Linear and quadratic growth models for continuous and dichotomous outcomes

Applied Quantitative Analysis in Education and the Social Sciences

Volumn , Issue , 2013, Pages 125-168

  O’Connell, Ann A ^a   Logan, Jessica A R ^a   Pentimonti, Jill M ^a   McCoach, D Betsy ^b  

^a OHIO STATE UNIVERSITY   (United States)

^b UNIVERSITY OF CONNECTICUT   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84917490208     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4324/9780203108550     Document Type: Chapter

References (39)

1. Breslow, N. (2003). Whither PQL? (University of Washington Biostatistics Working Paper Series, No. 192). Retrieved from http://www.bepress.com/uwbiostat/paper192
2. Brown, H., & Prescott, R. (2006). Applied mixed models in medicine (2nd ed.). West Sussex, England: Wiley.
3. Campbell, D. T., & Kenny, D. A. (1999). A primer on regression artifacts. New York, NY: Guilford Press.
4. Christ, T. J., Silberglitt, B., Yeo, S., & Cormier, D. (2010). Curriculum-based measurement of oral reading: An evaluation of growth rates and seasonal effects among students served in general and special education. School Psychology Review, 39, 447–462.
5. Collins, L. M. (2006). Analysis of longitudinal data: The integration of theoretical model, temporal design, and statistical model. Annual Reviews in Psychology, 57, 505–528.
6. Crowe, E. C., Connor, C. M., & Petscher, Y. (2009). Examining the core: Relations among reading curricula, poverty, and fi rst through third grade reading achievement. Journal of Educational Psychology, 47, 187–214.
7. Curran, P. J. (2003). Have multilevel models been structural equation models all along? Multivariate Behavioral Research, 38, 529–569.
8. Diaz, R. E. (2007). Comparison of PQL and Laplace 6 estimates of hierarchical linear models when comparing groups of small incident rates in cluster randomized trials. Computational Statistics and Data Analysis, 51, 2871–2888.
9. Duncan, T. E., Duncan, S. C., & Stryker, L. A. (2006). An introduction to latent variable growth curve modeling: Concepts, issues, and applications (2nd ed.). Mahwah, NJ: Erlbaum.
10. Dunn, L. M., & Markwardt, F. C. (1970). Peabody Individual Achievement Test: Manual. Circle Pines, MN: American Guidance Service.
11. Enders, C. K. (2010). Applied missing data analysis. New York, NY: Guilford Press.
12. Fitzmaurice, G., & Molenberghs, G. (2009). Advances in longitudinal data analysis: An historical perspective. In G. Fitzmaurice, M. Davidian, G. Verbeke, & G. Molenberghs (Eds.), Longitudinal data analysis (pp. 3–27). Boca Raton, FL: Chapman & Hall/CRC Press.
13. Fox, J. (2008). Applied regression analysis and generalized linear models. Thousand Oaks, CA: Sage.
14. Francis, D. J., Schatschneider, C., & Carlson, C. D. (2000). Introduction to individual growth curve analysis. In D. Drotar (Ed.), Handbook of research in pediatric and clinical child psychology (pp. 51–73). Norwell, MA: Kluwer Academic/Plenum Publishers.
15. Grimm, K. J., Ram, N., & Hamagami, F. (2011). Nonlinear growth curves in developmental research. Child Development, 82, 1357–1371.
16. Hedeker, D. R., & Gibbons, R. D. (2006). Longitudinal data analysis. Hoboken, NJ: Wiley.
17. Hosmer, D. W., & Lemeshow, S. (2000). Applied logistic regression (2nd ed.). New York, NY: Wiley.
18. Long, J. S. (1997). Regression models for categorical and limited dependent variables. Thousand Oaks, CA: Sage.
19. Maas, C. M., & Hox, J. (2004). Robustness issues in multilevel regression analysis. Statistica Neerlandica, 58(2), 127–137.
20. McCoach, D. B., & Black, A. C. (2008). Evaluation of model fi t and adequacy. In A.A. O’Connnell & D. B. McCoach (Eds.), Multilevel modeling of educational data (pp. 245–271). Charlotte, NC: Information Age Publishing.
21. McCoach, D. B., & Kaniskan, B. (2010). Using time-varying covariates in multilevel growth models. Frontiers in Psychology, 1: 17. doi: 10.3389/fpsyg.2010.00017 http://www.frontiersin.org/ Psychology.
22. Meredith, W., & Tisak, J. (1990). Latent curve analysis. Psychometrika, 55, 107–122.
23. Muthen, B., & Asparouhov, T. (2009). Growth mixture analysis: Models with non-Gaussian random effects. In G. Fitzmaurice, M. Davidian, G. Verbeke, & G. Molenberghs (Eds.), Longitudinal Data Analysis (pp. 143–165). Boca Raton: FL: Chapman & Hall/CRC Press.
24. National Center for Education Statistics. (2002). User’s manual for the ECLS-K fi rst grade publicuse data files and electronic code book: NCES 2002-135. Washington, DC: U.S. Department of Education.
25. National Longitudinal Surveys. (2005). NLS handbook. Washington, DC: Bureau of Labor Statistics. Available online [http://www.bls.gov/nls/nlsdoc.htm].
26. O’Connell, A. A. (2006). Logistic regression models for ordinal response variables. Thousand Oaks, CA: Sage.
27. O’Connell, A. A., Goldstein, J., Rogers, H. J., & Peng, C. Y. J. (2008). Multilevel logistic models for dichotomous and ordinal data. In A. A. O’Connell and D. B. McCoach (Eds.), Multilevel Modeling of Educational Data (pp. 199–242). Charlotte, NC: Information Age Publishing.
28. O’Connell, A. A., & McCoach, D. B. (2004). Applications of hierarchical linear models for evaluations of health interventions. Evaluation & the Health Professions, 27(2), 119–151.
29. O’Connell, A. A., Reed, S., Ren, W., & Li, J. (2010, April 29–May 4). Estimation methods and software comparison for hierarchical generalized linear models. Paper presented at the 91st Annual American Educational Research Association Annual Meeting, Denver, CO.
30. Osborne, J. W., & Waters, E. (2002). Four assumptions of multiple regression that researchers should always test. Practical Assessment, Research & Evaluation, 8(2). Retrieved from http://pare online.net/getvn.asp?v=8&n=2
31. Pinheiro, J. C., & Bates, D. M. (2006). Approximations to the log-likelihood function in the nonlinear mixed effects model. Journal of Computational and Graphical Statistics, 4, 12–35.
32. Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Application and data analysis methods. Thousand Oaks, CA: Sage.
33. Raudenbush, S. W., Bryk, A., Cheong, Y.F., Congdon R., & du Toit, M. (2011). HLM7: Hierarchical Linear and Nonlinear Models. Lincolnwood, IL: SSI.
34. Raudenbush, S. W., & Liu, X. F. (2001). Effects of study duration, frequency of observation, and sample size on power in studies of group differences in polynomial change. Psychological Methods, 6(4), 387–401.
35. Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. New York, NY: Oxford University Press.
36. Snijders, T. A. B., & Berkhof, J. (2008). Diagnostic checks for multilevel models. In H. Goldstein, J. De Leeuw, & W. Meijer (Eds.), Handbook of multilevel analysis (pp. 141–176). New York, NY: Springer.
37. West, J., Denton, K., & Germino-Hausken, E. (2000). America’s Kindergartners: Findings from the early childhood longitudinal study, kindergarten class of 1998–99: Fall 1998 (NCES 2000–070). Washington, DC: USDE, National Center for Education Statistics.
38. Willett, J. B., Singer, J. D., & Martin, N. C. (1998). The design and analysis of longitudinal studies of development and psychopathology in context: Statistical models and methodological recommendations. Development and Psychopathology, 10(2), 395–426.
39. Zill, N., & West, J. (2001). Entering kindergarten: A portrait of American children when they begin school (NCES 2001–035). Washington, DC: USDE, National Center for Education Statistics.