**ERIC Number:**ED205540

**Record Type:**RIE

**Publication Date:**1981-Apr

**Pages:**73

**Abstractor:**N/A

**Reference Count:**0

**ISBN:**N/A

**ISSN:**N/A

Causal Models with Unmeasured Variables: An Introduction to LISREL.

Wolfle, Lee M.

Whenever one uses ordinary least squares regression, one is making an implicit assumption that all of the independent variables have been measured without error. Such an assumption is obviously unrealistic for most social data. One approach for estimating such regression models is to measure implied coefficients between latent variables for which one had multiple manifest indicators. The problem with this approach is that overidentified models yield multiple estimates of the associations among latent variables. Maximum likelihood estimation (MLE) can be used to obtain estimates of these overidentified parameters. The recent development of a computer program for confirmatory factor analysis by Joreskog, Gruvaeus, and Van Thillo has made MLE computational procedures practicable. The resulting variances and covariances of the latent factors can be used to estimate the parameters of a structural model assumed to exist among the factors, and Joreskog and Sorbom have developed a program which incorporates MLE procedures for both the confirmatory factor analysis model and the linear structure model. This program is called LISREL, an acronym for linear structural relationships. This paper provides a nonmathematical introduction to LISREL. (Author/BW)

**Publication Type:**Speeches/Meeting Papers; Reports - Research; Guides - Non-Classroom

**Education Level:**N/A

**Audience:**N/A

**Language:**English

**Sponsor:**N/A

**Authoring Institution:**N/A

**Identifiers:**Causal Models; Confirmatory Factor Analysis; LISREL Computer Program

**Note:**Paper presented at the Annual Meeting of the American Educational Research Association (65th, Los Angeles, CA, April 13-17, 1981).