With the passing of Paul E. Meehl, Regents Professor of Psychology at the University of Minnesota, on February 14, 2003, the world lost one of the most influential clinical psychologists of the 20th century. The breadth of his interests, the preciseness and clarity of his thinking, the elegance of his writing, and his ability to integrate scientific and clinical matters of import were hallmarks of his illustrious career (see, e.g., Meehl, 1954, 1973, 1991). Yet, it is the very magnitude of his professional pursuits that defy categorization or even placement within a single field of inquiry. Whether they pertain to philosophical matters, measurement and psychodiagnostic issues, or elucidating psychopathological processes, Paul Meehl's contributions were seminal and established a base on which scholars could build their own theoretical and research perspectives. Although Paul certainly did not consider himself to be a developmental psychopathologist, his influence can be seen in the theoretical and methodological streams that have nurtured the emergence and growth of the field. Thus, it seems a fitting tribute to Paul that this Special Issue, “Conceptual, Methodological, and Statistical Issues in Developmental Psychopathology,” be dedicated in his honor.

Developmental psychopathologists have criticized categorical classification systems for their inability to account for within-group heterogeneity in biological, etiological, developmental, and cultural influences on behavior. Dichotomizing continuous scores of symptom severity is also inadvisable statistically. Perhaps because of a resulting wariness of categorizing, few explorations into the ontological status of traits or disorders as dimensional versus discrete have been conducted. It is argued here that the limitations of categorizing have little to do with the ontological status of traits and that developmental psychopathologists should be concerned with identifying discrete behavioral syndromes. Common taxometric methods for resolving discrete traits are described, and questions of concern to developmental psychopathologists are outlined that can be addressed through taxometrics studies. These include (a) identifying children who are at risk for future psychopathology, (b) identifying discrete subtypes within current diagnostic classes, (c) locating sensitive periods in the development of discrete pathological traits, (d) discovering moderators of treatment outcome, and (e) elucidating mechanisms of equifinality and multifinality. Although most behavioral traits probably are distributed continuously, identifying those that are discrete will advance the science of developmental psychopathology. Disorders for which taxometric analyses might be applied include anxiety, attention deficit hyperactivity disorder, autism spectrum disorders, conduct problems, depression, and schizophrenia.

The question of whether to view psychopathology as categorical or dimensional continues to provoke debate. We review the many facets of this argument. These include the pragmatics of measurement; the needs of clinical practice; our ability to distinguish categories from dimensions empirically; methods of analysis appropriate to each and how they relate; and the potential theoretical biases associated with each approach. We conclude that much of the debate is misconceived in that we do not observe pathology directly; rather, we observe its properties. The same pathology can have some properties that are most easily understood using a dimensional conceptualization while at the same time having other properties that are best understood categorically. We suggest replacing Meehl's analogy involving qualitatively distinct species with an alternative analogy with the “duality” of light, a phenomenon with both wave- and particle-like properties.

This article deals with alternative research strategies for developmental psychopathology. It argues that most applications of statistical methods in empirical research are variable centered, not person oriented. As a result, conclusions are often drawn that fail to do justice to the diverse nature of populations. It is recommended that we take seriously the importance of the implications of data aggregation. The difficulties of making inferences from a more aggregated level of analysis to a less aggregated level are explained and exemplified. We explain that a set of variables displays dimensional identity if the variable relationships remain unchanged across the levels or categories of other variables. Data examples of intelligence divergence and of Child Behavior Checklist subpopulation differences show that lack of dimensional identity can lead to incorrect conclusions. Schmitz' theorems on aggregation and the validity of results at the aggregate level for individuals are illustrated using data from a study on the development of alcoholism and discussed from a person-oriented perspective. Statistical methods suitable for person-oriented data analysis are reviewed.

The field of developmental psychopathology is faced with a dual challenge. On the one hand, we must build interdisciplinary theoretical models that adequately reflect the complexity of normal and abnormal human development over time. On the other hand, to remain a viable empirical science, we must rigorously evaluate these theories using statistical methods that fully capture this complexity. The degree to which our statistical models fail to correspond to our theoretical models undermines our ability to validly test developmental theory. The broad class of random coefficient trajectory (or growth curve) models allow us to test our theories in ways not previously possible. Despite these advantages, there remain certain limits with regard to the types of questions these models can currently evaluate. We explore these issues through the pursuit of three goals. First, we provide an overview of a variety of trajectory models that can be used for rigorously testing many hypotheses in developmental psychopathology. Second, we highlight what types of research questions are well tested using these methods and what types of questions currently are not. Third, we describe areas for future statistical development and encourage the ongoing interchange between developmental theory and quantitative methodology.

Transactional models have informed research design and interpretation in studies relevant to developmental psychopathology. Bidirectional effects between individuals and social contexts have been found in many behavioral and cognitive domains. This review will highlight representative studies where the transactional model has been explicitly or implicitly tested. These studies include experimental, quasiexperimental, and naturalistic designs. Extensions of the transactional model have been made to interventions designed to target different aspects of a bidirectional system in efforts to improve developmental outcomes. Problems remain in the need to theoretically specify structural models and to combine analyses of transactions in the parent–child relationship with transactions in the broader social contexts. Longitudinal studies with sufficient time points to assess reciprocal processes continue to be important. Such longitudinal investigations will permit identifying developmental periods where the child or the context may be most influential or most open to change.

A survey of dynamic systems (DS) methods appropriate for testing systems-based models in developmental psychopathology is provided. The rationale for developing new methods for the field is reviewed first. In line with other scholars, we highlight the fundamental incompatibility between developmentalists' organismic, open systems models and the mechanistic research methods with which these models are tested. Key DS principles are explained and their commensurability with developmental psychopathologists' core theoretical concerns are discussed. Next, a survey of research designs and methodological techniques currently being used and refined by developmental DS researchers is provided. The strengths and limitations of each approach are discussed throughout this review. Finally, we elaborate on one specific dynamic systems method, state space grids, which addresses many of the limitations of previous DS techniques and may prove most useful for the discipline. This approach was developed as a middle road between DS methods that are mathematically heavy on the one hand and purely descriptive on the other. Examples of developmental and clinical studies that have applied state space grids are reviewed and suggestions for future analyses are made. We conclude with some implications for the application of this new methodology for studying change processes in clinical research.

This paper reviews alternative conceptual and descriptive models of how early exposure to risk may predict later development. Particular attention is paid to the methodological and data analytic strategies for differentiating among the models. Example empirical illustrations of human studies are then provided that highlight the potential significance of early experiences for behavioral/emotional development. Finally, implications of these models of early experiences for intervention and prevention are examined and identified as an important methodological tool for further research.

Attachment theory and research have offered fundamental insights into early sociopersonality development for the past quarter-century. As its scope expands throughout the life course with applications to developmental psychopathology, however, attachment work faces important conceptual and methodological challenges. These include (a) expanding Bowlby's theoretical formulations to address developmental changes in the nature of attachment organization beyond infancy, the converging influence of multiple attachment relationships, and the nature and development of internal working models; (b) systematically validating assessments of attachment security for older ages in the context of enhanced theoretical understanding of how attachment itself changes with age; (c) new methodological approaches to understanding the relations between attachment and later behavior in light of empirical evidence of stability and change in attachment security and the need for explicit theoretical predictions of the sequelae of attachment security; and (d) more complex conceptualizations of the associations among attachment, contextual risk, and later behavior. These are similar to the challenges facing the original pioneers of attachment theory and research, suggesting that familiar problems must now be reconsidered against the landscape of new applications of attachment work and the insights of contemporary developmental science.

In recent years, the role of gender in the development of psychopathology has increasingly attracted the attention of researchers, theoreticians, and other professionals interested in the well-being of children and adolescents. This interest has taken diverse forms, ranging from the examination of sex differences in the prevalence of adjustment difficulties to the exploration of unique etiologies and trajectories in the development of psychopathology for boys versus girls. In this paper we (a) critically examine the current status of available theories, research, and methods related to the study of gender and psychopathology and provide recommendations for future work; (b) identify promising new trends that appear to have utility for enhancing our understanding of the role of gender in the development of adjustment difficulties; and (c) generate conclusions regarding gender and psychopathology by integrating information from past and present work with new ideas about fruitful directions for future inquiry.

As the US population becomes more diverse in the 21st century, researchers face many conceptual and methodological challenges in working with diverse populations. We discuss these issues for racially and ethnically diverse youth, using Spencer's phenomenological variant of ecological systems theory (PVEST) as a guiding framework. We present a brief historical background and discuss recurring conceptual flaws in research on diverse youth, presenting PVEST as a corrective to these flaws. We highlight the interaction of race, culture, socioeconomic status, and various contexts of development with identity formation and other salient developmental processes. Challenges in research design and interpretation of data are also covered with regard to both assessment of contexts and developmental processes. We draw upon examples from neighborhood assessments, ethnic identity development, and attachment research to illustrate conceptual and methodological challenges, and we discuss strategies to address these challenges. The policy implications of our analysis are also considered.

Empirical investigations of resilience over the past 30 years have examined a wide range of psychosocial correlates of, and contributors to, this phenomenon. Thus far, theoretical treatments of resilience have focused almost exclusively on psychosocial levels of analysis to derive explanatory models. However, there have been no formal discussions of either theory or research that have examined the biological contributors to, or correlates of, competent functioning despite the experience of adversity. This paper seeks to fill this gap and sets forth a preliminary theoretical framework and outline of empirical strategies for studying the biological underpinnings of resilience. The initial sections of the paper discuss the particular suitability of a transactional organizational theoretical perspective as a conceptual foundation for including a biological level of analysis within the extant theoretical framework of resilience. Subsequently, other important theoretical considerations for the inclusion of a biological perspective on resilience are discussed, including the avoidance of an approach that would reduce resilience to merely a biological process, the application of the constructs of multifinality and equifinality to a biological perspective on resilience, as well as a general discussion of the potential for utilization of brain imaging and other technologies in the study of resilience. The possible relation between the mechanisms of neural plasticity and resilience are examined in some detail, with specific suggestions concerning research questions needed to examine this association. Sections of the paper discuss the likely relation of several areas of brain and biological functioning with resilience, including emotion, cognition, neuroendocrine and immune functioning, and genetics. The paper concludes with a discussion of the implications of a biological perspective on resilience for preventive interventions.

Brain imaging studies in developmentally based psychopathologies most often use magnetic resonance imaging (MRI) to study regional volumes, task-related activity, neurometabolite concentrations, or the paths of fiber tracts within the brain. Methodological challenges for the use of MRI in studying these disorders include understanding the ultrastructural correlates of brain structure and function that are below the limits of resolution of this imaging modality and developing better methods for approximating the anatomical boundaries of the cytoarchitectonic units that are defined by those ultrastructural characteristics. Conceptual challenges include distinguishing findings that represent pathophysiologically central causes from compensatory and epiphenomenal effects, a difficulty that stems directly from the inherently correlational nature of imaging data. The promise of functional imaging studies must capitalize on the specificity of the cognitive and behavioral probes that are used to illuminate core features of the pathophysiology of developmental disorders, while recognizing the assumptions and limitations of the subtraction paradigms that are used to isolate the brain functions of interest. Statistical challenges include incorporating adequate statistical models for scaling effects within the brain, as well as modeling important demographic correlates that contribute to the substantial interindividual variability inherent in most imaging data. Statistical analyses need to consider the substantial intercorrelation of measures across the brain and the importance of correcting for multiple statistical comparisons, as well as the need for improved methods for brain warping and for assessing effective connectivity in functional imaging studies.