Monte Carlo research increasingly seems to favor the use of parallel analysis as a method for determining the "correct" number of factors in factor analysis or components in principal components analysis. We present a regression equation for predicting parallel analysis values used to decide the number of principal components to retain. This equation is appropriate for predicting criterion mean eigenvalues and was derived from random data sets containing between 5 and 50 variables and between 50 and 500 subjects. This relatively simple equation is more accurate for predicting mean eigenvalues from random data matrices with unities in the diagonals than a previously published equation. Moreover, given that the parallel analysis decision rule may be too dependent on chance, our equation is also used to predict the 95th percentile point in distributions of eigenvalues generated from random data matrices. Multiple correlations for all analyses were at least .95. Regression weights for predicting the first 33 mean and 95th percentile eigenvalues are given in easy-to-use tables.

Does the salience of an individual group member's gender depend on the group's sex composition? According to McGuire's distinctiveness theory, even for "momentary" or ad hoc groups, gender would be more salient in the spontaneous self-concepts of members of the minority sex in mixed-sex groups than in other conditions. To test this prediction experimentally, we manipulated the sex composition of 3-peison groups, which resulted in four types: All male, all female, lone male, and lone female. Within these group contexts, subjects responded to two open-ended probes of spontaneous self-concept (i.e., "Tell me what you are" and "Tell me what you are not"), with order counterbalanced, and subsequently completed a structured measure of gender identity (Personal Attributes Questionnaire). Chi-square analyses of whether gender was mentioned on the "Tell me about yourself" probe supported distinctiveness theory. Implications of this finding for distinctiveness theory and the psychology of self are discussed.

Group cohesion is an important construct in understanding the behavior of groups. However, there has been ongoing controversy on how to define and measure this construct. Central to this debate is the structure of cohesion. The authors critically review the literature on unidimensional and multidimensional models of cohesion and describe cohesion as a multidimensional construct with primary and secondary dimensions. Primary dimensions are applicable to describing the cohesiveness of all or most types of groups, whereas secondary dimensions are applicable to describing the cohesiveness of specific types of groups. Viewing group cohesion as consisting of primary and secondary dimensions is discussed.

Selecting the "correct" number of components to retain in principal components analysis is crucial. Parallel analysis, which requires a comparison of eigenvalues from observed and random data, is a highly promising strategy for making this decision. This paper focuses on linear interpolation, which has been shown to be an accurate method of implementing parallel analysis. Specifically, this article contains tables of 95th percentile eigenvalues from random data that can be used when the sample size is between 50 and 500 and when the number of variables is between 5 and 50. An empirical example is provided illustrating linear interpolation, direct computation, and regression methods for obtaining 95th percentile eigenvalues from random data. The tables of eigenvalues given in this report will hopefully enable more researchers to use parallel analysis because interpolation is an accurate and simple method of obviating the Monte Carlo requirements of parallel analysis.

Of the various methods and rules-of-thumb for deciding the 4correct" number of components to retain in principal components analysis, parallel analysis is arguably the most useful. Recently, researchers have become interested in developing alternative methods of implementing parallel analysis and of comparing their accuracy. The accuracy of three methods of implementing parallel analysis with mean eigenvalues (regression, interpolation, and computation with three samples of random data) was compared. The index of accuracy was the proportion of agreement on the number of components for extraction suggested by each of these three procedures and those suggested by implementing parallel analysis with 40 runs of random data, in a sample of 28 published correlation matrices. Moreover, the accuracy of two methods of implementing parallel analysis with 95th percentile eigenvalues (regression and interpolation) was compared using the aforementioned index of accuracy. In separate analyses for mean and 95th percentile eigenvalue strategies, no evidence of differential accuracy emerged. Implications for implementing parallel analysis are discussed.