Trait Ratings from Descriptions of Behavior As Mediated by Components of Meaning.

This study examined the role language plays in mediating the influence of verbal descriptions of persons on trait ratings of those persons. Subjects were given written descriptions of the behavior of fictitious persons in a work situation and were asked to rate them on fifteen trait- adjective scales. In one condition of the experiment, specific information about certain traits was withheld, forcing subjects to rate persons on traits for which they had no direct behavioral clues. In the other two conditions, the specific information was provided. Providing specific information about a trait directly influenced ratings on that trait even when sufficient general information on that trait was given. In one condition, the influence on the ratings of the additional behavioral clues was such that a new latent variable representing an additional component of meaning was called for in the structural equation model.

Limited Information Parameter Estimates for Latent or Mixed Manifest and Latent Variable Models.

We argue for separate analyses of the measurement and structural portions of latent or mixed manifest and latent variable models. We present limited information (single equation) procedures for estimating parameters in the structural portion of these models. These include parameter estimation procedures for recursive or nonrecursive relations, and procedures for testing zero-effect hypotheses. We then compare full and limited information estimates in a Monte Carlo analysis of sample correlation matrices that contained structural model misspecifications. Both full and limited information estimates identified misspecified nonzero effects reasonably well. However, limited information estimates were far superior in detecting misspecified zero-effect hypotheses. We recommend limited information parameter estimation procedures over full information techniques for (a) testing specific causal hypotheses and (b) locating specific structural model misspecifications.

A Brief History of the Philosophical Foundations of Exploratory Factor Analysis.

Exploratory factor analysis derives its key ideas from many sources. From the Greek rationalists and atomists comes the idea that appearance is to be explained by something not observed. From Aristotle comes the idea of induction and seeking common features of things as explanations of them. From Francis Bacon comes the idea of an automatic algorithm for inductively discovering common causes. From Descartes come the ideas of analysis and synthesis that underlie the emphasis on analysis of variables into orthogonal or linearly independent factors and focus on reproducing (synthesizing) the correlation matrix from the factors. From empiricist statisticians like Pearson and Yule comes the idea of exploratory, descriptive statistics. Also from the empiricist heritage comes the false expectation some have that factor analysis yields unique and unambiguous knowledge without prior assumptions -- the inductivist fallacy. This expectation founders on the indeterminacy of factors, even after their loadings are defined by rotation. Indeterminacy is unavoidable in the interpretation of common factors because the process of interpretation is inductive and inductive inferences are not uniquely determined by the data on which they are based. But from Kant we learn not to discard inductive inferences but to treat them as hypotheses that must be tested against additional data to establish their objectivity. And so the conclusions of exploratory factor analyses are never complete without a subsequent confirmatory analysis with additional variables and new data.

On Simple Structure and the Solution to Thurstone's Invariant Box Problem.

Thurstone's invariant box problem has 2 features which prevent the finding of the obvious simple structure by any of the usual analytic methods of rotation.