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Author: Tong, Xin

Resulting in 5 citations.

1.	Tong, Xin Zhang, Zhiyong
1.	Diagnostics of Robust Growth Curve Modeling Using Student's t Distribution Multivariate Behavioral Research 47,4 (2012): 493-518. Also: http://www.tandfonline.com/doi/full/10.1080/00273171.2012.692614 Cohort(s): NLSY97 Publisher: Taylor & Francis Keyword(s): Modeling, Growth Curve/Latent Trajectory Analysis; Peabody Individual Achievement Test (PIAT- Math) Growth curve models with different types of distributions of random effects and of intraindividual measurement errors for robust analysis are compared. After demonstrating the influence of distribution specification on parameter estimation, 3 methods for diagnosing the distributions for both random effects and intraindividual measurement errors are proposed and evaluated. The methods include (a) distribution checking based on individual growth curve analysis; (b) distribution comparison based on Deviance Information Criterion, and (c) post hoc checking of degrees of freedom estimates for t distributions. The performance of the methods is compared through simulation studies. When the sample size is reasonably large, the method of post hoc checking of degrees of freedom estimates works best. A web interface is developed to ease the use of the 3 methods. Application of the 3 methods is illustrated through growth curve analysis of mathematical ability development using data on the Peabody Individual Achievement Test Mathematics assessment from the National Longitudinal Survey of Youth 1997 Cohort (Bureau of Labor Statistics, U.S. Department of Labor, 2005).
	Bibliography Citation Tong, Xin and Zhiyong Zhang. "Diagnostics of Robust Growth Curve Modeling Using Student's t Distribution." Multivariate Behavioral Research 47,4 (2012): 493-518.

2.	Tong, Xin Zhang, Zhiyong
2.	Outlying Observation Diagnostics in Growth Curve Modeling Multivariate Behavioral Research 52,6 (2017): 768-788. Also: http://www.tandfonline.com/doi/full/10.1080/00273171.2017.1374824 Cohort(s): NLSY97 Publisher: Taylor & Francis Keyword(s): Modeling, Growth Curve/Latent Trajectory Analysis; Monte Carlo; Peabody Individual Achievement Test (PIAT- Math); Statistical Analysis Growth curve models are widely used for investigating growth and change phenomena. Many studies in social and behavioral sciences have demonstrated that data without any outlying observation are rather an exception, especially for data collected longitudinally. Ignoring the existence of outlying observations may lead to inaccurate or even incorrect statistical inferences. Therefore, it is crucial to identify outlying observations in growth curve modeling. This study comparatively evaluates six methods in outlying observation diagnostics through a Monte Carlo simulation study on a linear growth curve model, by varying factors of sample size, number of measurement occasions, as well as proportion, geometry, and type of outlying observations. It is suggested that the greatest chance of success in detecting outlying observations comes from use of multiple methods, comparing their results and making a decision based on research purposes. A real data analysis example is also provided to illustrate the application of the six outlying observation diagnostic methods.
	Bibliography Citation Tong, Xin and Zhiyong Zhang. "Outlying Observation Diagnostics in Growth Curve Modeling." Multivariate Behavioral Research 52,6 (2017): 768-788.

3.	Tong, Xin Zhang, Zhiyong
3.	Robust Bayesian Approaches in Growth Curve Modeling: Using Student's t Distributions versus a Semiparametric Method Structural Equation Modeling: A Multidisciplinary Journal published online (11 November 2019): DOI: 10.1080/10705511.2019.1683014. Also: https://www.tandfonline.com/doi/full/10.1080/10705511.2019.1683014 Cohort(s): NLSY97 Publisher: Lawrence Erlbaum Associates ==> Taylor & Francis Keyword(s): Bayesian; Modeling, Growth Curve/Latent Trajectory Analysis; Monte Carlo; Peabody Individual Achievement Test (PIAT- Math) Permission to reprint the abstract has been denied by the publisher.
	Bibliography Citation Tong, Xin and Zhiyong Zhang. "Robust Bayesian Approaches in Growth Curve Modeling: Using Student's t Distributions versus a Semiparametric Method." Structural Equation Modeling: A Multidisciplinary Journal published online (11 November 2019): DOI: 10.1080/10705511.2019.1683014.

4.	Tong, Xin Zhang, Zhiyong Yuan, Ke-Hai
4.	Evaluation of Test Statistics for Robust Structural Equation Modeling With Nonnormal Missing Data Structural Equation Modeling: A Multidisciplinary Journal 21,4 (2014): 553-565. Also: https://www.tandfonline.com/doi/full/10.1080/10705511.2014.919820 Cohort(s): NLSY97 Publisher: Lawrence Erlbaum Associates ==> Taylor & Francis Keyword(s): Missing Data/Imputation; Modeling, Growth Curve/Latent Trajectory Analysis; Peabody Individual Achievement Test (PIAT- Math); Statistical Analysis Permission to reprint the abstract has been denied by the publisher.
	Bibliography Citation Tong, Xin, Zhiyong Zhang and Ke-Hai Yuan. "Evaluation of Test Statistics for Robust Structural Equation Modeling With Nonnormal Missing Data." Structural Equation Modeling: A Multidisciplinary Journal 21,4 (2014): 553-565.

5.	Wang, Lijuan Zhang, Zhiyong Tong, Xin
5.	Mediation Analysis with Missing Data through Multiple Imputation and Bootstrap Working Paper, Department of Psychology, University of Notre Dame, January 2014 Cohort(s): Children of the NLSY79 Publisher: Department of Psychology, University of Notre Dame Keyword(s): Behavior Problems Index (BPI); Home Observation for Measurement of Environment (HOME); Missing Data/Imputation; Mothers, Education; Peabody Individual Achievement Test (PIAT- Math); Peabody Individual Achievement Test (PIAT- Reading); Statistical Analysis Permission to reprint the abstract has not been received from the publisher. A method using multiple imputation and bootstrap for dealing with miss- ing data in mediation analysis is introduced and implemented in SAS. Through simulation studies, it is shown that the method performs well for both MCAR and MAR data without and with auxiliary variables. It is also shown that the method works equally well for MNAR data if auxiliary vari- ables related to missingness are included. The application of the method is demonstrated through the analysis of a subset of data from the National Longitudinal Survey of Youth.
	Bibliography Citation Wang, Lijuan, Zhiyong Zhang and Xin Tong. "Mediation Analysis with Missing Data through Multiple Imputation and Bootstrap." Working Paper, Department of Psychology, University of Notre Dame, January 2014.

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