Assessment

Most commonly used clinical assessment tools cannot fully capture the dynamic psychological processes often hypothesized as core mechanisms of psychopathology and psychotherapy. There is therefore a gap between our theories of problems and interventions for those problems and the tools we use to understand clients. The purpose of this special issue is to connect theory about clinical dynamics to practice by focusing on methods for collecting dynamic data, statistical models for analyzing dynamic data, and conceptual schemes for implementing dynamic data in applied settings. In this introductory article, we argue for the importance of assessing dynamic processes, highlight recent advances in assessment science that enable their measurement, review challenges in using these advances in applied practice, and adumbrate the articles in this issue.

Observational measurement plays an integral role in a variety of scientific endeavors within biology, psychology, sociology, education, medicine, and marketing. The current article provides an interdisciplinary primer on observational measurement; in particular, it highlights recent advances in observational methodology and the challenges that accompany such growth. First, we detail the various types of instrument that can be used to standardize measurements across observers. Second, we argue for the importance of validity in observational measurement and provide several approaches to validation based on contemporary validity theory. Third, we outline the challenges currently faced by observational researchers pertaining to measurement drift, observer reactivity, reliability analysis, and time/expense. Fourth, we describe recent advances in computer-assisted measurement, fully automated measurement, and statistical data analysis. Finally, we identify several key directions for future observational research to explore.

In recent years, significant technological advances have changed our understanding of dynamic processes in clinical psychology. A particularly important agent of change has been ambulatory assessment (AA). AA is the assessment of individuals in their daily lives, combining the twin benefits of increased ecological validity and minimized retrospective biases. These benefits make AA particularly well-suited to the assessment of dynamic processes, and recent advancements in technology are providing exciting new opportunities to understand these processes in new ways. In the current article, we briefly detail the capabilities currently offered by smartphones and mobile physiological devices, as well as some of the practical and ethical challenges of incorporating these new technologies into AA research. We then provide several examples of recent innovative applications of AA methodology in clinical research, assessment, and intervention and provide a case example of AA data generated from a study utilizing multiple mobile devices. In this way, we aim to provide a sense of direction for researchers planning AA studies of their own.

Multivariate psychological processes have recently been studied, visualized, and analyzed as networks. In this network approach, psychological constructs are represented as complex systems of interacting components. In addition to insightful visualization of dynamics, a network perspective leads to a new way of thinking about the nature of psychological phenomena by offering new tools for studying dynamical processes in psychology. In this article, we explain the rationale of the network approach, the associated methods and visualization, and illustrate it using an empirical example focusing on the relation between the daily fluctuations of emotions and neuroticism. The results suggest that individuals with high levels of neuroticism had a denser emotion network compared with their less neurotic peers. This effect is especially pronounced for the negative emotion network, which is in line with previous studies that found a denser network in depressed subjects than in healthy subjects. In sum, we show how the network approach may offer new tools for studying dynamical processes in psychology.

Time series analysis is a technique that can be used to analyze the data from a single subject and has great potential to investigate clinically relevant processes like affect regulation. This article uses time series models to investigate the assumed dysregulation of affect that is associated with bipolar disorder. By formulating a number of alternative models that capture different kinds of theoretically predicted dysregulation, and by comparing these in both bipolar patients and controls, we aim to illustrate the heuristic potential this method of analysis has for clinical psychology. We argue that, not only can time series analysis elucidate specific maladaptive dynamics associated with psychopathology, it may also be clinically applied in symptom monitoring and the evaluation of therapeutic interventions.

The nomothetic approach (i.e., the study of interindividual variation) dominates analyses of clinical data, even though its assumption of homogeneity across people and time is often violated. The idiographic approach (i.e., the study of intraindividual variation) is best suited for analyses of heterogeneous clinical data, but its person-specific methods and results have been criticized as unwieldy. Group iterative multiple model estimation (GIMME) combines the assets of the nomothetic and idiographic approaches by creating person-specific maps that contain a group-level structure. The maps show how intensively measured variables predict and are predicted by each other at different time scales. In this article, GIMME is introduced conceptually and mathematically, and then applied to an empirical data set containing the negative affect, detachment, disinhibition, and hostility composite ratings from the daily diaries of 25 individuals with personality pathology. Results are discussed with the aim of elucidating GIMME’s potential for clinical research and practice.

Individuals in both clinical and research settings increasingly provide data across numerous time points. Examples include measurements collected from wearable technology (e.g., accelerometers), psychophysiological measures, coded observations, social media behaviors, and daily diary data. When numerous observations are available for each individual, the data fall under the class of time series data and can be examined from within a dynamic systems perspective. We provide a broad overview of current analytic methods for quantifying relations among dyads using this data type. The techniques include those from within a linear modeling framework, approaches that include a measurement model, and methods for examining cyclical relations. We also discuss some special topics, such as methods that allow for models to shift across time and that accommodate heterogeneity across individuals. Finally, methods that account for similar shapes of nonlinear curves across time are described. From this breadth of options, we hope to help guide practitioners, clinicians, and researchers in choosing the optimal method for their data and line of questions. To further aid in this choice, we indicate programs available for each technique. Example dyads presented here range from mother–infant, patient–caretaker, and husband–wife; however, the analytic methods can be applied to any type of dyad.

Dynamic psychological processes are most often assessed using self-report instruments. This places a constraint on how often and for how long data can be collected due to the burden placed on human participants. Smartphones are ubiquitous and highly personal devices, equipped with sensors that offer an opportunity to measure and understand psychological processes in real-world contexts over the long term. In this article, we present a novel smartphone approach to address the limitations of self-report in bipolar disorder where mood and activity are key constructs. We describe the development of MoodRhythm, a smartphone application that incorporates existing self-report elements from interpersonal and social rhythm therapy, a clinically validated treatment, and combines them with novel inputs from smartphone sensors. We reflect on lessons learned in transitioning from an existing self-report instrument to one that involves smartphone sensors and discuss the potential impact of these changes on the future of psychological assessment.

Borderline personality disorder (BPD) is a diagnosis defined by impairments in several dynamic processes (e.g., interpersonal relating, affect regulation, behavioral control). Theories of BPD emphasize that these impairments appear in specific contexts, and emerging results confirm this view. At the same time, BPD is a complex construct that encompasses individuals with heterogeneous pathology. These features—dynamic processes, situational specificity, and individual heterogeneity—pose significant assessment challenges. In the current study, we demonstrate assessment and analytic methods that capture both between-person differences and within-person changes over time. Twenty-five participants diagnosed with BPD completed event-contingent, ambulatory assessment protocols over 21 days. We used p-technique factor analyses to identify person-specific psychological structures consistent with clinical theories of personality. Five exemplar cases are selected and presented in detail to showcase the potential utility of these methods. The presented cases’ factor structures reflect not only heterogeneity but also suggest points of convergence. The factors also demonstrated significant associations with important clinical targets (self-harm, interpersonal violence).

Clinicians have long recognized the importance of tailoring psychotherapy interventions to the needs and characteristics of the individual patient. However, traditional approaches to clinical assessment, service delivery, and intervention research have not been conducive to such personalization. Contrary to traditional nomothetic approaches, idiographic assessment and modeling of intraindividual dynamic processes holds tremendous promise for tailoring the implementation of psychotherapy to the individual patient. In this article, we (a) present an argument for assessing person-specific dynamics, (b) provide a detailed description of a method that harnesses person-specific dynamic assessment and modeling for use in routine psychotherapy, (c) present exemplar clinical cases illustrating these methods, and (d) discuss how these methods can be translated into routine clinical assessment and psychotherapy.

In this article, we organize multimethod, multitimescale data around the interpersonal situation, a conceptual framework that can be used to integrate personality, psychopathology, and psychotherapy constructs in order to guide the assessment of clinical dynamics. We first describe the key variables of the interpersonal situation model and articulate methods for assessing those variables as they manifest (a) across different levels of personality, (b) across situations, and (c) within situations. We next use a case to demonstrate how to assess aspects of the interpersonal situation in a manner that enhances case conceptualization and facilitates the evaluation of clinical hypotheses. We also use this case to highlight challenges and decisions involved in implementing dynamic assessment in psychotherapy. We conclude by outlining areas in need of further exploration toward a more sophisticated approach to clinical practice that involves the routine assessment of dynamic processes.

A vast majority of psychological science focuses on snapshots of individuals. Clinical outcome studies may integrate multiple snapshots, typically with yearly intervals. However, there is much to learn about psychological processes as they unfold over real time, including minutes, days, weeks, and months. This special issue contains several articles that make significant advances in real-time assessment of psychological processes using state-of-the-art measurement. This is a brief summary of the specific innovations of this special issue. The summary includes suggestions for applying these measurement innovations to the study of functional dynamics that lead to clinically and socially significant events. Several examples provide ways in which a dynamical analysis of function may be revealing. Most important, the assessment of dynamic mechanisms underlying the amplification of psychopathology seems especially useful for tailoring clinical interventions to meet clients’ specific needs.