Intercept Centering and Time Coding in Latent Difference Score Models

REFERENCES

• Bell , R. Q. 1954 . An experimental test of the accelerated longitudinal approach . Child Development , 25 : 281 – 286 .
   PubMed Web of Science ®Google Scholar
• Biesanz , J. C. , Deeb-Sossa , N. , Papadakis , A. A. , Bollen , K. A. and Curran , P. J. 2004 . The role of coding time in estimating and interpreting growth curve models . Psychological Methods , 9 : 30 – 52 .
   PubMed Web of Science ®Google Scholar
• Bollen , K. A. and Curran , P. J. 2004 . Autoregressive latent trajectory models: A synthesis of two traditions . Sociological Methods & Research , 32 : 336 – 383 .
   Web of Science ®Google Scholar
• Bollen , K. A. and Curran , P. J. 2006 . Latent curve models: A structural equation perspective New York , NY : Wiley. .
   Google Scholar
• Center for Human Resource Research . 2004 . A guide to the 1979–2002National Longitudinal Survey of Youth Washington , DC : U.S. Department of Labor, Bureau of Labor Statistics. .
   Google Scholar
• Curran , P. J. 2003 . Have multilevel models been structural equation models all along? . Multivariate Behavioral Research , 38 : 529 – 569 .
   PubMed Web of Science ®Google Scholar
• Curran , P. J. and Bollen , K. A. 2001 . “ The best of both worlds: Combining autoregressive and latent curve models. ” . In New methods for the analysis of change Edited by: Collins , L. M. and Sayer , A. G. 105 – 136 . Washington , DC : American Psychological Association. .
   Google Scholar
• Dunn , L. M. and Markwardt , F. L. 1970 . Peabody Individual Achievement Test Manual Circle Pines , MN : American Guidance Service. .
   Google Scholar
• Ferrer , E. , Hamagami , F. and McArdle , J. J. 2004 . Modeling latent growth curves with incomplete data using different types of structural equation modeling and multilevel software . Structural Equation Modeling , 11 : 452 – 483 .
   Web of Science ®Google Scholar
• Grimm , K. J. and Widaman , K. F. 2010 . Residual structures in latent growth curve modeling . Structural Equation Modeling , 17 : 424 – 442 .
   Web of Science ®Google Scholar
• Hamagami , F. and McArdle , J. J. 2001 . “ Advanced studies of individual differences linear dynamic models for longitudinal data analysis. ” . In New developments and techniques in structural equations modeling Edited by: Marcoulides , G. and Schumacker , R. 203 – 246 . Mahwah , NJ : Lawrence Erlbaum Associates, Inc. .
   Google Scholar
• Jöreskog , K. G. 1970 . Estimation and testing of simplex models . British Journal of Mathematical and Statistical Psychology , 23 : 121 – 145 .
   Web of Science ®Google Scholar
• Jöreskog , K. G. 1974 . “ Analyzing psychological data by structural analysis of covariance matrices. ” . In Contemporary developments in mathematical psychology Edited by: Atkinson , R. C. , Krantz , D. H. , Luce , R. D. and Suppas , P. 1 – 56 . San Francisco , CA : Freeman. .
   Google Scholar
• Kreft , I. G. G. , de Leeuw , J. and Aiken , L. S. 1995 . The effect of different forms of centering in hierarchical linear models . Multivariate Behavioral Research , 30 : 1 – 21 .
   PubMed Web of Science ®Google Scholar
• Little , R. T. A. and Rubin , D. B. 1987 . Statistical analysis with missing data New York , NY : Wiley. .
   Google Scholar
• Mc Ardle , J. J. 2001 . “ A latent difference score approach to longitudinal dynamic structural analysis. ” . In Structural equation modeling: Present and future Edited by: Cudeck , R. , du Toit , S. and Sörbom , D. 342 – 380 . Lincolnwood , IL : Scientific Software International. .
   Google Scholar
• Mc Ardle , J. J. 2005 . “ The development of RAM rules for latent variable structural equation modeling. ” . In Contemporary advances in psychometrics Edited by: Madeau , A. and McArdle , J. J. 225 – 273 . Mahwah , NJ : Lawrence Erlbaum Associates, Inc. .
   Google Scholar
• Mc Ardle , J. J. and Epstein , D. B. 1987 . Latent growth curves within developmental structural equation models . Child Development , 58 : 110 – 133 .
   PubMedGoogle Scholar
• Mc Ardle , J. J. and Grimm , K. J. 2010 . “ Five steps in latent curve and latent change score modeling with longitudinal data. ” . In Longitudinal research with latent variables Edited by: van Montfort , K. , Oud , J. and Satorra , A. 245 – 274 . Heidelberg , , Germany : Springer-Verlag. .
   Google Scholar
• Mc Ardle , J. J. and Hamagami , F. 2001 . “ Linear dynamic analyses of incomplete longitudinal data. ” . In Methods for the analysis of change Edited by: Collins , L. and Sayer , A. 137 – 176 . Washington , DC : APA Press. .
   Google Scholar
• Mc Ardle , J. J. and McDonald , R. P. 1984 . Some algebraic properties of the Reticular Action Model for moment structures . The British Journal of Mathematical and Statistical Psychology , 37 : 234 – 251 .
   PubMedGoogle Scholar
• Mc Ardle , J. J. and Nesselroade , J. R. 1994 . “ Using multivariate data to structure developmental change. ” . In Life-span developmental psychology: Methodological innovations Edited by: Cohen , S. H. and Reese , H. W. 223 – 267 . Hillsdale , NJ : Lawrence Erlbaum Associates, Inc. .
   Google Scholar
• Mehta , P. D. and West , S. G. 2000 . Putting the individual back into individual growth curves . Psychological Methods , 5 : 23 – 43 .
   PubMed Web of Science ®Google Scholar
• Meredith , W. and Tisak , J. 1990 . Latent curve analysis . Psychometrika , 55 : 107 – 122 .
   Web of Science ®Google Scholar
• Muthén , L. K. and Muthén , B. O. 1998–2007 . Mplus user's guide, , 5th ed. Los Angeles , CA : Muthén & Muthén. .
   Google Scholar
• Raudenbush , S. W. 2001 . Comparing personal trajectories and drawing causal inferences from longitudinal data . Annual Review of Psychology , 50 : 501 – 525 .
   Google Scholar
• Rogosa , D. R. and Willett , J. B. 1985 . Understanding correlates of change by modeling individual differences in growth . Psychometrika , 50 : 203 – 228 .
   Web of Science ®Google Scholar
• Schuster , C. and von Eye , A. 1998 . Determining the meaning of parameters in multilevel models for longitudinal data . International Journal of Behavioral Development , 22 : 475 – 491 .
   Web of Science ®Google Scholar