Interactive and higher-order effects of social influences on drug use.

The study of moderators and higher-order effects of social influences on drug use has many implications for theories of health behavior. In the present study, we investigated the longitudinal predictive effects of some of the prominent moderator variables that represent forms of susceptibility toward social influence in teenage drug use. We also studied the possibility that social influence may predict drug use in nonlinear (quadratic) forms, consistent with theories proposing that threshold or decelerating effects may occur in social influences on normatively sanctioned behaviors. Results showed that several of the interactive and quadratic predictive effects were significant. The findings supported the views that certain moderator variables act as buffers, which either protect the individual from social pressures to use drugs, or make the individual more susceptible to such pressures. In addition, two of the obtained quadratic effects of social influence lent support to the application of social impact theory to drug use. Overall, our findings suggest that interactive and nonlinear approaches to social influences on drug use provide a unique and viable theoretical perspective from which to construe this problem health behavior.     

Cohabitation before marriage

This study compares 159 married couples who had or had not lived together previous to marriage with respect to a number of demographic and personality variables. There were few differences between the two groups on background variables but many on personality traits. Males who had cohabited perceived themselves as more androgynous, attractive, and less religious than males who did not cohabit. Females who cohabited saw themselves as more interested in art, attractive, extroverted, intelligent, liberal, androgynous, and having more leadership qualities; while being less religious, clothes-conscious, and law-abiding than women who did not cohabit. Couples who cohabited showed significantly greater sexual experimentation and self-perception accuracy than the couples who did not cohabit. Cohabitors revealed some background and trait within-group differences as a function of the length of their cohabital experiences. The majority of differences between the groups were in terms of variables assessed on the females. Theoretical implications of these findings are discussed. Authors' Note: This investigation was supported in part by a Research Scientist Development Award (K02-DA00017) and a research grant (DA01070) from the U.S. Public Health Service. The assistance of Deborah Cabin is gratefully acknowledged.     

Finite Normal Mixture SEM Analysis by Fitting Multiple Conventional SEM Models

This paper proposes a two-stage maximum likelihood (ML) approach to normal mixture structural equation modeling (SEM), and develops statistical inference that allows distributional misspecification. Saturated means and covariances are estimated at stage-1 together with a sandwich-type covariance matrix. These are used to evaluate structural models at stage-2. Techniques accumulated in the conventional SEM literature for model diagnosis and evaluation can be used to study the model structure for each component. Examples show that the two-stage ML approach leads to correct or nearly correct models even when the normal mixture assumptions are violated and initial models are misspecified. Compared to single-stage ML, two-stage ML avoids the confounding effect of model specification and the number of components, and is computationally more efficient. Monte-Carlo results indicate that two-stage ML loses only minimal efficiency under the condition where single-stage ML performs best. Monte-Carlo results also indicate that the commonly used model selection criterion BIC is more robust to distribution violations for the saturated model than that for a structural model at moderate sample sizes. The proposed two-stage ML approach is also extremely flexible in modeling different components with different models. Potential new developments in the mixture modeling literature can be easily adapted to study issues with normal mixture SEM.     

Consistency of Normal Distribution Based Pseudo Maximum Likelihood Estimates When Data Are Missing at Random

This paper shows that, when variables with missing values are linearly related to observed variables, the normal-distribution-based pseudo MLEs are still consistent. The population distribution may be unknown while the missing data process can follow an arbitrary missing at random mechanism. Enough details are provided for the bivariate case so that readers having taken a course in statistics/probability can fully understand the development. Sufficient conditions for the consistency of the MLEs in higher dimensions are also stated, while the details are omitted.     

Structural Equation Modeling With Robust Covariances

Existing methods for structural equation modeling involve fitting the ordinary sample covariance matrix by a proposed structural model. Since a sample covariance is easily influenced by a few outlying cases, the standard practice of modeling sample covariances can lead to inefficient estimates as well as inflated fit indices. By giving a proper weight to each individual case, a robust covariance will have a bounded influence function as well as a nonzero breakdown point. These robust properties of the covariance estimators will be carried over to the parameter estimators in the structural model if a technically appropriate procedure is used. We study such a procedure in which robust covariances replace ordinary sample covariances in the context of the Wishart likelihood function. This procedure is easy to implement in practice. Statistical properties of this procedure are investigated. A fit index is given based on sampling from an elliptical distribution. An estimating equation approach is used to develop a variety of robust covariances, and consistent covariances of these robust estimators, needed for standard errors and test statistics, follow from this approach. Examples illustrate the inflated statistics and distorted parameter estimates obtained by using sample covariances when compared with those obtained by using robust covariances. The merits of each method and its relevance to specific types of data are discussed.     

MULTILEVEL COVARIANCE STRUCTURE ANALYSIS BY FITTING MULTIPLE SINGLE-LEVEL MODELS

Data in social and behavioral sciences are often hierarchically organized. Multilevel statistical procedures have been developed to analyze such data while taking into account the dependence of observations. When simultaneously evaluating models at all levels, a significant statistic provides no information on the level at which the model is misspecified. Model misspecification can exist at one or several levels simultaneously. When one level is misspecified, the other levels may be affected even when they are correctly specified. Motivated by these observations, we propose to separate a multilevel covariance structure into multiple single-level covariance structure models and to fit these single-level models as in conventional covariance structure analysis. A procedure for segregating the multilevel model into single-level models is developed. Five test statistics for evaluating a model at each level are provided. Standard error formulas for the separate estimators are also provided, and their efficiency is compared to simultaneous estimators. Empirical and Monte Carlo results demonstrate the advantages of the segregated procedure over the simultaneous procedure. Computer programs that will allow the developed procedure to be used in practice are also presented.