La véritable explosion de l'utilisation des modèles d'équations structurelles semble toucher bon nombre de domaines dont le marketing. Malheureusement, ce phénomène a bien souvent pour conséquence une mauvaise spécification des modèles, ou une interprétation « peu stricte » des résultats. Cet article s'attache à revisiter la nature formative ou réflective des indicateurs, laquelle a d'énormes conséquences sur l'estimation des paramètres impliqués et plus généralement sur la validité du modèle. Une synthèse des travaux portant sur les conséquences d'une mauvaise spécification et les solutions à apporter est proposée. La conclusion est en forme d'interrogation : les indicateurs formatifs ont-ils leur place au sein des modèles d'équations structurelles ?
AndersonJ.C.GerbingD.W. (1988), Structural equation modeling in practice: A review and recommended twostep approach, Psychological Bulletin, 103, 3, 411–423.
2.
AndersonR.D.VastagG. (2004), Causal modeling alternatives in operations research: Overview and application, European Journal of Operational Research, 156, 1, 92–111.
3.
BagozziR.P. (1977), Structural equation models in experimental research, Journal of Marketing Research, 14, 2, 209–226.
4.
BagozziR.P. (1984), A prospectus for theory construction in marketing, Journal of Marketing, 48, 1, 11–30.
5.
BagozziR.P. (1994), Structural equation models in marketing research: Basic principles, Principles of marketing research, éd. BagozziR.P., Cambridge, MA, Blackwell, 317–385.
6.
BagozziR.P.FornellC. (1982), Theoretical concepts, measurement, and meaning, A second generation of multivariate analysisVol. 2, éd. FornellC., New York, Praeger, 24–38.
7.
BaumgartnerH.HomburgC. (1996), Applications of structural equation modeling in marketing and consumer research: A review, International Journal of Research in Marketing, 13, 2, 139–162.
8.
BlalockH.M. (1964), Causal inferences in nonexperimental research, Chapel Hill, University of North Carolina Press.
9.
BollenK.A. (1984), Multiple indicators: Internal consistency or no necessary relationship?Quality and Quantity, 18, 377–385.
10.
BollenK.A. (1989), Structural equations with latent variables, Series in probability and mathematical statistics, New York, Wiley.
11.
BollenK.A.LennoxR. (1991), Conventional wisdom on measurement: A structural equation perspective, Psychological Bulletin, 110, 2, 305–314.
12.
BollenK.A. (1996), An alternative two stage least squares (2SLS) estimator for latent variable equations, Psychometrika, 61, 1, 109–121.
13.
BollenK.A. (2001), Indicator: Methodology, International encyclopedia of the social and behavioral sciences, éds. SmelserN.J.BaltesP.B., Oxford, UK, Elsevier Science, 7282–7287.
14.
BollenK.A.TingK. (1993), Confirmatory tetrad analysis, Sociological methodology, éd. MarsdenP.M., Washington DC, American Sociological Association, 147–175.
15.
BollenK.A.TingK.F. (2000), A tetrad test for causal indicators, Psychological Methods, 5, 1, 3–22.
16.
BorsboomD.MellenberghG.van HeerdenJ. (2003), The theoretical status of latent variables, Psychological Review, 110, 2, 203–219.
17.
BrannickM.T. (1995), Critical comments on applying covariance structure modelling, Journal of Organizational Behavior, 16, 3, 201–214.
18.
BrecklerS.J. (1990), Application of covariance structure modeling in psychology: Cause for concern?, Psychological Bulletin, 107, 2, 260–272.
19.
BuffaE.S.DyerJ.S. (1977), Managerial use of dynamic structural models, Decision Sciences, 8, 1, 73–94.
20.
ChinW.W.GopalA. (1995), Adoption intention in GSS: Relative importance of beliefs, Data Base, 26, 3, 42–63.
21.
ChinW.W. (1998), Issues and opinion on structural equation modelling, Management Information Systems Quaterly, 22, 1, 7–16.
22.
ChowG.C. (1960), Tests of equality between sets of coefficients in two linear regressions, Econometrica, 28, 3, 591–605.
23.
ChurchillG.A. (1979), A paradigm for developing better measures of marketing constructs, Journal of Marketing Research, 16, 1, 64–73.
24.
CliffN. (1983), Some cautions concerning the application of causal modelling methods, Multivariate Behavioral Research, 18, 1, 115–126.
25.
CohenP.CohenJ.TeresiJ.MarchiM.VelezC.N. (1990), Problems in the measurement of latent variables in structural equations causal models, Applied Psychological Measurement, 14, 2, 183–196.
26.
CostnerH.L. (1969), Theory, deduction and the rules of correspondence, American Journal of Sociology, 75, 2, 245–263.
27.
CronbachL.J. (1951), Coefficient alpha and the internal structure of tests, Psychometrika, 16, 3, 297–334.
28.
CurranP.J. (2003), Have multilevel models been structural equation models all along?, Multivariate Behavioral Research, 38, 4, 529–569.
29.
DiamantopoulosA. (2005), The C-OAR-SE procedure for scale development in marketing: A comment, International Journal of Research in Marketing, 22, 1, 1–9.
30.
DiamantopoulosA.WinklholferH.M. (2001), Index construction with formative indicators: An alternative to scale development, Journal of Marketing Research, 38, 2, 269–277.
31.
DudgeonP.LiP. (2004), Report on SEMNET: questionnaire about inspecting fit indices, papier de recherche, Département de psychologie, Université de Melbourne, Vic.
32.
EdwardsJ.R.BagozziR.P. (2000), On the nature and direction of relationships between constructs and measures, Psychological Methods, 5, 2, 155–174.
33.
EdwardsJ.R. (2001), Multidimensional constructs in organizational behavior research: An integrative analytical framework, Organizational Research Methods, 4, 2, 144–192.
34.
FornellC.BooksteinF.L. (1982), Two structural equation models: LISREL and PLS applied to consumer exitvoice theory, Journal of Marketing Research, 19, 4, 440–453.
35.
FornellC.RheeB.YiY. (1991), Direct regression, reverse regression, and covariance structure analysis, Marketing Letters, 2, 3, 309–320.
36.
FornellC. (1992), A national customer satisfaction barometer: The Swedish experience, Journal of Marketing, 56, 1, 6–21.
37.
FornellC.JohnsonM.D.AndersonE.W.ChaJ.BryantB.E. (1996), The American customer satisfaction index: Nature, purpose and findings, Journal of Marketing, 60, 4, 7–18.
38.
GerbingD.W.AndersonJ.C. (1984), On the meaning of within-factor correlated measurement errors, Journal of Consumer Research, 11, 1, 572–582.
39.
GlaserD.N. (2002), Structural equation modeling texts: A primer for the beginner, Journal of Clinical Child and Adolescent Psychology, 31, 4, 573–578.
40.
HauserR.M.GoldbergerA.S. (1971), The treatment of unobservable variables in path analysis, Sociological methodology, éd. CostnerH.L., San Francisco, Jossey-Bass, 81–117.
41.
HauserR.M. (1973), Disaggregating a social-psychological model of educational attainment, Structural equation models in the social sciences, éds. GoldbergerA.S.DuncanO.D., New York, Academic Press, 255–284.
42.
HippJ.R.BollenK.A. (2003), Model fit in structural equation models with censored, ordinal, and dichotomous variables: Testing vanishing tetrads, Sociological Methodology, 33, 9, 267–305.
43.
HippJ.R.BauerD.J.BollenK.A. (2005), Conducting tetrad tests of model fit and contrasts of tetrad-nested models: A new SAS macro, Structural Equation Modeling, 12, 1, 76–93.
44.
JarvisC.B.MackenzieS.B.PodsakoffP.M. (2003), A critical review of construct indicators and measurement model misspecification in marketing and consumer research, Journal of Consumer Research, 30, 2, 199–218.
45.
JöreskogK.G.GoldbergerA.S. (1975), Estimation of a model with multiple indicators and multiple causes of a single latent variable, Journal of the American Statistical Association, 70, 3, 631–639.
46.
JöreskogK.G.SörbomD. (1978), LISREL IV: analysis of linear structural relationships by the method of maximum likelihood, Chicago, National Educational Resources.
47.
JöreskogK.G.YangF. (1996), Nonlinear structural equation models: The Kenny-Judd model with interaction effects, Advanced structural equation modeling techniques, éds. MarcoulidesG.A.SchumackerR.E., Hillsdale, NJ, Lawrence Erlbaum, 57–88.
48.
JöreskogK.G. (1967), Some contributions to maximum likelihood factor analysis, Psychometrika, 32, 4, 443–482.
49.
JöreskogK.G. (1970), A general method for analysis of covariance structures, Biometrika, 57, 2, 239–251.
50.
JöreskogK.G. (1971), Simultaneous factor analysis in several populations, Psychometrika, 57, 4, 409–426.
51.
KaplanD. (2000), Structural equation modelling: foundations and extensions, Thousand Oaks, CA, Sage.
52.
KellowayE.K. (1995), Structural equation modeling in perspective, Journal of Organizational Behavior, 16, 3, 215–225.
53.
KennyD.JuddC.M. (1984), Estimating the nonlinear and interactive effects of latent variables, Psychological Bulletin, 96, 2, 201–210.
54.
KlineR.B. (2004), Principles and practice of structural equation modelling, 2e édition, New York, Guilford Publications.
55.
LawK.S.WongC. (1999), Multidimensional constructs in structural equation analysis: An illustration using the job perception and job satisfaction constructs, Journal of Management, 25, 2, 143–160.
56.
LordF.M.NovickM.R. (1968), Statistical theories of mental test scores, Reading, Londres, Addison-Wesley.
57.
MacCallumR.C.BrowneM.W. (1993), The use of causal indicators in covariance structure models: Some practical issues, Psychological Bulletin, 114, 3, 533–541.
58.
MacCallumR.C. (1998), Commentary on quantitative methods in industrial-organizational research, Ohio State University News Letter.
MacKenzieS.B. (2003), The danger of poor construct conceptualization, Journal of the Academy of Marketing Science, 31, 3, 323–326.
61.
McDonaldR.A.SeifertC.F.LorenzetS.J.GivensS.JaccardJ. (2002), The effectiveness of methods for analyzing multivariate factorial data, Organizational Research Methods, 5, 3, 255–275.
62.
MillJ.S. (1843), A system of logic, Londres, Oxford University Press.
63.
MuthénB.O.MasynK. (2004), Discrete-time survival mixture analysis, Journal of Educational and Behavioral Statistics, 30, 1, 27–58.
NunnallyJ.C.BernsteinI.H. (1994), Psychometric theory, 3e édition, New York, McGraw-Hill.
66.
NunnallyJ.C. (1978), Psychometric theory, 2e édition, New York, McGraw-Hill.
67.
PeterJ.P. (1981), Construct validity: A review of basic issues and marketing practices, Journal of Marketing Research, 18, 2, 133–146.
68.
PingR.A. (2003), Latent variable interactions and quadratics in survey data: a source book for theoretical model testing (2e édition), [on-line monograph], http://home.att.net/∼rpingjr/intquad/toc2.htm.
69.
PopperK. (1959, 1973), La logique de la découverte scientifique, Paris, Payot.
70.
PrescottC.A. (2004), Using the Mplus computer program to estimate models for continuous and categorical data from twins, Behavior Genetics, 34, 1, 17–40.
71.
ReillyM.D. (1982), Working wives and convenience consumption, Journal of Consumer Research, 8, 1, 407–417.
72.
RigdonE. (2004), Formative measurement: reality or illusion?, papier présenté à la conférence Winter AMA, San Diego, CA.
73.
RindskopfD.RoseT. (1988), Some theory and applications of confirmatory second order factor analysis, Multivariat Behavioral Research, 23, 1, 51–67.
74.
RindskopfD. (1984), Using phantom and imaginary latent variables to parameterize constraints in linear structural models, Psychometrika, 49, 37–47.
75.
RogersW.SchmittN. (2004), Parameters recovery and model fit using multidimensional composites: A comparison of four empirical parceling composites, Multivariate Behavioral Research, 39, 3, 379–413.
76.
RossiterJ.R. (2002), The C-OAR-SE procedure for scale development in marketing, International Journal of Research in Marketing, 19, 4, 305–335.
77.
ShookC.L.KetchenD.J.JrHultG.T.M.KacmarK.M. (2004), An assessment of the use of structural equation modeling in strategic management research, Strategic Management Journal, 25, 4, 397–404.
78.
SkrondalA.Rabe-HeskethS. (2005), Structural equation modeling: Categorical variables, Encyclopedia of statistics in behavioral science, New York, Wiley.
79.
SpearmanC. (1904), General intelligence, objectively determined and measured, American Journal of Psychology, 15, 201–293 (article téléchargeable à http://www.fa100.info/spearman.pdf).
80.
SteenkampJ-B.BaumgartnerH. (2000), On the use of structural equation models for marketing modelling, International Journal of Research in Marketing, 17, 2, 195–202.
81.
SteigerJ.H. (2001), Driving fast in reverse, the relationship between software development, theory, and education in structural equation modeling, Journal of the American Statistical Association, 96, 453, 331–338.
82.
StephensonM.T.HolbertR.L. (2003), A Monte-Carlo simulation of observable versus latent variable structural equation modeling techniques, Communication Research, 30, 3, 332–354.
83.
TenenhausM. (1998), La régression PLS: théorie et pratique, Paris, Technip.
TomarkenA.J.WallerN.G. (2003), Potential problems with « well fitting » models, Journal of Abnormal Psychology, 112, 4, 578–597.
87.
Valette-FlorenceP. (1988), Spécificité et apports des méthodes d'analyse multivariée de la deuxième génération, Recherche et Applications en Marketing, 3, 4, 23–56.
88.
WilliamsL.J. (1995), Covariance structure modeling in organizational research: Problems with the method versus applications of the method, Journal of Organizational Behavior, 16, 3, 225–234.
89.
WilliamsL.J.EdwardsJ.R.VandenbergR.J. (2003), Recent advances in causal modeling methods for organizational research, Journal of Management, 29, 6, 903–936.
90.
WoldH. (1966), Estimation of principal components and related models by iterative least squares, Multivariate analysis, éd. KrishnaiahP.R., New York, Academic Press, 391–420.
91.
WoldH. (1982), Soft modeling: The basic design and some extensions, System under indirect observation, vol. 2, éds. JöreskogK.G.WoldH., Amsterdam, North Holland, 1–54.
92.
WoldH. (1985), Partial least square, Encyclopedia of statistical sciences, éds. KotzS.JohnsonN., New York, Wiley, 581–591.
