Path analysis is presented as a technique that can be used to test on a priori model based on a theoretical conceptualization involving a network of selected variables. This being an introductory source, no previous knowledge of path analysis is assumed, although some understanding of the fundamentals of multiple regression analysis might be…

The purpose of this paper is to illuminate the advantages of path analysis for the exposition of results in data analytic papers. Probably the greatest advantage is that it provides a means by which the nature of the problem may be handily summarized. The method of path analysis, although conceived over sixty years ago by Sewell Wright, has only…

A Monte Carlo simulation was used to ascertain the degree of inflation that can occur in regression estimates when samples contain randomly occurring instances of a pattern among correlations called cooperative suppression. Ten thousand samples of scores on three variables were randomly drawn from a population in which the correlations among the…

Recent theoretical and empirical study has considerably refined knowledge as to the dynamics of peer processes operative within secondary schools, but has tended to neglect models incorporating reciprocal effects. This situation results partly from a scarcity of appropriate data, and partly from limitations of older methodologies of estimating…

Path analysis, a technique related to multiple regression analysis is used for ascribing causal relationships among variables. Path analysis involves the construction of explicitly formulated causal models and makes the reasoning explicit in the form of path diagrams and structural equations. Regression analysis is then used to construct path…

The performance of the University of Arizona College of Medicine's graduating class of 1976 on the National Board of Medical Examiners (NBME) Parts I and II was analyzed in a structural equation model. Although the primary emphasis of this paper is toward model development, the effects of several background and demographic variables such as age,…

Structural equation models incorporating unmeasured variables make possible the rigorous testing of theories previously difficult to test adequately because of fallible measures of the theoretic variables. This paper first discusses a simple causal model; incorporating a single unmeasured variable for the purpose of exposition. A substantive…