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Author: MacKinnon, David P.
Resulting in 2 citations.
1. Davis, Caroline H.
MacKinnon, David P.
Schultz, Amy
Sandler, Irwin
Cumulative Risk and Population Attributable Fraction in Prevention
Journal of Clinical Child and Adolescent Psychology 32,2 (May 2003): 228-235.
Also: http://www.tandfonline.com/doi/abs/10.1207/S15374424JCCP3202_7
Cohort(s): Children of the NLSY79, NLSY79
Publisher: Lawrence Erlbaum Associates ==> Taylor & Francis
Keyword(s): Behavior Problems Index (BPI); Behavior, Antisocial; Behavioral Problems; Britain, British; British Cohort Study (BCS); CESD (Depression Scale); Child Health; Cross-national Analysis; Depression (see also CESD); Divorce; Family Income; Health Factors; NCDS - National Child Development Study (British); Poverty; Public Sector

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

Bibliography Citation
Davis, Caroline H., David P. MacKinnon, Amy Schultz and Irwin Sandler. "Cumulative Risk and Population Attributable Fraction in Prevention." Journal of Clinical Child and Adolescent Psychology 32,2 (May 2003): 228-235.
2. O'Rourke, Holly P.
Fine, Kimberly L.
Grimm, Kevin J.
MacKinnon, David P.
The Importance of Time Metric Precision When Implementing Bivariate Latent Change Score Models
Multivariate Behavioral Research published online (1 February 2021): DOI: 10.1080/00273171.2021.1874261.
Also: https://www.tandfonline.com/doi/full/10.1080/00273171.2021.1874261
Cohort(s): Children of the NLSY79
Publisher: Taylor & Francis
Keyword(s): Modeling; Peabody Individual Achievement Test (PIAT- Math); Peabody Individual Achievement Test (PIAT- Reading); Statistical Analysis; Test Scores/Test theory/IRT

The literature on latent change score models does not discuss the importance of using a precise time metric when structuring the data. This study examined the influence of time metric precision on model estimation, model interpretation, and parameter estimate accuracy in bivariate LCS (BLCS) models through simulation. Longitudinal data were generated with a panel study where assessments took place during a given time window with variation in start time and measurement lag. The data were analyzed using precise time metric, where variation in time was accounted for, and then analyzed using coarse time metric indicating only that the assessment took place during the time window. Results indicated that models estimated using the coarse time metric resulted in biased parameter estimates as well as larger standard errors and larger variances and covariances for intercept and slope. In particular, the coupling parameter estimates--which are unique to BLCS models--were biased with larger standard errors. An illustrative example of longitudinal bivariate relations between math and reading achievement in a nationally representative survey of children is then used to demonstrate how results and conclusions differ when using time metrics of varying precision. Implications and future directions are discussed.
Bibliography Citation
O'Rourke, Holly P., Kimberly L. Fine, Kevin J. Grimm and David P. MacKinnon. "The Importance of Time Metric Precision When Implementing Bivariate Latent Change Score Models." Multivariate Behavioral Research published online (1 February 2021): DOI: 10.1080/00273171.2021.1874261.