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Principles for reporting analyses using structural equation modeling are reviewed, with the goal of supplying readers with complete and accurate information. It is recommended that every report give a detailed justification of the model used, along with plausible alternatives and an account of identifiability. Nonnormality and missing data problems should also be addressed. A complete set of parameters and their standard errors is desirable, and it will often be convenient to supply the correlation matrix and discrepancies, as well as goodness-of-fit indices, so that readers can exercise independent critical judgment. A survey of fairly representative studies compares recent practice with the principles of reporting recommended here.

Structural equation modeling (SEM), also known as path analysis with latent variables, is now a regularly used method for representing dependency (arguably “causal”) relations in multivariate data in the behavioral and social sciences. Following the seminal work of Jöreskog (1973), a number of models for linear structural relations have been developed (Bentler & Weeks, 1980; Lohmoller, 1981; McDonald, 1978), and work continues on these. Commercial statistical packages include LISREL (Jöreskog & Sörbom, 1989, 1996), EQS (Bentler, 1985, 1995), CALIS (Hartmann, 1992), MPLUS (Muthén & Muthén, 1998), RAMONA (Browne, Mels, & Cowan, 1994), SEPATH (Steiger, 1995), and AMOS (Arbuckle, 1997). Available freeware includes COSAN (Fraser & McDonald, 1988) and Mx (Neale, 1997).

McArdle and McDonald (1984) proved that different matrix formulations of a path model with latent variables are essentially equivalent. Programs such as those listed supply essentially the same basic information, with minor variations in the details supplied. Thus, the eight parameter LISREL model, which arose out of the work of Keesling and Wiley (see Wiley, 1973) and was subsequently developed to its current state by Jöreskog (see