Examining the latent structure of anxiety sensitivity in adolescents using factor mixture modeling

Covariate inclusion in factor mixture modeling: Evaluating one-step and three-step approaches under model misspecification and overfitting

Factor mixture modeling (FMM) has been increasingly used in behavioral and social sciences to examine unobserved population heterogeneity. Covariates (e.g., gender, race) are often included in FMM to help understand the formation and characterization of latent subgroups or classes. This Monte Carlo simulation study evaluated the performance of one-step and three-step approaches to covariate inclusion across three scenarios, i.e., correct specification (study 1), model misspecification (study 2), and model overfitting (study 3), in terms of direct covariate effects on factors. Results showed that the performance of these two approaches was comparable when class separation was large and the specification of covariate effect was correct. However, one-step FMM had better class enumeration than the three-step approach when class separation was poor, and was more robust to the misspecification or overfitting concerning direct covariate effects. Recommendations regarding covariate inclusion approaches are provided herein depending on class separation and sample size. Large sample size (1000 or more) and the use of sample size-adjusted BIC (saBIC) in class enumeration are recommended.

Testing Measurement Invariance Across Unobserved Groups: The Role of Covariates in Factor Mixture Modeling

Factor mixture modeling (FMM) has been increasingly used to investigate unobserved population heterogeneity. This study examined the issue of covariate effects with FMM in the context of measurement invariance testing. Specifically, the impact of excluding and misspecifying covariate effects on measurement invariance testing and class enumeration was investigated via Monte Carlo simulations. Data were generated based on FMM models with (1) a zero covariate effect, (2) a covariate effect on the latent class variable, and (3) covariate effects on both the latent class variable and the factor. For each population model, different analysis models that excluded or misspecified covariate effects were fitted. Results highlighted the importance of including proper covariates in measurement invariance testing and evidenced the utility of a model comparison approach in searching for the correct specification of covariate effects and the level of measurement invariance. This approach was demonstrated using an empirical data set. Implications for methodological and applied research are discussed.

Positive alcohol use expectancies moderate the association between anxiety sensitivity and alcohol use across adolescence

Anxiety sensitivity (AS), or the fear of anxious symptoms and the belief that these symptoms may have negative physical, social, and cognitive consequences, is one personality trait that emerges in early adolescence and may be linked to alcohol use. However, findings are equivocal as to whether elevated AS during adolescence directly predicts alcohol use. Adolescents do report increases in positive alcohol use expectancies during this developmental period, and these expectancies have been found to be significantly associated with alcohol use. The current study examined whether positive alcohol use expectancies and AS in early adolescence predicted changes in alcohol use throughout adolescence. This aim was examined via secondary data analyses from a longitudinal study examining the development of risk behaviors in adolescents. Results of univariate latent growth curve modeling suggest that AS alone was not a significant predictor of baseline alcohol use or change in use over time after controlling for gender, age, and self-reported anxiety. However, AS in early adolescence was found to be a significant predictor of increases in alcohol use across adolescence for youth who reported greater positive alcohol use expectancies. These results indicate that beliefs regarding the positive effects of alcohol use are an important moderator in the relation between AS and change in alcohol use during adolescence.

Longitudinal investigation of anxiety sensitivity growth trajectories and relations with anxiety and depression symptoms in adolescence

Anxiety sensitivity (AS), the belief that anxious arousal is harmful, is a malleable risk factor that has been implicated in anxiety and depression symptoms in adolescents. Although there is some evidence that adolescents possess distinct developmental trajectories, few studies have explored this topic. This study examined the developmental trajectory of AS in 248 adolescents (M age = 11.0 years, SD = 0.82; 56% male) across 6 years, beginning when children were age 11. This study also examined the influence of AS trajectories on anxiety and depression at age 16. Finally, this study examined the utility of AS classes in identifying anxiety and depression growth. Three AS classes were found, described by normative-stable, high-stable, and high-unstable trajectories. Adolescents in the high-stable and the high-unstable AS classes had higher levels of anxiety and depression at age 16 than did adolescents in the normative-stable AS class. In addition, the anxiety and depression trajectories fit by AS class mirrored the AS class trajectories. These findings suggest three AS trajectories can be identified in adolescents. These trajectories are discussed in relation to a developmental perspective of AS.

Identification of anxiety sensitivity classes and clinical cut-scores in a sample of adult smokers: results from a factor mixture model

Anxiety sensitivity (AS), a multidimensional construct, has been implicated in the development and maintenance of anxiety and related disorders. Recent evidence suggests that AS is a dimensional-categorical construct within individuals. Factor mixture modeling was conducted in a sample of 579 adult smokers (M age=36.87 years, SD=13.47) to examine the underlying structure. Participants completed the Anxiety Sensitivity Index-3 and were also given a Structured Clinical Interview for DSM-IV-TR. Three classes of individuals emerged, a high AS (5.2% of the sample), a moderate AS (19.0%), and a normative AS class (75.8%). A cut-score of 23 to identify high AS individuals, and a cut-score of 17 to identify moderate-to-high AS individuals were supported in this study. In addition, the odds of having a concurrent anxiety disorder (controlling for other Axis I disorders) were the highest in the high AS class and the lowest in the normative AS class.