The Role of Social Position Within Peer Groups in Distress-Motivated Smoking Among Adolescents.

OBJECTIVE: The relationship between smoking and adolescents' peer relationships is complex, with studies showing increased risk of smoking for adolescents of both very high and very low social position. A key question is whether the impact of social position on smoking depends on an adolescent's level of coping motives (i.e., their desire to use smoking to mitigate negative affect). METHOD: We assessed how social position predicts nicotine dependence in a longitudinal sample (N = 3,717; 44.8% male; mean age = 13.41 years) of adolescent lifetime smokers measured between 6th and 12th grades. Using both social network analysis and multilevel modeling, we assessed this question at the between-person and within-person level, hypothesizing that within-person decreases in social position would lead to increased risk of nicotine dependence among those with high levels of coping motives. RESULTS: In contrast to our hypotheses, only interactions with the between-person measures of social position were found, with a slight negative relationship at low levels of coping motives. In addition, the main effect of coping motives was considerably stronger than that of social position at the between-person level, and social position had no significant within-person main effect on nicotine dependence risk. CONCLUSIONS: These results suggest that adolescents with higher overall levels of social position among their peers may have slightly decreased risk for nicotine dependence, but only when coping motives are low. Counter to expectations, higher levels of nicotine dependence risk were not linked to fluctuations in social position.

Social network isolation mediates associations between risky symptoms and substance use in the high school transition.

The current study examined whether social status and social integration, two related but distinct indicators of an adolescent's standing within a peer network, mediate the association between risky symptoms (depressive symptoms and deviant behavior) and substance use across adolescence. The sample of 6,776 adolescents participated in up to seven waves of data collection spanning 6th to 12th grades. Scores indexing social status and integration were derived from a social network analysis of six schools and subsequent psychometric modeling. Results of latent growth models showed that social integration and status mediated the relation between risky symptoms and substance use and that risky symptoms mediated the relation between social standing and substance use during the high school transition. Before this transition, pathways involving deviant behavior led to high social integration and status and in turn to substance use. After this transition, both deviant behavior and depressive symptoms led to low social integration and status and in turn greater substance use. These findings suggest that the high school transition is a risky time for substance use related to the interplay of increases in depressive symptoms and deviant behavior on the one hand and decreases in social status and integration on the other.

Using phantom variables in structural equation modeling to assess model sensitivity to external misspecification.

External misspecification, the omission of key variables from a structural model, can fundamentally alter the inferences one makes without such variables present. This article presents 2 strategies for dealing with omitted variables, the first a fixed parameter approach incorporating the omitted variable into the model as a phantom variable where all associated parameter values are fixed, and the other a random parameter approach specifying prior distributions for all of the phantom variable's associated parameter values under a Bayesian framework. The logic and implementation of these methods are discussed and demonstrated on an applied example from the educational psychology literature. The argument is made that such external misspecification sensitivity analyses should become a routine part of measured and latent variable modeling where the inclusion of all salient variables might be in question. (PsycINFO Database Record

The Role of Measurement Quality on Practical Guidelines for Assessing Measurement and Structural Invariance.

Although differences in goodness-of-fit indices (ΔGOFs) have been advocated for assessing measurement invariance, studies that advanced recommended differential cutoffs for adjudicating invariance actually utilized a very limited range of values representing the quality of indicator variables (i.e., magnitude of loadings). Because quality of measurement has been found to be relevant in the context of assessing data-model fit in single-group models, this study used simulation and population analysis methods to examine the extent to which quality of measurement affects ΔGOFs for tests of invariance in multiple group models. Results show that ΔMcDonald's NCI is minimally affected by loading magnitude and sample size when testing invariance in the measurement model, while differences in comparative fit index varies widely when testing both measurement and structural variance as measurement quality changes, making it difficult to pinpoint a common value that suggests reasonable invariance.

Using Lasso for Predictor Selection and to Assuage Overfitting: A Method Long Overlooked in Behavioral Sciences.

Ordinary least squares and stepwise selection are widespread in behavioral science research; however, these methods are well known to encounter overfitting problems such that R(2) and regression coefficients may be inflated while standard errors and p values may be deflated, ultimately reducing both the parsimony of the model and the generalizability of conclusions. More optimal methods for selecting predictors and estimating regression coefficients such as regularization methods (e.g., Lasso) have existed for decades, are widely implemented in other disciplines, and are available in mainstream software, yet, these methods are essentially invisible in the behavioral science literature while the use of sub optimal methods continues to proliferate. This paper discusses potential issues with standard statistical models, provides an introduction to regularization with specific details on both Lasso and its related predecessor ridge regression, provides an example analysis and code for running a Lasso analysis in R and SAS, and discusses limitations and related methods.

Modeling sparsely clustered data: design-based, model-based, and single-level methods.

Recent studies have investigated the small sample properties of models for clustered data, such as multilevel models and generalized estimating equations. These studies have focused on parameter bias when the number of clusters is small, but very few studies have addressed the methods' properties with sparse data: a small number of observations within each cluster. In particular, studies have yet to address the properties of generalized estimating equations, a possible alternative to multilevel models often overlooked in behavioral sciences, with sparse data. This article begins with a discussion of population-averaged and cluster-specific models, provides a brief overview of both multilevel models and generalized estimating equations, and then conducts a simulation study on the sparse data properties of generalized estimating equations, multilevel models, and single-level regression models for both normal and binary outcomes. The simulation found generalized estimating equations estimate regression coefficients and their standard errors without bias with as few as 2 observations per cluster, provided that the number of clusters was reasonably large. Similar to the previous studies, multilevel models tended to overestimate the between-cluster variance components when the cluster size was below about 5.