Applications of structural equation modeling in psychological research.

This chapter presents a review of applications of structural equation modeling (SEM) published in psychological research journals in recent years. We focus first on the variety of research designs and substantive issues to which SEM can be applied productively. We then discuss a number of methodological problems and issues of concern that characterize some of this literature. Although it is clear that SEM is a powerful tool that is being used to great benefit in psychological research, it is also clear that the applied SEM literature is characterized by some chronic problems and that this literature can be considerably improved by greater attention to these issues.

Annotated bibliography of structural equation modelling: technical work.

Researchers must be familiar with a variety of source literature to facilitate the informed use of structural equation modelling. Knowledge can be acquired through the study of an expanding literature found in a diverse set of publishing forums. We propose that structural equation modelling publications can be roughly classified into two groups: (a) technical and (b) substantive applications. Technical materials focus on the procedures rather than substantive conclusions derived from applications. The focus of this article is the former category; included are foundational/major contributions, minor contributions, critical and evaluative reviews, integrations, simulations and computer applications, precursor and historical material, and pedagogical textbooks. After a brief introduction, we annotate 294 articles in the technical category dating back to Sewall Wright (1921).

Occupational attainment as a function of abilities and interests: a longitudinal analysis using project TALENT data.

Ability, interest, gender, and family socioeconomic status of 13,248 tenth-grade participants in Project TALENT were studied as they relate to occupational attainment by using discriminant analysis. Individuals were classified into 12 broad categories reported 11 years after graduation. Accuracy analyses indicated correct classifications significantly above chance for all except the Technical and Sales categories. Within-category classification percentages were higher for all groups except Construction. Five canonical discriminant functions that jointly accounted for 96.8% of the between-groups variance were interpreted. The first 2 accounted for 81.9% of the variance. Function 1 was a general ability function; Function 2 differentiated the categories on the basis of mathematics ability and gender. Functions 3 through 5 accounted for 14.9% of the between-groups variance. Some implications of these findings are discussed.