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Title: Comparing Three Modern Approaches to Longitudinal Data Analysis: An Examination of a Single Developmental Sample
Resulting in 1 citation.
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Curran, Patrick J. |
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Comparing Three Modern Approaches to Longitudinal Data Analysis: An Examination of a Single Developmental Sample Presented: Washington, DC, Symposium at the Biennial Meeting of the Society for Research in Child Development, April 1997 Cohort(s): Children of the NLSY79 Publisher: Society for Research in Child Development (SRCD) Keyword(s): Behavior Problems Index (BPI); Home Observation for Measurement of Environment (HOME); Modeling, Growth Curve/Latent Trajectory Analysis; Peabody Individual Achievement Test (PIAT- Reading) Permission to reprint the abstract has not been received from the publisher. Introduction Excerpt: The empirical study of human development is mearly synonymous with longitudinal data analysis. To study development over time, one must consider the study of change over time. Despite the critical role longitudinal data analysis plays in the developmental research, how to best accomplish this task has been a long and sometimes hotly debated issue in the social sciences. In addition to the existence of many traditional data analytic techniques, the past decade has given rise to remarkable advances in the development of new and powerful methods for studying change over time. Such developments include latent variable growth modeling, hierarchial linear nodeling, general mixture modeling, generalized estimating equations, and exploratory growth modeling. Symposium participants: Mark Appelbaum, Patrick J. Curran, John J. McArdle, Stephen W. Radenbush, and Michael H. Seltzer. Discussant: C. Hendricks Brown. |
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Bibliography Citation
Curran, Patrick J. "Comparing Three Modern Approaches to Longitudinal Data Analysis: An Examination of a Single Developmental Sample." Presented: Washington, DC, Symposium at the Biennial Meeting of the Society for Research in Child Development, April 1997. |