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CONTENTS
INVITED ARTICLE
ORIGINAL RESEARCH
SPECIAL SERIES: ADVANCES IN LATENT VARIABLE ANALYSIS: Guest Editor: Timothy A. Brown
ABSTRACTS
Over 40 years of development of cognitive behavioral approaches to treating anxiety and related emotional disorders have left us with highly efficacious treatments that are increasingly widely accepted. Nevertheless, these manualized protocols have become numerous and somewhat complex, restricting effective training and dissemination. Deepening understanding of the nature of emotional disorders reveals that commonalities in etiology and latent structure among these disorders supercedes differences. This suggests the possibility of distilling a set of psychological procedures that would comprise a unified intervention for emotional disorders. Based on theory and data emerging from the fields of learning, emotional development and regulation, and cognitive science, we identify three fundamental therapeutic components relevant to the treatment of emotional disorders generally. These three components include (a) altering antecedent cognitive reappraisals; (b) preventing emotional avoidance; and (c) facilitating action tendencies not associated with the emotion that is dysregulated. This treatment takes place in the context of provoking emotional expression (emotional exposure) through situational, internal, and somatic (interoceptive cues), as well as through standard mood-induction exercises, and differs from patient to patient only in the situational cues and exercises utilized. Theory and rationale supporting this new approach are described along with some preliminary experience with the protocol. This unified treatment may represent a more efficient and possibly a more effective strategy in treating emotional disorders, pending further evaluation.
Patients who had taken part in a randomizd clinical trial of the treatment of chronic PTSD by either cognitive therapy or imaginal exposure were reassessed after 5 years. At 5-year follow-up a clear superiority of cognitive therapy over imaginal exposure emerged, although there had been no difference between the two treatment groups up to 12 months posttreatment. The cognitive therapy group showed significant differences on the primary outcome measures: total PTSD symptoms on the CAPS and percentage of PTSD cases. At 5 years no patients who received cognitive therapy were diagnosed with full PTSD compared to 29% of those who received imaginal exposure. All secondary outcomes showed lower scores for cognitive therapy, of which 3 were significant. Those who were not assessed at 5 years scored significantly higher on assessment measures, especially avoidance, at posttreatment.
The authors examined Hammen’s (1991) model of stress generation in depression in a Black adolescent population. The longitudinal sample of 1,766 participants entered the study at ages 13 to 18. Stressful events and depressive and other symptom occurrence over a 1-year period were analyzed. Results supported the stress generation model. Depressive symptoms were associated with an increase in negative stressful events. In addition, the study supported the symptom specificity of stress generation to depression versus anxious and conduct disorder symptoms.
The present investigation evaluated the initial efficacy of a modular approach to cognitive behavior therapy (CBT) for anxiety disorders in youth. Modular CBT consists of the guided combination of individually scripted techniques that are explicitly matched to the child’s individual strengths and needs. Eleven youth primarily of Asian and Pacific Island ethnicity ranging in age from 7 to 13 were referred for treatment. Comparisons in a multiple baseline across children provided preliminary support for the efficacy of the intervention. Among the 7 completers, all principal diagnoses were absent at posttreatment and 6-month follow-up assessments, and measures of anxiety symptoms and life functioning almost uniformly evidenced clinically significant improvements. SPECIAL SERIES ADVANCES IN LATENT VARIABLE ANALYSIS: APPLICATIONS TO CLINICAL RESEARCH
Latent variable approaches to data analysis offer a variety of advantages over traditional methods. For various reasons, innovations in these methodologies are slow to imbue the conduct of applied mental health research. The goal of this series is to provide a practical discussion and guidelines for several forms of statistical analysis with latent variables that are germane to clinical research: confirmatory factor analysis, latent growth modeling, multilevel modeling, and taxometrics. Conceptual, procedural, and methodological aspects of these strategies are discussed and illustrated using applied, clinically relevant examples.
A latent variable modeling approach to reliability and measurement invariance evaluation for multiple-component measuring instruments is outlined. An initial discussion deals with the limitations of coefficient alpha, a frequently used index of composite reliability. A widely and readily applicable structural modeling framework is next described that allows point and interval estimation of scale reliability. An easily employed procedure for testing measurement and reliability invariance in multiple population and longitudinal studies is then discussed. Unlike coefficient alpha that is in general a misestimator of scale reliability already at the population level, the article is based on the formal definition of the reliability coefficient, and its underlying modeling approach does not share alpha’s limitations. The described reliability and measurement invariance evaluation procedures are illustrated on a set of data resulting from a Social Interaction Anxiety Scale, and source codes for one of the most popular structural modeling programs, LISREL, are provided, which can be used to apply the outlined methods.
Over the past 3 decades we have witnessed an increase in the complexity of theoretical models that attempt to explain development in a number of behavioral domains. The conceptual movement to examine behavior from both developmental and contextual perspectives parallels recent methodological advances in the analysis of change. These new analysis techniques have fundamentally altered how we conceptualize and study change, and have prompted researchers to identify larger frameworks to integrate knowledge. One such framework is latent growth modeling. This article presents a basic latent growth modeling approach for analyzing repeated measures data and delineates several of its extensions, including analyses for multiple populations, accelerated designs, multivariate associative models, and a framework for sample size selection and power estimation.
We demonstrate graphical and analytical methods for multilevel (2- and 3-level) models using the analysis of observed microsocial interaction between parent-child dyads as an example. We also present multilevel path diagrams and argue that while not as compact as equations, path diagrams may communicate results better to a wider audience. The example data were drawn from an urban risk sample of kindergarten children and their parents observed in a 1-hour structured lab task and coded for behavioral process using the Family Peer Process (FPP) Code. Four repeated assessments of aversive parent discipline across the 1-hour observation session were nested within dyads, which were nested under coders, which created a 3-level model. The model also included the random effect of observed child aversive behavior as a time-varying predictor of parent aversive discipline at Level 1, child school antisocial behavior as a Level 2 predictor and coder gender as a Level 3 predictor. Our results indicated that coders accounted for about 13% of the variance, families accounted for about 20% of the variance, leaving 67% of the variance in parent aversive discipline due to random, time-specific influences. We discuss the psychometric implications of the small (20%) trait-like component of parent discipline in detail. We also found that observed child aversive behavior provoked parent aversive discipline more strongly in some families than others but child school antisocial behavior was not related to the magnitude of the child aversive effect on the parent.
The taxometric method is an increasingly popular statistical approach that tests whether the structure of a latent construct is categorical or continuous. This article presents the key conceptual and methodological issues that should be addressed in an informative taxometric investigation. We aim to help potential users of taxometrics determine: (a) when a taxometric analysis is scientifically justified, (b) whether their data are suitable for taxometric analysis, (c) whether they have properly implemented a sufficient variety of taxometric procedures, (d) whether they have appropriately presented and interpreted the obtained results, and (e) whether they have adequately articulated the implications of their structural solution. Annotated program code and empirical examples are provided to illustrate how taxometric analysis is applied in practice.
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DAVID H. BARLOW, LAURA B. ALLEN, AND MOLLY L. CHOATE. Toward a Unified Treatment for Emotional Disorders