The effectiveness of stand-alone cognitive-behavioural therapy for body image: a meta-analysis

This meta-analysis examined the effectiveness of stand-alone body image (BI) cognitive-behavioural therapy (CBT). Nineteen studies met inclusion criteria. The behavioural dimension of BI improves most after BI CBT while the investment dimension improves the least. Individuals with clinical BI disturbances improve more than do college student samples. Treatments addressing the attitudinal, behavioural, and perceptual components of BI are more effective than treatments addressing attitude and behaviour only. Therapist-assisted therapy is more effective than is self-directed therapy. BI variables improve somewhat more than do general psychological variables, while the latter show equivalent improvement. Finally, treatment gains are maintained at follow-up. More research is needed to better understand and treat the investment dimension of BI.     

A Bayesian Modeling Approach for Generalized Semiparametric Structural Equation Models

In behavioral, biomedical, and psychological studies, structural equation models (SEMs) have been widely used for assessing relationships between latent variables. Regression-type structural models based on parametric functions are often used for such purposes. In many applications, however, parametric SEMs are not adequate to capture subtle patterns in the functions over the entire range of the predictor variable. A different but equally important limitation of traditional parametric SEMs is that they are not designed to handle mixed data types—continuous, count, ordered, and unordered categorical. This paper develops a generalized semiparametric SEM that is able to handle mixed data types and to simultaneously model different functional relationships among latent variables. A structural equation of the proposed SEM is formulated using a series of unspecified smooth functions. The Bayesian P-splines approach and Markov chain Monte Carlo methods are developed to estimate the smooth functions and the unknown parameters. Moreover, we examine the relative benefits of semiparametric modeling over parametric modeling using a Bayesian model-comparison statistic, called the complete deviance information criterion (DIC). The performance of the developed methodology is evaluated using a simulation study. To illustrate the method, we used a data set derived from the National Longitudinal Survey of Youth.     

The Recovery of Correlation Between Latent Abilities Using Compensatory and Noncompensatory Multidimensional IRT Models

This research examined correlation estimates between latent abilities when using the two-dimensional and three-dimensional compensatory and noncompensatory item response theory models. Simulation study results showed that the recovery of the latent correlation was best when the test contained 100% of simple structure items for all models and conditions. When a test measured weakly discriminated dimensions, it became harder to recover the latent correlation. Results also showed that increasing the sample size, test length, or using simpler models (i.e., two-parameter logistic rather than three-parameter logistic, compensatory rather than noncompensatory) could improve the recovery of latent correlation.