Search Results
Author: MacKinnon, David P.
Resulting in 3 citations.
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Davis, Caroline H. MacKinnon, David P. Schultz, Amy Sandler, Irwin |
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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. |
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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.
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| 2. |
MacKinnon, David P. Lamp, Sophia J. |
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A Unification of Mediator, Confounder, and Collider Effects Prevention Science published online (23 June 2021): DOI: 10.1007/s11121-021-01268-x. Also: https://link.springer.com/article/10.1007/s11121-021-01268-x Cohort(s): NLSY97 Publisher: Springer Keyword(s): Family Income; Health/Health Status/SF-12 Scale; Modeling; Personality/Big Five Factor Model or Traits; Statistical Analysis Permission to reprint the abstract has not been received from the publisher. Third-variable effects, such as mediation and confounding, are core concepts in prevention science, providing the theoretical basis for investigating how risk factors affect behavior and how interventions change behavior. Another third variable, the collider, is not commonly considered but is also important for prevention science. This paper describes the importance of the collider effect as well as the similarities and differences between these three third-variable effects. The single mediator model in which the third variable (T) is a mediator of the independent variable (X) to dependent variable (Y) effect is used to demonstrate how to estimate each third-variable effect. We provide difference in coefficients and product of coefficients estimators of the effects and demonstrate how to calculate these values with real data. Suppression effects are defined for each type of third-variable effect. Future directions and implications of these results are discussed. |
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Bibliography Citation
MacKinnon, David P. and Sophia J. Lamp. "A Unification of Mediator, Confounder, and Collider Effects." Prevention Science published online (23 June 2021): DOI: 10.1007/s11121-021-01268-x.
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| 3. |
O'Rourke, Holly P. Fine, Kimberly L. Grimm, Kevin J. MacKinnon, David P. |
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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. |
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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.
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