On the use of structural equation models in experimental designs: Two extensions

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Abstract

Bagozzi and Yi (1989) recently introduced new procedures for using structural equation models in experimental designs with lisrel. We extend their research by showing that the structural equation analysis of experimental designs can be accomplished via Wold's partial least squares (pls) approach, which can be used without many of the assumptions necessary for maximum likelihood estimation in lisrel. We show that pls is applicable not only to the basic design, but also to other complex designs. We also identify two restrictive assumptions implicit in Bagozzi and Yi's step-down analysis procedures, and describe a more general approach that can be used even when these assumptions are not met. The proposed procedures are illustrated with Bagozzi and Yi's data, and the conditions suitable for alternative procedures are discussed.

References (27)

  • T. Dijkstra

    Some comments on maximum likelihood and partial least squares methods

    Journal of Econometrics

    (1983)
  • J.C. Anderson et al.

    The effect of sampling error on convergence, improper solutions, and goodness-of-fit indices for maximum likelihood confirmatory factor analysis

    Psychometrika

    (1984)
  • R.P. Bagozzi et al.

    On the use of structural equation models in experimental designs

    Journal of Marketing Research

    (1989)
  • D.W. Barclay

    Jackknifing in pls

    (1983)
  • M.S. Bartlett

    The use of transformations

    Biometrics

    (1947)
  • W.O. Bearden et al.

    Sample size effects on chi square and other statistics used in evaluating causal models

    Journal of Marketing Research

    (1982)
  • BMDP Statistical Software Manual

    (1988)
  • G.E.P. Box et al.

    Analysis of transformations

    Journal of Royal Statistical Society

    (1964)
  • J.H. Bray et al.

    Multivariate analysis of variance

    (1985)
  • M.W. Browne

    Asymptotically distribution-free methods for the analysis of covariance structures

    British Journal of Mathematical and Statistical Psychology

    (1984)
  • B. Cooil et al.

    Cross-validation for prediction

    Journal of Marketing Research

    (1987)
  • B. Efron et al.

    A leisurely look at the bootstrap, the jackknife, and cross-validation

    The American Statistician

    (1983)
  • I. Fenwick

    Techniques in market measurement: The jackknife

    Journal of Marketing Research

    (1979)
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    The authors thank the editor and anonymous 1JRM reviewers for their helpful comments on a previous version of this article. The financial assistance of The University of Michigan's School of Business Administration is also gratefully acknowledged.

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