Measuring vaping-related expectancies in young adults: Psychometric evaluation of the Electronic Nicotine Vaping Outcomes (ENVO) scale.

OBJECTIVE: Electronic cigarettes are the most commonly used tobacco products by young adults. Measures of beliefs about outcomes of use (i.e., expectancies) can be helpful in predicting use, as well as informing and evaluating interventions to impact use. METHODS: We surveyed young adult students (N = 2296, Mean age=20.0, SD=1.8, 64 % female, 34 % White) from a community college, a historically black university, and a state university. Students answered ENDS expectancy items derived from focus groups and expert panel refinement using Delphi methods. Factor Analysis and Item Response Theory (IRT) methods were used to understand relevant factors and identify useful items. RESULTS: A 5-factor solution [Positive Reinforcement (consists of Stimulation, Sensorimotor, and Taste subthemes, α = .92), Negative Consequences (Health Risks and Stigma, α = .94), Negative Affect Reduction (α = .95), Weight Control (α = .92), and Addiction (α = .87)] fit the data well (CFI=0.95; TLI=0.94; RMSEA=0.05) and was invariant across subgroups. Factors were significantly correlated with relevant vaping measures, including vaping susceptibility and lifetime vaping. Hierarchical linear regression demonstrated factors were significant predictors of lifetime vaping after controlling for demographics, vaping ad exposure, and peer/family vaping. IRT analyses indicated that individual items tended to be related to their underlying constructs (a parameters ranged from 1.26 to 3.18) and covered a relatively wide range of the expectancies continuum (b parameters ranged from -0.72 to 2.47). CONCLUSIONS: A novel ENDS expectancy measure appears to be a reliable measure for young adults with promising results in the domains of concurrent validity, incremental validity, and IRT characteristics. This tool may be helpful in predicting use and informing future interventions. IMPLICATIONS: Findings provide support for the future development of computerized adaptive testing of vaping beliefs. Expectancies appear to play a role in vaping similar to smoking and other substance use. Public health messaging should target expectancies to modify young adult vaping behavior.

Item Parameter Estimation With the General Hyperbolic Cosine Ideal Point IRT Model.

Over the last decade, researchers have come to recognize the benefits of ideal point item response theory (IRT) models for noncognitive measurement. Although most applied studies have utilized the Generalized Graded Unfolding Model (GGUM), many others have been developed. Most notably, David Andrich and colleagues published a series of papers comparing dominance and ideal point measurement perspectives, and they proposed ideal point models for dichotomous and polytomous single-stimulus responses, known as the Hyperbolic Cosine Model (HCM) and the General Hyperbolic Cosine Model (GHCM), respectively. These models have item response functions resembling the GGUM and its more constrained forms, but they are mathematically simpler. Despite the apparent impact of Andrich's work on ensuing investigations, the HCM and GHCM have been largely overlooked by applied researchers. This may stem from questions about the compatibility of the parameter metric with other ideal point estimation and model-data fit software or seemingly unrealistic parameter estimates sometimes produced by the original joint maximum likelihood (JML) estimation software. Given the growing list of ideal point applications and variations in sample and scale characteristics, the authors believe these HCMs warrant renewed consideration. To address this need and overcome potential JML estimation difficulties, this study developed a marginal maximum likelihood (MML) estimation algorithm for the GHCM and explored parameter estimation requirements in a Monte Carlo study manipulating sample size, scale length, and data types. The authors found a sample size of 400 was adequate for parameter estimation and, in accordance with GGUM studies, estimation was superior in polytomous conditions.

A psychometric investigation of gender differences and common processes across borderline and antisocial personality disorders.

The comorbidity between borderline personality disorder (BPD) and antisocial personality disorder (ASPD) is well-established, and the 2 disorders share many similarities. However, there are also differences across disorders: most notably, BPD is diagnosed more frequently in women and ASPD in men. We investigated if (a) comorbidity between BPD and ASPD is attributable to 2 discrete disorders or the expression of common underlying processes, and (b) if the model of comorbidity is true across sex. Using a clinical sample of 1,400 drug users in residential substance abuse treatment, we tested 3 competing models to explore whether the comorbidity of ASPD and BPD should be represented by a single common factor, 2 correlated factors, or a bifactor structure involving a general and disorder-specific factors. Next, we tested whether our resulting model was meaningful by examining its relationship with criterion variables previously reported to be associated with BPD and ASPD. The bifactor model provided the best fit and was invariant across sex. Overall, the general factor of the bifactor model significantly accounted for a large percentage of the variance in criterion variables, whereas the BPD and AAB specific factors added little to the models. The association of the general and specific factor with all criterion variables was equal for men and women. Our results suggest common underlying vulnerability accounts for both the comorbidity between BPD and AAB (across sex), and this common vulnerability drives the association with other psychopathology and maladaptive behavior. This in turn has implications for diagnostic classification systems and treatment. (PsycINFO Database Record

MIMIC Methods for Detecting DIF Among Multiple Groups: Exploring a New Sequential-Free Baseline Procedure.

A simulation study was conducted to investigate the efficacy of multiple indicators multiple causes (MIMIC) methods for multi-group uniform and non-uniform differential item functioning (DIF) detection. DIF was simulated to originate from one or more sources involving combinations of two background variables, gender and ethnicity. Three implementations of MIMIC DIF methods were compared: constrained baseline, free baseline, and a new sequential-free baseline. When the MIMIC assumption of equal factor variance across comparison groups was satisfied, the sequential-free baseline method provided excellent Type I error and power, with results similar to an idealized free baseline method that used a designated DIF-free anchor, and results much better than a constrained baseline method, which used all items other than the studied item as an anchor. However, when the equal factor variance assumption was violated, all methods showed inflated Type I error. Finally, despite the efficacy of the two free baseline methods for detecting DIF, identifying the source(s) of DIF was problematic, especially when background variables interacted.