The effects of individually varying times of observations on growth parameter estimations in piecewise growth model

References

• M.S. Aber and J.J. McArdle, Latent growth curve approaches to modeling the development of competence, in Criteria for Competence: Controversies in the Conceptualization and Assessment of Children's Abilities, M. Chandler and M. Chapman, eds., Lawrence Erlbaum Associates, Inc, Hillsdale, NJ, 1991, pp. 231–258.
   Google Scholar
• V.W. Berninger, R.D. Abbott, W. Nagy, and J. Carlisle, Growth in phonological, orthographic, and morphological awareness in Grades 1 to 6, J. Psycholinguistic Res. 39 (2010), pp. 141–163. doi: 10.1007/s10936-009-9130-6
   PubMed Web of Science ®Google Scholar
• J.C. Biesanz, N. Deeb-Sossa, A.A. Papadakis, K.A. Bollen, and P.J. Curran, The role of coding time in estimating and interpreting growth curve models, Psychol Methods. 9 (2004), pp. 30–52. doi: 10.1037/1082-989X.9.1.30
   PubMed Web of Science ®Google Scholar
• D.M. Capaldi, M. Stoolmiller, H.K. Kim, and K. Yoerger, Growth in alcohol use in at-risk adolescent boys: Two-part random effects prediction models, Drug Alcohol Dependence. 105 (2009), pp. 109–117. doi: 10.1016/j.drugalcdep.2009.06.013
   PubMed Web of Science ®Google Scholar
• C.-P. Chou, P.M. Bentler, and M.A. Pentz, Comparisons of two statistical approaches to study growth curves: The multilevel model and the latent curve analysis, Struct. Equ. Model. 5 (1998), pp. 247–266. doi: 10.1080/10705519809540104
   Web of Science ®Google Scholar
• K.R. Choudhury, I. Kasman, and G.D. Plowman, Analysis of multi-arm tumor growth trials in xenograft animals using phase change adaptive piecewise quadratic models, Stat. Med. 29 (2010), pp. 2399–409.
   PubMed Web of Science ®Google Scholar
• P.J. Curran, Have multilevel models been structural equation models all along? Multivariate Behav Res. 38 (2003), pp. 529–569. doi: 10.1207/s15327906mbr3804_5
   PubMed Web of Science ®Google Scholar
• T.E. Duncan and S.C. Duncan, An introduction to latent growth curve modeling, Behavior Therapy. 35 (2004), pp. 333–363. doi: 10.1016/S0005-7894(04)80042-X
   Web of Science ®Google Scholar
• E. Ferrer, F. Hamagami, and J.J. McArdle, Modeling latent growth curves with incomplete data using different types of structural equation modeling and multilevel software, Struct. Equ. Model. 11 (2004), pp. 452–483. doi: 10.1207/s15328007sem1103_8
   Web of Science ®Google Scholar
• J.-L. Heeb, G. Gmel, J. Rehm, and M. Mohler-Kuo, Exploring daily variations of drinking in the Swiss general population. A growth curve analysis, Int. J. Methods Psychiatric Res. 17 (2008), pp. 1–11. doi: 10.1002/mpr.238
   PubMed Web of Science ®Google Scholar
• M. Kieffer, Before and after third grade: Longitudinal evidence for the shifting role of socioeconomic status in reading growth, Read. Writ. 25 (2012), pp. 1–22. doi: 10.1007/s11145-010-9244-0
   Web of Science ®Google Scholar
• Y.-S. Kim, Y. Petscher, C. Schatschneider, and B. Foorman, Does growth rate in oral reading fluency matter in predicting reading comprehension achievement? J. Educ. Psychol. 102 (2010), pp. 652–667. doi: 10.1037/a0019643
   Web of Science ®Google Scholar
• O.-M. Kwok, W. Luo, and S.G. West, Using modification indexes to detect turning points in longitudinal data: A Monte Carlo study, Struct. Equ. Model. 17 (2010), pp. 216–240. doi: 10.1080/10705511003659359
   Web of Science ®Google Scholar
• F. Li, T.E. Duncan, S.C. Duncan, and H. Hops, Piecewise growth mixture modeling of adolescent alcohol use data, Struct. Equ. Model. 8 (2001), pp. 175–204. doi: 10.1207/S15328007SEM0802_2
   Web of Science ®Google Scholar
• Y. Liu, Q. Zhao, and H. Liu, Methods comparison of piecewise growth modeling, Psychol. Exploration. 33 (2013), pp. 415–422.
   Google Scholar
• J.A.R. Logan and Y. Petscher, School profiles of at-risk student concentration: Differential growth in oral reading fluency, J. School Psychol. 48 (2010), pp. 163–186. doi: 10.1016/j.jsp.2009.12.002
   PubMed Web of Science ®Google Scholar
• W. Meredith and J. Tisak, Latent curve analysis, Psychometrika. 55 (1990), pp. 107–122. doi: 10.1007/BF02294746
   Web of Science ®Google Scholar
• B. Muthén, Beyond SEM: General latent variable modeling, Behaviormetrika. 29 (2002), pp. 81–117. doi: 10.2333/bhmk.29.81
   Google Scholar
• B. Muthén, Mplus Statistical Analysis with Latent Variables: Technical Appendices, Muthén & Muthén, Los Angeles, CA, 2004.
   Google Scholar
• B. Muthén and T. Asparouhov, Beyond multilevel regression modeling: multilevel analysis in a general latent variable framework, in The Handbook of Advanced Multilevel Analysis, J.J.. Hox and J.K. Roberts, eds., Routledge, New York, 2001, pp. 15–40.
   Google Scholar
• B. Muthén, General growth mixture modeling for randomized preventive interventions, Biostatistics. 3 (2002), pp. 459–475. doi: 10.1093/biostatistics/3.4.459
   PubMed Web of Science ®Google Scholar
• B. Muthén, L. Muthén, and T. Asparouhov, Latent Variable Modeling Using Mplus, National Survey Reserach Center at Renmin University of China, Beijing, 2012.
   Google Scholar
• L. Muthén and B. Muthén, Mplus User's Guide, 6th ed., Muthén & Muthén, Los Angeles, CA, 2010.
   Google Scholar
• K.L. Nylund, T. Asparouhov, and B. Muthén, Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study, Struct. Equ. Model. 14 (2007), pp. 535–569. doi: 10.1080/10705510701575396
   Web of Science ®Google Scholar
• K.J. Preacher, P.J. Curran, and D.J. Bauer, Computational tools for probing interactions in multiple linear regression, multilevel modeling, and latent curve analysis, J. Educ. Behav. Stat. 31 (2006), pp. 437–448. doi: 10.3102/10769986031004437
   Web of Science ®Google Scholar
• S. Raudenbush and A. Bryk, Hierarchical Linear Models: Applications and Data Analysis Methods, 2nd ed., Sage Publications, Thousand Oaks, CA, 2002.
   Google Scholar
• J.D. Singer and J.B. Willett, Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence, Oxford University Press, New York, 2003.
   Google Scholar
• T.A.B. Snijiders and R.J. Bosker, Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling, Sage, London, 1999.
   Google Scholar
• D.J. Spiegelhalter, N.G. Best, B.P. Carlin, and A. van der Linde, Bayesian measures of model complexity and fit, J. R. Statist. Soc. Ser. B (Statist. Methodol.). 64 (2002), pp. 583–639. doi: 10.1111/1467-9868.00353
   Web of Science ®Google Scholar
• A. Tolvanen, Latent Growth Mixture Modeling: A Simulation Study, University of Jyväskylä, Jyväskylä, 2007.
   Google Scholar
• J.B. Willett and A.G. Sayer, Using covariance structure analysis to detect correlates and predictors of individual change over time, Psychol. Bull. 116 (1994), pp. 363–381. doi: 10.1037/0033-2909.116.2.363
   Web of Science ®Google Scholar
• D. Zhang and V.L. Willson, Comparing empirical power of multilevel structural equation models and hierarchical linear models: Understanding cross-level interactions, Struct. Equ. Model. 13 (2006), pp. 615–30. doi: 10.1207/s15328007sem1304_6
   Web of Science ®Google Scholar