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Author: Wang, Lijuan
Resulting in 4 citations.
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Wang, Lijuan |
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Generalized Mixed Models with Mixture Links for Multivariate Zero-Inflated Count Data Ph.D. Dissertation, University of Virginia, 2008. Cohort(s): NLSY97 Publisher: ProQuest Dissertations & Theses (PQDT) Keyword(s): Bayesian; Behavioral Problems; Modeling, Logit; Sample Selection; Substance Use Permission to reprint the abstract has not been received from the publisher. Count data with excessive zeros are often observed in substance use or problem behavior research. When multiple items which could produce zero-inflated count data are used to measure a construct (e.g., substance use), a traditional way to estimate individuals' trait levels of the construct is to form composite scores of the items. However, the main disadvantage of this method is that the composite scores' distribution is negatively skewed and the weight of each item is usually simply set as 1. In this study, I introduce a generalized mixed model with mixture links such as a logit link and a log link to estimate individuals' trait levels and investigate the psychometrics properties of the multiple items for multivariate zero-inflated count data. Simulation studies are conducted to assess the possible influence of factors such as sample size, number of items, proportion of zeros, and estimation method on the estimation of the proposed model and to compare the performance of the proposed model with that of previously employed alternative methods. Application of the model is illustrated by analyzing the substance use data from the NLSY study. The simulation results showed that the proposed model can recover the true trait levels more accurately than the selected alternative methods and the estimation of the person trait levels is more accurate with more items and lower proportions of zeros. Regarding the accuracy of the item parameter estimates, middle proportions of zeros, larger sample size, and more items provide more accurate estimates under the tested conditions. When sample size was larger than 2000, the item parameters were estimated accurately in most conditions. The simulation results also showed that both marginal maximum likelihood estimation method (MMLE) and Bayesian estimation (BE) methods can provide accurate item parameter estimates with large enough sample sizes. Each estimation method had its own advantages and disadvantages in computation ti me and convergence rate. The empirical results included many outcomes that were not obtained using previous methods, especially in investigating the psychometric properties of the multiple substance use items from both propensity and level perspectives. Limitations and future directions of this study are discussed. |
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Bibliography Citation
Wang, Lijuan. Generalized Mixed Models with Mixture Links for Multivariate Zero-Inflated Count Data. Ph.D. Dissertation, University of Virginia, 2008.. |
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Wang, Lijuan |
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IRT–ZIP Modeling for Multivariate Zero-Inflated Count Data Journal of Educational and Behavioral Statistics 35,6 (December 2010): 671-692. Also: http://jeb.sagepub.com/content/35/6/671.abstract Cohort(s): NLSY97 Publisher: Sage Publications Keyword(s): Data, Zero-inflated Count; Modeling, Mixed Effects; Modeling, Multilevel; Modeling, Poisson (IRT–ZIP); Propensity Scores; Sample Selection Permission to reprint the abstract has not been received from the publisher. This study introduces an item response theory–zero-inflated Poisson (IRT–ZIP) model to investigate psychometric properties of multiple items and predict individuals' latent trait scores for multivariate zero-inflated count data. In the model, two link functions are used to capture two processes of the zero-inflated count data. Item parameters are included to investigate item performance from both propensity and level perspectives. The application of the model was illustrated by analyzing the substance use data from the National Longitudinal Study of Youth (97 cohort). A simulation study based on the empirical data analysis scenario showed that the item parameters can be recovered accurately and precisely with adequate sample sizes. Limitations and future directions are discussed. |
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Bibliography Citation
Wang, Lijuan. "IRT–ZIP Modeling for Multivariate Zero-Inflated Count Data." Journal of Educational and Behavioral Statistics 35,6 (December 2010): 671-692.
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Wang, Lijuan Zhang, Zhiyong Tong, Xin |
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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. |
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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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Zhang, Zhiyong Hamagami, Fumiaki Wang, Lijuan Nesselroade, John R. Grimm, Kevin J. |
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Bayesian Analysis of Longitudinal Data Using Growth Curve Models International Journal of Behavioral Development 31,4 (July 2007): 374-383. Also: http://jbd.sagepub.com/content/31/4/374.abstract Cohort(s): Children of the NLSY79 Publisher: Taylor & Francis Keyword(s): Bayesian; Growth Curves; Methods/Methodology; Modeling, Growth Curve/Latent Trajectory Analysis; Peabody Individual Achievement Test (PIAT- Reading); Statistical Analysis Bayesian methods for analyzing longitudinal data in social and behavioral research are recommended for their ability to incorporate prior information in estimating simple and complex models. We first summarize the basics of Bayesian methods before presenting an empirical example in which we fit a latent basis growth curve model to achievement data from the National Longitudinal Survey of Youth. This step-by-step example illustrates how to analyze data using both noninformative and informative priors. The results show that in addition to being an alternative to the maximum likelihood estimation (MLE) method, Bayesian methods also have unique strengths, such as the systematic incorporation of prior information from previous studies. These methods are more plausible ways to analyze small sample data compared with the MLE method. Data Data in this example are two subsets from the National Longitudinal Survey of Youth (NLSY).2 The first subset includes repeated measurements of N = 173 children. At the first measurement in 1986, the children were about 6–7 years of age. The same children were then repeatedly measured at 2-year intervals for three additional measurement occasions (1988, 1990, and 1992). Missing data existed for some of the children. The second subset includes repeated measurements of N = 34 children. At their first measurement in 1992, the children were also about 6–7 years of age. The same children were also measured again at an approximate 2-year interval for another three times in years 1994, 1996, and 1998. Missing data also existed for several of the children. The children from both data sets were tested using the Peabody Individual Achievement Test (PIAT) Reading Recognition subtest that measured word recognition and pronunciation ability. The total score for this subtest ranged in value from 0 to 84. In the present study, this score was rescaled by dividing by 10. [ABSTRACT FROM AUTHOR] Copyright of International Journal of Behavioral Development is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts) |
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Bibliography Citation
Zhang, Zhiyong, Fumiaki Hamagami, Lijuan Wang, John R. Nesselroade and Kevin J. Grimm. "Bayesian Analysis of Longitudinal Data Using Growth Curve Models." International Journal of Behavioral Development 31,4 (July 2007): 374-383.
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