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The ability of several process variables to predict therapy outcome was tested with 30 depressed clients who received cognitive therapy with or without medication. Two types of process variables were studied: 1 variable that is unique to cognitive therapy and 2 variables that this approach is assumed to share with other forms of treatment. The client's improvement was found to be predicted by the 2 common factors measured: the therapeutic alliance and the client's emotional involvement (experiencing). The results also indicated, however, that a unique aspect of cognitive therapy (i.e., therapist's focus on the impact of distorted cognitions on depressive symptoms) correlated negatively with outcome at the end of treatment. Descriptive analyses that were conducted to understand this negative correlation suggest that therapists sometimes increased their adherence to cognitive rationales and techniques to correct problems in the therapeutic alliance. Such increased focus, however, seems to worsen alliance strains, thereby interfering with therapeutic change.Despite support for the effectiveness of cognitive therapy for depression, researchers are still confronted with a high degree of uncertainty about its underlying processes of change (Whisman, 1993). As recently noted by Beck and Haaga (1992), the refinement of our understanding of the mechanisms of action in the treatment of depression will take a predominant place in the future of cognitive therapy. The present study is an attempt to better understand the process of change in cognitive therapy for depression.As recommended by several workers in the field (e.g., Kazdin, 1986;Lambert, Shapiro, & Bergin, 1986), two types of processes were investigated: variables that are unique to cognitive

This article recommends an alternative method for testing multifaceted constructs. Researchers often have to choose between two problematic approaches for analyzing multifaceted constructs: the total score approach and the individual score approach. Both approaches can result in conceptual ambiguity. The proposed bifactor model assesses simultaneously the general construct shared by the facets and the specific facets, over and above the general construct. We illustrate the bifactor model by examining the construct of Extraversion as measured by the Revised NEO Personality Inventory (NEO-PI-R; , with two college samples (N = 383 and 378). The analysis reveals that the facets of the NEO-PI-R Extraversion correlate with criteria in opposite directions after partialling out the general construct. The direction of gender differences also varies by facets. Bifactor models combine the advantages but avoid the drawbacks of the 2 existing methods and can lead to greater conceptual clarity.Psychological constructs are often characterized by several related facets. Examples can be found in most areas of psychology, including personality traits, depression, subjective well-being, attention, interpersonal functioning, cognitive flexibility, and health behaviors. There is, however, a long-standing and unresolved debate in personality research on how to measure and test such multifaceted constructs (e

The study of discontinuities and nonlinear change has been a fruitful endeavor across the sciences, as these shifts can provide a window into the organization of complex systems and the processes that are associated with transition. A common assumption in psychotherapy research has been that change is gradual and linear. The research designs and statistics used to study change often reflect this assumption, but some recent research reveals other patterns of change. We briefly review relevant literature on dynamical systems theory and on life transition and post-traumatic growth to highlight the significance of nonlinear and discontinuous change across areas of psychology. We describe recent applications of these ideas and methods to the study of change in psychotherapy and encourage their use to complement more traditional clinical trial designs.Some change can be gradual and incremental, but many systems in nature show periods of turbulence and instability, with dramatic changes or growth spurts. Ilya Prigogine, a Nobel laureate known for his theory of dissipative structures in chemistry, argues that instabilities play an important role in transformation and that "most of reality, instead of being orderly, stable, and equilibrial, is seething and bubbling with change, disorder, and process" (Prigogine & Stengers, 1984, p. xv). The study of discontinuities has been a fruitful endeavor across the sciences, as these shifts can provide a window into the organization of a system and the processes that are associated with transition.A common assumption in psychotherapy research is that change is gradual and linear. The research designs and statistics used to study change often reflect this assumption. The hypothesized predictors of change are measured once or twice and then compared between groups or correlated with symptom change at the end of treatment. Most research also

Bishop et al. (this issue) propose an operational definition of mindfulness developed by a recent consensus panel.The group provides a solid empirical framework from which to develop measures of mindfulness, and they propose an exciting research agenda. We describe measurement development work from our research group that provides initial support for the proposed consensus definition and that examines mindfulness in relation to emotion regulation variables. We extend the discussion by describing how mindfulness can enhance the stabilizing and destabilizing aspects of therapeutic change, and we illustrate this in the context of our treatment program for depression.

Significant shifts or discontinuities in symptom course can mark points of transition and reveal important change processes. The authors investigated 2 patterns of change in depression-the rapid early response and a transient period of apparent worsening that the authors call a depression spike. Participants were 29 patients diagnosed with major depressive disorder who enrolled in an open trial of an exposure-based cognitive therapy. Hierarchical linear modeling revealed an overall cubic shape of symptom change and that both the rapid response and spike patterns predicted lower posttreatment depression. Patients wrote weekly narratives about their depression. Early narratives of rapid responders were coded as having more hope than those of nonrapid responders. The narratives of patients with a depression spike had more cognitive-emotional processing during this period of arousal than those without a spike. Findings are discussed in the context of cognitive-emotional processing theories in depression and anxiety disorders.

As the number of psychotherapies with demonstrated efficacy accumulates, an important task is to identify principles and processes of change. This information can guide treatment refinement, integration, and future development. However, the standard randomized control trial (RCT) design can limit the questions that can be asked and the statistical analyses that can be conducted. We discuss the importance of examining the shape of change, in addition to identifying mediators and moderators of change. We suggest methodological considerations for longitudinal data collection that can improve the kinds of therapy process questions that can be examined. We also review some data analytic approaches that are being used in other areas of psychology that have the potential to capture the complexity and dynamics of change in psychotherapy.The central question of interest in the study of psychotherapy is change over time. Patients come into therapy with certain behavioral, emotional, and/or cognitive difficulties, and they seek relief from these problems and an improved quality of life by the time therapy is completed. One way to determine this change is to assess problems prior to treatment (point A) and at the end of treatment (point B). When it comes to understanding change in psychotherapy, is it enough to know simply that there has been improvement from point A to point B? Process researchers argue no-that is, in addition to knowing that change occurs in response to a treatment, it is crucial to understand how individuals change from point A to point B. We present some important methodological issues to consider to improve the quality of data and statistical analyses in studies of change in clinical trials.In the first section of this paper, we briefly describe the types of questions that can be the focus of process research. In the second section, we discuss some of the ways in which the standard outcome study design, the randomized control trial (RCT) design, can limit the types of process questions that can be asked and the statistical analyses that can be conducted. We present two methodological recommendations that can address some of these limitations. First, we recommend that psychotherapy researchers increase the number of repeated assessments of symptoms and putative mediators or covariates of change over the course of treatment and follow-up. Second, we recommend that psychotherapy researchers carefully consider the timing of the effect of an intervention so that assessments are taken over an appropriate period.In the third section of this paper, we follow these methodological issues with a description of what might be considered the current statistical state of the art in assessing change and Correspondence regarding this paper can be addressed to Jean-Philippe Laurenceau,

Researchers often debate about whether there is a meaningful differentiation between psychological well-being and subjective well-being. One view argues that psychological and subjective well-being are distinct dimensions, whereas another view proposes that they are different perspectives on the same general construct and thus are more similar than different. The purpose of this investigation was to examine these two competing views by using a statistical approach, the bifactor model, that allows for an examination of the common variance shared by the two types of well-being and the unique variance specific to each. In one college sample and one nationally representative sample, the bifactor model revealed a strong general factor, which captures the common ground shared by the measures of psychological well-being and subjective well-being. The bifactor model also revealed four specific factors of psychological well-being and three specific factors of subjective well-being, after partialling out the general well-being factor. We further examined the relations of the specific factors of psychological and subjective well-being to external measures. The specific factors demonstrated incremental predictive power, independent of the general well-being factor. These results suggest that psychological well-being and subjective well-being are strongly related at the general construct level, but their individual components are distinct once their overlap with the general construct of well-being is partialled out. The findings thus indicate that both perspectives have merit, depending on the level of analysis.