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Source: Multivariate Behavioral Research
Resulting in 8 citations.
1. Choi, Ji Yeh
Kyung, Minjung
Hwang, Heungsun
Park, Ju-Hyun
Bayesian Extended Redundancy Analysis: A Bayesian Approach to Component-based Regression with Dimension Reduction
Multivariate Behavioral Research published online (25 April 2019): DOI: 10.1080/00273171.2019.1598837.
Also: https://www.tandfonline.com/doi/full/10.1080/00273171.2019.1598837
Cohort(s): Children of the NLSY79
Publisher: Taylor & Francis
Keyword(s): Bayesian; Markov chain / Markov model; Monte Carlo; Peabody Individual Achievement Test (PIAT- Math); Peabody Individual Achievement Test (PIAT- Reading); Peabody Picture Vocabulary Test (PPVT)

Extended redundancy analysis (ERA) combines linear regression with dimension reduction to explore the directional relationships between multiple sets of predictors and outcome variables in a parsimonious manner. It aims to extract a component from each set of predictors in such a way that it accounts for the maximum variance of outcome variables. In this article, we extend ERA into the Bayesian framework, called Bayesian ERA (BERA). The advantages of BERA are threefold. First, BERA enables to make statistical inferences based on samples drawn from the joint posterior distribution of parameters obtained from a Markov chain Monte Carlo algorithm. As such, it does not necessitate any resampling method, which is on the other hand required for (frequentist’s) ordinary ERA to test the statistical significance of parameter estimates. Second, it formally incorporates relevant information obtained from previous research into analyses by specifying informative power prior distributions. Third, BERA handles missing data by implementing multiple imputation using a Markov Chain Monte Carlo algorithm, avoiding the potential bias of parameter estimates due to missing data. We assess the performance of BERA through simulation studies and apply BERA to real data regarding academic achievement.
Bibliography Citation
Choi, Ji Yeh, Minjung Kyung, Heungsun Hwang and Ju-Hyun Park. "Bayesian Extended Redundancy Analysis: A Bayesian Approach to Component-based Regression with Dimension Reduction." Multivariate Behavioral Research published online (25 April 2019): DOI: 10.1080/00273171.2019.1598837.
2. Lu, Zhenqiu Laura
Zhang, Zhiyong
Lubke, Gitta H.
Bayesian Inference for Growth Mixture Models with Latent Class Dependent Missing Data
Multivariate Behavioral Research 46,4 (2011): 567-597.
Also: http://www.tandfonline.com/doi/abs/10.1080/00273171.2011.589261
Cohort(s): NLSY97
Publisher: Taylor & Francis
Keyword(s): Bayesian; Missing Data/Imputation; Modeling, Growth Curve/Latent Trajectory Analysis

Growth mixture models (GMMs) with nonignorable missing data have drawn increasing attention in research communities but have not been fully studied. The goal of this article is to propose and to evaluate a Bayesian method to estimate the GMMs with latent class dependent missing data. An extended GMM is first presented in which class probabilities depend on some observed explanatory variables and data missingness depends on both the explanatory variables and a latent class variable. A full Bayesian method is then proposed to estimate the model. Through the data augmentation method, conditional posterior distributions for all model parameters and missing data are obtained. A Gibbs sampling procedure is then used to generate Markov chains of model parameters for statistical inference. The application of the model and the method is first demonstrated through the analysis of mathematical ability growth data from the National Longitudinal Survey of Youth 1997 (Bureau of Labor Statistics, U.S. Department of Labor, 1997). A simulation study considering 3 main factors (the sample size, the class probability, and the missing data mechanism) is then conducted and the results show that the proposed Bayesian estimation approach performs very well under the studied conditions. Finally, some implications of this study, including the misspecified missingness mechanism, the sample size, the sensitivity of the model, the number of latent classes, the model comparison, and the future directions of the approach, are discussed.
Bibliography Citation
Lu, Zhenqiu Laura, Zhiyong Zhang and Gitta H. Lubke. "Bayesian Inference for Growth Mixture Models with Latent Class Dependent Missing Data." Multivariate Behavioral Research 46,4 (2011): 567-597.
3. Malone, Patrick S.
Lamis, Dorian A.
Masyn, Katherine E.
Northrup, Thomas F.
A Dual-Process Discrete-Time Survival Analysis Model: Application to the Gateway Drug Hypothesis
Multivariate Behavioral Research 45,5 (2010): 790-805.
Also: http://www.informaworld.com/smpp/content~db=all~content=a929458147~frm=abslink
Cohort(s): NLSY97
Publisher: Taylor & Francis
Keyword(s): Drug Use; Modeling; Statistical Analysis; Time Theory

The gateway drug model is a popular conceptualization of a progression most substance users are hypothesized to follow as they try different legal and illegal drugs. Most forms of the gateway hypothesis are that 'softer' drugs lead to 'harder,' illicit drugs. However, the gateway hypothesis has been notably difficult to directly test-that is, to test as competing hypotheses in a single model that licit drug use might lead to illicit drug use or the reverse. This article presents a novel statistical technique, dual-process discrete-time survival analysis, which enables this comparison. This method uses mixture-modeling software to estimate 2 concurrent time-to-event processes and their effects on each other. Using this method, support for the gateway hypothesis in the National Longitudinal Survey of Youth, 1997, was weak. However, this article was not designed as a strong test of causal direction but more as a technical demonstration and suffered from certain technological limitations. Both these limitations and future directions are discussed. [ABSTRACT FROM AUTHOR]
Bibliography Citation
Malone, Patrick S., Dorian A. Lamis, Katherine E. Masyn and Thomas F. Northrup. "A Dual-Process Discrete-Time Survival Analysis Model: Application to the Gateway Drug Hypothesis." Multivariate Behavioral Research 45,5 (2010): 790-805.
4. O'Keefe, Patrick
Rodgers, Joseph Lee
Double Decomposition of Level-1 Variables in Multilevel Models: An Analysis of the Flynn Effect in the NLSY Data
Multivariate Behavioral Research 52,5 (2017): 630-647.
Also: http://www.tandfonline.com/doi/full/10.1080/00273171.2017.1354758
Cohort(s): Children of the NLSY79
Publisher: Taylor & Francis
Keyword(s): Flynn Effect; I.Q.; Modeling, Multilevel

This paper introduces an extension of cluster mean centering (also called group mean centering) for multilevel models, which we call "double decomposition (DD)." This centering method separates between-level variance, as in cluster mean centering, but also decomposes within-level variance of the same variable. This process retains the benefits of cluster mean centering but allows for context variables derived from lower level variables, other than the cluster mean, to be incorporated into the model. A brief simulation study is presented, demonstrating the potential advantage (or even necessity) for DD in certain circumstances. Several applications to multilevel analysis are discussed. Finally, an empirical demonstration examining the Flynn effect, our motivating example, is presented. The use of DD in the analysis provides a novel method to narrow the field of plausible causal hypotheses regarding the Flynn effect, in line with suggestions by a number of researchers.
Bibliography Citation
O'Keefe, Patrick and Joseph Lee Rodgers. "Double Decomposition of Level-1 Variables in Multilevel Models: An Analysis of the Flynn Effect in the NLSY Data." Multivariate Behavioral Research 52,5 (2017): 630-647.
5. O'Keefe, Patrick
Rodgers, Joseph Lee
The Corrosive Influence of the Flynn Effect on Age Normed Tests
Multivariate Behavioral Research published online (7 February 2019): DOI: 10.1080/00273171.2018.1562322.
Also: https://www.tandfonline.com/doi/full/10.1080/00273171.2018.1562322
Cohort(s): Children of the NLSY79
Publisher: Taylor & Francis
Keyword(s): Children, Academic Development; Cognitive Ability; Flynn Effect; I.Q.; Test Scores/Test theory/IRT

This project provides empirical evidence for this built-in FE [Flynn Effect]. Using the National Longitudinal Survey of Youth-Children dataset (the NLSYC) and a variety of multilevel models, we: (1) Show a within person effect with individuals scoring higher over time and (2) Do not find evidence for practice. Previous work (O’Keefe & Rodgers, 2017 O’Keefe, P., & Rodgers, J. L. (2017) with this sample suggests the within person effect is not the FE itself. The NLSYC is well-suited to the task because it includes a known FE and longitudinal data. We conclude that there may be an artificial FE built into ability instruments because of this norming bias.
Bibliography Citation
O'Keefe, Patrick and Joseph Lee Rodgers. "The Corrosive Influence of the Flynn Effect on Age Normed Tests." Multivariate Behavioral Research published online (7 February 2019): DOI: 10.1080/00273171.2018.1562322.
6. Ou, Lu
Chow, Sy-Miin
Ji, Linying
Molenaar, Peter C.M.
(Re)evaluating the Implications of the Autoregressive Latent Trajectory Model Through Likelihood Ratio Tests of Its Initial Conditions
Multivariate Behavioral Research 52,2 (2017): 178-199.
Also: http://www.tandfonline.com/doi/full/10.1080/00273171.2016.1259980
Cohort(s): NLSY79
Publisher: Taylor & Francis
Keyword(s): Family Income; Modeling, Growth Curve/Latent Trajectory Analysis; Monte Carlo

The autoregressive latent trajectory (ALT) model synthesizes the autoregressive model and the latent growth curve model. The ALT model is flexible enough to produce a variety of discrepant model-implied change trajectories. While some researchers consider this a virtue, others have cautioned that this may confound interpretations of the model's parameters. In this article, we show that some--but not all--of these interpretational difficulties may be clarified mathematically and tested explicitly via likelihood ratio tests (LRTs) imposed on the initial conditions of the model. We show analytically the nested relations among three variants of the ALT model and the constraints needed to establish equivalences. A Monte Carlo simulation study indicated that LRTs, particularly when used in combination with information criterion measures, can allow researchers to test targeted hypotheses about the functional forms of the change process under study. We further demonstrate when and how such tests may justifiably be used to facilitate our understanding of the underlying process of change using a subsample (N = 3,995) of longitudinal family income data from the National Longitudinal Survey of Youth.
Bibliography Citation
Ou, Lu, Sy-Miin Chow, Linying Ji and Peter C.M. Molenaar. "(Re)evaluating the Implications of the Autoregressive Latent Trajectory Model Through Likelihood Ratio Tests of Its Initial Conditions." Multivariate Behavioral Research 52,2 (2017): 178-199.
7. Tong, Xin
Zhang, Zhiyong
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.
8. Tong, Xin
Zhang, Zhiyong
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.