No abstract

This paper considers the problem of applying factor analysis to non‐normal categorical variables. A Monte Carlo study is conducted where five prototypical cases of non‐normal variables are generated. Two normal theory estimators, ML and GLS, are compared to Browne's (1982) ADF estimator. A categorical variable methodology (CVM) estimator of Muthén (1984) is also considered for the most severely skewed case. Results show that ML and GLS chi‐square tests are quite robust but obtain too large values for variables that arc severely skewed and kurtotic. ADF, however, performs well.Parameter estimate bias appears non‐existent for all estimators. Results also show that ML and GLS estimated standard errors are biased downward. For ADF no such standard error bias was found. The CVM estimator appears to work well when applied to severely skewed variables that had been dichotomized. ML and GLS results for a kurtosis only case showed no distortion of chi‐square or parameter estimates and only a slight downward bias in estimated standard errors. The results are compared to those of other related studies.

A comprehensive measure of alcohol outcome expectancies was developed through the use of exploratory and confirmatory factor analyses. The questionnaire assesses both positive and negative expected effects of alcohol as well as the subjective evaluation of those effects. The measure was found to demonstrate adequate internal consistency, temporal stability, and construct validity. Criterion validity was demonstrated through structural regression analyses of the independent and combined influences of outcome expectancies and subjective evaluation on three measures of alcohol use. Information on subjects' dose-related expectancies provided further validation of the expectancy construct and yielded information about the effects people associate with drinking different amounts of alcohol.

A general latent variable model is given which includes the specification of a missing data mechanism. This framework allows for an elucidating discussion of existing general multivariate theory bearing on maximum likelihood estimation with missing data. Here, missing completely at random is not a prerequisite for unbiased estimation in large samples, as when using the traditional listwise or pairwise present data approaches. The theory is connected with old and new results in the area of selection and factorial invariance. It is pointed out that in many applications, maximum likelihood estimation with missing data may be carried out by existing structural equation modeling software, such as LISREL and LISCOMP. Several sets of artifical data are generated within the general model framework. The proposed estimator is compared to the two traditional ones and found superior.

As reported by parents, children in China went to bed later and woke up earlier and their sleep duration was 1 hour shorter than the US children. Chinese children were reported to have more sleep problems than their US counterparts. Daytime sleepiness was determined by sleep duration only for those who slept insufficiently. Unique school schedules and sleep practices may contribute to the differences in the sleep patterns and sleep problems of children from the United States and China.