Although simulations can be useful for illustrative purposes or for exploring the effects of sample size and population variance on model statistics, the analysis of nonstatistical issues in structural equations should not rely on simulations. Acito and Anderson's results can be derived analytically, without simulations. Moreover, because of differences in the definition of measurement and theory, their results have limited relevance to what was suggested in our original article. In this reply we discuss these issues, question the usefulness of Acito and Anderson's approach, and further clarify the basic proposition of our original article—that reliance on covariance fit as the sole criterion for theory testing may give the analyst an incomplete basis for evaluation.
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References
1.
AcitoFranklin, and AndersonRonald D. (1984), “On Simulation Methods for Investigating Structural Modeling,”Journal of Marketing Research, 21(February), 107–12.
2.
BagozziRichard P. (1981), “Evaluating Structural Equation Models with Unobservable Variables and Measurement Error: A Comment,”Journal of Marketing Research, 18(August), 375–81.
3.
BeardenWilliam O., and SharmaSubhash, and TeelJesse E. (1982), “Sample Size Effects on Chi Square and Other Statistics Used in Evaluating Causal Models,”Journal of Marketing Research, 19(November), 425–30.
4.
BeardenWilliam O., and ShimpTerence (1982), “The Use of Extrinsic Cues to Facilitate Product Adoption,”Journal of Marketing Research, 19(May), 229–39.
5.
BeardenWilliam O., and TeelJesse (1983), “Selected Determinants of Consumer Satisfaction and Complaint Reports,”Journal of Marketing Research, 20(February), 21–8.
6.
BentlerPeter M., and BonettDouglas G. (1980), “Significance Tests and Goodness of Fit in the Analysis of Covariance Structures,”Psychological Bulletin, 88(3), 588–606.
7.
BielbyWilliam T., and HauserRobert M. (1977), “Structural Equation Models,”Annual Review of Sociology, 3, 137–61.
8.
De PijperW. M., and SarisW. E. (1982), “The Effect of Identification Restrictions on the Test Statistics in Latent Variable Models,” in Systems Under Indirect Observation: Causality, Structure, Prediction, JöreskogKarl, and WoldHerman, eds. Amsterdam: North Holland,175–82.
9.
FornellClaes(1983), “Issues in the Application of Covariance Structure Analysis: A Comment,”The Journal of Consumer Research, 9(March), 443–8.
10.
FornellClaes, and BooksteinFred L. (1982), “Two Structural Equation Models: LISREL and PLS Applied to Consumer Exit-Voice Theory,”Journal of Marketing Research, 18(November) 440–52.
11.
FornellClaes, and Gur-ArieOded (1983), “Validity Assessment in Covariance Structure Analysis via Selection Procedures,” in Winter Conference Proceedings.Chicago: American Marketing Association,253–7.
12.
FornellClaes, and LarckerDavid F. (1981a), “Evaluating Structural Equation Models with Unobservable Variables and Measurement Error,”Journal of Marketing Research, 18(February), 39–50.
13.
FornellClaes, and LarckerDavid F. (1981b), “Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics,”Journal of Marketing Research, 18(August), 383–8.
14.
FornellClaes, and RobinsonWilliam T. (1983), “Industrial Organization and Consumer Satisfaction/Dissatisfaction,”The Journal of Consumer Research, 9(March), 403–12.
15.
FornellClaes Gerhard, TellisJ., and ZinkhanGeorge M. (1982), “Validity Assessment: A Structural Equations Approach Using Partial Least Squares,” in Educators’ Conference Proceedings.Chicago: American Marketing Association,405–9.
16.
PunjGirish N., and StaelinRichard (1983), “A Model of Consumer Information Search Behavior for New Automobiles,”The Journal of Consumer Research,9(March), 366–80.
17.
RyanMichael J. (1982), “Behavior Intention Formation: The Interdependency of Attitudinal and Social Influence Variables,”The Journal of Consumer Research,9(December), 263–78.
18.
SörbomDag, and JöreskogKarl G. (1982), “The Use of Structural Equation Models in Evaluation Research,” in A Second Generation of Multivariate Analysis: Vol. II, Measurement and Evaluation, FornellClaes, ed. New York: Praeger,381–418.
