References

 

(To request a Muthén paper, please email bmuthen@ucla.edu and refer to the number in parenthesis.)

 

Analysis With Longitudinal Data

 

Introductory

 

Bijleveld, C. C. J. H., & van der Kamp, T. (1998).  Longitudinal data analysis: Designs, models, and methods. Newbury Park: Sage.

 

Collins, L.M. & Sayer, A. (Eds.) (2001).  New Methods for the Analysis of Change.  Washington, D.C.: APA. 

 

Curran, P.J., & Bollen, K.A. (2001).  The best of both worlds: Combining autoregressive and latent curve models.  In Collins, L. M. & Sayer, A. G. (Eds.), New Methods for the Analysis of Change (pp. 105-136).  Washington, DC:  American Psychological Association.

 

Duncan, T. E., Duncan, S. C., Strycker, L. A., Li, F., & Alpert, A. (1999).

An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues,

and Applications. Mahwah, NJ: Lawrence Erlbaum Associates.

 

Goldstein, H. (1995).  Multilevel statistical models.  Second edition.  London:  Edward Arnold.

 

Jennrich, R.I., & Schluchter, M.D. (1986).  Unbalanced repeated-measures models with structured covariance matrices.  Biometrics, 42, 805-820. 

 

Khoo, S.T. & Muthén, B. (2000).  Longitudinal data on families: Growth modeling alternatives.  In Multivariate Applications in Substance use Research, J. Rose, L. Chassin, C. Presson & J. Sherman (eds.), Hillsdale, N.J.:  Erlbaum, pp. 43-78. (#79)

 

Laird, N.M., & Ware, J.H.  (1982).  Random-effects models for longitudinal data.  Biometrics, 38, 963-974.

 

Lindstrom, M.J., & Bates, D.M.  (1988).  Newton-Raphson and EM algorithms for linear mixed-effects models for repeated-measures data.  Journal of the American Statistical Association, 83, 1014-1022.

 

Littell, R., Milliken, G.A., Stroup, W.W., & Wolfinger, R.D. (1996).  SAS system for mixed models.  Cary NC:  SAS Institute.

 

McArdle, J.J. & Epstein, D. (1987).  Latent growth curves within developmental structural equation models.  Child Development, 58, 110-133.

 

McArdle, J.J. & Hamagami, F. (2001).  Latent difference score structural models for linear dynamic analyses with incomplete longitudinal data.  In Collins, L. M. & Sayer, A. G. (Eds.), New Methods for the Analysis of Change (pp. 137-175).  Washington, DC:  American Psychological Association.

 

Meredith, W. & Tisak, J. (1990).  Latent curve analysis.  Psychometrika, 55, 107-122.

 

Muthén, B.  (1991).  Analysis of longitudinal data using latent variable models with varying parameters.  In L. Collins, & J. Horn (Eds.), Best Methods for the Analysis of Change.  Recent Advances, Unanswered Questions, Future Directions (pp. 1-17). Washington DC: American Psychological Association.  (#33)

 

Muthén, B. (1997).  Latent variable modeling with longitudinal and multilevel data.  In A. Raftery (ed), Sociological Methodology (pp. 453-480). Boston: Blackwell Publishers.  (#73)

 

Muthén, B.  (2000).  Methodological issues in random coefficient growth modeling using a latent variable framework: Applications to the development of heavy drinking.  In Multivariate Applications in Substance use Research, J. Rose, L. Chassin, C. Presson & J. Sherman (eds.), Hillsdale, N.J.:  Erlbaum, pp. 113-140.  (#81)

 

Muthén, B. & Curran, P. (1997).  General longitudinal modeling of individual differences in experimental designs: A latent variable framework for analysis and power estimation.  Psychological Methods, 2, 371-402.  (#71)

 

Muthén, B. & Khoo, S.T. (1998).  Longitudinal studies of achievement growth using latent variable modeling. Learning and Individual Differences, Special issue: latent growth curve analysis, 10, 73-101.  (#80)

 

Muthén, B. & Muthén, L. (2000).  The development of heavy drinking and alcohol-related problems from ages 18 to 37 in a U.S. national sample.  Journal of Studies on Alcohol, 61, 290-300.  (#83)

 

Rao, C.R. (1958).  Some statistical models for comparison of growth curves.  Biometrics, 14, 1-17.

 

Raudenbush, S.W. & Bryk, A.S. (2002).  Hierarchical linear models: Applications and data analysis methods.  Second edition.  Newbury Park, CA: Sage Publications. 

 

Singer, J.D. (1998).  Using SAS PROC MIXED to fit multilevel models, hierarchical models, and individual growth models.  Journal of Educational and Behavioral Statistics, 23, 323-355.

 

Singer, J.D. & Willett, J.B. (2003). Applied longitudinal data analysis.  Modeling change and event occurrence.  New York, NY:  Oxford University Press.

 

Snijders, T. & Bosker, R. (1999).  Multilevel analysis. An introduction to basic and advanced multilevel modeling.  Thousand Oakes, CA: Sage Publications.

 

Tucker, L.R. (1958).  Determination of parameters of a functional relation by factor analysis.  Psychometrika, 23, 19-23.

 

 

Advanced

 

Brown, C.H. & Liao, J. (1999).  Principles for designing randomized preventive trials in mental health: An emerging development epidemiologic perspective.  American Journal of Community Psychology, special issue on prevention science, 27, 673-709. 

 

Brown, C.H., Indurkhya, A. & Kellam, S.K. (2000).  Power calculations for data missing by design:  applications to a follow-up study of lead exposure and attention.  Journal of the American Statistical Association, 95, 383-395.

 

Collins, L.M. & Sayer, A. (Eds.), New Methods for the Analysis of Change.  Washington, D.C.: APA. 

 

Ferrer, E. & McArdle, J.J. (2003). Alternative structural models for multivariate longitudinal data analysis.  Structural Equation Modeling, 10, 493-524.

 

Khoo, S.T. & Muthén, B. (2000).  Longitudinal data on families:  Growth modeling alternatives.  In Multivariate Applications in Substance use Research, J. Rose, L. Chassin, C. Presson & J. Sherman (eds.), Hillsdale, N.J.:  Erlbaum, pp. 43-78 (#79)

 

Miyazaki, Y. & Raudenbush, S.W. (2000).  A test for linkage of multiple cohorts from an accelerated longitudinal design.  Psychological Methods, 5, 44-63.

 

Moerbeek, M., Breukelen, G.J.P. & Berger, M.P.F. (2000).  Design issues for experiments in multilevel populations.  Journal of Educational and Behavioral Statistics, 25, 271-284.

 

Muthén, B. (1996).  Growth modeling with binary responses.  In A. V. Eye, & C. Clogg (Eds.), Categorical Variables in Developmental Research:  Methods of Analysis (pp. 37-54).  San Diego, CA: Academic Press.  (#64)

 

Muthén, B. (1997).  Latent variable modeling with longitudinal and multilevel data.  In A. Raftery (ed), Sociological Methodology (pp. 453-480). Boston: Blackwell Publishers.  (#73)

 

Muthén, B. & Curran, P. (1997).  General longitudinal modeling of individual differences in experimental designs:  A latent variable framework for analysis and power estimation.  Psychological Methods, 2, 371-402.  (#71)

 

Muthén, B. & Muthén, L. (2000).  The development of heavy drinking and alcohol-related problems from ages 18 to 37 in a U.S. national sample.  Journal of Studies on Alcohol, 61, 290-300.  (#83)

 

Muthén, L.K. and Muthén, B.O. (2002).  How to use a Monte Carlo study to decide on sample size and determine power.  Structural Equation Modeling, 4, 599-620. (#97)

 

Raudenbush, S.W. (1997).  Statistical analysis and optimal design for cluster randomized trials.  Psychological Methods, 2, 173-185.

 

Raudenbush, S.W. & Liu, X. (2000).  Statistical power and optimal design for multisite randomized trials.  Psychological Methods, 5, 199-213.

 

Satorra, A. & Saris, W. (1985).  Power of the likelihood ratio test in covariance structure analysis.  Psychometrika, 51, 83-90.