Two-Part Predictors in Regression Models

References

• Baker, S. G., & Kramer, B. S. (2001). Good for women, good for men, bad for people: Simpson's paradox and the importance of sex-specific analysis in observational studies. Journal of Women's Health & Gender-Based Medicine, 10, 867–872. https://doi.org/10.1089/152460901753285769
   PubMedGoogle Scholar
• Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahway, New Jersey: Lawrence Erlbaum.
   Google Scholar
• Flora, D. B. (2011). Two-part modeling of semicontinuous longitudinal variables: A comparison of approaches. Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 7, 145–156. https://doi.org/10.1027/1614-2241/a000032
   Web of Science ®Google Scholar
• Greenland, S., & Finkle, W. D. (1995). A critical look at methods for handling missing covariates in epidemiologic regression analyses. American Journal of Epidemiology, 142, 1255–1264. https://doi.org/10.1093/oxfordjournals.aje.a117592
   PubMed Web of Science ®Google Scholar
• Hastie, T., Tibshirani, R., & Friedman, J. (2001). The elements of statistical learning: Data mining, inference, and prediction. New York, NY: Springer.
   Google Scholar
• Hedeker, D., Demirtas, H., & Mermelstein, R. J. (2009). A mixed ordinal location scale model for analysis of Ecological Momentary Assessment (EMA) data. Statistics and its Interface, 2, 391–401. https://doi.org/10.4310/SII.2009.v2.n4.a1
   PubMed Web of Science ®Google Scholar
• Hedeker, D., Mermelstein, R. J., & Demirtas, H. (2012). Modeling between- and within-subject variance in ecological momentary assessment (EMA) data using mixed-effects location scale models. Statistics in Medicine, 31, 3328–3336. https://doi.org/10.1002/sim.5338
   PubMed Web of Science ®Google Scholar
• Jones, M. P. (1996). Indicator and stratification methods for missing explanatory variables in multiple linear regression. Journal of the American Statistical Association, 91, 222–230. https://doi.org/10.2307/2291399
   Web of Science ®Google Scholar
• Kurland, B. F., Johnson, L. L., Egleston, B. L., & Diehr, P. H. (2009). Longitudinal data with follow-up truncated by death: Match the analysis method to research aims. Statistical Science, 24, 211. https://doi.org/10.1214/09-STS293
   PubMed Web of Science ®Google Scholar
• Manne, S., Kashy, D., Weinberg, D. S., Boscarino, J. A., & Bowen, D. J. (2012). Using the interdependence model to understand spousal influence on colorectal cancer screening intentions: A structural equation model. Annals of Behavioral Medicine, 43, 320–329. https://doi.org/10.1007/s12160-012-9344-y
   PubMed Web of Science ®Google Scholar
• Maxwell, S. E., & Delaney, H. D. (2004). Designing experiments and analyzing data: A model comparison perspective (2nd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.
   Google Scholar
• Myers, J. L., & Well, A. D. (2003). Research design and statistical analysis. Mahwah, NJ: Lawrence Erlbaum Associates.
   Google Scholar
• Olsen, M. K., & Schafer, J. L. (2001). A two-part random-effects model for semicontinuous longitudinal data. Journal of the American Statistical Association, 96, 730–745. https://doi.org/10.1016/j.jhealeco.2009.11.010
   Web of Science ®Google Scholar
• Parke, R. D., Coltrane, S., Duffy, S., Buriel, R., Dennis, J., Powers, J., French, S., & Widaman, K. F. (2004). Economic stress, parenting, and child adjustment in Mexican American and European American families. Child Development, 75, 1632–1656. https://doi.org/10.1111/j.1467-8624.2004.00807.x
   PubMed Web of Science ®Google Scholar
• Ramsey, F. L., & Schafer, D. W. (2002). The statistical sleuth (2nd ed.). Pacific Grove, CA: Duxbury.
   Google Scholar
• Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. New York, NY: Wiley.
   Google Scholar
• SAS Institute Inc. (2011). SAS/STAT® 9.3 user's guide. Cary, NC: SAS Institute Inc.
   Google Scholar
• Schafer, J. L. (1999). Multiple imputation: A primer. Statistical Methods in Medical Research, 8, 3–15. https://doi.org/10.1177/096228029900800102
   PubMed Web of Science ®Google Scholar
• Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7, 147–177. https://doi.org/10.1037/1082-989X.7.2.147
   PubMed Web of Science ®Google Scholar
• Thornberry, T. P. (2009). The apple doesn't fall far from the tree (Or does it?): Intergenerational patterns of antisocial behavior. Criminology, 47, 297–325. https://doi.org/10.1111/j.1745-9125.2009.00153.x
   PubMed Web of Science ®Google Scholar
• Thornberry, T. P., Freeman-Gallant, A., Lizotte, A. J., Krohn, M. D., & Smith, C. A. (2003). Linked lives: The intergenerational transmission of antisocial behavior. Journal of Abnormal Child Psychology, 31, 171–184. https://doi.org/10.1023/A:1022574208366
   PubMed Web of Science ®Google Scholar
• Thornberry, T. P., Krohn, M. D., Lizotte, A. J., Smith, C. A., & Tobin, K. (2003). Gangs and delinquency in developmental perspective. New York: Cambridge University Press.
   Google Scholar
• Thornberry, T. P., Matsuda, M., Greenman, S. J., Bears Augustyn, M., Henry, K. L., Smith, C. A., & Ireland, T. O. (2014). Adolescent risk factors for child maltreatment. Child Abuse & Neglect, 38, 706–722. https://doi.org/10.1016/j.chiabu.2013.08.009
   PubMed Web of Science ®Google Scholar
• Widaman, K. F., Grimm, K. J., Early, D. R., Robins, R. W., & Conger, R. D. (2013). Investigating factorial invariance of latent variables across populations when manifest variables are missing completely. Structural Equation Modeling, 20, 384–408. https://doi.org/10.1080/10705511.2013.797819
   PubMed Web of Science ®Google Scholar