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 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):  
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 Influences on Attitudes Regarding Potential COVID-19 Vaccination in the United States

Vaccines ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 582 ◽  
Author(s):  
Kendall Pogue ◽  
Jamie L. Jensen ◽  
Carter K. Stancil ◽  
Daniel G. Ferguson ◽  
Savannah J. Hughes ◽  
...  
Keyword(s):  
United States ◽  
Structural Equation Modeling ◽  
Side Effects ◽  
Structural Equation ◽  
The United States ◽  
Equation Modeling ◽  
Vaccine Acceptance ◽  
The World ◽  
Short And Long Term

The COVID-19 pandemic continues to ravage the world, with the United States being highly affected. A vaccine provides the best hope for a permanent solution to controlling the pandemic. However, to be effective, a vaccine must be accepted and used by a large majority of the population. The aim of this study was to understand the attitudes towards and obstacles facing vaccination with a potential COVID-19 vaccine. To measure these attitudes a survey was administered to 316 respondents across the United States by a survey corporation. Structural equation modeling was used to analyze the relationships of several factors with attitudes toward potential COVID-19 vaccination. Prior vaccine usage and attitudes predicted attitudes towards COVID-19 vaccination. Assessment of the severity of COVID-19 for the United States was also predictive. Approximately 68% of all respondents were supportive of being vaccinated for COVID-19, but side effects, efficacy and length of testing remained concerns. Longer testing, increased efficacy and development in the United States were significantly associated with increased vaccine acceptance. Messages promoting COVID-19 vaccination should seek to alleviate the concerns of those who are already vaccine-hesitant. Messaging directed at the benefits of vaccination for the United States as a country would address the second predictive factor. Enough time should be taken to allay concerns about both short- and long-term side effects before a vaccine is released.

Evaluating the Construct Validity of Ryff’s Scales of Psychological Well-Being Using Exploratory Structural Equation Modeling

2016 ◽  
Vol 35 (6) ◽  
pp. 633-638 ◽  
Author(s):  
Hsien-Yuan Hsu ◽  
Tze-Li Hsu ◽  
KoFan Lee ◽  
Lori Wolff
Keyword(s):  
Structural Equation Modeling ◽  
Construct Validity ◽  
Structural Equation ◽  
Discriminant Validity ◽  
Well Being ◽  
The United States ◽  
Equation Modeling ◽  
Exploratory Structural Equation Modeling ◽  
Psychological Well Being ◽  
Measurement Models

The purpose of this study was to evaluate the construct validity of Ryff’s Scales of Psychological Well-Being (SPWB) using exploratory structural equation modeling (ESEM). The data were drawn from the national survey of Midlife in the United States conducted during 1994 and 1995. Measurement models assuming different number of factors (1-6 factors) and considering the effect of negatively wording items were specified and compared to determine optimal number of underlying factors. The discriminant validity was assessed following Farrell’s suggestions. The results showed the discriminant validity was questionable due to five indicators with considerable cross-loadings.

scholarly journals Nurses’ Patient-Centeredness and Perceptions of Care among Medicaid Patients in Hospital Obstetrical Units

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Stephen J. Aragon ◽  
Liana J. Richardson ◽  
Wanda Lawrence ◽  
Sabina B. Gesell
Keyword(s):  
Structural Equation Modeling ◽  
Standard Deviation ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
The United States ◽  
Equation Modeling ◽  
Patient Centeredness ◽  
Patients Satisfaction ◽  
Random Samples ◽  
Multigroup Structural Equation Modeling

Objective. This study examined to what degree patient-centeredness—measured as an underlying ability of obstetrical nurses—influenced Medicaid patients’ satisfaction with care in hospital obstetrical units.Design. Multigroup structural equation modeling design, using three cross-sectional random samples (n=300each) from the 2003 Press Ganey National Inpatient Database.Setting. Self-administered mail surveys.Participants. 900 Medicaid recipients recently discharged from inpatient hospital obstetrical units across the United States.Methods. Multigroup structural equation modeling was used to test the goodness of fit between a hypothesized model based on the Primary Provider Theory and patients’ ratings of nurses.Results. The model fitted the data well, was stable across three random samples, and was sustained when compared to a competing model. The patient-centeredness of nurses significantly influenced overall patient satisfaction and explained 66% of its variability. When nurses’ patient-centeredness increased by one standard deviation, patients’ satisfaction increased by 0.80 standard deviation.Conclusion. This study offers a novel approach to the measurement of the patient-centeredness of nurses and a paradigm for increasing it and its influence on Medicaid patients’ satisfaction in hospital obstetrical units.

scholarly journals Does Stigma Toward Anal Sexuality Impede HIV Prevention Among Men Who Have Sex With Men in the United States? A Structural Equation Modeling Assessment

2020 ◽  
Vol 17 (3) ◽  
pp. 477-490
Author(s):  
Bryan A. Kutner ◽  
Jane M. Simoni ◽  
Kevin M. King ◽  
Steven M. Goodreau ◽  
Andrea Norcini Pala ◽  
...  
Keyword(s):  
United States ◽  
Structural Equation Modeling ◽  
Hiv Prevention ◽  
Structural Equation ◽  
The United States ◽  
Equation Modeling ◽  
Sex With Men

Test of Nyborg's General Trait Covariance (GTC) model for hormonally guided development by means of structural equation modeling

2003 ◽  
Vol 17 (3) ◽  
pp. 221-235 ◽  
Author(s):  
Martin Reuter ◽  
Petra Netter ◽  
Jürgen Hennig ◽  
Changiz Mohiyeddini ◽  
Helmuth Nyborg
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Fluid Intelligence ◽  
Group Analysis ◽  
Armed Forces ◽  
The United States ◽  
Negative Influence ◽  
Gonadal Hormones ◽  
Equation Modeling ◽  
Trait Covariance

Nyborg's General Trait Covariance (GTC) model for hormonally guided development investigates the influence of gonadal hormones and fluid intelligence on body build, achievement, and socioeconomic variables. According to the model, testosterone should be negatively related to height, fat/muscle ratio, intelligence, income, and education. It is conceived that this influence should be determined to a great extent by mutual relationships between these variables. The model was tested by means of structural equation modeling (SEM) in a sample of 4375 males who had served in the United States Armed Forces. The results largely confirm Nyborg's androtype model but in addition reflect the relationships between the variables included in a quantitative causal manner. It could be shown that testosterone has a negative influence on crystallized intelligence and that this effect is mainly mediated by the negative influence of testosterone on education. An additional multiple group analysis testing for structural invariance across age groups revealed that the mediating role of education is more pronounced in old veterans. Copyright © 2002 John Wiley & Sons, Ltd.

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