Jaehwa Choi

The Insecure Psychotherapy Base: Using Client and Therapist Attachment Styles to Understand the Early Alliance

The purpose of this study was to test the notion that complementary attachments are best for achieving a secure base in psychotherapy. Specifically, we predicted third to fifth session alliance from client- and therapist-rated attachment style interactions. Using a combined sample of 46 therapy dyads from a community mental health clinic and university counseling center, the client- and therapist-perceived therapy alliance, attachment anxiety, and attachment avoidance were examined at the beginning of therapy. The results of an Actor-Partner Interdependence Model (APIM; Kenny & Cook, 1999, Partner effects in relationship research: Conceptual issues, analytic difficulties, and illustrations. Personal Relationships, 6, 433-448.) indicated that there was no direct effect of either client or therapist attachment style on therapist or client early ratings of the alliance. One significant interaction emerged and indicated that client-perceived alliance was influenced by therapist and client attachment anxiety. The client-perceived early alliance was higher when more anxious therapists worked with clients with decreasing anxiety. The client early alliance was higher when less anxious therapists worked with clients with increasing anxiety. The findings partially support the notion that different attachment configurations between the therapist and client facilitate greater alliance, but this was the case only when assessing client-perceived early alliance and only with regards to the dimension of attachment anxiety. There were no significant main effects or interactions when exploring therapist-perceived alliance. Implications of the findings are discussed along with recommendations for future study and clinical training.

Posttraumatic growth among combat veterans: A proposed developmental pathway

With the large number of combat veterans returning from war, there is an ever-increasing need to understand ways to help soldiers and veterans successfully navigate their return to life after combat. Posttraumatic growth (PTG) offers strong protective elements following combat, including reduction in suicidal ideation (Bush et al., 2011). The purpose of this study was to explore a proposed psychosocial developmental pathway between posttraumatic stress symptoms and PTG among combat veterans of the Afghanistan and Iraq wars. The indirect pathway from posttraumatic symptoms to PTG through negative psychosocial development was found to be significant and positive. It appears that psychosocial development may indeed mediate the process by which combat veterans can make meaning from their experiences, improving overall well-being.

A Comparison of Maximum Likelihood and Bayesian Estimation for Polychoric Correlation Using Monte Carlo Simulation

The purpose of this study is to compare the maximum likelihood (ML) and Bayesian estimation methods for polychoric correlation (PCC) under diverse conditions using a Monte Carlo simulation. Two new Bayesian estimates, maximum a posteriori (MAP) and expected a posteriori (EAP), are compared to ML, the classic solution, to estimate PCC. Different types of prior distributions are used to investigate the sensitivity of a prior distribution onto the Bayesian PCC estimation. In this simulation study, it appears that the MAP would be the estimator of choice for the PCC. The performance of the MAP is not only better than the ML but also appears to overcome the limitations of the EAP (i.e., the shrinkage effect).

A Note on Confidence Intervals for Two-Group Latent Mean Effect Size Measures

This note suggests delta method implementations for deriving confidence intervals for a latent mean effect size measure for the case of 2 independent populations. A hypothetical kindergarten reading example using these implementations is provided, as is supporting LISREL syntax.

An Empirical Evaluation of Mediation Effect Analysis With Manifest and Latent Variables Using Markov Chain Monte Carlo and Alternative Estimation Methods

Recently, the Markov chain Monte Carlo (MCMC) estimation method has become explosively popular in a variety of quantitative research methods. In mediation effect analysis (MEA), the MCMC estimation methods can be a promising tool and an important alternative as compared with traditional methods (e.g., the z test using the delta method and the bias-corrected bootstrapping method) in addressing issues such as nonconvergence and complex modeling. In this article, a subject-level MCMC approach for the single MEA is empirically evaluated and compared with traditional methods through Monte Carlo simulation. The evaluation covers point and interval estimates of both manifest and latent variables across conditions including sample size, effect size, and magnitude of factor loadings. BUGS codes for MEA with both manifest and latent variables are provided that can be easily adapted to fit various MEA models in practice.

Estimation of the latent mediated effect with ordinal data using the limited-information and Bayesian full-information approaches.

It is common to encounter latent variables with ordinal data in social or behavioral research. Although a mediated effect of latent variables (latent mediated effect, or LME) with ordinal data may appear to be a straightforward combination of LME with continuous data and latent variables with ordinal data, the methodological challenges to combine the two are not trivial. This research covers model structures as complex as LME and formulates both point and interval estimates of LME for ordinal data using the Bayesian full-information approach. We also combine weighted least squares (WLS) estimation with the bias-corrected bootstrapping (BCB; Efron Journal of the American Statistical Association, 82, 171–185, 1987) method or the traditional delta method as the limited-information approach. We evaluated the viability of these different approaches across various conditions through simulation studies, and provide an empirical example to illustrate the approaches. We found that the Bayesian approach with reasonably informative priors is preferred when both point and interval estimates are of interest and the sample size is 200 or above.

College Student Persistence: A Multilevel Analysis of Distance Learning Course Completion at the Crossroads of Disability Status.

With over 15 million students enrolling in institutes of higher education, it is critical to understand the factors that lead to course completion and graduation. This becomes especially important when considering students with disabilities, as this is historically a vulnerable population. Modern technology and online classes may be one way to meet these needs. The purpose of this study was to investigate whether course completion rates differ when taking online versus traditional courses, among students with and without disabilities. The participants of the study were a purposive sample of 3,488 undergraduate and graduate students, from a comprehensive, midsized, Historically Black College located on east coast of the United States. Hierarchical general linear modeling was used to determine whether disability status and course delivery format were related to completion, while controlling for student-level and course-level variables. The results show that students were significantly less likely to complete online courses after controlling for individual level (e.g., gender, cumulative GPA, and previous course credit hours) and class level covariates. Yet, students with disabilities were just as likely as peers without disabilities to complete courses, and the cross-level interaction between disability status and delivery format was not statistically significant (i.e., course completion differences over delivery format does not statistically significantly differ over student disability status). The results have important implications for college students with disabilities.