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.

One hundred and sixty‐seven female undergraduates completed a series of questionnaires assembled to assess indices of eating disorders relative to tobacco and alcohol use, self‐control, and sex‐role ideology. Data were subjected to correlational, factorial, regression and analysis of variance procedures. In general, results showed the eating disorder indices to be independent of smoking and drinking behaviour. Smoking status and alcohol consumption were significantly related in that nonsmokers consumed less alcohol and reported being drunk less often than either smokers or ex‐smokers. Self‐control scores showed the greatest association with a measure of social desirability, although there were weak links to eating disorder indices and level of alcohol use. Self‐control also emerged as the sole predictor of eating disorder measures. Finally, sex‐role ideology was found to be a weak moderator of alcohol consumption such that the more feminist the ideology, the lower the level of intake.

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