Advertisement

Latent Profile Analysis

  • Cody S. Ding
Chapter

Abstract

The MDS is discussed as a profile analysis approach of re-parameterizing the linear latent variable model in such a way that the latent variables can be interpreted in terms of profile patterns rather than factors. It is used to identify major patterns among psychological variables and can serve as the basis for further study of correlates and/or predictors of profiles and other background and external variables. I outline the procedure of MDS profile analysis and discuss the issues that are related to parameter estimation and interpretation of the results.

Keyword

Profile analysis 

References

  1. Affnso, V. C., Oakland, T. D., Larocca, R., & Spanakos, A. (2000). The course on individual cognitive assessment. School Psychology Review, 29(1), 52–64.Google Scholar
  2. Allport, G. W. (1937). Personality: A psychological interpretation. Oxford: Holt.Google Scholar
  3. Bechtel, G. G., Tucker, L. R., & Chang, W. C. (1971). A scalar product model for the multidimensional scaling of choice. Psychometrika, 36, 369–388.MathSciNetCrossRefGoogle Scholar
  4. Benzecri, J. P. (1969). Statistical analysis as a tool to make patterns emerge from data. In S. Watanabe (Ed.), Methodologies of pattern recognition (pp. 35–74). New York: Academic Press.CrossRefGoogle Scholar
  5. Bergman, L. R., & Magnusson, D. (1997). A person-oriented approach in research on developmental psychopathology. Development and Psychopathology, 9(2), 291–319.CrossRefGoogle Scholar
  6. Borg, I., & Groenen, P. J. F. (2005). Modern multidimensional scaling: Theory and applications (2nd ed.). New York, NY: Springer.Google Scholar
  7. Cattell, R. B. (1967). The three basic factor analytic research designs: Their interrelations and derivatives. In D. N. Jackson & S. Messick (Eds.), Problems in human assessment (pp. 288–299). New York: McGraw-Hill.Google Scholar
  8. Collins, L. M., & Lanza, S. T. (2010). Latent class and latent transition analysis: With applications in the social, behavioral, and health sciences. New York: Wiley.Google Scholar
  9. Cronbach, L. J., & Gleser, G. C. (1953). Assessing similarity between profiles. Psychological Bulletin, 50, 456–473.CrossRefGoogle Scholar
  10. Davison, M., & Jones, L. E. (1976). A similarity-attraction model for predicting sociometric choice from perceived group structure. Journal of Personality and Social Psychology, 33, 601–612.CrossRefGoogle Scholar
  11. Davison, M. L. (1983). Multidimensional scaling. New York: Wiley.zbMATHGoogle Scholar
  12. Davison, M. L. (1994). Multidimensional scaling models of personality responding. In S. Strack & M. Lorr (Eds.), Differentiating normal and abnormal personality (pp. 196–215). New York: Springer.Google Scholar
  13. 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.CrossRefGoogle Scholar
  14. Davison, M. L., & Sireci, S. G. (2000). Multidimensional scaling. In H. E. A. Tinsley & S. D. Brown (Eds.), Handbook of applied multivariate statistics and mathematical modeling (pp. 323–352). San Diego: Academic Press.CrossRefGoogle Scholar
  15. Davison, M. L., & Skay, C. L. (1991). Multidimensional scaling and factor models of test and item responses. Psychological Bulletin, 110, 551–556.CrossRefGoogle Scholar
  16. Ding, C. (2005). Determining the significance of scale values from multidimensional scaling profile analysis using a resampling method. Behavioral Research Methods, 37(1), 37–47.CrossRefGoogle Scholar
  17. Greenacre, M. J. (1984). Theory and applications of correspondence analysis. Orlando: Academic Press.zbMATHGoogle Scholar
  18. Kandel, D. B., & Davies, M. (1982). Epidemiology of depressive mood in adolescents. Archives of General Psychiatry, 39, 1205–1212.CrossRefGoogle Scholar
  19. Kruskal, J. B. (1964). Nonmetric scaling: A numerical method. Psychometrika, 29, 28–42.MathSciNetzbMATHGoogle Scholar
  20. Lamiell, J. T. (1981). Toward an idiothetic psychology of personality. American Psychologist, 36, 276–289.CrossRefGoogle Scholar
  21. Magnusson, D. (1998). The logic and implications of a person-oriented approach. In R. B. Cairns, L. R. Bergman, & J. Kagan (Eds.), Methods and models for studying the individual (pp. 33–64). Thousand Oaks: Sage.Google Scholar
  22. Muthen, B. O., & Muthen, L. K. (2000). Integrating person-centered and variable-centered analyses: Growth mixture modeling with latent trajectory classes. Alcoholism: Clinical and Experimental Research, 24, 882–891.CrossRefGoogle Scholar
  23. Paddock, J. R., & Nowicki, S. J. (1986). The circumplexity of Leary’s interpersonal circle: A multidimensional scaling perspective. Journal of Personality Assessment, 50, 279–289.CrossRefGoogle Scholar
  24. Pavot, W., & Diener, E. (1993). Review of the satisfaction with life scale. Psychological Assessment, 5, 164–172.CrossRefGoogle Scholar
  25. Petersen, A. C., Schulenberg, J., Abramowitz, R. H., Offer, D., & Jarcho, H. D. (1984). A self-image questionnaire for young adolescents (SIQYA): Reliability and validity studies. Journal of Youth and Adolescence, 13, 93–111.CrossRefGoogle Scholar
  26. Pfeiffer, S. I., Reddy, L. A., Kletzel, J. E., Schmelzer, E. R., & Boyer, L. M. (2000). The practitioners’s view of IQ testing and profile analysis. School Psychology Quarterly, 15, 376–385.CrossRefGoogle Scholar
  27. Russell, D., Peplau, L. A., & Cutrona, C. E. (1980). The revised UCLA loneliness scale: Concurrent and discriminant validity evidence. Journal of Personality and Social Psychology, 39, 472–480.CrossRefGoogle Scholar
  28. Speece, D. L., & Cooper, D. H. (1990). Ontogeny of school failure: Classification of first-grade children. American Educational Research Journal, 27, 119–140.CrossRefGoogle Scholar
  29. Tucker, L. R. (1960). Intra-individual and inter-individual multidimensionality. In H. Gulliksen & S. Messick (Eds.), Psychological scaling: Theory and applications (pp. 155–167). New York: Wiley.Google Scholar
  30. Weller, S. C., & Romney, A. K. (1990). Metric scaling: Correspondence analysis. Newbury Park: Sage.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Cody S. Ding
    • 1
    • 2
  1. 1.Department of Education Science and Professional ProgramUniversity of Missouri-St. LouisSt. LouisUSA
  2. 2.Center for NeurodynamicsUniversity of Missouri-St. LouisSt. LouisUSA

Personalised recommendations