Estimating, testing, and comparing specific effects in structural equation models: the phantom model approach.

The phantom model approach for estimating, testing, and comparing specific effects within structural equation models (SEMs) is presented. The rationale underlying this novel method consists in representing the specific effect to be assessed as a total effect within a separate latent variable model, the phantom model that is added to the main model. The following favorable features characterize the method: (a) It enables the estimation, testing, and comparison of arbitrary specific effects for recursive and nonrecursive models with latent and manifest variables; (b) it enables the bootstrapping of confidence intervals; and (c) it can be applied with all standard SEM programs permitting latent variables, the specification of equality constraints, and the bootstrapping of total effects. These features along with the fact that no manipulation of matrices and formulas is required make the approach particularly suitable for applied researchers. The method is illustrated by means of 3 examples with real data sets.

[1]  M. Sobel Some New Results on Indirect Effects and Their Standard Errors in Covariance Structure Models , 1986 .

[2]  Kenneth A. Bollen,et al.  Total, Direct, and Indirect Effects in Structural Equation Models , 1987 .

[3]  L. A. Kurdek,et al.  The Nature and Predictors of the Trajectory of Change in Marital Quality Over the First 4 Years of Marriage for First-Married Husbands and Wives , 1998 .

[4]  Mike W.-L. Cheung,et al.  Comparison of Approaches to Constructing Confidence Intervals for Mediating Effects Using Structural Equation Models , 2007 .

[5]  Kenneth A. Bollen,et al.  Structural Equations with Latent Variables , 1989 .

[6]  G. A. Marcoulides,et al.  Advanced structural equation modeling : issues and techniques , 1996 .

[7]  E. Ziegel,et al.  Bootstrapping: A Nonparametric Approach to Statistical Inference , 1993 .

[8]  Clark C. Presson,et al.  Multivariate applications in substance use research : new methods for new questions , 2000 .

[9]  Wei-Chi Tsai,et al.  Test of a model linking employee positive moods and task performance. , 2007, The Journal of applied psychology.

[10]  Erik Woody,et al.  Structural equation models for interchangeable dyads: being the same makes a difference. , 2005, Psychological methods.

[11]  D. Mackinnon Contrasts in multiple mediator models. , 2000 .

[12]  James L. Arbuckle,et al.  Full Information Estimation in the Presence of Incomplete Data , 1996 .

[13]  David P Mackinnon,et al.  Confidence Limits for the Indirect Effect: Distribution of the Product and Resampling Methods , 2004, Multivariate behavioral research.

[14]  R. Purc-Stephenson,et al.  Reporting practices in confirmatory factor analysis: an overview and some recommendations. , 2009, Psychological methods.

[15]  D. Rindskopf Using phantom and imaginary latent variables to parameterize constraints in linear structural models , 1984 .

[16]  K. Bollen,et al.  DIRECT AND INDIRECT EFFECTS: CLASSICAL AND BOOTSTRAP ESTIMATES OF VARIABILITY , 1990 .

[17]  D. A. Kenny,et al.  Models of Non-Independence in Dyadic Research , 1996 .

[18]  P. Shrout,et al.  Mediation in experimental and nonexperimental studies: new procedures and recommendations. , 2002, Psychological methods.

[19]  William D. Berry Nonrecursive Causal Models , 1984 .