a spouse. In general, the impact of life transitions is contingent on contextual factors such as socio-demographic characteristics or relationship quality. However, there is limited research on how marital quality before spousal loss and gender influence the association between spousal loss and cognitive change. The current study examines the effects of spousal loss on change in cognitive functioning as well as the moderating effects of pre-loss marital quality and gender. Data from two waves of the Midlife in the United States (MIDUS) study were analyzed (MIDUS2: 2004-05, MIDUS3: 2013-14). The analytic sample consists of two groups: (1) 179 bereaved adults who were age 54 or older at MIDUS2 (M = 65.2, SD = 9.5) and whose spouses died between MIDUS2 and MIDUS3, and (2) 179 non-bereaved adults, matched with the bereaved group on age and gender, who did not experience spousal loss between the two waves. Cognitive function was assessed via BTACT (Brief Telephone Adult Cognition Test) at both waves. Regression results show that both pre-loss marital quality and gender significantly moderate the association between spousal loss and change in cognitive functioning. Specifically, relative to their counterparts, men and those who reported better marital relationships prior to spousal death had a greater risk of cognitive decline after a spouse’s death. The findings suggest the significance of pre-loss marital quality and gender for cognitive changes in widowhood and have implications for the development of efficient interventions

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