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Partial least squares analysis in developmental psychopathology

Published online by Cambridge University Press:  31 October 2008

Robert D. Ketterlinus ,
Fred L. Bookstein ,
Paul D. Sampson  and
Michael E. Lamb
Robert D. Ketterlinus*
Affiliation:
National Institute of Child Health and Human Development
Fred L. Bookstein
Affiliation:
University of Michigan
Paul D. Sampson
Affiliation:
University of Washington
Michael E. Lamb
Affiliation:
National Institute of Child Health and Human Development
*
Address requests for reprints and copies of the computer program to: Robert D. Ketterlinus, SSED/LCE/NICHD, BSA Building – Room 331, 9000 Rock-ville Pike, Bethesda MD 20892.
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Abstract

Despite extensive theoretical and empirical advances in the last two decades, little attention has been paid to the development of statistical techniques suited for the analysis of data gathered in studies of developmental psychopathology. As in most other studies of developmental processes, research in this area often involves complex constructs, such as intelligence and antisocial behavior, measured indirectly using multiple observed indicators. Relations between pairs of such constructs are sometimes reported in terms of latent variables (LVs): linear combinations of the indicators of each construct. We introduce the assumptions and procedures associated with one method for exploring these relations: partial least squares (PLS) analysis, which maximizes covariances between predictor and outcome LVs; its coefficients are correlations between observed variables and LVs, and its LVs are sums of observable variables weighted by these correlations. In the least squares logic of PLS, familiar notions about simple regressions and principal component analyses may be reinterpreted as rules for including or excluding particular blocks in a model and for “splitting” blocks into multiple dimensions. Guidelines for conducting PLS analyses and interpreting their results are provided using data from the Goteborg Daycare Study and the Seattle Longitudinal Prospective Study on Alcohol and Pregnancy. The major advantages of PLS analysis are that it (1) concisely summarizes the intercorrelations among a large number of variables regardless of sample size, (2) yields coefficients that are readily interpretable, and (3) provides straightforward decision rules about modeling. The advantages make PLS a highly desirable technique for use in longitudinal research on developmental psychopathology. The primer is written primarily for the nonstatistician, although formal mathematical details are provided in Appendix 1.

Type
Research Article
Information
Development and Psychopathology , Volume 1 , Issue 4 , October 1989 , pp. 351 - 371
Copyright
Copyright © Cambridge University Press 1989

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References

Achenbach, T. (1990). What is “developmental” about developmental psychopathology? In Rolf, J., Matsen, A., Cicchetti, D., Neuchterlein, K., & Wein-traub, S. (Eds.), Risk and protective factors in the development of psychopathology (pp. 29–48). New York: Cambridge University Press.CrossRefGoogle Scholar
Biddle, B. J., & Marlin, M. M. (1987). Causality, confirmation, credulity, and structural equation modeling. Child Development, 58, 4–17.CrossRefGoogle Scholar
Bookstein, F. L. (1982). The geometric meaning of soft modeling, with some generalizations. In Wold, H. & Joreskog, K. (Eds.), Systems under indirect observation: Causality, structure, prediction (Vol. II, pp. 55–74). Amsterdam: North-Holland.Google Scholar
Bookstein, F. L. (1986). The elements of latent variable models: A cautionary lecture. In Lamb, M. E., Brown, A. L., & Rogoff, B. (Eds.), Advances in developmental psychology (Vol. 4, pp. 203–230). Hillsdale, NJ: Erlbaum.Google Scholar
Broberg, A., Hwang, C.-R., Lamb, M. E., & Ket-terlinus, R. D. (1989). Child care effects on socioemotional and intellectual competence in Swedish preschoolers. In Lande, J. S., Scarr, S., & Gunzenhauser, N. (Eds.), Caring for children: Challenge to America (pp. 49–76). Hillsdale, NJ: Erlbaum.Google Scholar
Broberg, A., Lamb, M. E., Hwang, C.-P., & Book-stein, F. L. (1987). Determinants of verbal abilities in Swedish preschoolers. Unpublished manuscript, University of Goteborg.Google Scholar
Cicchetti, D. (1984). The emergence of developmental psychopathology. Child Development, 55, 1–7.CrossRefGoogle ScholarPubMed
Cicchetti, D. (1989). Developmental psychopathology: Some thoughts on its evolution. Development and Psychopathology, 1, 1–4.CrossRefGoogle Scholar
Cicchetti, D. (1990). An historical perspective on the discipline of developmental psychopathology. In Rolf, J., Masten, A., Cicchetti, D., Neuchterlein, K., & Weintraub, S. (Eds.), Risk and protective factors in the development of psychopathology (pp. 2–28). New York: Cambridge University Press.CrossRefGoogle Scholar
Cicchetti, D., & Aber, J. L. (1986). Early precursors of later depression: An organizational perspective. In Lipsitt, L. & Rovee-Collier, C. (Eds.), Advances in infancy (Vol. 4, pp. 87–138). Norwood, NJ: Ablex.Google Scholar
Cicchetti, D., & Schneider-Rosen, K. (1984). Toward a transactional model of childhood depression. In Cicchetti, D. & Schneider-Rosen, K. (Eds.), Childhood depression. New directions in child development (No. 26, pp. 5–27). San Francisco: Jossey-Bass.Google Scholar
Cicchetti, D., & Sroufe, L. A. (1978). An organizational view of affect: Illustration from the study of Down syndrome infants. In Lewis, M. & Rosenblum, L. (Eds.), The development of affect (pp. 309–350). New York: Plenum.CrossRefGoogle Scholar
Engel, G. (1977). The need for a new medical model: A challenge for biomedicine. Science, 196, 129–135.CrossRefGoogle Scholar
Fornell, C., & Bookstein, F. L. (1982). Two structural equations models: LISREL and PLS applied to market data. Journal of Marketing Research, 19, 440–452.CrossRefGoogle Scholar
Freedman, D. A. (1987). As others see us: A case study in path analysis. Journal of Educational Statistics, 12, 101–128.CrossRefGoogle Scholar
Golub, G. H., & Reinsch, C. (1971). Singular value decomposition and least squares solutions. In Wilkinson, J. H. & Reinsch, C. (Eds.), Linear algebra. Die Grundlehren der mathematischen Wissenchafteen (Vol. 186, pp. 134–151). New York: Springer Verlag.CrossRefGoogle Scholar
Jones, K. L., & Smith, D. W. (1973). Recognition of the fetal alcohol syndrome in early infancy. Lancet, ii, 999–1001.CrossRefGoogle Scholar
Joreskog, K. G., & Sorbom, D. (1984). LISREL IV: Analysis of structural relationships by maximum likelihood, instrumental variables, and least squares methods (3rd ed.). Mooresville, IN: Scientific Software.Google Scholar
Joreskog, K. G., & Wold, H. (Eds.) (1982). Systems under indirect observation: Casuality, structure, prediction (Vols. 1 & 2). Amsterdam: North Holland.Google Scholar
Lamb, M. E., Hwang, C.-R., Bookstein, F. L., Broberg, A., Hult, G., & Frodi, M. (1988). The development of social competence in Swedish preschoolers. Developmental Psychology, 24, 58–70.CrossRefGoogle Scholar
Lamb, M. E., Hwang, C.-R., Broberg, A., & Book-stein, F. L. (1988). The effects of out-of-home care on the development of social competence in Sweden: A longitudinal study. Early Childhood Research Quarterly, 3, 379–402.CrossRefGoogle Scholar
Lohmoller, J.-B. (1984). VPLS program manual. Latent variables path analysis with partial leastsquares analysis. Koln: University of Koln.Google Scholar
Lohmoller, J.-B., & Wold, H. (1980). Three-mode path models with latent variables and Partial Least Squares estimates. Research report 80–03, Hochschule der Bundeswehr, Munich.Google Scholar
Maccoby, E. E., & Martin, J. A. (1983). Socialization in the context of the family: Parent-child interactions. In Mussen, P. H. (Ed.), Handbook of child psychology, (Vol. IV, pp. 1–101). New York: Wiley.Google Scholar
Muthen, B. O. (1987). Response to Freedman's critique of path analysis: Improve credibility by better methodological training. Journal of Educational Statistics, 12, 178–184.Google Scholar
Myklebust, H. R. (1981). The pupil rating scale revised: Screening for learning disabilities. New York: Grune & Stratton.Google Scholar
Rutter, M., & Garmezy, N. (1983). Developmental psychopathology. In Hetherington, E. M. (Ed.), Carmichael's manual of child psychology Vol. 4: Social and Personality Development (pp. 775–911) New York: Wiley.Google Scholar
Sameroff, A. J. (1983). Developmental systems: Contexts and evolution. In Mussen, P. H. (Ed.), Handbook of child psychology (Vol. I, pp. 237–294). New York: Wiley.Google Scholar
Sameroff, A. J., & Chandler, M. J. (1975). Reproductive risk and the continuum of caretaking causally. In Horowitz, F. D., Hetherington, M., Scarr-Salaptek, S., & Siegel, G. (Eds.), Review of child development research (Vol. 4, pp. 187–244). Chicago: University of Chicago Press.Google Scholar
Sampson, P. D., Streissguth, A. P., Barr, H. M., & Bookstein, F. L. (1989). Neurobehavioral effects of prenatal alcohol, Part II: Partial Least Squares analyses. Neurotoxocology and Teratology, 11, 477–491.CrossRefGoogle Scholar
Sampson, P. D., Streissguth, A. P., Vega-Gonzalez, S. C., Barr, H. M., & Bookstein, F. L. (1987). Predicting classroom behavior ratings by prenatal alcohol exposure: Latent variable modeling and nonlinear scaling. Technical Report No. 103, Department of Statistics, University of Washington, Seattle, WA.Google Scholar
Silvern, L. (1984). Emotional-behavioral disorders: A failure of system functions. In Gollin, E. (Ed.), Malformations of development (pp. 95–152). New York: Academic.Google Scholar
Sroufe, L. A. (1979). The coherence of individual development. American Psychologist, 34, 834–841.CrossRefGoogle Scholar
Sroufe, L. A., & Rutter, M. (1984). The domain of developmental psychopathology. Child Development, 55, 17–29.CrossRefGoogle ScholarPubMed
Sroufe, L. A., & Waters, E. (1977). Attachment as an organizational construct. Child Development, 48, 1184–1199.CrossRefGoogle Scholar
Sternberg, K. J., Lamb, M. E., Hwang, C.-P., Broberg, A., Ketterlinus, R. D., & Bookstein, F. L. (1988). Does out-of-home care affect compliance in preschoolers? Unpublished manuscript, National Institute of Child Health and Human Development.Google Scholar
Streissguth, A. P., Martin, D. C., Martin, J. C., & Barr, H. M. (1981). The Seattle longitudinal prospective study on alcohol and pregnancy. Neurobehavioral Toxicology and Teratology, 3, 223–233.Google ScholarPubMed
Tanaka, J. S. (1987). “How big is big enough?”: Sample size and goodness of fit in structural equations models with latent variables. Child Development, 58, 134–146.CrossRefGoogle Scholar
Wold, H. (1975). Path models with latent variables: The NIPALS approach. In Blalock, H. M., Borodkin, F. M., Boudon, R., & Capecchi, V. (Eds.), Quantitative sociology: International perspectives on mathematical and statistical modeling (pp. 307–357). New York: Academic.CrossRefGoogle Scholar
Wold, H. (1982). Soft modeling: The basic design and some extensions. In Wold, H. & Joreskog, K. (Eds.), Systems under indirect observation: Causality, structure, prediction (Vol. II, pp. 55–74). Amsterdam: North-Holland.Google Scholar
Wold, H. (1985). Partial least squares. In Kotz, S. & Johnson, N. L. (Eds.), Encyclopedia of statistical sciences (Vol. 6, pp. 581–591). New York: Wiley.Google Scholar