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Author: McArdle, John J.
Resulting in 3 citations.
1. McArdle, John J.
Bell, Richard Q.
An Introduction to Latent Growth Models for Developmental Data Analysis
In: Modeling Longitudinal and Multilevel Data: Practical Issues, Applied Approaches, and Specific Examples. T. Little, K. Schnabel, and J. Baumert, eds., Mahwah, NJ: Lawrence Erlbaum Associates, 2000.
Also: http://kiptron.usc.edu/publications/jjm.html
Cohort(s): Children of the NLSY79, NLSY79
Publisher: Lawrence Erlbaum Associates ==> Taylor & Francis
Keyword(s): Change Scores; Children, Poverty; Data Quality/Consistency; Home Observation for Measurement of Environment (HOME); LISREL; Modeling; Modeling, Growth Curve/Latent Trajectory Analysis; Peabody Individual Achievement Test (PIAT- Reading); Poverty; Test Scores/Test theory/IRT

Permission to reprint the abstract has been denied by the publisher.

Bibliography Citation
McArdle, John J. and Richard Q. Bell. "An Introduction to Latent Growth Models for Developmental Data Analysis" In: Modeling Longitudinal and Multilevel Data: Practical Issues, Applied Approaches, and Specific Examples. T. Little, K. Schnabel, and J. Baumert, eds., Mahwah, NJ: Lawrence Erlbaum Associates, 2000.
2. McArdle, John J.
Hamagami, Fumiaki
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).
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
3. Zhang, Zhiyong
McArdle, John J.
Nesselroade, John R.
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.
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.