Developmental Psychopathology, Theory and Method

By Dante Cicchetti

The seminal reference for the latest research in developmental psychopathology

Developmental Psychopathology is a four-volume compendium of the most complete and current research on every aspect of the field. Volume One: Theory and Method focuses on the theoretical and empirical work that has contributed to dramatic advancements in understanding of child and adult development, including findings in the areas of genetics and neurobiology, as well as social and contextual factors. Now in its third edition, this comprehensive reference has been fully updated to reflect the current state of the field and its increasingly multilevel and interdisciplinary nature and the increasing importance of translational research. Contributions from expert researchers and clinicians provide insight into how multiple levels of analysis may influence individual differences, the continuity or discontinuity of patterns, and the pathways by which the same developmental outcomes may be achieved.

Advances in developmental psychopathology have burgeoned since the 2006 publication of the second edition ten years ago, and keeping up on the latest findings in multiple avenues of investigation can be burdensome to the busy professional and researcher from psychology and related fields. This reference solves the problem by collecting the best of the best, as edited by Dante Cicchetti, a recognized leader in the field, into one place, with a logical organization designed for easy reference.

• Get up to date on the latest research from the field
• Explore new models, emerging theory, and innovative approaches
• Learn new technical analysis and research design methods
• Understand the impact of life stage on mental health

The complexity of a field as diverse as developmental psychopathology deepens with each emerging theory and new area of study, as made obvious by the exciting findings coming out of institutions and clinics around the world. Developmental Psychopathology Volume One: Theory and Method brings these findings together into a cohesive, broad-reaching reference.

• Language: English
• Publisher: Wiley
• Release date: Dec 22, 2015
• ISBN: 9781119125440

Book preview

Table of Contents

Title Page

Copyright

Dedication

Preface to Developmental Psychopathology, Third Edition

References

Contributors

Chapter 1: Assessment of Psychopathology in Young Children

Introduction

Early Problems Matter

Important Considerations in Young Child Assessment

Domains of Development

Selecting an Assessment Approach and Tool

Assessment Tools

Conclusions and Directions for Future Research

References

Chapter 2: Developmental Issues in Assessment, Taxonomy, and Diagnosis of Psychopathology: Life Span and Multicultural Perspectives

Life Span Perspectives

Multicultural Perspectives

Developmental Psychopathology

The Developmental Component

The Psychopathology Component

A Framework for the Developmental Study of Psychopathology

The Roles of Assessment and Taxonomy in the Developmental Study of Psychopathology

Diagnosis

Longitudinal Designs for the Developmental Study of Psychopathology

Life Span Applications

Standardized Assessment and Taxonomic Models in Multiple Longitudinal Studies

Multicultural Findings and Applications

Advancing Assessment, Taxonomy, and Diagnosis

Quantitative Aids to Meeting the Challenges

Efforts to Quantify Diagnostic Taxa

Directions for Continued Research

Summary

References

Chapter 3: Developmental Epidemiology

What Is Epidemiology?

Characteristics of Research in Psychiatric Epidemiology

What Is Developmental Epidemiology?

From Child Psychiatric Epidemiology to Developmental Epidemiology: A Brief History

Epidemiology as a Developmental Method

Future Directions: Developmental Epidemiology

Summary: Translational Epidemiology

References

Chapter 4: Using Natural Experiments to Test Environmental Mediation Hypotheses

Introduction

What Is Meant by a Cause?

Noncausal Alternative Explanations for an Association

Natural Experiments

Are Natural Experiments Really Needed?

Overview of Natural Experiment Methodology

References

Chapter 5: Developmental Models and Mechanisms for Understanding the Effects of Early Experiences on Psychological Development

Introduction

Alternative Developmental Models and Hypotheses in Psychological Research on Early Experience

Developmental Mechanisms

Conceptual and Methodological Considerations for Investigating Developmental Models and Mechanisms

Model Systems and Paradigms for Studying Developmental Models and Mechanisms

Developmental Models Are Shaping Clinical and Public Health Strategies

Conclusions and Future Directions

References

Chapter 6: Emotional Security Theory and Developmental Psychopathology

The Substance of Emotional Security in Historical Perspective

Emotional Security as a Mediator of Children's Adjustment to Interparental Conflict

Contextual Characteristics

Developmental Parameters

Future Directions

Conclusions

References

Chapter 7: Emotion and the Development of Psychopathology

Introduction

The Nature of Emotion

Emotion and Psychopathology

Emotional Competence

Development of Emotional Competence

Conclusion and Future Directions

References

Chapter 8: Attachment and Developmental Psychopathology

Overview

Historical Overview of Attachment Theory

Individual Differences in Attachment

Internal Working Models of Attachment

Measurement of Individual Differences in Attachment

The Determinants of Individual Differences in Attachment

Continuity and Change in Attachment Security Across the Life Course

Children's Attachment Security and Psychopathology

The Search for Mediators

Adult Attachment and Psychopathology

Mechanisms in Adult Attachment and Psychopathology

Attachment and Intervention

Conclusions

References

Chapter 9: Autonomy and Autonomy Disturbances in Self-Development and Psychopathology: Research on Motivation, Attachment, and Clinical Process

Introduction

Autonomous Regulation and Facilitative Environments

Autonomy and Autonomy Support in Major Developmental Processes: Attachment, Intrinsic Motivation, Internalization, Emotion Regulation, and Identity Formation

Autonomy Disturbances in Development and Psychopathology

SDT and the Study of Development and Psychopathology: Conclusions and Implications

References

Chapter 10: Roots of Typical Consciousness: Implications for Developmental Psychopathology

General Aim

What Does it Mean to Be Conscious?

Experiential Awareness at Birth

Constraints on Early Experience

Roots of Intersubjectivity

Conclusions: Implications for Developmental Psychopathology

References

Chapter 11: I-Self and Me-Self Processes Affecting Developmental Psychopathology and Mental Health

Introduction

How the I-Self Contributes Positively to the Developmental Construction of the Me-Self, Including Jamesian I-Self Functions

Self-Enhancement and Self-Serving Biases: The I-Self/Me-Self Conspiracy

Socialization Practices That Can Compromise Jamesian I-Self Functions

Cross-Cultural Differences in Self-Enhancement

Self-Evaluations in Global Self-Esteem and Self-Concept: I-Self and Me-Self Functions

Why Can't Those With Low Self-Esteem Alter the Perceptions of Their Overall Lack of Worth?

Cross-Cultural Differences in Self-Esteem

Depression and Suicidal Behaviors

Eating-Disordered Behavior

The Role of Self Processes, Humiliation, and Violent Revenge

Summary, Thus Far

Cross-Cultural Differences in Self-Coherence

Mindfulness as an I-Self Process: A Buddhist Perspective

Further Implications for Mental Health: The Role of the Positive Psychology Movement

Positive Psychology's Emphasis on Issues Related to Perceptions of Control: Cognitive Plus Emotional Components

Conclusions

References

Chapter 12: Peer Relations and Developmental Psychopathology

Introduction

Infancy and Early Childhood

School-Age Children

Adolescence

Conclusion and Future Directions

References

Chapter 13: Family Systems from a Developmental Psychopathology Perspective

Inviting Family Systems Theory to the Table

Historical Origins and Theoretical Underpinnings of Family Systems Theories

What Makes a Family Theory Systemic?

Psychopathology and Its Classification from a Family Systems Perspective

Convergence and Divergence Between Family Systems and Developmental Psychopathology

Challenges to Family Systems Theory

Family Systemic Research Methods

Empirical Investigations of Family Systems Constructs

Gender Dynamics in Family Process

Toward the Future: Growth Points for Developmental Psychopathology from a Family Systems Perspective

Concluding Thoughts

References

Chapter 14: Adolescent/Young Adult Romantic Relationships and Psychopathology

Introduction

Adolescent/Young Adult Romantic Relationships

Internalizing Problems and Disorders

Externalizing Problems and Disorders

Eating Disorders

Attention-Deficit/Hyperactivity Disorder and Other Disorders

Comorbidity

Adolescent Parenthood and its Association with Psychopathology

Same-Sex Relationships

The Peer Context

The Family Context: Intergenerational Transmission and Implications for Long-Term Outcomes

Conclusions and Future Directions

References

Chapter 15: What Can Dynamic Systems Models of Development Offer to the Study of Developmental Psychopathology?

What Can Dynamic Systems Theories of Development Offer to the Study of Developmental Psychopathology?

Rethinking Psychopathology: From Fixed Forms to Dynamic Trajectories

Dynamic Systems Theory and Developmental Psychopathology

The Development of Affective Dissociation in Maltreated Children

Self-Sustaining Learning Trajectories in Children with Attention and Emotional-Behavioral Difficulties

The Development Socioemotional Adjustment in Children with Emotional and Behavioral Difficulties

Beyond Best Practices and Evidence-Based Intervention: Translating Dynamic Systems into Prevention and Practice

Conclusions: The Self-Organization of Order, Variability, and Disorder in Structures of Thinking, Feeling, and Acting

References

Chapter 16: A Survey of Dynamic Systems Methods for Developmental Psychopathology

Introduction

The Developmentalist's Dilemma

Principles of Dynamic Systems

Research Design Strategies Informed by DS Principles

Suitable Data for DS Analyses

DS Methods

State Space Grid Analysis: A Graphical and Statistical Middle Road

Future Directions: Implications for Clinical Research

Conclusion

References

Chapter 17: Missing Data

Statistical Issues: What Happens When Data Go Missing?

Mechanisms of Missingness

Modern Missing Data Methods

Full Information Maximum Likelihood

Practical Considerations

Review of Missing Data Practices in Psychological Research

Why Change?

Planned Missing Data Designs

Conclusions

References

Chapter 18: Person-Oriented Approaches

Chapter Overview

Description of the Variable- and Person-Oriented Approaches

Three Early Protagonists of the Person-Oriented Approach

Statistical Approaches

Conclusion and Future Directions

References

Chapter 19: Person-Specific Approaches to the Modeling of Intraindividual Variation in Developmental Psychopathology

Introduction

Modeling Interindividual Variation Through Group-Based Individual Difference Models

The Data Box and Factor Analysis

Data Examples

Discussion

Future Directions

References

Chapter 20: Configural Frequency Analysis for Research on Developmental Processes

Introduction

Predicting End Points of Development

Discussion

References

Chapter 21: Moderation and Mediation in Interindividual Longitudinal Analysis

Introduction

The Role of Moderation and Mediation Analyses in Developmental Psychopathology Research

ECLS-K Data Examples

Basic Moderation

Basic Mediation Model

Combining Basic Mediation and Moderation

Longitudinal Data

Multilevel Modeling

Multilevel Modeling Approaches for Longitudinal Data

Structural Equation Modeling

Structural Equation Models for Longitudinal Data

Models for Moderation and Mediation in Longitudinal Data

Modern Causal Inference and Longitudinal Mediation Models

Future Directions

References

Chapter 22: Latent Growth Modeling and Developmental Psychopathology

Core Theoretical Principles of Developmental Psychopathology and Latent Growth Modeling Approaches

Basic Assumptions and Longitudinal Descriptive Analyses

The Latent Growth Modeling

Multilevel Modeling for Studying Developmental Trajectories

Latent Change (Difference) Score Modeling

Growth Mixture Modeling

Conclusion and Future Perspective

References

Chapter 23: Integrative Data Analysis for Research in Developmental Psychopathology

Chapter Overview

Utilities for Research in Developmental Psychopathology

Research Synthesis

Data Examples

Translational Implications of Integrative Data Analysis

Future Directions and Limitations

Conclusions

References

Author Index

Subject Index

End User License Agreement

List of Illustrations

Chapter 1: Assessment of Psychopathology in Young Children

Figure 1.1 Illustration of age effects in social competence from 12 to 36 months of age.

Chapter 2: Developmental Issues in Assessment, Taxonomy, and Diagnosis of Psychopathology: Life Span and Multicultural Perspectives

Figure 2.1 Cartoon by Emil Kraepelin in the 1896 Heidelberg Bierzeitung (Beer Newspaper). In English translation, the caption says, Psychiatrists of Europe! Protect your most sacred diagnoses!

Figure 2.2 The top-down approach to assessment and taxonomy of psychopathology.

Figure 2.3 The bottom-up approach to assessment and taxonomy of psychopathology.

Figure 2.4 Within-cohort (same subjects) predictive correlations over intervals of two to eight years and between-cohort (matched subjects) predictive correlations over six years for Dutch children. Each predictive correlation for the Aggressive Behavior syndrome was significantly greater than for the corresponding Delinquent Behavior syndrome at p < .001.

Figure 2.5 Mean Aggressive Behavior and Delinquent Behavior syndrome scores averaged across seven birth cohorts of Dutch children, separately for males and females.

Figure 2.6 Predictive pathways to scores for Aggressive Behavior, Delinquent Behavior, Attention Problems and syndrome scores in an American national sample.

Figure 2.7 Relations between assessment, taxonomy, and diagnosis. As explained in text, diagnosis includes diagnostic processes (gathering data), formal diagnoses (classifying cases and disorders), and diagnostic formulations (integrating all relevant data for each case).

Chapter 3: Developmental Epidemiology

Figure 3.1 Contributions of developmental epidemiology to translational science.

Figure 3.2 Mean separation anxiety symptoms by age and source of information.

Chapter 6: Emotional Security Theory and Developmental Psychopathology

Figure 6.1 Key pathways and processes in emotional security theory that are proposed to inform an understanding of children's heightened vulnerability to interparental conflict.

Figure 6.2 A graphical depiction of the bidirectional associations between the latent goal of preserving emotional security and the three component processes of emotional reactivity, regulation of exposure to parent affect, and internal representations.

Figure 6.3 A model illustrating how the SDS impacts children's competence in multiple domains by altering the operation of ethological systems that organize approach motives and behaviors.

Figure 6.4 A model illustrating how parent–child relationship processes may inform how interparental conflict increases children's risk for psychopathology though their experiences with insecurity in the interparental and parent–child relationships.

Figure 6.5 Models in developmental psychopathology.

Chapter 9: Autonomy and Autonomy Disturbances in Self-Development and Psychopathology: Research on Motivation, Attachment, and Clinical Process

Figure 9.1 Graphic overview of the role of antecedents of parental need support and need thwarting.

Figure 9.2 Graphic overview of selected psychopathologies reflecting autonomy disturbances.

Chapter 11: I-Self and Me-Self Processes Affecting Developmental Psychopathology and Mental Health

Figure 11.1 Path-Analytical Model for Predictors of Homicidal Ideation and Suicidal Ideation

Figure 11.2 Measure to Assess Integration or Differentiation of Multiple Selves

Chapter 13: Family Systems from a Developmental Psychopathology Perspective

Figure 13.1 Wynne's model of epigenetic processes in relational systems.

Figure 13.2 Karnaugh map displaying mother–child transactions in typical and clinically referred groups.

Figure 13.3 Schermerhorn and Cummings's model of transactional family dynamics.

Figure 13.4 The Revised Family Cohesion Index.

Chapter 15: What Can Dynamic Systems Models of Development Offer to the Study of Developmental Psychopathology?

Figure 15.1 Trajectories in the development of diverse conduct problems from 4 to 18 years of age.

Figure 15.2 The coactive person–environment system.

Figure 15.3 State space model of shifting relations between motivation and performance over time in a child with attention difficulties.

Figure 15.4 The epigenetic nature of development.

Figure 15.5 Hierarchical levels of skill development.

Figure 15.6 Levels within the representational tier of development.

Figure 15.7 The developmental web.

Figure 15.8 Modeling of two scenarios of dissociation-coordination after trauma occurring at 8 years of age; trajectories are determined by dynamic interactions between coordination and dissociation.

Figure 15.9 Changes in matches, mismatches and self-iterative utterances between a child and tutor in mathematics instruction.

Figure 15.10 Independent analysis of growth for matches, mismatches, teacher self-iterations and student self-iterations.

Figure 15.11 Relation between changes in matches, mismatches and self-iterations and the growth of mathematical performance.

Figure 15.12 Interaction dynamics in prototypical responsive and nonresponsive learning sessions with a boy with emotional and behavioral difficulties.

Figure 15.13 Growth of mathematical performance in EBD children.

Figure 15.14 Modeling growth in teaching and learning with children with attentional and emotional problems.

Figure 15.15 The dynamics of socioemotional adjustment for negative emotions.

Figure 15.16 Pathways Toward Adaptive and Maladaptive Socioemotional Adjustment. Phrases along individual trajectories indicate order of developing skills or deficits; gray boxes indicate social effects on developmental pathways; dashed lines indicate co-morbidity of anxiety and depression.

Figure 15.17 Dynamics of staff–child interaction in a group home.

Chapter 16: A Survey of Dynamic Systems Methods for Developmental Psychopathology

Figure 16.7 Schematic diagram of a behavioral interaction with some of the components that can be analyzed through event history analysis.

Figure 16.14 Example of a state space grid with a hypothetical trajectory representing 10 seconds of coded behavior, one arrow head per second. Plotting begins in the lower left part of the cell and moves in a diagonal as each second is plotted, ending in the upper right.

Figure 16.1 A state space with three attractors (the wells) and one repellor (the hill).

Figure 16.2 Alternative developmental trajectories. Phase transitions occur at regular junctures in development. Increased variability at phase transitions is shown in magnified segment.

Figure 16.3 Example of a time-series for an antisocial youth with a positive rule-break bout slope.

Figure 16.4 Example of a time-series for a normal adolescent with a negative rule-break bout slope.

Figure 16.5 Recurrence plots of two exemplary dyads across observation sessions. Gray diagonal line represents the line of identity. Dyad in the upper row is showing the peak in entropy and dyad in the lower row does not show a peak in entropy.

Figure 16.6 Result from the LCGA analysis of values for entropy of the diagonal line structures in the recurrence plots of each dyad across the six observation sessions (95% confidence intervals are based on 15,000 bootstrap replications).

Figure 16.8 Schematic of a four-variable Karnaugh map. Each cell is a unique combination of four binary variables. The arrow shows a sample transition from one state, where only variable D is present (i.e., only D equals 1), to the next state in time, where all 4 variables are present (i.e., all equal 1).

Figure 16.9 Null clines and the stability of steady states. Steady states are circled and represented by the point at which the null clines intersect.

Figure 16.10 Moving min-max graph representing one child's acquisition of spatial prepositions (time window of 18 days, last window, 15 days)

Figure 16.11 Steenbeek and van Geert's (2005) peer interaction simulation model. The first row of boxes represents the first moment in time, t, and the second row represents the second moment, t + 1. Thin arrows represent the iterative feedback components (the output of one iteration is the input for the next).

Figure 16.12 Basic cusp catastrophe model.

Figure 16.13 Cusp catastrophe model representing the relation between anger and frustration in different contexts.

Figure 16.15 Pre- and postperturbation state space grids for an EXT dyad.

Figure 16.16 Pre- and postperturbation state space grids for a MIXED dyad.

Figure 16.17 Examples of three state space grids from Lewis, Lamey, and Douglas (1999).

Chapter 17: Missing Data

Figure 17.1 The mechanisms of missing data and their potential associations with the missing data and the missing values. (Note: The MCAR variables are necessarily uncorrelated with MAR variables and MNAR variables. MAR and MNAR variables can have varying overlap and the higher the overlap the less the influence of the MNAR process can be. The regression betas reflect the implied strength of the multiple linear prediction of the missing values from the set of possible variables that are classified as either in MCAR, MAR, or MNAR set.)

Figure 17.2 A graphical representation of the supermatrix technique as applied to a data set with four observations and three variables.

Figure 17.3 Two variations of a two-method planned missing model. (Note: Only a subset of individuals are randomly selected to receive the expensive measure.)

Figure 17.4 The three traditional sequential designs used to disentangle age, cohort, and time of measurement effects.

Chapter 18: Person-Oriented Approaches

Figure 18.1 Five biopsychological profiles of women experiencing intimate partner violence.

Figure 18.2 Path model of aggressive impulses, verbal aggression against adults and physical aggression against peers. All predicted from gender.

Figure 18.3 Two-group model of aggressive impulses, verbal aggression against adults, and physical aggression against peers; standardized solution for female respondents given.

Figure 18.4 Developmental trajectories of self-perceived aggressive impulses in 114 adolescents.

Figure 18.5 Dendrogram of Ward's clustering of aggressive impulses in adolescents.

Figure 18.6 Density plots of fear, joy, and anxiety in three clusters (Ward's method).

Figure 18.7 Density plots of fear, joy, and anxiety in three clusters (complete linkages).

Chapter 19: Person-Specific Approaches to the Modeling of Intraindividual Variation in Developmental Psychopathology

Figure 19.1 A cross-lagged regression model (regression coefficients only).

Figure 19.2 A first-order cross-legged vector autoregression model (regression coefficients only).

Figure 19.3 The Data Box.

Figure 19.4 A comparison of scree plots of the Borkenau data.

Chapter 20: Configural Frequency Analysis for Research on Developmental Processes

Figure 20.1 Constructing models of personality.

Figure 20.2 Method of differences.

Figure 20.3 Cross-lagged relationship.

Figure 20.4 Cross-lagged relationship between beer consumption and mood.

Figure 20.5 Individual and averaged developmental trajectories.

Figure 20.6 Autocorrelograms for lags between 1 and 50 for respondent 3050 (Panel 1) and 3053 (Panel 2).

Figure 20.7 Autocorrelogram for the aggregate of respondents 3050 and 3053.

Chapter 21: Moderation and Mediation in Interindividual Longitudinal Analysis

Figure 21.1 Path diagrams for the mediation model.

Figure 21.2 Path diagram for the two-predictor, three-mediator in the ECLS-K data.

Figure 21.3 Path diagram of the Fairchild and MacKinnon (2009) approach to moderated mediation and mediated moderation.

Figure 21.4 Illustration of a treatment group decaying exponentially across time while the control group remains constant.

Figure 21.5 SEM mediation model for manifest X, M, and Y variables.

Figure 21.6 Mediation model with latent X, M, and Y variables.

Figure 21.7 An SEM with an interaction between manifest variables.

Figure 21.8 Autoregressive model with cross-lagged paths.

Figure 21.9 SEM latent growth curve model with L1 and L2 predictions.

Figure 21.10 A four-wave latent change score model.

Figure 21.11 Multiple-indicator latent change score model.

Figure 21.12 A four-wave exponential decay latent growth curve model.

Figure 21.13 An autoregressive model with longitudinal mediation across two waves.

Figure 21.14 An autoregressive mediation model with longitudinal and contemporaneous mediation across two waves.

Figure 21.15 An autoregressive mediation model with longitudinal mediation across four waves.

Figure 21.16 An autoregressive mediation model with longitudinal and contemporaneous mediation across four waves.

Figure 21.17 A latent growth curve mediation model across three waves.

Figure 21.18 An autoregressive latent trajectory mediation model across four waves.

Figure 21.19 Parallel process latent change score mediation model.

Figure 21.20 Alternate parallel process latent change score mediation model.

Figure 21.21 An exponential decay latent growth curve model with a continuous X variable and direct paths between observed variables.

Figure 21.22 A parallel process exponential decay latent latent growth curve model with a dichotomous X variable and direct paths between latent growth parameters.

Chapter 22: Latent Growth Modeling and Developmental Psychopathology

Figure 22.1 Longitudinal plots of individual changes in height based on different time metrics for a sample of female participants of the National Longitudinal Survey of Youth–Children and Young Adults.

Figure 22.2 A latent growth curve model.

Figure 22.3 Multivariate growth model of self-esteem and depressive symptoms with maltreatment subtypes as predictors.

Figure 22.4 A bivariate growth curve model.

Figure 22.5 A time-varying covariate growth curve model.

Figure 22.6 Latent change score model of the moderation of inattention between anger and externalizing problems. Values given are unstandardized coefficients with critical ratio (CR) in parentheses. For each path, the coefficients (CR) are listed for poor attention/good attention groups. * c22-math-0338 .

Figure 22.7 Latent change score model of the moderation or inattention between anger and internalizing problems. Values are given unstandardized coefficients with critical ratio (CR) in parentheses. For each path, the coefficients (CR) are listed for poor attention/good attention groups. * c22-math-0353 .

Figure 22.8 A bivariate dual change score model.

Figure 22.9 Latent change score model of emotion lability/negativity predicting internalizing symptomatology, mediated by emotion regulation. Unstandardized parameter estimates (SE) are presented.

Figure 22.10 Estimated mean growth trajectories of ego-resiliency.

Figure 22.11 Estimated mean growth trajectories of ego-control.

Chapter 23: Integrative Data Analysis for Research in Developmental Psychopathology

Figure 23.1 Integrative data analysis (IDA) and meta-analysis using individual participant-level data (IPD) and aggregated data (AD). AD meta-analysis can be conducted either as part of IDA when IPD is available (see the circle inside the rectangle on left) or based on reported AD in publications (the circle inside the right rectangle).

Figure 23.2 Analytical approaches under IDA.

Figure 23.3 Proportion of the articles citing meta-analysis studies published in Development and Psychopathology from 1997 to 2012.

Figure 23.4 Current and future directions of research synthesis. Black arrows indicate the emerging directions and dotted arrows indicate the flow of research synthesis from data to analysis. Note that IPD approaches are computationally intensive and may not always be feasible.

Figure 23.5 Symmetrical (top) and asymmetrical (bottom) funnel plots. The bottom Figure shows evidence of publication bias.

Figure 23.6 A fixed-effects model. It assumes a common underlying mean c23-math-0049 and different variances c23-math-0050 due to sampling variability for study i. This Figure was drawn based on Figure 3 from Normand (1999).

Figure 23.7 A random-effects model. It assumes a superpopulation with a mean c23-math-0051 and a variance c23-math-0052 (top figure) from which distributions with different means c23-math-0053 and variances c23-math-0054 (bottom figure) are drawn. Figure 23.7 was drawn based on Figure 4 from Normand (1999).

Figure 23.8 Network meta-analysis. The Figure on the left shows an example of an indirect comparison between interventions B and C. The Figure on the right shows mixed networks of evidence (both direct and indirect) for interventions B and C. Interventions B and C are connected via both solid and dotted lines.

Figure 23.9 Protective behavioral strategies at the first follow-up under the fixed-effects model for 10 studies.

Figure 23.10 PBS latent trait means across studies estimated from the GPCM analysis. The second, third, and fourth bars indicate data from Studies 8a, 8b, and 8c, respectively. The error bars indicate two times standard errors in each direction. Participants in Studies 2 and 16 were estimated to utilize PBS more often than students in other studies. Study 12 was an outlying study—Participants in Study 12 were least likely to utilize PBS.

Figure 23.11 PBS at the first follow-up under the fixed-effects model for nine studies (Study 12 removed).

Figure 23.12 Model-based estimates of NR latent trait scores at baseline, 6 months and 12 months post intervention by group. NR = Neglecting responsibilities due to drinking; AlcEd = Alcohol Education; PF = Stand-alone Personalized Feedback; MI.PF = Motivational Interview plus Personalized Feedback (MI + PF); y-axis indicates NR scores. Higher scores (i.e., shorter bars in this figure) indicate greater severity. Error bars indicate 95% confidence intervals. Relatively speaking, there was a trend toward improving for those in the PF and MI + PF conditions.

Figure 23.14 Multivariate meta-analysis of multiple intervention comparisons, showing a graphic demonstration of the significant MI + PF intervention effect compared with control. NR = Neglecting responsibilities due to drinking; AlcEd = Alcohol Education; PF = Stand-alone Personalized Feedback; MI.PF = Motivational Interview plus Personalized Feedback (MI + PF). Error bars indicate 95% confidence intervals. Confidence intervals that do not include zero indicate that the intervention condition at a given time point differed significantly in the estimated NR trait scores, compared with control. MI + PF, compared with control, showed significantly lower levels of NR trait scores at both 6 months and 12 months post intervention.

Figure 23.13 Model-based estimates of change at 6 months and 12 months post intervention by group. NR = Neglecting responsibilities due to drinking; AlcEd = Alcohol Education; PF = Stand-alone Personalized Feedback; MI.PF = Motivational Interview plus Personalized Feedback (MI + PF); y-axis indicates NR change scores. Error bars indicate 95% confidence intervals for these change scores. Confidence intervals that do not include zero indicate a statistically significant reduction at a given time point within groups. All groups with the exception of Alcohol Education showed a statistically significant reduction at 6 months. The significant reduction disappeared at 12 months for all three groups.

List of Tables

Chapter 1: Assessment of Psychopathology in Young Children

Table 1.1 Illustrative Examples of Sociocultural Factors that Impact Assessment Findings

Table 1.2 General Characteristics of Different Types of Parent and Other Caregiver Report Tools

Table 1.3 Types of Reliability

Table 1.4 Reliability Statistics and Rules of Thumb for Interpreting Reliability

Table 1.5 Brief Checklist/Questionnaire Measures That Address Social-Emotional and Behavioral Problems Broadly

Table 1.7 Breadth and Depth of Coverage by Comprehensive Checklist Measures

Table 1.6 Comprehensive Checklist/Questionnaire Measures That Address Social-Emotional and Behavioral Problems Broadly

Table 1.8 Diagnostic Interviews Used with Young Children

Table 1.9 Test–Retest Reliability of Diagnostic Interviews Used with Young Children

Chapter 2: Developmental Issues in Assessment, Taxonomy, and Diagnosis of Psychopathology: Life Span and Multicultural Perspectives

Table 2.1 Challenges in Advancing Assessment, Taxonomy, and Diagnosis

Table 2.2 Potential Advantages of Quantifying Criterial Features and Taxonomic Constructs

Chapter 3: Developmental Epidemiology

Table 3.1 Homotypic and Heterotypic Continuity, with and Without Controls for Comorbidity

Chapter 6: Emotional Security Theory and Developmental Psychopathology

Table 6.1 Comparison of Security in the Interparental and Parent-Child Relationships Along Ethological Parameters

Table 6.2 Synopsis of Findings from Studies Examining Multiple Signs of Emotional Insecurity as Mediators of Associations Between Interparental Conflict and Child Adjustment

Table 6.3 Developmental Challenges Faced by Children From Infancy Through Adolescence

Chapter 10: Roots of Typical Consciousness: Implications for Developmental Psychopathology

Table 10.1 Levels of Intersubjectivity Unfolding in Typical Development

Chapter 16: A Survey of Dynamic Systems Methods for Developmental Psychopathology

Table 16.1 Summary of DS Techniques and DS Concepts and Examples of Studies That Have Applied These Techniques

Table 16.2 Regression Results Predicting Adolescent Authority Conflict and Substance Abuse in Midadolescence from the Strength of the Deviant Talk Attractor in Early Adolescence

Table 16.3 Parameter profiles for various types of dyads

Chapter 17: Missing Data

Table 17.1 Methods of Treating Missing Data

Table 17.2 Missing Data Mechanisms

Table 17.3 Software Packages That Implement Multiple Imputation

Table 17.4 The Solomon Four-Group Design as a Planned Missing Design with the Addition of Two Additional Groups for a Complete Planned Missing Design

Table 17.5 Example of a Three-Form Planned Missing Design

Table 17.6 Possible Candidate Constructs for Use with the Two-Method Planned Missing Data Design

Table 17.7 Converting a Cohort-Sequential Data Set into an Accelerated Longitudinal Design

Chapter 18: Person-Oriented Approaches

Table 18.1 An Example Demonstrating the Complete Confounding of Contextual and Individual Effects in Ecologic Data

Table 18.2 Observed and Expected Frequencies for Log-Linear Model of the Development of Aggressive Impulses and Physical Aggression Against Peers in Adolescents

Table 18.3 Parameters of the Model of the Development of Aggressive Impulses and Physical Aggression Against Peers in Adolescents

Table 18.4 Cross-Classification of Two Cluster Solutions

Table 18.5 Cross-Tabulation of the Three-Cluster Solutions from Ward's Method (Rows) and Complete Linkage (Columns)

Table 18.6 Cross-Classification of JoyD, FearD, and AnxietyD with the Three-Cluster Solution Created with Ward's Method

Table 18.7 Space-Segment Clusters of the Cross-Classification of the Dichotomized Variables Fear, Joy, and Anxiety

Chapter 19: Person-Specific Approaches to the Modeling of Intraindividual Variation in Developmental Psychopathology

Table 19.1 Positive and Negative Items for the Two-Factor Solution

Table 19.2 Confirmatory Factor Analysis for Stepsons 1, 5, and 6

Chapter 20: Configural Frequency Analysis for Research on Developmental Processes

Table 20.1 First-Order CFA of the Cross-Classification of PTSD (P), Income (I), and Depression (D)

Table 20.12 First-order CFA of the Cross-Classification of Monotonic Trend Information of Three Data Waves of Depression (D), Anxiety (A), and PTSD (P)

Table 20.2 First-Order CFA of the Cross-Classification of Narrowed Consciousness (C), Thought Disturbance (T), and Affective Disturbance (A)

Table 20.3 First-Order CFA of the Cross-Classification of Crime (C), Hyperactivity (H), and Adrenaline Level (A)

Table 20.4 Calculation of First Through Fourth Differences

Table 20.5 Descriptive Statistics for the Variables PADIF1 = PAAP85 − PAAP83 and PADIF2 = PAAP87 − PAAP85

Table 20.6 Two-Group CFA of the Gender (G) × PADIF1 (P1) × PADIF2 (P2) Cross-Classification, with Gender as the Grouping Variable

Table 20.7 First-Order CFA of the Gender (G) × PADIF1 (P1) × PADIF2 (P2) Cross-Classification

Table 20.8 First-Order CFA of the P1 × P2 × ΔP Cross-Classification; Structural Zeros Not Taken into Account

Table 20.9 First-Order CFA of the P1 × P2 × ΔP Cross-Classification; Structural Zeros Taken into Account

Table 20.10 First-Order CFA of the Gender × P1 × P2 × ΔP Cross-Classification; Structural Zeros Taken into Account

Table 20.11 First-order CFA of Means and Linear Trends of Two State Anxiety Questionnaire Parallel Forms

Table 20.13 Two-Group CFA of the Cross-Classification of Monotonic Trend Information of Three Data Waves of Depression (D), Anxiety (A), and PTSD (P), with PTSD as the Grouping Variable

Table 20.14 Evaluation of Treatment of Schizophrenics with Neuroleptic Drugs in a Pre–Post Study

Table 20.15 Cross-Classification of Facility and Research Diagnoses for 223 Psychiatric Patients

Table 20.16 2 × 2 × 2 Table for the Comparison of Pattern Shift in Two Groups

Table 20.17 2 × 2 Table for the Comparison of Pattern Shift in Two Groups

Table 20.18 Cross-Classification of Depression (D), PTSD (P), and Frequency of Violence (F)

Table 20.19 2 × 2 Table for the Comparison of Two Groups in a Two-Variable Pattern

Table 20.20 Cross-Classification of Treatment and Outcome Pattern and Cell Frequencies

Table 20.21 First-Order CFA of the Cross-Classification of Numbers of Subjective Health (H), Mood (M), and Beer Consumed (B), for Respondent 3000

Table 20.22 Prediction CFA of the Cross-Classification of Numbers of Beer Consumed (B) and Subjective Health (H), over the First and the Second Halves of the Observation Period (P); for Respondent 3004

Table 20.23 First-Order CFA of the PR × VR × AR Cross-Classification

Table 20.24 2-Group CFA of the PR × VR × AR Cross-Classification, with AR as the Grouping Variable

Table 20.25 Series with Lags 1, 2, and 3

Table 20.26 Cross-Classification of a Series of Scores with Itself, for a Lag of k

Table 20.27 Cross-Classification of Beer Consumption for a Lag of One Day (Respondent 3053)

Table 20.28 Cross-Classification of Liquor Consumption for a Lag of One Day (Respondent 3053)

Table 20.29 2 × 2 Cross-Classification with Observed Frequencies mij and Expected Frequencies c20-math-0307

Table 20.30 Cross-Classification of Mood Ratings for a Lag of One Day (Respondent 3053)

Table 20.31 First-order CFA of Mood Ratings on Consecutive Days (Respondent 3053)

Table 20.32 First-Order CFA of Beer and Liquor Consumption and Mood Ratings on the Following Day (Respondent 3053)

Table 20.33 Correlation Matrix for X1, X2, Y1, and Y2

Table 20.34 Cross-Lagged CFA of the B × M × B1 × M1 Cross-Classification

Table 20.35 Parameter Estimates for Log-Linear Cross-Lagged Model

Table 20.36 First-order CFA of the Cross-Classification of B, B1, M1, and ID

Table 20.37 Two-Group CFA of the Cross-Classification of B, B1, M1, and ID With ID as the Grouping Variable

Table 20.38 First-Order CFA of the DV3 × DV4 × DV5 × SX5 Cross-Classification

Table 39 Predicting SX5 from the Serial Pattern of DV in the DV3 × DV4 × DV5 × SX5 Cross-Classification

Table 20.40 First-Order CFA of the DV1 × SX3 × SX4 × SX5 Cross-Classification

Table 20.41 2-Group CFA of the DV1 × SX3 × SX4 × SX5 Cross-Classification with DV1 as the Grouping Variable

Chapter 21: Moderation and Mediation in Interindividual Longitudinal Analysis

Table 21.1 Coefficients and Standard Errors for Moderation Models

Table 21.2 Coefficients, Standard Errors, Tests of Mediation, and Bootstrap Confidence Intervals for Single- and Multiple-Mediator Models

Table 21.3 Coefficients, Standard Errors, and Tests of Moderated Mediation and Mediated Moderation

Table 21.4 Coefficients and Standard Errors for Multilevel LAM Models

Table 21.5 Coefficients and Standard Errors for Multilevel Models Including Time-Varying Predictor by Time Interactions

Table 21.6 Combinations of Significant and Nonsignificant Coefficients That Produce Various Trends of the Mediated Effect over Time

Table 21.7 Coefficients, Standard Errors, and Mediated Effect Trends for the M-COT Model

Table 21.8 Coefficients and Standard Errors for Tests of Longitudinal Mediation in an Autoregressive Panel Model

Table 21.9 Coefficients and Standard Errors for Tests of Longitudinal and Contemporaneous Mediation in an Autoregressive Panel Model

Table 21.10 Coefficients and Standard Errors in an LGM Mediation Model

Table 21.11 Coefficients and Standard Errors in LCS Parallel Process Mediation Models

Table 21.12 Coefficients and Standard Errors in LCS Mediation Models with Paths from True Scores to Latent Change

Chapter 22: Latent Growth Modeling and Developmental Psychopathology

Table 22.1 Comparisons of Fitted Growth Curve Models for Self-Esteem and Depressive Symptoms of Maltreated and Nonmaltreated Children

Table 22.2 Comparisons of Fitted Growth Curve Models for Self-Esteem and Depressive Symptoms of Maltreated and Nonmaltreated Children

Table 22.3 Fit Indices for Univariate Growth Mixture Modeling of Personality Processes and Behavioral Problems in Maltreated and Nonmaltreated Children

Chapter 23: Integrative Data Analysis for Research in Developmental Psychopathology

Table 23.1 Research Synthesis Methods

Table 23.2 Model Setting of 2 × 2 Table for k Independent Studies

Table 23.3 Pooling Data from 2 × 2 Table Using the Peto's Odds Ratio and the Mantel-Haenszel Method

Table 23.4 Project INTEGRATE: Study Designs (Adapted from Mun et al., 2015)

Table 23.5 Project INTEGRATE: Intervention Groups Across Studies After Removing Ineligible Studies for the Multivariate Meta-analysis Data Example

Table 23.6 Patterns of Covariates by Study for IPD Multivariate Meta-analysis

Table 23.7 Parameter Estimates from IPD Multivariate Meta-analysis

Table 23.8 Software Programs for Meta-analysis

DEVELOPMENTAL PSYCHOPATHOLOGY

THIRD EDITION

Volume One: Theory and Method

Editor

DANTE CICCHETTI

Title Page

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Library of Congress Cataloging-in-Publication Data:

Developmental psychopathology / editor, Dante Cicchetti. – Third edition.

pages cm

Includes index.

ISBN 978-1-118-12087-3 (volume 1 : cloth : alk. paper) – ISBN 978-1-118-12091-0 (volume 2 : alk. paper) – ISBN 978-1-118-12092-7 (volume 3 : alk. paper) – ISBN 978-1-118-12093-4 (volume 4 : alk. paper) 1. Mental illness–Etiology. 2. Developmental psychology. 3. Mental illness–Risk factors. 4. Adjustment (Psychology) I. Cicchetti, Dante.

RC454.4.D483 2016

616.89–dc23

These volumes are dedicated to Marianne Gerschel in recognition of her great vision and staunch support of the field of developmental psychopathology.

Preface to Developmental Psychopathology, Third Edition

A decade has passed since the second edition of Developmental Psychopathology was published. The two prior editions (Cicchetti & Cohen, 1995, 2006) have been very influential in the growth of the field of developmental psychopathology. The volumes have been highly cited in the literature and have served as an important resource for developmental scientists and prevention and intervention researchers alike. In the present third edition, we have expanded from the three volumes contained in the second edition to four volumes. The increased number of volumes in this current edition reflects the continued knowledge gains that have occurred in the field over the past decade.

A not insignificant contributor to this growth can be found in the very principles of the discipline (Cicchetti, 1984, 1990, 1993); Cicchetti & Sroufe, 2000; Cicchetti & Toth, 1991, 2009); Rutter & Sroufe, 2000; Sroufe & Rutter, 1984). Theorists, researchers, and prevention scientists in the field of developmental psychopathology adhere to a life span framework to elucidate the numerous processes and mechanisms that can contribute to the development of mental disorders in high-risk individuals as well as those operative in individuals who already have manifested psychological disturbances or who have averted such disorders despite their high-risk status (Cicchetti, 1993; Masten, 2014; Rutter, 1986, 1987, 2012). Not only is knowledge of normal genetic, neurobiological, physiological, hormonal, psychological, and social processes very helpful for understanding, preventing, and treating psychopathology, but also deviations from and distortions of normal development that are seen in pathological processes indicate in innovative ways how normal development may be better investigated and understood. Similarly, information obtained from investigations of experiments of nature, high-risk conditions, and psychopathology can augment the comprehension of normal development (Cicchetti, 1984, 1990, 1993); Rutter, 1986; Rutter & Garmezy, 1983; Sroufe, 1990; Weiss, 1969).

Another factor that has expedited growth within the field of developmental psychopathology has been its ability to incorporate knowledge from diverse disciplines and to encourage interdisciplinary and translational research (Cicchetti & Gunnar, 2009; Cicchetti & Toth, 2006). In keeping with its integrative focus, contributions to developmental psychopathology have come from many disciplines of the biological and social sciences. A wide array of content areas, scientific disciplines, and methodologies has been germane. Risk and protective factors and processes have been identified and validated at multiple levels of analysis and in multiple domains.

The increased emphasis on a multilevel, dynamic systems approach to psychopathology and resilience, the increased attention paid to gene–environment interplay in the development of psychopathology and resilience, and the application of a multiple levels of analysis developmental perspective to mental illnesses that have traditionally been examined nondevelopmentally (e.g., bipolar disorder, schizophrenia, and the personality disorders) not only have contributed to a deeper understanding of the dysfunctions but also have educated the public about the causes and consequences of mental disorder (see Cicchetti & Cannon, 1999; Cicchetti & Crick, 2009a, 2009b); Miklowitz & Cicchetti, 2006, 2010); Tackett & Sharp, 2014).

Advances in genomics, GxE interactions, and epigenetics; growth in our understanding of neurobiology, neural plasticity, and resilience; and progress in the development of methodological and technological tools, including brain imaging, neural circuitry, hormone assays, immunology, social and environmental influences on brain development, and statistical analysis of developmental change, pave the way for interdisciplinary and for multiple levels of analysis research programs that will significantly increase the knowledge base of the development and course of maladaptation, psychopathology, and resilience. Moreover, randomized control prevention and intervention trials are being conducted based on theoretical models and efforts to elucidate the mechanisms and processes contributing to developmental change at both the biological and psychological levels (Belsky & van IJzendoorn, 2015; Cicchetti & Gunnar, 2008).

Despite the significant advances that have occurred in the field of developmental psychopathology, much important work lies ahead. Undoubtedly these future developments will build on the venerable contributions of the past; however, as work in the field becomes increasingly interdisciplinary, multilevel, and technologically sophisticated, it is essential that even more emphasis be directed toward the process of development (Harter, 2006; Sroufe, 2007, 2013). It is not only genes and environments but also the cumulative developmental history of the individual that influences how future development will unfold (Sroufe, 2007, 2013).

Developmental psychopathologists have incorporated concepts and methods derived from other disciplinary endeavors that are too often isolated from each other, thereby generating advances in knowledge that might have been missed in the absence of cross-disciplinary dialogue. The continuation and elaboration of the mutually enriching interchanges that have occurred within and across disciplines interested in normal and abnormal development not only will enhance the science of developmental psychopathology but also will increase the benefits to be derived for individuals with high-risk conditions or mental disorders, families, and society as a whole.

Dante Cicchetti, Ph.D.

Minneapolis, MN

January 2015

References

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Cicchetti, D. 1990. A historical perspective on the discipline of developmental psychopathology. In J. Rolf, A. Masten, D. Cicchetti, K. Nuechterlein, & S. Weintraub (Eds.), Risk and protective factors in the development of psychopathology (pp. 2–28). New York, NY: Cambridge University Press.

Cicchetti, D. 1993. Developmental psychopathology: Reactions, reflections, projections. Developmental Review, 13, 471–502.

Cicchetti, D., & Cannon, T. 1999. Neurodevelopmental processes in the ontogenesis and epigenesis of psychopathology. Development and Psychopathology, 11, 375–393.

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Contributors

Thomas M. Achenbach, PhD

University of Vermont

Burlington, VT

Adrian Angold, MRCPsych

Duke University Medical Center

Durham, North Carolina

Marian J. Bakermans-Kranenburg, PhD

Leiden University

Leiden, Netherlands

Lars R. Bergman, PhD

Stockholm University

Stockholm, Sweden

G. Anne Bogat, PhD

Michigan State University

East Lansing, Michigan

Margaret J. Briggs-Gowan, PhD

University of Connecticut

Farmington, Connecticut

Deborah M. Capaldi, PhD

Oregon Social Learning Center

Eugene, Oregon

Alice S. Carter, PhD

University of Massachusetts

Boston, Massachusetts

JeeWon Cheong, PhD

University of Florida

Gainesville, Florida

Dante Cicchetti, PhD

Institute of Child Development

University of Minnesota

Minneapolis, Minnesota

Pamela M. Cole, PhD

Pennsylvania State University

University Park, Pennsylvania

E. Jane Costello, PhD

Duke University Medical Center

Durham, North Carolina

Patrick T. Davies, PhD

University of Rochester

Rochester, New York

Joanne Davila, PhD

Stony Brook University

Stony Brook, New York

Edward L. Deci, PhD

University of Rochester

Rochester, New York

R. M. Pasco Fearon, PhD, DClinPsy

University College London

London, United Kingdom

Kurt W. Fischer, PhD

Harvard University

Cambridge, Massachusetts

Matthew S. Fritz, PhD

University of Nebraska

Lincoln, Nebraska

Matteo Giletta, PhD

Tilburg University

Tilburg, Netherlands

Leandra Godoy, PhD

Children's National Medical Center

Washington, District of Columbia

Isabela Granic, PhD

Radboud University Nijmegen

Nijmegen, Netherlands

Kevin J. Grimm, PhD

Arizona State University

Tempe, Arizona

Ashley M. Groh, PhD

University of Missouri

Columbia, Missouri

Susan Harter, PhD

University of Denver

Denver, Colorado

Amy Heberle, MS

University of Massachusetts

Boston, Massachusetts

Tom Hollenstein, PhD

Queen's University

Kingston, Canada

Yang Jiao, MS

Rutgers, the State University of New Jersey

Piscataway, New Jersey

Patricia K. Kerig, PhD

University of Utah

Salt Lake City, Utah

Jungmeen Kim-Spoon, PhD

Virginia Tech

Blacksburg, Virginia

Jennifer L. Krull, PhD

University of California

Los Angeles, California

Annette M. La Greca, PhD, ABPP

University of Miami

Coral Gables, Florida

Kyle M. Lang, PhD

Texas Tech University

Lubbock, Texas

Anna Lichtwarck-Aschoff, PhD

Radboud University Nijmegen

Nijmegen, Netherlands

Todd D. Little, PhD

Texas Tech University

Lubbock, Texas

David P. MacKinnon, PhD

Arizona State University

Tempe, Arizona

Meredith J. Martin, PhD

University of Rochester

Rochester, New York

Michael F. Mascolo, PhD

Merrimack College

North Andover, Massachusetts

Peter C. M. Molenaar, PhD

Pennsylvania State University

University Park, Pennsylvania

Eun-Young Mun, PhD

Rutgers, the State University of New Jersey

Piscataway, New Jersey

Thomas G. O'Connor, PhD

University of Rochester Medical Center

Rochester, New York

Mitchell J. Prinstein, PhD, ABPP

University of North Carolina

Chapel Hill, North Carolina

Leslie A. Rescorla, PhD

Bryn Mawr College

Bryn Mawr, Pennsylvania

Mijke Rhemtulla, PhD

University of Amsterdam

Amsterdam, Netherlands

Philippe Rochat, PhD

Emory University

Atlanta, Georgia

Glenn I. Roisman, PhD

University of Minnesota

Minneapolis, Minnesota

Michael J. Rovine, PhD

Pennsylvania State University

University Park, Pennsyvania

Michael L. Rutter, MD, FRS, FRC Psych., FBA, FAC Med Sci

King's College London

London, United Kingdom

Richard M. Ryan, PhD

Australian Catholic University

Strathfield, Australia

Henderien Steenbeek, PhD

University of Groningen

Groningen, Netherlands

Melissa L. Sturge-Apple, PhD

University of Rochester

Rochester, New York

Anita Thapar, MD, PhD

Cardiff University

Cardiff, United Kingdom

Paul van Geert, PhD

University of Groningen

Groningen, Netherlands

Marinus H. van IJzendoorn, PhD

Leiden University

Leiden, Netherlands

Maarten Vansteenkiste, PhD

University of Ghent

Ghent, Belgium

Alexander von Eye, PhD

Michigan State University

East Lansing, Michigan

Wei Wu, PhD

University of Kansas

Lawrence, Kansas

Minge Xie, PhD

Rutgers, the State University of New Jersey

Piscataway, New Jersey

Chapter 1

Assessment of Psychopathology in Young Children

Margaret J. Briggs-Gowan, Leandra Godoy, Amy Heberle, and Alice S. Carter

INTRODUCTION

EARLY PROBLEMS MATTER

Progress in Psychiatric Diagnosis in Young Children

IMPORTANT CONSIDERATIONS IN YOUNG CHILD ASSESSMENT

Reliance on Caregivers for Information

Sensitivity to Contextual Influences, Including Caregiving Contexts

DOMAINS OF DEVELOPMENT

SELECTING AN ASSESSMENT APPROACH AND TOOL

Types of Tools

Understanding Psychometric Properties

Reliability

Validity

Validity of Classification

Normatization

Cultural Validity and Cultural Norms

Knowing What Problems Are Really Being Assessed

Response Formats

Summary

ASSESSMENT TOOLS

Screening Methods

Screening Methods Characteristics of Screening Tools

Selected Screening Tools

Comprehensive Dimensional Tools for Assessing Social-Emotional/Behavioral Problems

Selected Dimensional Checklists

Variation in Emphasis of the Domains That Are Assessed

Diagnostic Approaches

Selected Diagnostic Interviews

Psychometric Properties of Diagnostic Interviews

Observational Assessment

Assessing Impairment

CONCLUSIONS AND DIRECTIONS FOR FUTURE RESEARCH

REFERENCES

Introduction

The past 20 years have witnessed a sea change for young children's mental health. It is now recognized that early childhood (0–5 years) is a crucial period for the development of self-regulation, a critical set of competencies that have implications for adaptive functioning in school and through the life span. Early childhood is also recognized as a time when psychopathology may begin to emerge and disrupt young children's developmental progress. In addition, enormous progress has been made in demonstrating that, when psychiatric disorders are defined in a manner that is developmentally meaningful, even very young children suffer from psychiatric disorders that are valid, impairing, and clinically very similar to those experienced by older children (Egger & Angold, 2006; Egger et al., 2006). Indeed, recent research has indicated that psychiatric disorders are just as prevalent in early childhood as they are in school-age children (Egger & Angold, 2006). Moreover, when young children manifest psychopathology that is impairing, it is often persistent and predicts later difficulties once they become of school age. Equally important, there is increasing awareness that these problems can interfere with learning within early childhood and may set in motion a developmental cascade that likely predicts challenges to lifespan functioning in multiple domains. Research focused on specific disorders has driven discovery of neurobiologic substrates, which has further validated the relevance and reality of early life psychopathology (Luby, Belden, Pautsch, Si, & Spitznagel, 2009; Luby, Si, Belden, Tandon, & Spitznagel, 2009; Stalets & Luby, 2006). These advances in our understanding of young child psychopathology are paralleled by, and one might argue largely driven by, an explosion in reliable, valid, developmentally sensitive measures for assessing a full range of self-regulation, social-emotional development in young children. Specifically, over the past 15-plus years, a number of instruments have been developed to assess parent and other caregiver appraisals of social-emotional functioning utilizing both questionnaire and interview methods. There have also been advances in observational tools to assess clinically significant emotional and behavior problems. With greater acceptance and building on advances in measurement, we are poised to evaluate the benefits of a broad range of prevention and intervention efforts and see increasing discovery of biological and environmental influences on young children's mental health.

As the field presses forward to address the mental health needs of young children both efficiently and effectively, success will be optimized by a well-informed approach to assessment that (1) acknowledges contextual factors, including the caregiving environments at home and in other settings, such as child care and early education environments, caregiver influences on social-emotional functioning and assessment, recent changes in family structure or contextual stressors, and sociocultural factors; (2) is framed within the context of a child's functioning in other developmental domains, such as language, cognition, adaptive functioning, health, and sensory; (3) is tailored to the goals and purposes of the assessment and evaluation setting (e.g., pediatric clinic, day care center, mental health clinic, or private practice); (4) utilizes reliable, valid, developmentally sensitive tools; and (5) employs an approach to interpretation that views the whole child in relation to contextual and developmental factors and evaluates his or her capacities and participation in developmentally appropriate activities and settings (i.e., impairment).

A primary goal of this chapter is to help clinicians and researchers determine the most suitable measures to use from a wide array of parent and other caregiver report, observational, and direct assessment measures that are now available. Rather than trying to offer an exhaustive list of all existing measures of social-emotional functioning and psychopathology appropriate for young children, we highlight some of the most widely employed and promising tools and approaches, including those that reflect advances in screening, comprehensive dimensional parent- and other caregiver-report instruments, and diagnostic approaches to young child evaluation. These assessment tools can be categorized as follows: (1) parent and other caregiver report instruments that focus on general problem behaviors; (2) parent and other caregiver report instruments that focus on specific problem areas or disorders (e.g., anxiety, disruptive behavior); (3) parent and other caregiver report instruments designed to assess both problem behaviors and competencies; (4) comprehensive diagnostic interviews for parents of young children; and (5) observational tools and methods. Within the first three categories, measures can be further divided according to whether they are brief tools appropriate for screening or longer checklist tools or diagnostic interviews that provide more detailed information. Finally, we will close the chapter with a discussion of ongoing challenges, future directions, and opportunities in research on and clinical applications with assessment of young child psychopathology. Although many researchers and clinicians continue to express discomfort about pathologizing, or labeling, young children, our focus is on assessment tools that enhance the recognition and detection of early emerging psychopathology to address mental health needs in an effort to minimize adverse developmental cascades. Moreover, we argue optimistically that by labeling systematic behavioral patterns observed within young children (rather than labeling individual children) we create the potential to develop and disseminate guidance regarding appropriate contextual supports and specific behavioral interventions that are tailored to the needs of children with different behavioral profiles; these early prevention and targeted interventions can be designed to support family beliefs, values, and goals and children's developmental progress while minimizing child and family distress.

Early Problems Matter

There is now a consensus among child clinicians that children as young as 2 years of age can suffer from significant social-emotional and behavior problems, or psychopathology. Prevalence estimates of clinically significant problems in nonreferred samples have ranged considerably, from as low as 7% to as high as 26%, depending on whether problems are defined in terms of meeting criteria for psychiatric diagnosis or by exceeding a clinical cutoff on a checklist measure (Briggs-Gowan, Carter, Skuban, & Horwitz, 2001; Egger & Angold, 2006; Gleason et al., 2011; Karabekiroglu et al., 2013; Keenan et al., 1997; Lavigne, Lebailly, Hopkins, Gouze, & Binns, 2009; Wichstrom et al., 2012). Rates also tend to be higher among young children exposed to poverty and other psychosocial risk factors (McCue Horwitz et al., 2012; Qi & Kaiser, 2003; Weitzman, Edmonds, Davagnino, & Briggs-Gowan, 2014). Early social-emotional and behavioral problems are linked with impairment in child and family functioning as well as increased parenting stress and worry (Briggs-Gowan & Carter, 2008a; Briggs-Gowan, Carter, Bosson-Heenan, Guyer, & Horwitz, 2006; Briggs-Gowan et al., 2001; Egger & Angold, 2006; Fuchs, Klein, Otto, & von Klitzing, 2013; Keenan et al., 2007; Lavigne et al., 1996; Luby, Belden, Pautsch, et al., 2009). Moreover, social-emotional/behavior problems in young children have been associated with concomitant delays in child social-emotional competence (Briggs-Gowan & Carter, 2008a; Briggs-Gowan et al., 2001) and shown to predict poorer social competence in elementary school (Briggs-Gowan, Carter, & Ford, 2011). Intervening in preschool to address both social emotional problems and competencies is associated with greater improvements in both areas, including on experimental tasks of emotion knowledge and social problem-solving strategies (Ştefan & Miclea, 2013). Furthermore, contrasting with the historical belief that young children's difficult behavior is just a phase (Keenan & Wakschlag, 2000), there is consistent evidence that for some children these early emergent social-emotional and behavioral problems are persistent and predict poorer functioning at later ages (Briggs-Gowan & Carter, 2008b; Briggs-Gowan et al., 2006; Kim-Cohen et al., 2005; Lavigne et al., 1998; Mathiesen & Sanson, 2000; O'Neill, Schneiderman, Rajendran, Marks, & Halperin, 2014; Shaw, Lacourse, & Nagin, 2005; Speltz, McClellan, DeKlyen, & Jones, 1999; Spence, Najman, Bor, O'Callaghan, & Williams, 2002; Stalets & Luby, 2006; Wakschlag, Briggs-Gowan, et al., 2008). Notably, persistence has been documented for a wide range of problems, including anxiety, depression, attention-deficit hyperactivity, and disruptive behaviors. Thus, consistent and convincing evidence indicates that young children can and do suffer from a wide spectrum of social-emotional and behavioral problems that are often persistent—the presence of impairment further underscores the importance of early identification and prevention in this developmental period.

Progress in Psychiatric Diagnosis in Young Children

Empirical and conceptual work in the areas of posttraumatic stress disorder (PTSD), depression, and disruptive behavior disorders illustrates the role that developmental factors can play in how psychopathology manifests and how several groups have endeavored to establish the relevance of these psychopathologies in young children.

PTSD

Considerable work by Michael Scheeringa and colleagues documents that children as young as 9 months of age can and do suffer from PTSD (Scheeringa, 2007, 2008); Scheeringa, Myers, Putnam, & Zeanah, 2012; Scheeringa, Peebles, Cook, & Zeanah, 2001). This work highlights the importance of considering the developmental capacities of young children when determining the appropriateness of criteria employed for older children and illustrates the utility of adopting a multi-informant, multimethod approach to assessment (Hunsley & Mash, 2007). Scheeringa et al.'s work in this area has driven important recognition of developmental factors that affect how PTSD presents in young children. For example, many avoidance and numbing symptoms are either developmentally implausible (e.g., sense of a foreshortened future) or internal in quality (e.g., avoidance of internal thoughts, feelings, or reminders of the event), making them very difficult to identify in young children who have limited verbal skills (Scheeringa, 2008). Scheeringa also noted that these types of symptoms may manifest differently in young children. For example, markedly diminished interest in significant activities is often observed as constriction of play, and feeling of detachment or estrangement from others is often observed as social withdrawal.

Scheeringa and colleagues further documented the central role that parental reactions play in the emergence, promotion, and maintenance of symptoms in young children (Scheeringa & Zeanah, 2001), highlighting the importance of assessing young children's possible PTSD symptoms in the context of parent– or caregiver–child relationships.

Depression

Luby and colleagues' work has established the presence of early manifestations of clinically significant signs and symptoms of depressive disorders in young children (Luby, Belden, Pausch, et al., 2009; Luby & Navsaria, 2010; Luby, Si, et al., 2009; Stalets & Luby, 2006). Their research addresses the complicated developmental question of whether young children's emotional repertoire is itself sufficiently differentiated to encompass true depressive or elated affect and how to differentiate atypical from normative developmental manifestations. They have further noted that greater variability in young children's mood states calls into question the relevance of duration criteria employed by diagnostic systems developed for older children (Gaffrey, Belden, & Luby, 2011). Luby and colleagues utilized a multimethod, multi-informant approach that included (1) parent reports on both dimensional ratings scales and in an age-appropriate diagnostic interview; (2) comprehensive observation of the young child's affective range via a laboratory based temperament assessment, thematic play, and parent–child interaction across structured and unstructured conditions; (3) a developmentally sensitive direct interview for preschoolers; (4) cognitive assessment of the child; and (5) neurocognitive assessment of the child. By employing these