Evidence for extending the circumplex model of personality trait language to self-reported moods

We show that self-ratings by 141 undergraduates on 17 items form a circumplex that closely agrees with Conte and Plutchik's (1981) circumplex of personality traits. The order of items on a circumplex, generated by self-ratings following a here-and-now instruction set, scarcely differs from that of a circumplex representing the same ratings averaged over a 2-week period. The configuration becomes more elliptical as one moves from the state-like measures (here-and-now ratings) to pure trait measures (averages), but measurement bias is shown to be the most probable cause of the distortion. Allowing for the limitations of small samples of items and raters, the findings show that the circumplex model represents a valid theoretical construct and does not arise from measurement error or from an illusory implicit personality theory.     

Correspondence analysis used complementary to loglinear analysis

Loglinear analysis and correspondence analysis provide us with two different methods for the decomposition of contingency tables. In this paper we will show that there are cases in which these two techniques can be used complementary to each other. More specifically, we will show that often correspondence analysis can be viewed as providing a decomposition of the difference between two matrices, each following a specific loglinear model. Therefore, in these cases the correspondence analysis solution can be interpreted in terms of the difference between these loglinear models. A generalization of correspondence analysis, recently proposed by Escofier, will also be discussed. With this decomposition, which includes classical correspondence analysis as a special case, it is possible to use correspondence analysis complementary to loglinear analysis in more instances than those described for classical correspondence analysis. In this context correspondence analysis is used for the decomposition of the residuals of specific restricted loglinear models.     

Iterative least squares estimates of communality: Initial estimate need not affect stabilized value

A common criticism of iterative least squares estimates of communality is that method of initial estimation may influence stabilized values. As little systematic research on this topic has been performed, the criticism appears to be based on cumulated experience with empirical data sets. In the present paper, two studies are reported in which four types of initial estimate (unities, squared multiple correlations, highestr, and zeroes) and four levels of convergence criterion were employed using four widely available computer packages (BMDP, SAS, SPSS, and SOUPAC). The results suggest that initial estimates have no effect on stabilized communality estimates when a stringent criterion for convergence is used, whereas initial estimates appear to affect stabilized values employing rather gross convergence criteria. There were no differences among the four computer packages for matrices without Heywood cases.