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.

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.

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):  
Structural Equation Modeling ◽  
Cognitive Functioning ◽  
Structural Equation ◽  
The United States ◽  
Equation Modeling ◽  
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 structural equation modeling framework. The 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 M plus 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 M plus 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. While SAS and R are comprehensive statistical analytic packages and encompass numerous data manipulation capacities, M plus is designed primarily for structural equation modeling and therefore is 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 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 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

scholarly journals GENDER DISPARITY AMONGST TAVR OPERATORS IN THE UNITED STATES

2021 ◽  
Vol 77 (18) ◽  
pp. 901
Author(s):  
Timothy Simpson ◽  
Tamara Atkinson ◽  
Howard Song ◽  
Joaquin Cigarroa ◽  
Firas Zahr ◽  
...  
Keyword(s):  
United States ◽  
The United States ◽  
Gender Disparity

Menthol Cigarettes, Tobacco Dependence, and Smoking Persistence: The Need to Examine Enhanced Cognitive Functioning as a Neuropsychological Mechanism

2018 ◽  
Vol 22 (4) ◽  
pp. 466-472 ◽  
Author(s):  
Nancy C Jao ◽  
Marcia M Tan ◽  
Phoenix A Matthews ◽  
Melissa A Simon ◽  
Robert Schnoll ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
Cognitive Functioning ◽  
Basic Science ◽  
Tobacco Dependence ◽  
The United States ◽  
Science Literature ◽  
Cigarette Use ◽  
Menthol Cigarette ◽  
Menthol Cigarettes

Abstract Introduction Despite the overall decline in the prevalence of cigarette use in the United States, menthol cigarette use among smokers is rising, and evidence shows that it may lead to more detrimental effects on public health than regular cigarette use. One of the mechanisms by which nicotine sustains tobacco use and dependence is due to its cognitive enhancing properties, and basic science literature suggests that menthol may also enhance nicotine’s acute effect on cognition. Aims and Methods The purpose of this review is to suggest that the cognitive enhancing effects of menthol may be a potentially important neuropsychological mechanism that has yet to be examined. In this narrative review, we provide an overview of basic science studies examining neurobiological and cognitive effects of menthol and menthol cigarette smoking. We also review studies examining menthol essential oils among humans that indicate menthol alone has acute cognitive enhancing properties. Finally, we present factors influencing the rising prevalence of menthol cigarette use among smokers and the importance of this gap in the literature to improve public health and smoking cessation treatment. Conclusions Despite the compelling evidence for menthol’s acute cognitive enhancing and reinforcing effects, this mechanism for sustaining tobacco dependence and cigarette use has yet to be examined and validated among humans. On the basis of the basic science evidence for menthol’s neurobiological effects on nicotinic receptors and neurotransmitters, perhaps clarifying menthol’s effect on cognitive performance can help to elucidate the complicated literature examining menthol and tobacco dependence. Implications Menthol cigarette use has continued to be a topic of debate among researchers and policy makers, because of its implications for understanding menthol’s contribution to nicotine dependence and smoking persistence, as well as its continued use as a prevalent flavoring in tobacco and nicotine products in the United States and internationally. As international tobacco regulation policies have begun to target menthol cigarettes, research studies need to examine how flavoring additives, specifically menthol, may acutely influence neurobiological and cognitive functioning as a potential mechanism of sustained smoking behavior to develop more effective treatments.

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