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Author: Hamagami, Fumiaki
Resulting in 2 citations.
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McArdle, John J. Hamagami, Fumiaki |
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Latent Difference Score Structural Models for Linear Dynamic Analyses with Incomplete Longitudinal Data In: New Methods for the Analysis of Change. LM Collins and AG Sayer, eds. Washington, DC: American Psychological Association, 2001: pp. 139-175 Cohort(s): Children of the NLSY79 Publisher: American Psychological Association (APA) Keyword(s): Behavior Problems Index (BPI); Change Scores; Data Analysis; Data Quality/Consistency; LISREL; Modeling; Modeling, Growth Curve/Latent Trajectory Analysis; Peabody Individual Achievement Test (PIAT- Reading); Test Scores/Test theory/IRT Chapter: States that the creation of "best methods" for the analysis of change in longitudinal and developmental research has five key goals: the direct identification of intraindividual change; direction identification of interindividual differences in intraindividual change; analysis of interrelationships in change; analysis of determinants of intraindividual change; and analysis of determinants of interindividual differences in intraindividual change. The kind of longitudinal data dealt with in this chapter are multiple measures from data from the National Longitudinal Survey of Youth (NLSY). Some recent approaches to longitudinal data analysis have used structural equation modeling (SEM). The authors present a relatively new way to approach SEM-based analyses of longitudinal data, termed latent differences score (LDS) analysis (McArdle and Hamagami, 1995, 1998; McArdle and Nesselroade, 1994). This version of LDS is designed for ease of use with available SEM software (e.g., LISREL, Mx, RAMONA) and permits features of incomplete-data analyses. The chapter begins with a basic description of the available data and gives foundations of the LDS methods. A variety of LDS models using the available NLSY data are illustrated. (PsycINFO Database Record (c) 2000 APA, all rights reserved). |
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Bibliography Citation
McArdle, John J. and Fumiaki Hamagami. "Latent Difference Score Structural Models for Linear Dynamic Analyses with Incomplete Longitudinal Data" In: New Methods for the Analysis of Change. LM Collins and AG Sayer, eds. Washington, DC: American Psychological Association, 2001: pp. 139-175
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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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