The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Modeling incomplete longitudinal and cross-sectional data using latent growth structural models
Abstract
In this paper we describe some mathematical and statistical models for identifying and dealing with changes over age. We concentrate specifically on the use of a latent growth structural equation model approach to deal with issues of: (1) latent growth models of change, (2) differences in longitudinal and cross-sectional results, and (3) differences due to longitudinal attrition. This is a methodological paper using simulated data, but we base our models on practical and conceptual principles of modeling change in developmental psychology. Our results illustrate both benefits and limitations using structural models to analyze incomplete longitudinal data.
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Cites
RUBIN, D. B. (1976) · Biometrika · doi:10.1093/biomet/63.3.581 · Cited by 9,925
Random-Effects Models for Longitudinal Data
Laird, N. M., & Ware, J. H. (1982) · Biometrics · doi:10.2307/2529876 · Cited by 8,923
Cited by
Missing data analysis: making it work in the real world
Graham, J. W. (2009) · Annual Review of Psychology · doi:10.1146/annurev.psych.58.110405.085530 · Cited by 6,163
Age-related differences and change in positive and negative affect over 23 years.
Charles, S. T., Reynolds, C. A., & Gatz, M. (2001) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.80.1.136 · Cited by 1,049
Newman, D. A. (2003) · Organizational Research Methods · doi:10.1177/1094428103254673 · Cited by 626
Alcohol use from ages 9 to 16: A cohort-sequential latent growth model
Duncan, S. C., Duncan, T. E., & Strycker, L. A. (2006) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2005.06.001 · Cited by 273
Structural Factor Analysis Experiments with Incomplete Data
McArdle, J. J. (1994) · Multivariate Behavioral Research · doi:10.1207/s15327906mbr2904_5 · Cited by 198
Self-citation
Metadata
- Authors
- Mcardle, J. J., & Hamagami, F.
- Venue
- Experimental Aging Research
- Publisher
- Informa UK Limited
- Published
- 1992-09
- Volume
- 18
- Issue
- 3
- Pages
- 145-166
- ISSN
- 0361-073X (ISSN-L)
- 1096-4657
- OpenAlex
- W2075231192
- Cited by
- 171
- References
- 2
- FWCI
- 2.30
- Type
- journal article
- Language
- en
- Open access
- closed
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- Mcardle, J. J., & Hamagami, F. (1992). Modeling incomplete longitudinal and cross-sectional data using latent growth structural models. Experimental Aging Research. https://doi.org/10.1080/03610739208253917
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
