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Roman ZJ, Brandt H. A Latent Auto-Regressive Approach for Bayesian Structural Equation Modeling of Spatially or Socially Dependent Data. MULTIVARIATE BEHAVIORAL RESEARCH 2023; 58:90-114. [PMID: 34379011 DOI: 10.1080/00273171.2021.1957663] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Spatial analytic approaches are classic models in econometric literature, but relatively new in social sciences. Spatial analysis models are synonymous with social network auto-regressive models which are also gaining popularity in the behavioral sciences. These models have two major benefits. First, dependent data, either socially or spatially, must be accounted for to acquire unbiased results. Second, analysis of the dependence provides rich additional information such as spillover effects. Structural Equation Models (SEM) are widely used in psychological research for measuring and testing multi-faceted constructs. So far, SEM that allow for spatial or social dependency are limited with regard to their flexibility, for example, when estimating nonlinear effects. Here, we provide a cohesive framework which can simultaneously estimate latent interaction/polynomial effects and account for spatial effects with both exogenous and endogenous latent variables, the Bayesian Spatial Auto-Regressive Structural Equation Model (BARDSEM). First, we briefly outline classic auto-regressive models. Next, we present the BARDSEM and introduce simulation results to exemplify its performance. Finally, we provide an empirical example using the spatially dependent extended US southern homicide data to show the rich interpretations that are possible using the BARDSEM. Finally, we discuss implications, limitations, and future research.
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Da Silva Coelho C, Joly-Burra E, Ihle A, Ballhausen N, Haas M, Hering A, Künzi M, Laera G, Mikneviciute G, Tinello D, Kliegel M, Zuber S. Higher levels of neuroticism in older adults predict lower executive functioning across time: the mediating role of perceived stress. Eur J Ageing 2022; 19:633-649. [PMID: 36052201 PMCID: PMC9424398 DOI: 10.1007/s10433-021-00665-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/19/2021] [Indexed: 11/30/2022] Open
Abstract
Neuroticism has been associated with individual differences across multiple cognitive functions. Yet, the literature on its specific association with executive functions (EF) in older adults is scarce, especially using longitudinal designs. To disentangle the specific influence of neuroticism on EF and on coarse cognitive functioning in old adulthood, respectively, we examined the relationship between neuroticism, the Trail Making Test (TMT) and the Mini-Mental State Examination (MMSE) in a 6-year longitudinal study using Bayesian analyses. Data of 768 older adults (M age = 73.51 years at Wave 1) were included in a cross-lagged analysis. Results showed no cross-sectional link between neuroticism and TMT performance at Wave 1 and no longitudinal link between neuroticism at Wave 1 and MMSE at Wave 2. However, neuroticism at Wave 1 predicted TMT performance at Wave 2, indicating that the more neurotic participants were, the lower they performed on the TMT six years later. Additional analyses showed that this relation was fully mediated by participants' perceived stress. Our results suggest that the more neurotic older adults are the more stress they may perceive six years later, which in turn negatively relates to their EF. In sum, this study demonstrates that neuroticism may lead to lower EF in older age across six years. It further suggests older adults' perceived stress as mediator, thereby providing novel insights into the mechanisms underlying this relation. Possible intervention approaches to counter these effects are discussed.
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Affiliation(s)
- Chloé Da Silva Coelho
- Centre for the Interdisciplinary Study of Gerontology and Vulnerabilities (CIGEV), University of Geneva, Boulevard du Pont d’Arve 28, 1205 Geneva, Switzerland
- Department of Psychology, University of Geneva, Geneva, Switzerland
| | - Emilie Joly-Burra
- Centre for the Interdisciplinary Study of Gerontology and Vulnerabilities (CIGEV), University of Geneva, Boulevard du Pont d’Arve 28, 1205 Geneva, Switzerland
- Swiss National Centre of Competence in Research LIVES–Overcoming vulnerability: life course perspectives, Lausanne and Geneva, Switzerland
- Department of Psychology, University of Geneva, Geneva, Switzerland
| | - Andreas Ihle
- Centre for the Interdisciplinary Study of Gerontology and Vulnerabilities (CIGEV), University of Geneva, Boulevard du Pont d’Arve 28, 1205 Geneva, Switzerland
- Swiss National Centre of Competence in Research LIVES–Overcoming vulnerability: life course perspectives, Lausanne and Geneva, Switzerland
- Department of Psychology, University of Geneva, Geneva, Switzerland
| | - Nicola Ballhausen
- Centre for the Interdisciplinary Study of Gerontology and Vulnerabilities (CIGEV), University of Geneva, Boulevard du Pont d’Arve 28, 1205 Geneva, Switzerland
- Department of Developmental Psychology, Tilburg School of Social and Behavioral Sciences, Tilburg University, Tilburg, The Netherlands
| | - Maximilian Haas
- Centre for the Interdisciplinary Study of Gerontology and Vulnerabilities (CIGEV), University of Geneva, Boulevard du Pont d’Arve 28, 1205 Geneva, Switzerland
- Department of Psychology, University of Geneva, Geneva, Switzerland
| | - Alexandra Hering
- Centre for the Interdisciplinary Study of Gerontology and Vulnerabilities (CIGEV), University of Geneva, Boulevard du Pont d’Arve 28, 1205 Geneva, Switzerland
- Department of Psychology, University of Geneva, Geneva, Switzerland
- Department of Developmental Psychology, Tilburg School of Social and Behavioral Sciences, Tilburg University, Tilburg, The Netherlands
| | - Morgane Künzi
- Centre for the Interdisciplinary Study of Gerontology and Vulnerabilities (CIGEV), University of Geneva, Boulevard du Pont d’Arve 28, 1205 Geneva, Switzerland
- Swiss National Centre of Competence in Research LIVES–Overcoming vulnerability: life course perspectives, Lausanne and Geneva, Switzerland
- Department of Psychology, University of Geneva, Geneva, Switzerland
| | - Gianvito Laera
- Centre for the Interdisciplinary Study of Gerontology and Vulnerabilities (CIGEV), University of Geneva, Boulevard du Pont d’Arve 28, 1205 Geneva, Switzerland
- Swiss National Centre of Competence in Research LIVES–Overcoming vulnerability: life course perspectives, Lausanne and Geneva, Switzerland
- Department of Psychology, University of Geneva, Geneva, Switzerland
| | - Greta Mikneviciute
- Centre for the Interdisciplinary Study of Gerontology and Vulnerabilities (CIGEV), University of Geneva, Boulevard du Pont d’Arve 28, 1205 Geneva, Switzerland
- Swiss National Centre of Competence in Research LIVES–Overcoming vulnerability: life course perspectives, Lausanne and Geneva, Switzerland
| | - Doriana Tinello
- Centre for the Interdisciplinary Study of Gerontology and Vulnerabilities (CIGEV), University of Geneva, Boulevard du Pont d’Arve 28, 1205 Geneva, Switzerland
- Swiss National Centre of Competence in Research LIVES–Overcoming vulnerability: life course perspectives, Lausanne and Geneva, Switzerland
- Department of Psychology, University of Geneva, Geneva, Switzerland
| | - Matthias Kliegel
- Centre for the Interdisciplinary Study of Gerontology and Vulnerabilities (CIGEV), University of Geneva, Boulevard du Pont d’Arve 28, 1205 Geneva, Switzerland
- Swiss National Centre of Competence in Research LIVES–Overcoming vulnerability: life course perspectives, Lausanne and Geneva, Switzerland
- Department of Psychology, University of Geneva, Geneva, Switzerland
| | - Sascha Zuber
- Centre for the Interdisciplinary Study of Gerontology and Vulnerabilities (CIGEV), University of Geneva, Boulevard du Pont d’Arve 28, 1205 Geneva, Switzerland
- Swiss National Centre of Competence in Research LIVES–Overcoming vulnerability: life course perspectives, Lausanne and Geneva, Switzerland
- Institute on Aging and Lifelong Health (IALH), University of Victoria, Victoria, BC Canada
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Roman ZJ, Brandt H, Miller JM. Automated Bot Detection Using Bayesian Latent Class Models in Online Surveys. Front Psychol 2022; 13:789223. [PMID: 35572225 PMCID: PMC9093679 DOI: 10.3389/fpsyg.2022.789223] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 03/29/2022] [Indexed: 11/16/2022] Open
Abstract
Behavioral scientists have become increasingly reliant on online survey platforms such as Amazon's Mechanical Turk (Mturk). These platforms have many advantages, for example it provides ease of access to difficult to sample populations, a large pool of participants, and an easy to use implementation. A major drawback is the existence of bots that are used to complete online surveys for financial gain. These bots contaminate data and need to be identified in order to draw valid conclusions from data obtained with these platforms. In this article, we will provide a Bayesian latent class joint modeling approach that can be routinely applied to identify bots and simultaneously estimate a model of interest. This method can be used to separate the bots' response patterns from real human responses that were provided in line with the item content. The model has the advantage that it is very flexible and is based on plausible assumptions that are met in most empirical settings. We will provide a simulation study that investigates the performance of the model under several relevant scenarios including sample size, proportion of bots, and model complexity. We will show that ignoring bots will lead to severe parameter bias whereas the Bayesian latent class model results in unbiased estimates and thus controls this source of bias. We will illustrate the model and its capabilities with data from an empirical political ideation survey with known bots. We will discuss the implications of the findings with regard to future data collection via online platforms.
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Affiliation(s)
| | - Holger Brandt
- Department of Psychology, Faculty of Mathematics and Natural Sciences, University of Tübingen, Tübingen, Germany
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WEN Z, OUYANG J, FANG J. Standardized estimates for latent interaction effects: Method comparison and selection strategy. ACTA PSYCHOLOGICA SINICA 2022. [DOI: 10.3724/sp.j.1041.2022.00091] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Kiefer C, Mayer A. Accounting for Latent Covariates in Average Effects from Count Regressions. MULTIVARIATE BEHAVIORAL RESEARCH 2021; 56:579-594. [PMID: 32329366 DOI: 10.1080/00273171.2020.1751027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The effectiveness of a treatment on a count outcome can be assessed using a negative binomial regression, where treatment effects are defined as the difference between the expected outcome under treatment and under control. These treatment effects can to date only be estimated if all covariates are manifest (observed) variables. However, some covariates are latent variables that are measured by multiple fallible indicators. In such cases, it is important to control for measurement error of covariates in order to avoid attenuation bias and to get unbiased treatment effect estimates. In this paper, we propose a new approach to compute average and conditional treatment effects in regression models with a logarithmic link function involving multiple latent and manifest covariates. We extend the previously presented moment-based approach in several aspects: Building on a multigroup SEM framework for count variables instead of the generalized linear model, we allow for latent covariates and multiple covariates. We provide an illustrative example to explain the application and estimation in structural equation modeling software.
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Affiliation(s)
| | - Axel Mayer
- Institute of Psychology, RWTH Aachen University
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Hsiao YY, Kwok OM, Lai MHC. Modeling Measurement Errors of the Exogenous Composites From Congeneric Measures in Interaction Models. STRUCTURAL EQUATION MODELING : A MULTIDISCIPLINARY JOURNAL 2020; 28:250-260. [PMID: 34239281 PMCID: PMC8259412 DOI: 10.1080/10705511.2020.1782206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
We investigated the performance of two single indicator methods: latent moderated structural equation (LMS) and reliability-adjusted product indicator (RAPI) methods, on testing interaction effects with congeneric measures, which vary in factor loadings and error variances under a common factor. Additionally, in the simulation study, we compared the performance of four reliability estimates (Cronbach's alpha, omega composite, Coefficient H, and greatest lower bound [GLB]) to adjust for the exogenous composites' measurement errors. Results from the study showed that: while estimating interaction effects with exogenous composites from congeneric measures, the four reliability estimates performed comparably well. Recommendations on the choice of reliability estimates between the LMS and the RAPI methods under different sample sizes and population reliability conditions are further discussed.
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Affiliation(s)
- Yu-Yu Hsiao
- Department of Individual, Family, and Community Education, University of New Mexico
| | - Oi-Man Kwok
- Department of Educational Psychology, Texas A&M University
| | - Mark H. C. Lai
- Department of Psychology, University of Southern California
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Lodder P, Denollet J, Emons WHM, Nefs G, Pouwer F, Speight J, Wicherts JM. Modeling Interactions Between Latent Variables in Research on Type D Personality: A Monte Carlo Simulation and Clinical Study of Depression and Anxiety. MULTIVARIATE BEHAVIORAL RESEARCH 2019; 54:637-665. [PMID: 30977400 DOI: 10.1080/00273171.2018.1562863] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Several approaches exist to model interactions between latent variables. However, it is unclear how these perform when item scores are skewed and ordinal. Research on Type D personality serves as a good case study for that matter. In Study 1, we fitted a multivariate interaction model to predict depression and anxiety with Type D personality, operationalized as an interaction between its two subcomponents negative affectivity (NA) and social inhibition (SI). We constructed this interaction according to four approaches: (1) sum score product; (2) single product indicator; (3) matched product indicators; and (4) latent moderated structural equations (LMS). In Study 2, we compared these interaction models in a simulation study by assessing for each method the bias and precision of the estimated interaction effect under varying conditions. In Study 1, all methods showed a significant Type D effect on both depression and anxiety, although this effect diminished after including the NA and SI quadratic effects. Study 2 showed that the LMS approach performed best with respect to minimizing bias and maximizing power, even when item scores were ordinal and skewed. However, when latent traits were skewed LMS resulted in more false-positive conclusions, while the Matched PI approach adequately controlled the false-positive rate.
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Affiliation(s)
- Paul Lodder
- CoRPS-Center of Research on Psychology in Somatic diseases, Department of Medical and Clinical Psychology, Tilburg University, The Netherlands
- Department of Methodology and Statistics, Tilburg University, The Netherlands
| | - Johan Denollet
- CoRPS-Center of Research on Psychology in Somatic diseases, Department of Medical and Clinical Psychology, Tilburg University, The Netherlands
| | - Wilco H M Emons
- Department of Methodology and Statistics, Tilburg University, The Netherlands
| | - Giesje Nefs
- CoRPS-Center of Research on Psychology in Somatic diseases, Department of Medical and Clinical Psychology, Tilburg University, The Netherlands
| | - Frans Pouwer
- Department of Psychology, University of Southern Denmark, Denmark
- STENO Diabetes Center Odense, Odense, Denmark
| | - Jane Speight
- School of Psychology, Deakin University, Geelong, Australia
- The Australian Centre for Behavioural Research in Diabetes, Diabetes Victoria, Melbourne, Australia
- AHP Research, Hornchurch, UK
| | - Jelte M Wicherts
- Department of Methodology and Statistics, Tilburg University, The Netherlands
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Brandt H, Umbach N, Kelava A. The Standardization of Linear and Nonlinear Effects in Direct and Indirect Applications of Structural Equation Mixture Models for Normal and Nonnormal Data. Front Psychol 2015; 6:1813. [PMID: 26648886 PMCID: PMC4663265 DOI: 10.3389/fpsyg.2015.01813] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2015] [Accepted: 11/10/2015] [Indexed: 11/29/2022] Open
Abstract
The application of mixture models to flexibly estimate linear and nonlinear effects in the SEM framework has received increasing attention (e.g., Jedidi et al., 1997b; Bauer, 2005; Muthén and Asparouhov, 2009; Wall et al., 2012; Kelava and Brandt, 2014; Muthén and Asparouhov, 2014). The advantage of mixture models is that unobserved subgroups with class-specific relationships can be extracted (direct application), or that the mixtures can be used as a statistical tool to approximate nonnormal (latent) distributions (indirect application). Here, we provide a general standardization procedure for linear and nonlinear interaction and quadratic effects in mixture models. The procedure can also be applied to multiple group models or to single class models with nonlinear effects like LMS (Klein and Moosbrugger, 2000). We show that it is necessary to take nonnormality of the data into account for a correct standardization. We present an empirical example from education science applying the proposed procedure.
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Affiliation(s)
- Holger Brandt
- Hector Research Institute of Education Sciences and Psychology, Eberhard Karls Universität Tübingen Tübingen, Germany
| | - Nora Umbach
- Hector Research Institute of Education Sciences and Psychology, Eberhard Karls Universität Tübingen Tübingen, Germany
| | - Augustin Kelava
- Hector Research Institute of Education Sciences and Psychology, Eberhard Karls Universität Tübingen Tübingen, Germany
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Rdz-Navarro K, Alvarado JM. Reexamining Nonlinear Structural Equation Modeling Procedures: The Effect of Parallel and Congeneric Measures. MULTIVARIATE BEHAVIORAL RESEARCH 2015; 50:645-661. [PMID: 26717124 DOI: 10.1080/00273171.2015.1071236] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
The current study examines the performance of the extended unconstrained approach (EXUC) and the latent moderated structural equation modeling procedure (LMS) in situations where quadratic and interaction terms are tested simultaneously and investigates their limitations with regard to the employment of parallel and congeneric measures, relatively low indicator reliabilities, and relatively large numbers of indicators. By means of a Monte Carlo study, we found LMS to be the best option for testing multiple nonlinear effects given sufficient sample size (n ≥ 500) and normally distributed exogenous variables. Its advantages became more prominent when indicator reliabilities were heterogeneous and small. The EXUC was a viable option for estimating the model when indicators were parallel and exhibited large indicator reliabilities. An empirical example of the results is provided, and the relevance of measurement model characteristics to assess nonlinear relationships is discussed.
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Affiliation(s)
- Karina Rdz-Navarro
- a Department of Sociology , Faculty of Social Sciences, University of Chile
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Brandt H, Klein AG. A Heterogeneous Growth Curve Model for Nonnormal Data. MULTIVARIATE BEHAVIORAL RESEARCH 2015; 50:416-435. [PMID: 26610155 DOI: 10.1080/00273171.2015.1022639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
The heterogeneous growth curve model (HGM; Klein & Muthén, 2006 ) is a method for modeling heterogeneity of growth rates with a heteroscedastic residual structure for the slope factor. It has been developed as an extension of a conventional growth curve model and a complementary tool to growth curve mixture models. In this article, a robust version of the heterogeneous growth curve model (HGM-R) is presented that extends the original HGM with a mixture model to allow for an unbiased parameter estimation under the condition of nonnormal data. In two simulation studies, the performance of the method is examined under the condition of nonnormality and a misspecified heteroscedastic residual structure. The results of the simulation studies suggest an unbiased estimation of the heterogeneity by the HGM-R when sample size was large enough and a good approximation of the heteroscedastic residual structure even when the functional form of the heteroscedasticity was misspecified. The practical application of the approach is demonstrated for a data set from HIV-infected patients.
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Affiliation(s)
- Holger Brandt
- a Hector Research Institute of Education Sciences and Psychology, Eberhard Karls University , Tübingen
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Kelava A, Brandt H. A general non-linear multilevel structural equation mixture model. Front Psychol 2014; 5:748. [PMID: 25101022 PMCID: PMC4102910 DOI: 10.3389/fpsyg.2014.00748] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2013] [Accepted: 06/26/2014] [Indexed: 11/13/2022] Open
Abstract
In the past 2 decades latent variable modeling has become a standard tool in the social sciences. In the same time period, traditional linear structural equation models have been extended to include non-linear interaction and quadratic effects (e.g., Klein and Moosbrugger, 2000), and multilevel modeling (Rabe-Hesketh et al., 2004). We present a general non-linear multilevel structural equation mixture model (GNM-SEMM) that combines recent semiparametric non-linear structural equation models (Kelava and Nagengast, 2012; Kelava et al., 2014) with multilevel structural equation mixture models (Muthén and Asparouhov, 2009) for clustered and non-normally distributed data. The proposed approach allows for semiparametric relationships at the within and at the between levels. We present examples from the educational science to illustrate different submodels from the general framework.
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Affiliation(s)
- Augustin Kelava
- Department of Education, Center for Educational Science and Psychology, Eberhard Karls Universität Tübingen Tübingen, Germany
| | - Holger Brandt
- Department of Education, Center for Educational Science and Psychology, Eberhard Karls Universität Tübingen Tübingen, Germany
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Miller JW, Saldanha JP, Hunt CS, Mello JE. Combining Formal Controls to Improve Firm Performance. JOURNAL OF BUSINESS LOGISTICS 2013. [DOI: 10.1111/jbl.12028] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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