Abstract
This paper discusses how Multidimensional Scaling (MDS) can provide an exploratory technique for identifying major growth profiles, which may be indicative of growth associated with subgroups. It briefly overviews the conventional growth models and growth mixture models, examines the assumptions related to these models, and indicates some limitations associated with these models. It then proposes an exploratory growth profile analysis using the MDS model as a complement to more specification-oriented techniques. It describes the Profile Analysis via Multidimensional Scaling model (PAMS) and extends the model for longitudinal data. The MDS profile model can solve for the growth parameters such that each MDS dimension corresponds to a major growth profile. It is argued that the MDS model provides an exploratory tool for identifying growth trends and studying individual differences with respect to those growth trends. Since MDS has not traditionally been used for longitudinal studies, the MDS growth analysis can serve as the basis for studies of the kind discussed in the paper.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Amsel E., Renninger K.A. (eds) (1997). Change and Development: Issues of Theory, Method, and Application. Lawrence Erlbaum Associates, Mahwah, New Jersey
Bauer J.D., Curran P.J. (2003). Distributional assumptions of growth mixture models: Implications for overextraction of latent trajectory classes. Psychological Methods 8: 338–363
Behrens J.T. (1997). Principles and procedures of exploratory data analysis. Psychological Methods 2: 131–160
Davison M.L. (1983). Multidimensional Scaling. Wiley, New York
Davison M.L. (1994). Multidimensional scaling models of personality responding. In: Strack S., Lorr M. (eds) Differentiating Normal and Abnormal Personality. Springer, New York, pp. 196–215
Davison M.L., Gasser M., Ding S. (1996). Identifying major profile patterns in a population: An exploratory study of WAIS and GATB patterns. Psychological Assessment 8: 26–31
Hix-Small H., Duncan T.E., Duncan S.C., Okut H. (2004). A multivariate associative finite growth mixture modeling approach examining adolescent alcohol and marijuana use. Journal of Psychopathology and Behavioral Assessment 26(4): 255–270
Li F., Duncan T.E., Duncan S.C. (2001). Latent growth modeling of longitudinal data: A finite growth mixture modeling approach. Structural Equation Modeling 8: 493–530
Muthen B.O. (1989). Latent variable modeling in heterogeneous populations. Psychometrika 54: 557–587
Muthen B.O. (2001). Second-generation structural equation modeling with a combination of categorical and continuous latent variables: New opportunities for latent class/latent growth modeling. In: Collins L.M., Sayer A. (eds) New Methods for the Analysis of Change. APA, Washington, DC, pp. 291–322
Nagin D. (1999). Analyzing developmental trajectories: a semi-parametric, group-based approach. Psychological Methods 4: 139–157
Nagin D., Tremblay R. (2001). Analyzing developmental trajectories of distinct but related behaviors: A group-based method. Psychological Methods 6: 18–34
SAS 9.1 Language Reference: Dictionary (2004). Cary, NC: SAS Institute Inc.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ding, C. Modeling Growth Data Using Multidimensional Scaling Profile Analysis. Qual Quant 41, 891–903 (2007). https://doi.org/10.1007/s11135-006-9031-9
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11135-006-9031-9