Longitudinal studies. Desing and analysis models
DOI:
https://doi.org/10.24310/espsiescpsi.v2i1.13356Keywords:
Repeated measures designs, longitudinal data, repeated measures ANOVA, MANOVA, GMANOVA, linear mixed modelAbstract
The models that traditionally have been used to analyse repeated measure data are linear and follow an approach based on analysis of variance. Their main drawback is that they require balanced data, something that is difficult to achieve in applied contexts. Therefore, alternative models such as the study of growth curves have been developed, which in turn have been used to derive a large number of methods. These methods model both between- and within- individual variation and do not require balanced data. Today, linear mixed models are applied as a general analytical alternative. Mixed models estimate both the expected values of observations (fixed effects) and the variances and covariances of the observations (random effects). So what distinguishes the linear mixed model from the general linear model is the calculation of covariance parameters which allow the analysis of longitudinal data (correlated, incomplete, and with non-constant intervals between observations).
Downloads
Metrics
References
Albert, P. S. (1999). Longitudinal data analysis (repeated measures) in clinical trials. Statistics in Medicine, 18, 1707-1732.
Baltes, P. B. y Nesselroade, J. R. (1979). History and rationale of longitudinal research. En J. R. Nesselroade y P. B. Baltes (Eds.), Longitudinal research in the study of behaviour and development. New York: Academic Press.
Bock, R. D. (1975). Multivariate statistical methods in behavioural research. New York: McGraw-Hill.
Bock, R. D. (1979). Univariate and multivariate analysis of variance of time-structured data. En J. R. Nesselroade y P. B. Baltes (Eds.), Longitudinal research in the study of behavior and development. New York: Academic Press.
Bono, R., Arnau, J. y Vallejo, G. (2008). Técnicas de análisis aplicadas a datos longitudinales en Psicología y Ciencias de la Salud: Período 1985-2005. Papeles del Psicólogo, 29, 136-146.
Bryk, A. S. y Raudenbush S. W. (1992). Hierarchical linear models. Newbury Park, CA: Sage.
Cnaan, A., Laird, N. M. y Slasor, P. (1997). Using the general linear mixed model to analyse unbalanced repeated measures and longitudinal dada. Statistics in Medicine, 16, 2349-2380.
Davis, C. S. (1998). The analysis of longitudinal studies having non-normal responses. En B. S. Everitt y G. Dunn (Eds.), Statistical analysis of medical data. New developments. London: Arnold.
Diggle, P. J., Liang, K. Y. y Zeger, S. L. (1994). Analysis of longitudinal data. New York: Oxford University Press.
Edgington, E. (1974). A new tabulation of statistical procedures used in APA journals. American Psychologists, 29, 25-26.
Edwards, L. J. (2000). Modern statistical techniques for the analysis of longitudinal data in biomedical research. Pediatric Pulmony, 30, 330-344.
Elston, R. C. y Grizzle, J. F. (1962). Estimation of time response curves and their confidence bands. Biometrics,18, 148-159.
Finn, J. D. (1969). Multivariate analysis of repeated measures data. Multivariate Behavioral Research, 4, 391-413.
Fitzmaurice, G. M. (1998). Regression models for discrete longitudinal data. En B. S. Everitt y G. Dunn (Eds.), Statistical analysis of medical data. New developments. London: Arnold.
Gill, P. S. (2000). A robust mixed linear model analysis for longitudinal data. Statistics in Medicine, 19, 975- 987.
Goldstein, H. (1987). Multilevel models in educational and social research. London: Oxford University Press.
Gregoire, T. G., Brillinger, D. R. y Diggle, P. J. E. (Eds.) (1997). Modelling longitudinal and spatially correlated data. New York: Springer Verlag.
Grizzle, J. E. y Allen, D. M. (1969). Analysis of growth and dose response curves. Biometrics, 25, 357-381.
Hand, D. y Crowder, M. (1996). Practical longitudinal data analysis. London: Chapman & Hall.
Helms, R. W. (1992). Intentionally incomplete longitudinal designs: I. Methodology and comparison of some full span designs. Statistics in Medicine, 11, 1889-1993.
Hyunh, H. (1978). Some approximate tests for repeated measurement designs. Psychometrika, 43, 383- 386.
Keselman, H. J., Algina, J., Kowalchuk, R. K. y Wolfinger, R. D. (1999). A comparison of recent approaches to the analysis of repeated measurements. British Journal of Mathematical and Statistical Psychology, 52, 63-78.
Keselman, H. J. y Keselman, J. C. (1988). Comparing repeated measures means in factorial designs. Psychophysiology, 25, 612-618.
Kowalchuk, R. K., Keselman, H. J., Algina, J. y Wolfinger, R. D. (2004). The analysis of repeated measurements with mixed-model adjusted F tests. Educational and Psychological Measurement, 64, 224-242.
Laird, N. M. y Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38, 963- 974.
Liang, K. Y. y Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models. Biometrika, 73, 13-22.
Lindquist, E. F. (1953). Design and analysis of experiments in psychology and education. Boston, MA: Houghton Mifflin.
Littell, R. C., Henry, P. R y Ammerman, C. B. (1998). Statistical analysis of repeated measures data using SAS procedures. Journal of Animal Science, 76, 1216- 1231.
Littell, R. C., Milliken, G. A., Stroup, W. W. y Wolfinger, R. D. (1996). SAS system for mixed models. Cary, NC: SAS Institute.
Mauchley, J. W. (1940). Significance test of sphericity of a normal n-variate distribution. Annals of Mathematical Statistics, 11, 204-209.
Menard, S. (1991). Longitudinal research. Newbury Park, CA: Sage.
Morrison, D. F. (1976). Multivariate statistical methods. New York: McGraw Hill.
Nesselroade, J. R. y Baltes, P. B. (Eds.) (1979). Longitudinal research in the study of behaviour and development. New York: Academic Press.
Potthoff, R. F. y Roy, S. N. (1964). A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika, 51, 313-326.
Rao, C. R. (1958). Some statistical methods for the comparison of growth. Biometrics, 4, 1-17.
Rao, C. R. (1959). Some problems involving linearhypothesis in multivariate analysis. Biometrika, 46, 49-58.
Rao, C. R. (1965). The theory of least squares when the parameters are stochastic and its application to the analysis of growth curves. Biometrika, 52, 447-458.
Raudenbush, S.W. (2001). Comparing personal trajectories and drawing causal inferences from longitudinal data. Annual Review of Psychology, 52, 501-525.
Rogan, J. C., Keselman, H. J. y Mendoza, J. L. (1979). Analysis of repeated measurements. British Journal of Mathematical and Statistical Psychology, 32, 269-286.
Singer, J. D. y Willett, J. B. (2005). Longitudinal research: Current status and future prospects. Consulta 12 de septiembre de 2007 de la World Wide Web: http://www.gseweb.harvard.edu/~faculty/singer/Presentations /Longitudinal%20research.ppt
Stevens, J. (1996). Applied multivariate statistics for the social sciences. Hillsdale, NJ: Lawrence Erlbaum.
Sullivan, L. M., Dukes, K. A. y Losina, E. (1999). An introduction to hierarchical linear modelling. Statistics in Medicine, 18, 855-888.
Tatsuoka, M. M. (1988). Multivariate analysis: Techniques for educational and psychological research (2ª ed.). New York: John Wiley
Timm, N. H. (1975). Multivariate analysis with application in education and psychology. Monterey: Brooks/Kole.
Timm, N. H. (1980). Multivariate analysis of variance of repeated measurements. En P. R. Krishnaiah (Ed.), Handbook of statistics (vol. I). Amsterdam: North- Holland.
Timm, N. H. y Mieczkowski, T. A. (1997). Univariate and multivariate general linear models: theory and applications using SAS® software. Cary, NC: SAS Institute Inc.
Van der Leeden, R. (1998). Multilevel analysis of repeated measures data. Quality & Quantity, 32, 15- 29.
Van der Leeden, R., Vrijburg, K. y De Leeuw, J. (1996). A review of two different approaches for the analysis of growth data using longitudinal mixed linear models: comparing hierarchical linear regression (ML3, HLM) and repeated measures design with structures covariance matrices (BMDP5V). Computational Statistics and Data Analysis, 21, 581-605.
Verbeke, G. y Molenberghs, G. (1997). Linear mixedmodels in practice. New York: Springer-Verlag.
Visser, R. A. (1985). Analysis of longitudinal data in behavioural and social research. Leiden: DSWO Press.
Wall, W. D. y Williams, H. L. (1970). Longitudinal studies and the social sciences. London: Heinemann.
Ware, J. H. y Liang, K. Y. (1996). The design and analysis of longitudinal studies: a historical perspective. En P. Armitage y H. A. David (Eds.), Advances in biometry. New York: John Wiley. Wolfinger, R. D. (1996). Heterogeneous variancecovariance structures for repeated measurements. Journal of Agricultural, Biological, and Enviromental Statistics, 1, 205-230.
Wu, Y. B., Clopper, R. y Wooldridge, P. J. (1999). A comparison of traditional approaches to hierarchical lineal modeling when analyzing longitudinal data. Research in Nursing & Healt, 22, 421-432.
Zeger S. L. y Liang, K. Y. (1992). An overview of methods for the analysis of longitudinal dada. Statistics in Medicine, 11, 1825-1839.
Zhang, D. (2004). Generalized linear mixed models with varying coefficients for longitudinal data. Biometrics, 60, 8-15.
Downloads
Published
How to Cite
Issue
Section
License
All contents published in Escritos de Psicología are protected under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. All about this license is available in the following link: <http://creativecommons.org/licenses/by-nc-sa/4.0>
Users can copy, use, redistribute, share and exhibit publicly as long as:
- The original source and authorship of the material are cited (Journal, Publisher and URL of the work).
- It is not used for comercial purposes.
- The existence of the license and its especifications are mentioned.
There are two sets of authors’ rights: moral and property rights. Moral rights are perpetual prerogatives, unrenounceable, not-transferable, unalienable, imprescriptible and inembargable. According to authors’ rights legislation, Escritos de Psicología recognizes and respects authors moral rights, as well as the ownership of property rights. The property rights are referred to the benefits that are gained by the use or the dissemination of works. Escritos de Psicología is published in an open access form and it is exclusively licenced by any means for doing or authorising distribution, dissemination, reproduction, , adaptation, translation or arrangement of works.
Authors are responsable for obtaining the necessary permission to use copyrighted images.
10.png)
ESCRITOS DE PSICOLOGÍA por 