Search Results
Author: Nesselroade, John R.
Resulting in 2 citations.
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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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Zhang, Zhiyong McArdle, John J. Nesselroade, John R. |
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Growth Rate Models: Emphasizing Growth Rate Analysis through Growth Curve Modeling Journal of Applied Statistics 39,6 (June 2012): 1241-1262. Also: http://www.tandfonline.com/doi/abs/10.1080/02664763.2011.644528 Cohort(s): Children of the NLSY79 Publisher: Taylor & Francis Group Keyword(s): Behavior Problems Index (BPI); Gender Differences; Modeling, Growth Curve/Latent Trajectory Analysis; Peabody Individual Achievement Test (PIAT- Math); Test Scores/Test theory/IRT Permission to reprint the abstract has not been received from the publisher. To emphasize growth rate analysis, we develop a general method to reparametrize growth curve models to analyze rates of growth for a variety of growth trajectories, such as quadratic and exponential growth. The resulting growth rate models are shown to be related to rotations of growth curves. Estimated conveniently through growth curve modeling techniques, growth rate models have advantages above and beyond traditional growth curve models. The proposed growth rate models are used to analyze longitudinal data from the National Longitudinal Study of Youth (NLSY) on children's mathematics performance scores including covariates of gender and behavioral problems (BPI). Individual differences are found in rates of growth from ages 6 to 11. Associations with BPI, gender, and their interaction to rates of growth are found to vary with age. Implications of the models and the findings are discussed. |
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Bibliography Citation
Zhang, Zhiyong, John J. McArdle and John R. Nesselroade. "Growth Rate Models: Emphasizing Growth Rate Analysis through Growth Curve Modeling." Journal of Applied Statistics 39,6 (June 2012): 1241-1262.
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