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Description of the MUSP Cohort

Inclusion criteria for original research publications, quality of supporting literature, predictors: maltreatment types, ethical approval, prevalence and co-occurrence of maltreatment subtypes, cognition and education outcomes, psychological and mental health outcomes, addiction and substance use outcomes, sexual health outcomes, physical health, magnitude of effects, abuse, neglect, and cognitive development, psychological maltreatment: emotional abuse and/or neglect, sexual abuse, physical abuse, limitations, conclusions, long-term cognitive, psychological, and health outcomes associated with child abuse and neglect.

POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose.

FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.

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Lane Strathearn , Michele Giannotti , Ryan Mills , Steve Kisely , Jake Najman , Amanuel Abajobir; Long-term Cognitive, Psychological, and Health Outcomes Associated With Child Abuse and Neglect. Pediatrics October 2020; 146 (4): e20200438. 10.1542/peds.2020-0438

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Video Abstract

Potential long-lasting adverse effects of child maltreatment have been widely reported, although little is known about the distinctive long-term impact of differing types of maltreatment. Our objective for this special article is to integrate findings from the Mater-University of Queensland Study of Pregnancy, a longitudinal prenatal cohort study spanning 2 decades. We compare and contrast the associations of specific types of maltreatment with long-term cognitive, psychological, addiction, sexual health, and physical health outcomes assessed in up to 5200 offspring at 14 and/or 21 years of age. Overall, psychological maltreatment (emotional abuse and/or neglect) was associated with the greatest number of adverse outcomes in almost all areas of assessment. Sexual abuse was associated with early sexual debut and youth pregnancy, attention problems, posttraumatic stress disorder symptoms, and depression, although associations were not specific for sexual abuse. Physical abuse was associated with externalizing behavior problems, delinquency, and drug abuse. Neglect, but not emotional abuse, was associated with having multiple sexual partners, cannabis abuse and/or dependence, and experiencing visual hallucinations. Emotional abuse, but not neglect, revealed increased odds for psychosis, injecting-drug use, experiencing harassment later in life, pregnancy miscarriage, and reporting asthma symptoms. Significant cognitive delays and educational failure were seen for both abuse and neglect during adolescence and adulthood. In conclusion, child maltreatment, particularly emotional abuse and neglect, is associated with a wide range of long-term adverse health and developmental outcomes. A renewed focus on prevention and early intervention strategies, especially related to psychological maltreatment, will be required to address these challenges in the future.

Child maltreatment is a major public health issue worldwide, with serious and often debilitating long-term consequences for psychosocial development as well as physical and mental health. 1   In the United States alone, 3.5 million children are reported for suspected maltreatment each year, with an annual substantiated maltreatment rate of 9.1 per 1000 children. 2   Some of the long-term adverse outcomes associated with maltreatment include cognitive disability, anxiety and depression, psychosis, teen-aged pregnancy, addiction disorders, obesity, and cardiovascular disease. 3   Understanding the distinctive impact of differing types of maltreatment may help medical professionals provide more wholistic care and treatment recommendations as well as identify more specific public health targets for primary prevention.

Unfortunately, however, little is known about the long-term effects of differing types of child maltreatment, which include sexual abuse, physical abuse, emotional abuse, and neglect. 4   According to a meta-analysis review, 5   research on child maltreatment has predominantly been focused on sexual abuse, with far less attention paid to psychological maltreatment (emotional abuse and/or neglect) and the co-occurrence of different types of maltreatment. In addition, most of the current evidence is derived from cross-sectional studies, which may be subject to recall bias, 6 – 8   in which an outcome status (such as depression) may influence recall of the exposure (ie, previous maltreatment). Few previous studies have adequately controlled for confounding variables, such as perinatal risk, socioeconomic adversity, parental psychopathology, and impaired early childhood development, which may predispose to both child maltreatment and later adverse health outcomes.

Longitudinal studies offer evidence that is more robust, but these studies are relatively few in number and have generally been limited to certain sociodemographic groups 9   or to specific types of child maltreatment, such as sexual abuse. 1 , 10   Other longitudinal studies have relied on retrospective recall of maltreatment rather than prospectively collected agency-reported data. 11 – 13   In studies in which prospective data have been collected, 7 , 13 – 17   only a few have compared different types of child maltreatment. 7 , 16 , 17  

In this special article, we review findings from the Mater-University of Queensland Study of Pregnancy (MUSP), a now 40-year longitudinal prenatal cohort study from Brisbane, Australia, involving >7000 women and their children. 18   Unique features of the MUSP include its use of a population-based sample, its use of prospectively substantiated child maltreatment reports, and its consideration of different subtypes of maltreatment. In addition, the study design controlled for a wide range of confounders and covariates, including both maternal and child sociodemographic and mental health variables. This combined body of work, which includes numerous publications over the past decade, has documented a broad range of adverse outcomes associated with child maltreatment, including deficits in cognitive and educational outcomes 19 – 21   ; mental health problems, such as anxiety, depression, posttraumatic stress disorder (PTSD), psychosis, delinquency, and intimate partner violence (IPV) 22 – 25   ; substance abuse and addiction 26 – 30   ; sexual health problems 31   ; physical growth and health deficits 32 – 35   ; and overall decreased quality of life. 36  

Our purpose for this special article is to compare the effects of 4 differing types of maltreatment on long-term cognitive, psychological, addiction, and health outcomes assessed in the offspring at ∼14 and/or 21 years of age. Rather than providing a systematic review or meta-analysis of the current literature, which would include diverse study designs and purposes, we report and compare the findings of individual articles that used a common data set and standard methodology to study a broad array of outcomes. We particularly highlight the long-term impact of emotional abuse and neglect, which has received far less attention in the literature.

Between 1981 and 1983, 8556 consecutive pregnant women who attended their first prenatal clinic visit at the Mater Mothers’ Hospital in Brisbane, Australia, agreed to participate ( Fig 1 ). After excluding mothers who did not deliver a singleton infant at the Mater Mothers’ Hospital or withdrew consent, the MUSP birth cohort consisted of 7223 mother-infant dyads, who were followed over 2 decades: at 3 to 5 days, 6 months, 5 years, 14 years and 21 years. Midway through the study, this rich data set was anonymously linked to state reports of child abuse and neglect, which identified some form of suspected maltreatment in >10% of cases. 37   Notified cases, which had been referred from the community or by general medical practitioners, were investigated by the Queensland government child protection agency. Substantiated maltreatment was determined after a formal investigation when there was “reasonable cause to believe that the child had been, was being, or was likely to be abused or neglected.” 38   Substantiated maltreatment occurred when a notified case was confirmed for (1) sexual abuse, “exposing a child to or involving a child in inappropriate sexual activities”; (2) physical abuse, “any non-accidental physical injury inflicted by a person who had care of the child”; (3) emotional abuse, “any act resulting in a child suffering any kind of emotional deprivation or trauma”; or (4) neglect, “failure to provide conditions that were essential for the healthy physical and emotional development of a child,” which encompassed physical, emotional and medical neglect. 37  

Overview of the MUSP enrollment and testing.

We searched PubMed from inception to April 2020 for published MUSP articles in which agency-reported child maltreatment was evaluated as the predictor of a range of outcomes. Studies needed to meet the following criteria for inclusion in the review: (1) notified or substantiated abuse and neglect was listed as a main predictor variable and (2) outcomes included standardized measurements of cognitive, psychological, behavioral, or health functioning. From ∼340 published MUSP studies, we identified 24 articles dealing with child maltreatment, of which 21 included state-reported maltreatment versus self-reported maltreatment data ( n = 3). Nineteen of the 21 articles met all inclusion criteria and were evaluated in this review ( Fig 2 ). One study was excluded because it only examined outcomes associated with sexual abuse. 8   Another article was excluded because its outcome measures were similar to another included study. 29  

Published studies from the Mater-University of Queensland Study of Pregnancy, linking long-term outcomes with specific maltreatment subtypes (adjusted coefficients or odds ratios ± 95% confidence intervals). CES-D, Center for Epidemiologic Studies–Depression Scale; CI, confidence interval; N , number of offspring in sample; N (Mal) , number of offspring who experienced maltreatment. a In different articles adjusting for co-occurrence of maltreatment subtypes was handled in different ways: (1) statistical adjustment: each maltreatment subtype predictor was statistically adjusted for the other maltreatment subtypes (eg, neglect was adjusted for the occurrence of physical, sexual, and emotional abuse) and is reflected in the table’s odds ratios and coefficients; (2) exclusive categories: different combinations of maltreatment types are included in mutually exclusive groups (eg, physical abuse only, physical abuse and emotional abuse only, physical and emotional abuse and neglect [without sexual abuse], etc; see Table 1 ); (3) nonexclusive categories: maltreatment categories may overlap with other categories (eg, any substantiated abuse [sexual, physical, or emotional] versus any substantiated neglect); and (4) none: no statistical adjustments or combined categories were presented for co-occurring maltreatment subtypes. b Adjusted coefficients (95% CI) were reported as statistical association measures rather than adjusted odds ratios. c Cases of notified (rather than substantiated) maltreatment. In the study by Mills et al, 26   a sensitivity analysis was performed after exclusion of unsubstantiated cases of maltreatment. The associations between any maltreatment and substance use were similar to those seen in the original analysis after full adjustment. d Medium effect size, based on magnitude of the adjusted odds ratio (2 ≤ odds ratio ≤ 4). e Large effect size, based on magnitude of the adjusted odds ratio (odds ratio > 4).

Each of the reviewed articles followed Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for the conduct of cohort studies. 41   The quality of the studies was also evaluated by using a modified version of the Newcastle-Ottawa Scale, which is used to assess the following domains: sample representativeness and size, comparability between respondents and nonrespondents, ascertainment of outcomes, and statistical quality. 42   On the basis of this assessment, all of the MUSP studies were determined to be of low risk of bias, with a score of 4 out of 5 points ( Supplemental Information ).

In all but 2 studies (which used notified maltreatment 21 , 26   ) events were dichotomized and coded as substantiated maltreatment versus no substantiated maltreatment. According to a validated classification of maltreatment types, 43   specific categories and co-occurring forms of childhood maltreatment 44   were used to predict outcomes. In 2 studies, 19 , 20   all types of abuse were combined into 1 category and compared to neglect, whereas in another study, sexual abuse was compared to any combination of nonsexual maltreatment. 21   In 2 other studies, 26 , 40   emotional abuse and neglect (examples of psychological maltreatment) were combined, partly because of overlapping definitional constructs from the government child protection agency (emotional abuse included “emotional deprivation,” and neglect included the failure to provide for “healthy…emotional development”). In all but 2 of the included articles, 25 , 33   co-occurrence of different types of maltreatment was considered, either by examining specific combinations of maltreatment types (in exclusive or nonexclusive overlapping categories) or by statistically adjusting for all remaining types of maltreatment ( Fig 2 ).

All of the odds ratios, mean differences, or coefficients were adjusted for potential confounding variables ( Fig 3 ). All articles adjusted for a variety of sociodemographic variables, such as age, race, education, income, and marital status. Perinatal and/or childhood factors, such as birth weight, gestational age, and breastfeeding status, were used as covariates, particularly in articles in which cognitive and educational outcomes were examined. Psychological and mental health variables (such as internalizing and externalizing behavior problems, maternal depression, chronic stress, or exposure to violence) were primarily included as covariates in mental health outcome studies, especially for psychosis. Addiction studies adjusted for youth and maternal alcohol or tobacco use, among other covariates, and physical health outcome studies adjusted for relevant covariates (such as BMI in a study of dietary fat intake and parental height when studying offspring height). In selected articles, maltreatment subtypes were also statistically adjusted for the other types of maltreatment to determine independent effects.

Covariates used in published articles from the MUSP to adjust for possible confounding. a Race: child’s race, parental race, and maternal or paternal racial origin at pregnancy. b Child age: child age and gestational age. c Maternal age: maternal age at the first visit clinic or at pregnancy. d Maternal education: maternal education (prenatal or at birth). e Family income: annual family income, familial income over the first 5 years or family poverty before birth or over the first 5 years of life, family income before birth, and annual family income. f Maternal marital status and social support: same partner at birth and 14 years and social support at 5 years. g Maternal depression: maternal depression during pregnancy, 3- to 6-month follow-up, or 21-year follow-up; chronic maternal depression. h Maternal alcohol use: maternal alcohol use at 3- to 6-month or 14-year follow-up and binge drinking. i Maternal cigarette use: cigarette use during pregnancy, 6 months postpartum, or at 14-year follow-up. ADHD, attention-deficit/hyperactivity disorder; CES-D, Center for Epidemiologic Studies–Depression Scale; IPV, intimate partner violence. Covariates used in published articles from the MUSP to adjust for possible confounding.

A total of 46 outcomes were assessed at 14 years ( n = 5200) and/or 21 years ( n = 3778) ( Fig 1 ) and were grouped into 5 domains ( Fig 2 ):

Cognition and education outcomes included reading ability and perceptual reasoning measured in adolescence, and, at age 21, receptive verbal intelligence and failure to complete high school or be either enrolled in school or employed; attention problems were measured at both time points.

Psychological and mental health outcomes at 21 years included internalizing and externalizing behavior problems (which were also assessed at 14 years), lifetime anxiety disorder, depressive disorder and symptoms, PTSD, lifetime psychosis diagnosis, psychotic symptoms (such as delusional experience or visual and/or auditory hallucinations), delinquency, experience of IPV or harassment, and overall quality of life.

Addiction and substance use, measured at both time points, included alcohol and cigarette use at 14 and 21 years, and cannabis abuse and/or dependence (including early onset) and injecting-drug use at the 21-year follow-up.

Sexual health was investigated at age 21 in terms of early initiation of sexual experience, having multiple sexual partners, youth pregnancy, and miscarriage or termination.

Physical health outcomes measured at 21 years included symptoms of asthma, high dietary fat intake, poor sleep quality, and height deficits.

The 14-year assessments included a youth questionnaire ( n = 5172) and in-person cognitive testing ( n = 3796). The 21-year visit included an in-person assessment of mental health diagnoses in a subset of the cohort ( n = 2531) with the World Health Organization Composite International Diagnostic Interview (CIDI), which is based on Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition criteria 45   ( Fig 1 ). All of the questionnaire and interview measures were validated, except for reported frequencies of specific events (ie, pregnancy, number of cigarettes, etc).

Associations were described by using either adjusted odds ratios or mean differences and coefficients, along with the corresponding 95% confidence intervals, and were plotted to visualize and compare the statistical significance of each association across specific outcome categories and types of maltreatment ( Figs 4 – 8 ).

Child maltreatment and cognition and educational outcomes at 14 and 21 years. A, Adjusted coefficients ± 95% confidence intervals. B, Odds ratios ± 95% confidence intervals. * P < .05.

Child maltreatment and psychological and mental health outcomes at 14 and 21 years. A, Adjusted coefficients ± 95% confidence intervals. B, Odds ratios ± 95% confidence intervals. * P < .05.

Child maltreatment and addiction and substance use outcomes at 14 and 21 years (adjusted odds ratio ± 95% confidence interval). * P < .05.

Child maltreatment and sexual health outcomes at 21 years (adjusted odds ratio ± 95% confidence interval). * P < .05.

Child maltreatment and physical health outcomes at 21 years. A, Adjusted odds ratio ± 95% confidence interval. B, Adjusted coefficients ± 95% confidence interval. * P < .05.

The MUSP was approved by the Human Ethics Review Committee of The University of Queensland and the Mater Misericordiae Children’s Hospital. Ethical approval was obtained separately from the Human Ethics Review Committee of The University of Queensland for linking substantiated child maltreatment data to the 21-year follow-up data.

In this cohort of 7214 children ( Fig 1 ), 7.1% ( n = 511 children) experienced at least 1 episode of substantiated maltreatment. Substantiated sexual abuse was reported in 2.0% ( n = 147), physical abuse in 4.0% ( n = 287), emotional abuse in 3.7% ( n = 267), and neglect in 3.7% of cases ( n = 269) ( Table 1 ). Almost 60% of the children with substantiated maltreatment had multiple substantiated episodes (293 children; range: 2–14 episodes per child; median: 3 episodes per child 37   ). Of the 3778 young adults included in the 21-year follow-up, 4.5% ( n = 171) had a history of substantiated maltreatment, 39   including sexual abuse ( n = 53), physical abuse ( n = 60), emotional abuse ( n = 71), and neglect ( n = 89).

More than half of the children who experienced substantiated maltreatment were reported for ≥2 co-occurring maltreatment types ( Table 1 ). Of the substantiated sexual abuse cases, 57.1% of the children experienced ≥1 additional maltreatment types (84 of 147); for physical abuse, this proportion was 79.1% (227 of 287); for emotional abuse, 83.5% (223 of 267); and for neglect, 73.6% (198 of 269). In particular, emotional abuse and neglect co-occurred, with or without other types of maltreatment, in ∼59% of cases. 46  

Nonexclusive and Exclusive Categorization of Child Maltreatment Subtypes (Single and in Combination) Within the MUSP Cohort

Abuse (a combined category) and neglect were both associated with significantly lower cognitive scores at both 14 and 21 years, as well as with negative long-term educational and employment outcomes in young adulthood. 19 , 20   This was after adjusting for factors such as the child’s race, sex, birth weight, breastfeeding exposure, and age; family income; and maternal education and alcohol and/or tobacco use ( Fig 3 ). Specifically, proxy measures of IQ, such as reading ability and perceptual reasoning, at age 14 years were adversely associated with both substantiated abuse and neglect. 19   Sexual abuse was associated with attention problems in adolescence, whereas nonsexual maltreatment was associated with attention problems at both time points. 21   Young adults who experienced substantiated child maltreatment had reduced scores on the Peabody Vocabulary Test at 21 years. In terms of educational outcomes in young adulthood, both abuse and neglect manifested a threefold to fourfold increase in odds of failing to complete high school and a twofold to threefold increase in the likelihood of being unemployed at age 21 years 20   ( Figs 2 and 4 ).

During adolescence, physical abuse, emotional abuse, and neglect were all significantly associated with both internalizing and externalizing behavior problems, although this was not the case for physical abuse notifications without co-occurring emotional abuse or neglect. 22   After adjustment for relevant sociodemographic variables, the associations with emotional abuse and neglect remained significant at 21 years. 39   No statistically significant association was found between sexual abuse and these behavior problems at either time point.

Psychological maltreatment in childhood was associated with all of the other 15 psychological and mental health outcomes in young adulthood, except for delinquency in women. This was true after adjustment for sociodemographic variables and psychological and mental health problems (such as attention-deficit/hyperactivity disorder, aggressive behavior problems, and maternal depression or adverse life events, in the case of psychosis and/or IPV exposure outcomes) ( Fig 3 ). Specifically, both emotional abuse and neglect were significantly associated at 21 years with all of the following outcomes: anxiety, depression, PTSD, psychosis (with some exceptions), delinquency in men, and experiencing IPV and harassment (except for neglect). 22 – 25 , 39   Emotional abuse and neglect were the only maltreatment subtypes associated with a significant decrease in quality-of-life scores. 36  

The only mental health outcomes associated with sexual abuse were clinical depression, lifetime PTSD, and experiencing physical IPV. 8 , 25 , 39   Physical abuse was associated with externalizing behavior problems and delinquency (in men), internalizing behavior problems and depressive symptoms, experience of IPV, and PTSD 22 , 24 , 25 , 39   ( Figs 2 and 5 ).

Overall, emotional abuse and/or neglect were associated with all categories of substance use and addiction at both 14 and 21 years, whereas physical and sexual abuse were associated with surprisingly few substance abuse outcomes. Specifically, childhood emotional abuse and neglect were associated with adolescent substance use at age 14, including alcohol use and smoking. 26   This was after adjustment for sociodemographic factors and youth and maternal drug use. The association with cigarette and alcohol use persisted from adolescence to adulthood. The category of "any cigarette use" was the only addiction outcome associated with all 4 types of maltreatment. 40   At 21 years, emotional abuse and neglect were both associated with the early onset of cannabis abuse after adjustment for maternal stress and cigarette use. Additionally, physical abuse, emotional abuse, and neglect all revealed increased odds of cannabis dependence at age 21, with early onset associated with physical abuse and neglect. 28   In contrast, only emotional abuse significantly predicted injecting-drug use in young adult men, after adjustment for maternal alcohol use and depression, whereas all types of substantiated childhood maltreatment were associated with injecting-drug use in women. 27   Sexual abuse was not associated with any addiction or substance use outcome except for cigarette use at 21 years ( Figs 2 and 6 ).

All forms of maltreatment were significantly associated, at 21 years, with early onset of sexual activity and subsequent youth pregnancy. This was after adjustment for factors such as gestational age, youth psychopathology, and drug use. Neglect was the only type of maltreatment associated with having multiple sexual partners and was the maltreatment type most strongly associated with most other sexual health outcomes, especially youth pregnancy. Pregnancy miscarriage was modestly associated with emotional abuse, whereas termination of pregnancy was not associated with any maltreatment subtype 31   ( Figs 2 and 7 ).

Reduced adult height at 21 years, adjusted for parental height, was associated with all maltreatment subtypes except sexual abuse (which was not associated with any of the physical health outcomes). At 21 years, physical abuse was also associated with high dietary fat intake, a risk factor for obesity (adjusted for BMI), and poor sleep quality in men (adjusted for psychopathology and drug use). Asthma at 21 years revealed a modest association with emotional abuse. The combined category of any maltreatment was also associated with high dietary fat intake ( Figs 2 and 8 ).

To estimate the magnitude of potential effects of child maltreatment on long-term outcomes, other studies have used a number of statistical techniques. In one Australian study that used the MUSP and other data sets, the population attributable risk of child maltreatment causing anxiety disorders in men and women, was estimated to be 21% and 31%, respectively, and 16% and 23% for depressive disorders. 46   Similarly, in the MUSP study on cognitive and educational outcomes of maltreated youth, the population attributable risk of child maltreatment leading to “failure to complete high school” was 13%, and 14% for “failure to be in either education or employment at 21 years.” 20  

Based on one published metric of effect size using the magnitude of the adjusted odds ratio, 47   77% of the statistically significant associations in this review were considered to have a medium to large effect size (odds ratio ≥2), including 10% with a large effect size (odds ratio >4) ( Fig 2 ).

In summary, over the past decade, the MUSP has revealed that child maltreatment is associated with a broad array of adverse outcomes during adolescence and young adulthood, including the following:

deficits in cognitive development, attention, educational attainment, and employment;

serious mental health problems, including anxiety, depression, PTSD, and psychosis, as well as delinquency and the experience of IPV;

substance use and addiction problems;

sexual health problems; and

physical health limitations and risk.

These results were seen after adjustment for a broad range of relevant sociodemographic, perinatal, psychological, and other risk factors ( Fig 3 ). Many of the studies also adjusted for the other subtypes of child maltreatment and demonstrated that specific maltreatment types were closely associated with particular outcomes.

Significant cognitive delays and educational failure were seen for both abuse and neglect across adolescence and adulthood. In another study, the authors concluded that preexisting cognitive impairments at 3 or 5 years may explain this association, rather than maltreatment per se. 16   However, other research has revealed that children neglected over the first 4 years of life show a progressive decline in cognitive functioning, which is associated with a significantly reduced head circumference at 2 and 4 years of age. 48   In rodent models, contingent maternal behavior is linked with infant cognitive development, and possible mechanisms include increases in synaptic connections within the hippocampus 49   and reduced apoptotic cell loss. 50   Prolonged maternal separation, in contrast, is associated with impaired cognitive development in rodent and primate models. 51 , 52  

One of the most striking conclusions from this review was the broad association between emotional abuse and/or neglect and adverse outcomes in almost all areas of assessment ( Fig 2 ). In stark contrast, physical abuse and sexual abuse were associated with far fewer adverse outcomes. Overall, quality of life was lower for those who had experienced emotional abuse and neglect but not for those who had experienced physical or sexual abuse. Although emotional abuse and neglect often co-occur with other types of maltreatment, 46   the associated outcomes were generally robust even after statistical adjustment or separation into differing maltreatment categories ( Fig 2 ).

Emotional abuse and neglect in early childhood may lead to psychopathology via insecure attachment, 53 , 54   which has been associated with externalizing behavior problems 55   and impaired social competence. 56 , 57   Emotional neglect, in particular, may lead to deficits in emotion recognition and regulation, as well as insensitivity to reward, 3   potentially influencing social and emotional development. Neglected children are less able to discriminate facial expressions and emotions, 58   whereas youth who have been emotionally neglected show blunted development of the brain’s reward area, the ventral striatum. 59   Reduced reward activation may predict risk for depression, 59   addiction, 60   and other psychopathologies. 61  

Neglect was also associated with the early onset of sexual activity, multiple sexual partners, and youth pregnancy, even after adjustment for other maltreatment subtypes. This suggests that neglect may result in compensatory efforts to obtain sexual intimacy, consistent with other studies revealing higher rates of unprotected sex 62   and adolescent pregnancy in neglected children. 63   In the animal literature, female rodents that experience maternal deprivation tend to have an earlier onset of puberty and increased sexual receptivity, leading to elevated reproductive activity to help offset an environment of higher offspring risk. 64 , 65  

As observed elsewhere, 66   sexual abuse was associated with early sexual experimentation and youth pregnancy as well as symptoms of PTSD and depression. Risky sexual behaviors were independent of other types of maltreatment but were not specific for sexual abuse. An additional MUSP study comparing self-reported and agency-notified child sexual abuse revealed consistent associations with major depressive disorder, anxiety disorders, and PTSD. 8   The absence of associations with other adverse outcomes, however, may be, in part, due to the lower prevalence of substantiated sexual abuse, especially at the 21-year follow-up.

Outcomes associated with physical abuse differed from those associated with sexual abuse, with increased odds of externalizing behavior problems, and delinquency in men. Jaffee 3   suggests that physical abuse, in particular, may lead to a hypervigilance response to threat, including negative attentional bias, disproportionate to relatively mild threat cues. Studies have revealed that physically abused children show selective attention to anger cues, 67   have difficulty disengaging from them, 58 , 68   and are more likely to misinterpret facial cues as being angry or fearful. 69  

Although these studies demonstrated significant associations between maltreatment and a range of long-term outcomes, association does not equal causality. The causal mechanisms proposed above are tentative and may relate to multiple types of maltreatment.

Other limitations should also be considered. Firstly, selective attrition of socioeconomically disadvantaged and maltreated young people was evident in the MUSP cohort ( Supplemental Information ). However, based on multiple imputation calculations and inverse probability weighting of MUSP data, 18 , 70   differences in the rate of loss to follow-up, for both dependent and independent variables, made little difference to either the estimates or their precision, mirroring findings from other longitudinal studies. 71   In addition, the findings were mostly unchanged when using propensity analysis, which is used to assess the effects of nonrandom sampling variation by analyzing the probability of assignment to a particular category within an observational study given the observed covariates. 72   Specifically, the sample was weighted so that it better resembled sociodemographic characteristics at baseline to minimize bias from differential attrition in those with greater socioeconomic disadvantage.

Secondly, differences in the prevalence of specific maltreatment subtypes might have influenced the statistical power to detect true effects, particularly regarding sexual abuse ( Table 1 ).

Finally, the co-occurrence of different types of maltreatment may have impacted the ability to accurately predict the associations between specific types of maltreatment and outcomes. Other studies have revealed that emotional abuse and neglect, in particular, are more likely to co-occur with each other and with other types of maltreatment. 73   However, even in those articles that statistically adjusted for other co-occurring maltreatment subtypes, the associated outcomes linked with emotional abuse and/or neglect were generally robust. In articles that did not adjust for these co-occurrences, some of the strongest associations were still observed for emotional abuse and/or neglect.

Child maltreatment, particularly psychological maltreatment, is associated with a broad range of negative long-term health and developmental outcomes extending into adolescence and young adulthood. Although these data do not establish causality, neurodevelopmental pathways are likely influenced by stress and early social experience through epigenetic mechanisms, which may affect gene expression and regulation and, ultimately, behavior and development. 3 , 74  

Understanding the developmental roots of these adverse outcomes may motivate physicians to more systematically inquire about early-life trauma and refer patients to more appropriate treatment services. 75 , 76   Even more importantly, early intervention and prevention programs, such as prenatal and infancy nurse home visiting, 77   have demonstrated, in randomized clinical trials, diminished rates of child abuse and neglect. 78 , 79   Long-term benefits to the offspring include decreased childhood internalizing problems, 80   reduced antisocial behavior and substance abuse in adolescence, 81   and improved cognitive skills extending into young adulthood. 80 , 82   Supporting at-risk parents and young children should thus be an urgent priority.

Dr Strathearn conceptualized and designed the original study linking the Mater-University of Queensland Study of Pregnancy data set with substantiated reports of child maltreatment, drafted the special article, and reviewed and revised the manuscript; Dr Giannotti assisted in drafting the manuscript and prepared all tables and figures; Drs Mills, Kisely, and Abajobir conceptualized and wrote the original research articles summarized in this article; Dr Najman was the original principal investigator of the Mater-University of Queensland Study of Pregnancy; and all authors critically reviewed the manuscript for important intellectual content and approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

FUNDING: Partially supported by the US National Institute on Drug Abuse (R01DA026437). The content is solely the responsibility of the authors and does not necessarily represent the official views of this institute or the National Institutes of Health. Funded by the National Institutes of Health (NIH).

Composite International Diagnostic Interview

intimate partner violence

Mater-University of Queensland Study of Pregnancy

posttraumatic stress disorder

Competing Interests

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8 Research Methodology in Clinical Child and Adolescent Psychology

Jonathan S. Comer, Florida International University

Laura J. Bry Department of Psychology Florida International University Miami, FL, USA

• Published: 07 November 2018
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To continue to move the field of clinical child and adolescent psychology forward, researchers must systematically rely on research strategies that achieve favorable balances between scientific rigor and clinical relevance. This chapter presents an overview of modern methods and considerations that maximize both rigor and relevance in the evaluation of child and adolescent treatments. This research methodology chapter is organized around the four stages of a clinical trial: (a) planning a clinical trial; (b) conducting a clinical trial; (c) analyzing trial outcomes, and (d) reporting results. Sample selection, random assignment, control condition selection, treatment integrity, missing data, clinical significance, treatment mechanisms, and consolidated standards for communicating study findings to the scientific community are addressed. Collectively, the methods and design considerations detail modern research strategies for the continually evolving science of clinical child and adolescent psychology.

Children’s mental health problems impose a staggering public health burden. For example, roughly 40% of adolescents in the United States have reportedly suffered from a mental disorder in the past year (Kessler, Avenevoli, Costello, et al., 2012), and these disorders are associated with enormous individual, family, and societal costs. Youth mental disorders are associated with complex comorbid presentations (Kessler, Avenevoli, McLaughlin, et al., 2012); elevated substance use (Kendall & Kessler, 2002; Wu, Goodwin, Comer, Hoven, & Cohen, 2010 ); and medical comorbidities ( Merikangas et al., 2015 ). When left untreated, they persist into adulthood, during which time they are associated with family dysfunction; disability in major life roles ( Merikangas et al., 2007 ); poorer educational attainment ( Breslau, Lane, Sampson, & Kessler, 2008 ); criminality; suicide ( Nock & Kessler, 2006 ); and overall reduced health-related quality of life ( Comer et al., 2011 ).

Despite these daunting statistics, recent years have witnessed very promising advances in the development and evaluation of evidence-based interventions for the broad range of children’s mental health problems ( Kendall, 2012 ; Ollendick & King, 2004 ). Evaluations of therapeutic efficacy and effectiveness have evolved from a historical reliance on simply professional introspection and retrospective case histories to modern reliance on complex multimethod experimental investigations and well-controlled randomized trials across well-defined and increasingly generalizable samples.

However, much still remains to be learned about the treatment of child and adolescent mental health problems, and this should not be surprising. After all, whereas many sciences have been progressing for centuries (e.g., physics, chemistry, biology), it has been only relatively recently that empiricism and the scientific method have been applied systematically to clinical child and adolescent psychology ( Comer & Kendall, 2013b ). At this relatively early stage in the science of clinical child and adolescent psychology, most of the research is still ahead of us. As we face the challenge of optimally informing best practices in youth mental health care with data, the prepared investigator must be familiar with the portfolio of modern research strategies for conducting clinical evaluations of treatment methods—a set of “directions” so to speak for getting from “here” to “there” (see Comer & Kendall, 2013b ). Just as with any travel directions, where there may be many acceptable ways to get to the same destination (e.g., the scenic way, the quick way, the cheap way), for each testable question in clinical child and adolescent psychology, there are many methods that can be used to reveal meaningful information, each with limitations and strengths.

To continue to move the field of clinical child and adolescent psychology forward, investigators must systematically rely on research strategy “routes” that achieve favorable balances between scientific rigor and clinical relevance ( Comer & Kendall, 2013b ). This necessitates careful considerations regarding the trade-offs between internal validity (which is typically linked with rigor ) and external validity (which is typically linked with relevance ). Internal validity pertains to the extent to which the independent variable, rather than an extraneous influence, accounts for variance in the dependent variable. The more rigorous and tightly controlled a study design, the more persuasively the study is able to rule out the possibility that variables beyond the independent variable might be accounting for variance in the dependent variable. External validity, on the other hand, pertains to the extent to which study results generalize to people, settings, times, measures, and characteristics other than those included in a particular study. Accordingly, design decisions focusing on internal validity and aiming to improve interpretative conclusions typically have the consequence of reducing the external validity and generalizability of findings to broadly relevant settings and vice versa. With this in mind, we present an overview of modern methods and considerations that maximize both rigor and relevance in the evaluation of child and adolescent treatments.

Planning a Clinical Trial

When planning a clinical evaluation to examine the efficacy or effectiveness of treatment for child and adolescent mental health problems, six sets of considerations are essential: (a) design considerations; (b) control condition considerations; (c) independent variable considerations; (d) dependent variable considerations; (e) assessment point considerations; and (f) sample and setting considerations.

Design Considerations in Clinical Child and Adolescent Psychology

Broadly speaking, the development and evaluation of novel therapeutic interventions occur through a sequence of three progressive stages ( Rounsaville, Carroll, & Onken, 2001 ). Stage 1 encapsulates an iterative development process, combining previous research, clinical expertise, and consultation with experts. An experimental intervention is tested, preliminarily, on a small number of subjects of the population for which the treatment is intended. This idiographic approach provides initial evidence to show the relationship between treatment and symptoms on an individual basis. Stage 1 designs also address issues related to intervention feasibility and acceptability and provide opportunities for intervention refinement or tweaking before progressing to Stage 2, large-scale evaluation. In Stage 2, tightly controlled, systematic, and rigorous evaluations with high internal validity establish broad efficacy of the intervention by looking at nomothetic patterns. Stage 3 designs evaluate intervention effectiveness, prioritizing external validity, generalizability to a wider range of patients, and transportability across clinical settings and practitioners.

Depending on research goals and the stage of the intervention, a range of study designs is available to evaluate an experimental treatment. Selecting a study design involves finding a balance between one’s research question and goals and the limitations associated with each design option. We now turn our attention to leading design options available to investigators, including single-case and multiple-baseline experimental designs, the randomized controlled trial (RCT), and sequenced treatment designs.

Single-case and multiple-baseline designs.

Systematic research designs encompassing a single individual or a small sample of subjects are useful for informing our understanding of individual behavior change and revealing a signal of “how,” “why,” and “when” treatment-related changes may occur. This idiographic portrait of the relationship between an intervention and symptoms makes such designs particularly useful during Stages 1 and 3 of treatment evaluation ( Barlow & Nock, 2009 ). Indeed, these designs have played a prominent role in developing clinical guidelines and best practices, underscoring their importance in evidence-based practice ( American Psychological Association, 2002 ). Understanding treatment-related change at the individual level provides an opportunity for intervention refinement prior to initiating costly large-scale evaluations. After large-scale clinical evaluations have been conducted, single-case designs can again be useful to evaluate the intervention’s applicability to individuals in new settings or with different symptom profiles or when implemented by clinicians of different training backgrounds. Single-case and multiple-baseline trials are also relatively cost-efficient, making them valued designs in the context of limited funding.

Generally, single-case experimental designs employ a systematic, repeated-measures approach wherein data related to a specific dependent variable (i.e., clinical target) are collected across a baseline and treatment phase. Researchers must balance a data collection schedule that is frequent enough to provide clues about when treatment-related changes occur, while avoiding potential subject response fatigue ( Barlow, Nock, & Hersen, 2009 ; Gallo, Comer, & Barlow, 2013 ; Kazdin, 2001 ). Typically, the baseline phase is referred to as the “A” phase. Dependent variable data are collected across a baseline phase (rather than at a single time point) to document the stability of the target behavior as it occurs naturally. These data are then compared to observations collected during the treatment phase (or “B” phase) of the design. Capturing stability of the behavior during the baseline period is critical for attributing changes to the treatment, rather than attributing changes to a natural cycle of fluctuations of that behavior ( Gallo et al., 2013 ).

The most traditional representation of the single-case experimental design is the A–B design , in which a target behavior is measured repeatedly across both a baseline A phase and a treatment B phase. This design allows the researcher to capture data on a naturally occurring, preintervention behavior, which is then directly compared to observations of the behavior after the intervention has been introduced. A more rigorous permutation adds an additional A or baseline phase of data collection. In such an A–B–A design , the A and B phases are followed by an additional phase of data collection during which the intervention has been withdrawn (A). Such introduction and removal of treatment allow for stronger conclusions. Importantly, within clinical psychology, withdrawal designs are often hard to attain and perhaps even less desirable. For example, unlike psychopharmacology evaluations in which a patient can simply stop taking a medication, “unlearning” a specific coping skill or behavioral strategy can be difficult, if not impossible, and in some cases may even be unethical.

Some investigators will add a second B phase to an A–B–A design to address some of the shortcomings of the A–B–A design. Such an A–B–A–B design adds rigor by offering an inherent replication of findings and the ethical shortcomings of withdrawing an effective intervention. Another permutation, the B–A–B design allows the investigator to begin the evaluation with the intervention. This is useful when assessing a clinical behavior that requires immediate attention and for which waiting throughout a baseline period may be contraindicated (e.g., suicidal ideation, self-injury), but it does not provide observations of target behaviors as they occur naturally in the absence of intervention. Moreover, to control for possible placebo effects, the A–B–C–B design introduces a third “C” phase that corresponds to a placebo condition (e.g., education, support, and attention). Addition of a C phase allows investigators to more readily attribute improvements seen in the treatment B phase to the specific intervention, rather than broadly to any intervention that could have been applied.

For researchers evaluating psychosocial interventions that do not provide opportunity for withdrawal phases, a multiple-baseline design can serve as a valuable alternative. Multiple-baseline designs employ an A–B design that differentially extends the length of the baseline (A) phase across behaviors, subjects, or settings. To establish intervention efficacy, improvements in the target behavior must be seen only after the treatment phase (B) is initiated. The baseline phase length may be determined prior to beginning a study or a researcher may wait until the target behavior stabilizes in participants and then initiate treatment (B phase) only after stabilization is achieved.

Multiple-baseline designs may occur across behaviors , across subjects , and across settings . A multiple-baseline design across behaviors evaluates the effects of an intervention on different behaviors, but within the same individual. Improvements seen in the clinical targets are attributed to intervention only if they occur after initiation of the phase of the intervention in which they were specifically targeted. Multiple-baseline designs across subjects evaluate the effects of a single intervention on multiple individuals who share a similar clinical presentation (e.g., Comer et al., 2012 ; Jarrett & Ollendick, 2012 ; Ollendick, 1995 ; Suveg, Kendall, Comer, & Robin, 2006). Each subject is assigned to a baseline period of varying and randomly determined lengths, and efficacy is demonstrated when improvements in target behaviors occur after the treatment phase is initiated, regardless of the duration of the baseline period assigned. In multiple-baseline designs across settings, intervention is applied sequentially in different settings for the same individual (e.g., at home, at school). To demonstrate treatment efficacy, improvements in the target behavior should occur in a specific setting only after intervention has been implemented in that setting.

Strengths of the multiple-baseline design include its ability to circumvent challenges of withdrawal designs when applied to psychosocial interventions. Moreover, multiple-baseline designs allow researchers to examine an intervention across multiple behaviors, settings, or individuals, which yields more generalizable findings. Some have argued that the strength of the multiple-baseline design decreases when fewer than three or four behaviors, individuals, or settings are measured ( Barlow & Nock, 2009 ; Gallo et al., 2013 ), although there is some debate about this.

Randomized controlled trials.

Whereas single-case experimental designs offer idiographic data and inferences regarding treatment effects on individual children and adolescents, to examine causal impacts of therapeutic interventions in ways that can inform clinical and policy decision-making, a treatment must be tested with tightly controlled procedures derived from experimental science in a nomothetic manner. By maximizing internal validity and systematically manipulating the intervention as the independent variable in a randomized controlled trial, researchers can more confidently and robustly conclude whether observed changes in clinical targets resulted from the intervention itself or from other extraneous factors ( Kendall, Comer, & Chow, 2013 ).

The RCT can take the form of a small pilot RCT or a larger scale clinical trial. The small pilot RCT represents a randomized, controlled study design with a restricted sample size and is useful at the end of Stage 1 research following refinement of the intervention but before entering into a larger, more costly RCT (for an example, see Comer et al., 2017 ). Small pilot RCTs ensure the intervention is suitable for a randomized study design and identify issues related to feasibility to be addressed before reaching Stage 2 research. Large-scale RCTs represent Stage 2 evaluations of therapeutic intervention and use adequately powered sample sizes to examine nomothetic effects across groups of children and adolescents with similar clinical portraits.

Regardless of sample size, the defining characteristic of the RCT is random assignment between groups . Youth are randomly assigned to either an active treatment condition where the independent variable (e.g., a given therapeutic intervention) is applied or a control condition where the experimental intervention is absent. Assignment to treatment conditions must be determined randomly and independent of baseline symptom levels, family preferences, or therapist/investigator sense of which condition would be best for a given child. At trial outset, each child has an equal chance of being assigned to various conditions (although for variations, see Kendall et al., 2013 ). Including both a treatment and control condition allows researchers to directly compare observations of a target behavior across youth who have been similarly matched on key clinical characteristics. Because of the controlled study design, changes seen uniquely or more prominently in the treatment group can confidently be attributed to the therapeutic intervention.

Importantly, randomly assigning youth across treatment conditions does not guarantee ultimate comparability across conditions, although the likelihood of such is high. Simply due to chance, participants in one group may be older, more impaired, or different on any number of meaningful variables. After data collection is complete, researchers can evaluate the comparability of youth across groups, and if baseline group differences are found, such differences are attended to as covariates at the data analysis phase. Alternately, to ensure children and adolescents across groups are matched on key characteristics, researchers can use randomized block assignments . Participants are arranged into small, equal numbered subgroups based on comparability on key characteristics (e.g., subgroups of one boy and one girl to ensure gender comparability across groups). Randomization then occurs at the subgroup level, rather than at the individual child level, retaining the randomized element while also ensuring comparability across groups.

Sequenced treatment designs.

In clinical care settings, treatments result in a range of outcomes, including improvements on target symptoms (treatment response), worsening of target symptoms (deterioration), no change in target symptoms (nonresponse), or some, but not sufficient, improvement of target symptoms (partial response). Throughout the course of treatment, therapists make clinical decisions based on response to that point to determine what, if any, changes should be made to the child’s treatment plan (e.g., continuing with the treatment course vs. switching to another treatment). The rigor and structure of the traditional RCT does not allow for flexibility during treatment implementation and therefore cannot inform clinical decision-making in cases of nonresponse, partial response, or clinical deterioration during the course of treatment.

Sequenced treatment designs and adaptive treatment regimens retain randomization procedures while also systematically evaluating shifting treatment strategies across time for children and adolescents who are not sufficiently improving. The most common and increasingly popular adaptive treatment design is the sequential multiple-assignment randomized trial (i.e., the SMART design ) ( Dawson & Lavori, 2012 ; Murphy, 2005 ), which yields quality data with which to develop evidence-based adaptive treatment algorithms that differentially incorporate the benefits of intervention forms across critical points in treatment. A SMART includes multiple intervention stages, but as each child moves through intervention stages, randomization options at key decision points are determined by the child’s treatment response at that point (see Barlow & Comer, 2013 ). Indeed, the design of a SMART improves on traditional factorial RCT designs focused on broad main effects of treatment conditions across a single treatment phase and instead recognizes the true multiphase nature of the treatment process for the majority of children and adolescents in clinical practice. The sample SMART design illustrated in Figure 8.1 examines sequences of treatment in the context of behavioral parent training (BPT) and individual child therapy (ICT) and yields data to meaningfully inform eight distinct adaptive treatment regimens. This single design requires a very large sample size but can efficiently inform sequenced treatment decisions for children and adolescents showing a range of clinical responses to different forms of initial intervention. Despite the adaptive nature of children’s individual intervention courses, the randomization element of a SMART at critical decision points still affords causal conclusions ( Barlow & Comer, 2013 ; Lei, Nahum-Shani, Lynch, Oslin, & Murphy, 2012 ). Accordingly, the SMART offers a hybrid of the nomothetic groups-based (factorial) design strategy that typically informs policy decisions and the more idiographic single-case experimental designs that clarify individualized changes.

A sample sequential multiple-assignment randomized trial (SMART) design.

A recent SMART in clinical child and adolescent psychology ( Pelham et al., 2016 ), for example, found that central nervous system stimulant medication for attention deficit hyperactivity disorder (ADHD) is most effective when it is used as a supplemental second-line treatment following an adequate course of quality low-dose behavior therapy, rather than as a first-line treatment. Pelham and colleagues were also able to document that the behavioral-first treatment strategy was far less expensive for the healthcare system than starting treatment with medication. This SMART has the potential to meaningfully influence treatment sequencing for children with ADHD in primary care, where medication alone has traditionally been the most often used treatment, with poor long-term outcomes and high associated costs.

Control Condition Considerations in Clinical Child and Adolescent Psychology

Once the investigator has decided on an appropriate study design, the investigator must select an appropriate control condition. In a “controlled” evaluation, comparable children and adolescents are randomly assigned to either the treatment condition and receive the experimental intervention or a control condition and do not receive the intervention. By contrasting changes between youth across conditions, the efficacy of the intervention beyond outcomes produced by extraneous factors (e.g., passage of time, family expectations) can be assessed. Control conditions take many forms, each carrying a unique set of strengths and limitations that affect the inferences that can be made.

No-treatment control condition.

Youth assigned to groups in which they receive no treatment are considered to be in a no-treatment control condition . This straightforward design allows researchers to draw comparisons between treatment and no treatment and consider the effect of intervention above and beyond the passage of time. Comparing intervention outcomes to outcomes in a no-treatment control condition allows the investigator to rule out the possibility that intervention effects are simply due to the regression of extreme scores to the mean across the study time period. Importantly, however, a no-treatment control condition does not rule out other explanatory factors beyond the possibility that changes represent what might naturally unfold with the passage of time. Sometimes when participants simply know they are going to get treatment, it affects their expectancies, and they show symptom improvements. Accordingly, a no-treatment control condition cannot rule out the possibility that superior changes in the treatment condition are accounted for by differences in participant expectancies associated with being assigned to (any) treatment. Accordingly, no-treatment control conditions are best suited for early stages of treatment development and evaluation and are not appropriate to meaningfully address conceptual questions about treatment efficacy and active treatment components. That said, pragmatic considerations make no-treatment controlled designs hard to implement, given difficulties of recruiting and retaining participants in a no-treatment condition.

Wait-list control condition.

An improvement over the no-treatment control condition that accounts for patient expectancies is the wait-list control condition. In a wait-list controlled design, children are assigned to receive the treatment either immediately or after a predetermined waiting period. At outset, all participants know they will receive treatment at some point in the study and likely hold similar expectations that their symptoms will improve, regardless of condition. Target clinical behaviors are assessed at uniform intervals throughout both conditions. For example, if an experimental intervention is 12 weeks, then the wait-list interval would ideally be 12 weeks as well.

Although wait-list control conditions effectively account for the passage of time as well as patient expectancies of ultimate symptom improvement, wait-list control conditions do not account for inherent benefits associated with receiving care and attention from clinical staff that have nothing to do with the specific therapeutic components hypothesized to be responsible for treatment-related change. Further, participants in a wait-list control condition are prohibited from accessing other care services during the interim wait period. Accordingly, attrition from wait-list control conditions can be high. Moreover, it can be unethical to implement a wait-list control design when alternative treatments for the clinical target have been supported in previous work. In such cases, a multiple-treatment comparison design (discussed further in this chapter) is more appropriate.

With these limitations in mind, similar to the no-treatment control, wait-list control conditions are best suited for early stages of treatment development and evaluation. Importantly, wait-list and no-treatment control conditions can carry with them ethical dilemmas. Children and adolescents in these control conditions must be regularly monitored throughout study participation to ensure they do not show serious clinical deterioration that would suggest they should be withdrawn from their assigned condition. Indeed, these control condition designs are not suitable for clinical populations that cannot tolerate a wait-list or no-treatment phase (e.g., adolescents showing suicidal behaviors).

Attention-placebo control condition.

Attention-placebo control conditions are valuable for investigators looking to additionally rule out “common factors” associated with all therapeutic interventions (e.g., receiving care and attention from warm clinical staff, having an outlet through which problems can be discussed). These designs contrive a control condition that mimics elements of treatment by inviting participants to receive face-to-face interactions with attentive clinical staff. Importantly, the attention-placebo control condition is explicitly devoid of elements that are believed to be specifically effective in the experimental intervention. Attention-placebo control conditions typically consist of general psychoeducation, clinical monitoring, and broad patient support.

Despite the advantage that attention-placebo control conditions have for accounting for common, nonspecific therapeutic factors, it can be difficult to establish credibility (for patients and for therapists) when implementing these control conditions. It is useful for therapists to hold equally positive expectations of improvement for participants across conditions ( Kazdin, 2003 ), and establishing positive expectations for a condition oriented around nonspecific treatment factors can be difficult to achieve. Thus, researchers utilizing attention-placebo control conditions should measure participant expectations across conditions so that participant expectancy effects can be accounted for in analyses.

Standard-treatment comparison condition.

A standard-treatment comparison or treatment-as-usual control condition consists of an invention that is routinely given and allows the investigator to evaluate the incremental benefits of an experimental intervention over and above the existing standard of care. Ethical concerns that arise in no-treatment, wait-list, and attention-placebo conditions are minimized because children in this condition are receiving exactly what they would have received for their problems had the study never taken place. Moreover, attrition is minimized as all children receive active care, and patient and therapist expectations for change are likely to be more comparable. Despite these benefits, however, what exactly constitutes “treatment as usual” has been difficult to operationalize as it varies widely across settings, making it difficult to integrate findings across studies incorporating these control conditions. Further, differences between an experimental intervention and a treatment-as-usual condition might be attributed to differences in therapist quality, training, supervision, or organization, rather than to differences specific to the hypothesized active ingredients of the experimental intervention. Moreover, it can be difficult to match the intensity, dosing, or duration of treatments when comparing an experimental treatment condition to a treatment-as-usual condition. For example, suppose an experimental treatment protocol calls for weekly 60-minute sessions with a therapist for 12 weeks, whereas the standard care that is currently offered in a setting entails 20-minute sessions every other week for up to 8 weeks. If the investigator changes the treatment-as-usual condition to have control participants meet weekly and for longer periods of time with therapists, the control group is no longer a “standard care” condition; it is a new condition contrived by the investigator. Alternatively, if the investigator in this scenario compares the experimental condition to the true treatment as usual, it is possible that differences between the conditions could simply be due to differences in the frequency and intensity of care and not to the putative active ingredients of the experimental treatment.

Multiple-treatment comparisons.

Some more rigorous and revealing studies include multiple active treatment conditions and are thus able to address issues of relative or comparative efficacy. These studies offer direct comparisons of alternative active treatments. For multiple treatment comparisons, it is important that each treatment is comparable on a number of characteristics, including duration, session length, and frequency; setting; and level of credibility. For example, if children who received Treatment A were found to show superior outcomes to children in Treatment B, but Treatment A was 8 weeks and Treatment B was 4 weeks, the investigator would not be able to determine whether Treatment A had stronger effects than Treatment B or whether the study just found that 8 weeks of treatment was better than 4 weeks of treatment. Further, multiple-treatment comparison studies must ensure comparability of therapists across conditions. Therapists should be matched on their levels of training and experience, expertise in administration of study treatment protocols, and attitudes toward the treatments, including their allegiance to specific therapeutic approaches and their intervention expectancies. For example, it would be problematic if in a multiple-treatment comparison design a group of psychodynamic therapists conducted both a behavioral intervention (in which their expertise is low) and a psychodynamic therapy (in which their expertise is high). If outcomes differed across conditions, it would not be clear whether this was the result of true differences between behavioral and psychodynamic approaches or whether this was simply due to differences in therapist expectancies across the conditions.

Researchers using a multiple-treatment comparison design must also consider issues of sample size and outcome measurement. Whereas comparisons of active conditions against inactive control conditions typically yield large effect sizes, comparisons of multiple active conditions typically yield smaller effect sizes and accordingly require larger samples for adequate power. Moreover, to avoid potential biases, measures should cover a range of target clinical symptoms, and assessments should be equally sensitive to expected changes associated with each treatment type. For example, a measure that primarily evaluates children’s self-talk may be a well-suited measure for examining the impact of cognitive behavioral therapy but may not evaluate meaningful changes associated with antidepressant medication, for which the direct targeting of children’s self-talk is not a proposed mechanism of change ( Comer & Kendall, 2013a ).

Independent Variable Considerations in Clinical Child and Adolescent Psychology

In the context of a clinical trial, the independent variable that is manipulated is treatment assignment, that is, whether a child does or does not receive treatment or which treatment condition a child will receive. As in any experimental study, this independent variable must be carefully operationalized and implemented with integrity. Specifically, when evaluating an experimental intervention, the treatment must be adequately detailed and described in order to replicate the evaluation or to be able to communicate to others how to conduct the treatment ( Comer & Kendall, 2013a ). A treatment protocol that clearly defines the intervention and dictates how it is to be administered is critical for internal validity and ensuring the integrity of the independent variable. However, manualized intervention protocols can limit external validity, especially when attempting to generalize findings to settings and practitioners who do not typically use treatment manuals to guide their services. Some critics argue that manualized treatment protocols are overly rigid and do not afford clinicians needed flexibility to adapt to the complex and individualized patient needs encountered in routine practice settings ( Addis & Krasnow, 2000 ). Although most supported treatment manuals have always afforded a great deal of flexibility to individual patient needs, more modern treatment protocols are increasingly taking a modular approach, in which supported practices for specific identified problems are structured as free-standing modules, and decision flowcharts guide treatment component sequencing and module selection ( Chorpita, 2007 ; Comer, Elkins, Chan, & Jones, 2014 ). Modularized treatment protocols address complex comorbidities and shifting clinical needs by accommodating personalized tailoring of care for specific problems presenting in each child.

Dependent Variable Considerations in Clinical Child and Adolescent Psychology

The investigator must decide which dependent variables will be assessed and how they will be measured. Indeed, it is critical to measure outcomes using a variety of methods in order to minimize bias. Given research documenting poor cross-informant agreement in the assessment of child psychopathology (e.g., Comer & Kendall, 2004 ; De Los Reyes & Kazdin, 2005 ; Grills & Ollendick, 2003 ), investigators are wise to collect reports from multiple informants (e.g., parents, teachers, therapists, children). Such a multi-informant strategy allows researchers to evaluate symptoms that may differentially present across various contexts and life domains or that may be perceived differently across key people in children’s lives ( Silverman & Ollendick, 2005 ). Features of cognitive development can interfere with the accuracy of young children’s reports, and demand characteristics may cause children to offer what they believe to be desired responses. Accordingly, it is important to collect data simultaneously from important adults in children’s lives who observe their behavior across different settings. On the other hand, parents and teachers may not be privy to more internal and unobservable symptoms (e.g., anxiety).

Multimodal assessment strategies draw on multiple modes of assessment (e.g., observations, questionnaires) to evaluate the same dependent variable. For example, positive parenting practices might be measured via behavioral codings of structured parent–child interactions, as well as parent self-reports. For other dependent variables, objective records (e.g., medical or school records) might be collected. Data on peer relations might draw on sociometric data and peer nominations.

Finally, multiple targets should be assessed ( De Los Reyes & Kazdin, 2006 ). Improvement can take many forms, including decreased symptoms, loss of clinical diagnosis, improved quality of life, higher academic functioning, and improved interpersonal functioning. No single dependent variable independently and sufficiently captures treatment response. Inherent in a multiple-domain assessment strategy, however, is the fact that treatments rarely produce uniform effects across assessed domains. For example, one treatment might improve child anxiety but not peer relationships, whereas another treatment might improve children’s peer relationships but not anxiety. If a clinical trial were to compare these two treatments, it is not readily apparent which treatment should be deemed more efficacious ( Comer & Kendall, 2013a ). Typically, the investigator selects a primary outcome, as well as secondary and exploratory outcomes that provide more nuanced information about treatment responses. Importantly, selection of a primary outcome variable must occur prior to collection and review of the findings, so that decisions about which variables are most important are made a priori and are not biased by the significance of results. De Los Reyes and Kazdin (2006) have argued for a multidimensional conceptualization of intervention change, and similarly we caution consumers of the treatment literature against simplistic dichotomous appraisals of treatments as effective or not.

Assessment Point Considerations in Clinical Child and Adolescent Psychology

Evaluating a novel intervention through an experimental design requires a clinical researcher to take careful observations of the dependent variables across the duration of the study at key time points. Target clinical behaviors are selected for measurement and should be assessed at the outset of the study to provide baseline data . Baseline data serve as benchmarks against which subsequent observations of dependent variables are assessed. Post-treatment assessments are another critical time point for assessment, as those observations speak to acute treatment outcomes or the impact of an experimental intervention on clinical symptoms immediately after treatment is complete.

Although post-treatment data are critical, post-treatment data do not allow researchers to examine enduring treatment effects. To demonstrate lasting treatment gains or maintenance , researchers must also measure clinical outcomes at predetermined intervals after treatment has been completed (e.g., 3 months post-treatment, 6 months post-treatment). Such follow-up evaluations add methodological rigor to a study. For example, in a study comparing multiple active treatments, acute post-treatment outcomes may be comparable, but follow-up assessments may reveal that children in one experimental treatment condition showed higher maintenance of treatment gains with continued time. Importantly, for follow-up assessments to capture true lasting effects, participants should not have contact with other clinical services during the follow-up assessment period. Because follow-up intervals can be lengthy, it is not always feasible or ethical to prevent participants from receiving outside services during a follow-up interval. Many investigators, accordingly, include a naturalistic follow-up component that allows participants to seek outside services during the interval between post-treatment and follow-up evaluation. Additional service use after treatment completion may actually be a variable of interest, and when it is not, outside service use during the follow-up interval should be controlled for statistically.

Investigators are also increasingly incorporating assessments at different points during treatment, or midtreatment, to establish growth curves, consider the rate and shape of change during the treatment phase, and better understand potential mediators of treatment response. Midtreatment assessments provide revealing data on when symptom changes occur during treatment, at what pace, and how changes across different domains of response may unfold and interact with one another across time ( Chu, Skriner, & Zandberg, 2013 ; Gallo, Cooper-Vince, Hardway, Pincus, & Comer, 2014 ; Kendall et al., 2009 ; Marker, Comer, Abramova, & Kendall 2013 ).

Sample and Setting Considerations in Clinical Child and Adolescent Psychology

Careful consideration is needed when selecting a sample to best represent the clinical population of interest. Those youth chosen to participate in the trial will strongly influence the extent to which findings can be generalized to the larger population of youth who may benefit from the treatment. A genuine clinical sample made up of youth shown to have a disorder and who are seeking treatment will afford greatest external validity and generalizability. However, genuine clinical samples can be difficult for researchers to recruit into studies; moreover, they frequently carry more complex clinical portraits, which can threaten the internal validity of the study. Alternatively, analogue or selected samples can afford a higher degree of control and internal validity in study design, but youth in such samples are not necessarily comparable to the majority of patients typically seen in clinical practice.

Broadly speaking, it is important that the sample in a study evaluating an experimental intervention reflect the population for which that intervention is intended to ultimately benefit. Thus, in addition to considering a sample’s clinical characteristics, researchers in clinical child and adolescent psychology must consider sociodemographic diversity. Race, ethnicity, gender, socioeconomic status, education level, and other related demographic characteristics must all be considered when recruiting an appropriate study sample that can generalize to the general population.

The setting in which a study takes place will also have important implications for the generalizability of results. Early stage evaluations of therapeutic interventions are often conducted in clinical research laboratory settings and require investigators to recruit subjects to participate. Therapists in these trials are typically part of the investigator’s research team, and as such their outcomes may not generalize to the practices of front-line clinicians who differ from research staff clinicians with regard to experience, caseload size, supervision, and oversight. It is ultimately critical to demonstrate the transportability of an intervention to front-line service settings. Therefore, later stage evaluations of therapeutic interventions must evaluate outcomes beyond tightly controlled research settings.

Conducting a Clinical Trial

Once a clinical trial has been carefully designed, it does not simply run itself. The investigator must play a highly active role in organizing and implementing each aspect of the study in order to ensure a successful trial.

Training and organizing study staff merits special attention. Independent evaluators ( IEs ) refer to staff members who participate in assessment procedures and who are masked to each participant’s treatment assignment. IEs must be trained to a prespecified criterion (e.g., must match the diagnostic profile generated by the principal investigator on at least three consecutive diagnostic interviews) prior to their active participation on the trial, and throughout the course of the study periodic reliability checks are further necessary to ensure interrater reliability across study IEs. Systematic safeguards must be put in place to guarantee that IEs are kept unaware of each participant’s treatment assignment. IEs should try to avoid patient waiting rooms in which they might run into families assigned to treatment. IEs should not attend clinical supervision meetings that would reveal participant assignment information. For smaller teams in which the same staff members serve as both IEs and as therapists on different cases, multiple supervision teams are required, and staff members can only serve as IEs on cases carried on opposite supervision teams. Prior to post-treatment assessments, participating families should be reminded that their post-treatment assessor does not know which treatment they received (or even whether they received treatment if it is a wait-list controlled trial), and families should be cautioned against speaking about any treatment experiences during the interview.

Study therapists must be adequately trained. This typically involves initial didactic training on the study protocol and knowledge quizzes, followed by role plays. Ideally, there is opportunity for trainee therapists to shadow and then cotreat several cases using the study protocol prior to their active participation as a therapist on the study. Investigators should set a criterion that must be met by study therapists prior to their carrying study cases independently (e.g., complete didactic training, achieve a score of 80% or greater on a knowledge quiz, shadow one case, and cotreat one case). Regular supervision is critical to avoid therapist drift and to ensure treatment integrity.

Just because study therapists have been trained to criterion does not guarantee that they will deliver the independent variable (treatment) as intended. In the course of a study, the treatment that was assigned may not in fact be the treatment that is provided (see also Perepletchikova & Kazdin, 2005 ). To ensure that study treatments are implemented as intended, treatment integrity checks should be conducted. Therapy sessions should be regularly recorded such that independent raters can view them and provide quantifiable judgments on the implementation of key treatment components. McLeod, Islam, and Wheat (2013) provided more detailed descriptions of procedural issues in the conduct of quality assurance and treatment integrity checks.

Throughout the course of a clinical trial, someone must be responsible for monitoring the sample and the data and for ensuring that all data are collected as designed and intended. This person must be omniscient: The person must know the condition of every participant, must know who is assigned to which cases and who is responsible for collecting each piece of data, must know who can know about each participant’s condition, and must be aware of where each family is in the flow of study phases. Even with such a person dedicated to this role, data will inadvertently be missed, blinds will unintentionally be broken, and families will mistakenly not be contacted at their appropriate follow-up points. For a large clinical trial, this is a full-time job, but for smaller studies, a principal investigator can often perform in this role. Most important, the individual in charge of tracking cannot serve as an IE or as an IE supervisor because their role inherently unmasks them to all study-related information that could bias responses.

Throughout treatment, study staff must regularly monitor adverse events, and an individual or panel of individuals must be responsible for deciding whether a particular child suffering adverse events should be withdrawn from the study. This is particularly important when treatment conditions include medications that can introduce unfavorable side effects, but psychological treatments can also be stressful and associated with adverse events.

Retaining the sample throughout the study can be challenging. It is recommended that study staff phone, email, or text families weekly to “welcome” them to their next session or appointment. During the treatment phase, there is an attrition risk when study treatment cannot address complex and shifting patient needs that may present. For example, a family crisis, an emergent academic issue, or a serious peer conflict may present, and such unforeseen events may become a clinical priority for the family that is not explicitly addressed by the treatment protocol. Adjunctive services and attrition prevention ( ASAP ) procedures (e.g., Abikoff et al., 2002 ) are often implemented to maintain the sample, in which each case in a trial is allowed a prespecified number of additional sessions during the treatment phase to address exigencies or clinical crises that fall outside of the scope of the treatment protocol. Even children who do not complete treatment should be invited to participate in as many post-treatment and follow-up evaluations as possible. Monthly calls, birthday wishes, and holiday cards are recommended during follow-up intervals in order to maintain contact with families between the end of treatment and follow-up evaluation, thus maximizing sample participation at follow-up.

Analyzing Trial Outcomes

After conducting a clinical trial, the data analysis phase entails the active process through which the investigative team extracts relevant information from the collected data in ways that permit statistical inferences about the larger population of youth the sample was recruited to represent. A comprehensive outline of clinical trial data analysis is beyond the scope of the present chapter (the interested reader is referred to Jaccard & Guilamo-Ramos, 2002a , 2002b ; Read, Kendall, Carper, & Rausch, 2013 ); here, we briefly address (a) missing data and attrition; (b) evaluation of clinical significance; and (c) evaluation of change mechanisms.

Missing data and attrition.

Even in the most diligently organized and carefully implemented clinical trial, not every child randomized will actually complete participation. Mason (1999) estimated that on average roughly 20% of participants withdraw prematurely from their participation in a clinical trial. Attrition can be problematic for data analysis, particularly when large numbers of youth do not complete treatment or when rates of attrition vary across study conditions ( Leon et al., 2006 ).

When there is a meaningful discrepancy between the number of children randomized to the various treatment conditions and the number of children who completed their participation, the investigator can conduct and report two sets of data analyses: (a) treatment completer analyses that evaluate only those youth who completed the full course of their treatment and (b) intent-to-treat analyses that include all those initially randomized. Treatment completer analyses evaluate intervention effects when someone receives a full “dose” of treatment. Those who drop out of treatment, those who refuse treatment, and those who do not adhere to treatment are not included in such analyses ( Kendall et al., 2013 ). Treatment completer outcomes may be somewhat inflated because they only capture the results of children who fully adhered to and completed treatment. At the same time, treatment completer analyses directly examine outcomes associated with true exposure to the experimental manipulation and therefore provide very valuable information.

On the other hand, intent-to-treat analyses are more conservative and evaluate outcomes for all children involved at the point of randomization. Such analyses speak more directly to issues of generalizability of findings as they incorporate information about treatment tolerability. A simplistic method for handling missing data for intent-to-treat analyses is the last observation carried forward ( LOCF ) method, which assumes that the scores for children who withdraw from treatment remain constant from their last assessment point throughout the conclusion of the study. For example, if a family withdraws participation at Week 8, then the data values from that child’s Week 7 assessment (or most recently completed assessment) would be substituted for all subsequent assessment points. However, LOCF introduces systematic bias and fails to take into account the uncertainty of postdropout functioning ( Leon et al., 2006 ). Accordingly, LOCF methods have fallen out of fashion, in favor of (a) multiple imputation methods , which impute a range of values for missing data by incorporating the uncertainty of the true values of missing data ( Little & Rubin, 2002 ); and (b) mixed-effects modeling , which relies on regression modeling to address missing data in the context of random (e.g., child) and fixed (e.g., treatment condition, gender) effects. Mixed-effects modeling is a particularly strong approach to handling missing data when numerous assessments are collected across a treatment trial (e.g., weekly data are collected).

Evaluation of statistical and clinical significance.

Statistical significance is identified when the mean difference between treatment conditions is beyond that which could have resulted by chance alone (most commonly defined as p < .05). Tests of statistical significance are critical as they indicate how likely it is that observed differences between conditions were not due solely to chance. However, tests of statistical significance alone do not provide compelling information on the clinical significance of group differences. Relying solely on statistical significance can lead an investigator to interpret treatment gains as meaningful when in fact they may be clinically insignificant ( Kendall et al., 2013 ). For example, suppose that a treatment for disruptive behavior problems results in significantly lower scores on the Externalizing Scale of the Child Behavior Checklist (CBCL). An examination of CBCL means however reveals only a small but reliable shift from a mean of 81 to a mean of 78. With a large enough sample size, this change can achieve statistical significance at the conventional p < .05 level, but a 3-point change from 81 to 78 on the CBCL Externalizing Scale is of limited practical significance. At both baseline and still at post-treatment, the scores are within the clinically elevated range, and such a small magnitude of change may have little effect on a child’s functioning.

Clinical significance refers to the meaningfulness or persuasiveness of the magnitude of change ( Jacobson & Truax, 1991 ; Kendall, 1999 ). Whereas tests of statistical significance ask the question, “Were there intervention-related changes?” tests of clinical significance ask the question, “Were intervention-related changes convincing and meaningful?” Clinical significance can be evaluated by (a) considering the extent to which treated youth are returned within normal limits (i.e., they are indistinguishable from a normative sample of youth; Kendall, Marrs-Garcia, Nath, & Sheldrick, 1999 ); (b) evaluating the magnitude of effect sizes of change, regardless of statistical significance; or (c) computing the Reliable Change Index (RCI; Jacobson & Truax, 1991 ) across participants. To calculate the RCI, the investigator assesses the extent to which each individual participant’s change pre- to post-treatment was reliable, versus the possible result of simple measurement error. For each participant, the investigator calculates a difference score (e.g., post score minus baseline score) and compares it to the standard error of measurement (i.e., ±1.96 SE ). As such, the RCI is determined by two factors: (a) the magnitude of change and (b) the reliability of measurement. Each of these approaches to assessing clinical significance (e.g., normative comparisons, effect size interpretations, RCI) provides an important, but unique, perspective on the meaningfulness of treatment outcomes; thus, they are often used in conjunction with one another. For example, an investigator might use published norms of a measure to evaluate which participants crossed over from the clinical range to the nonclinical range and also calculate an RCI for each participant. The investigator can use these data to group participants into the following categories: recovered (i.e., passed both normative cutoff and RCI criteria), unchanged (i.e., passed neither criteria), improved (passed RCI but not normative cutoff criteria), or deteriorated (i.e., passed RCI criteria in the negative direction) ( Comer & Kendall, 2013a ; Jacobson & Truax, 1991 ; McGlinchey, Atkins, & Jacobson, 2002 ).

Evaluation of change mechanisms.

Researchers and funding agencies are increasingly interested in identifying the conditions that determine when an intervention is more or less potent (moderation) and the processes through which an intervention produces change (mediation). A moderator is a variable that delineates the conditions under which a given intervention is related to an outcome. Conceptually, moderators identify on whom and under which circumstances treatments have different effects, and they are usually measured prior to treatment ( Kendall et al., 2013 ; Kraemer, Wilson, Fairburn, & Agras, 2002 ). Functionally, a moderator is a variable that influences either the direction or magnitude of an association between the independent variable (treatment condition) and a dependent variable (outcome). Treatment moderators help identify which youth might be most responsive to which interventions and for which youth alternative interventions might be appropriate. Of note, when a variable broadly predicts treatment response across all treatment conditions in a clinical trial, conceptually that variable is simply a predictor , not a moderator (see Kraemer et al., 2002 ).

A mediator, on the other hand, is a variable that is measured during treatment and clarifies the process by which an intervention influences an outcome. Conceptually, mediators identify how and why treatments have the effect they do ( Kraemer et al., 2002 ). The mediator effect reveals the mechanism through which treatment is associated with outcomes. Significant meditation affords causal conclusions. If a supported treatment for child anxiety was found to influence negative self-talk, which in turn was found to have a significant influence on child anxiety and avoidance, then negative self-talk might be considered to mediate the treat-to-outcome association. Specific statistical methods used to evaluate the presence of treatment moderation and mediation can be found elsewhere ( MacKinnon, Lockhart, Baraldi, & Gelfand, 2013 ).

Funding agencies are increasingly prioritizing interventions research that explicitly examines mechanisms that can explain treatment effects. The experimental therapeutics paradigm has the researcher first hypothesize a “target” or mechanism of action. Rather than focusing on clinical effects and treatment response, the experimental therapeutics researcher studies an intervention first as a manipulation to verify whether the intervention has a predicted effect on the target mechanism (i.e., target engagement). Once target engagement has been documented, the experimental therapeutics researcher then examines whether clinical outcomes are indeed related to successful target engagement.

Reporting Results

Presenting the written study findings to the scientific community in a peer-reviewed outlet is the final step of a clinical trial. A well-constructed data report must present all relevant methodological and study-related information with enough context to afford meaningful interpretation of results and to allow for replication. Study aims and results must be placed in the context of related research to illustrate how the current findings compare to previous results, and the investigator must discuss how the results build on, support, or diverge from other findings in the field. A candid and nondefensive articulation of study limitations and shortcomings is also critical in order to direct future research.

To avoid potential bias in the reporting of clinical trial results, a multidisciplinary panel of experts established a checklist of guidelines for maximizing transparency in reporting (i.e., CONSORT guidelines; see Begg et al., 1996 ). CONSORT (i.e., Consolodated Standards of Reporting Trials) guidelines offer a minimum set of recommendations for preparing reports of clinical trial findings that ensure transparent, comprehensive reporting to facilitate critical evaluation and interpretation. Chief among the CONSORT items is inclusion of a graphical representation of the flow of study participation from baseline to study completion. Such a “CONSORT chart” provides important information on recruitment, randomization, retainment, and participant attrition across treatment conditions and assessment time points.

We remain at a relatively nascent stage in the science of clinical child and adolescent psychology, with the majority of work ahead of us. Having reviewed key considerations for planning, conducting, analyzing, and reporting clinical evaluations of child and adolescent treatments, it is clear that no individual investigation, even with optimal design and procedures, is able to adequately answer all relevant questions. Rather, a collection and series of investigations, drawing on a broad range of methodological strategies, is needed to progress our understanding of best practices for the widely diverse range of mental health problems that present in childhood and adolescence.

Those looking for the “correct” research methodology with which to address all questions in clinical child and adolescent psychology are misguided. Throughout this chapter, we have outlined how for each testable question there are many research strategies that can be used to reveal meaningful information, each with strengths and limitations. Collectively, the methods and design considerations outlined in this chapter detail a portfolio of modern research strategies for the continually evolving science of clinical child and adolescent psychology: a set of alternative and complementary “directions” so to speak for advancing our field from where we are now to where we need to be.

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Psychosocial Development Research in Adolescence: a Scoping Review

• Original Article
• Published: 01 February 2022
• Volume 30 , pages 640–669, ( 2022 )

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• Nuno Archer de Carvalho   ORCID: orcid.org/0000-0001-6620-0804 1 , 2 &
• Feliciano Henriques Veiga   ORCID: orcid.org/0000-0002-2977-6238 1 , 2  

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Erikson’s psychosocial development is a well-known and sound framework for adolescent development. However, despite its importance in scientific literature, the scarcity of literature reviews on Erikson’s theory on adolescence calls for an up-to-date systematization. Therefore, this study’s objectives are to understand the extent and nature of published research on Erikson’s psychosocial development in adolescence (10–19 years) in the last decade (2011–2020) and identify directions for meaningful research and intervention. A scoping review was conducted following Arksey and O’Malley’s framework, PRISMA-ScR guidelines, and a previous protocol, including a comprehensive search in eight databases. From 932 initial studies, 58 studies were selected. These studies highlighted the burgeoning research on Erikson’s approach, with a more significant representation of North American and European studies. The focus of most studies was on identity formation, presenting cross-cultural evidence of its importance in psychosocial development. Most of the studies used quantitative designs presenting a high number of different measures. Regarding topics and variables, studies emphasized the critical role of identity in adolescents’ development and well-being and the relevance of supporting settings in psychosocial development. However, shortcomings were found regarding the study of online and school as privileged developmental settings for adolescents. Suggestions included the need to consider the process of identity formation in the context of lifespan development and invest in supporting adolescents’ identity formation. Overall, conclusions point out Erikson’s relevance in understanding adolescents’ current challenges while offering valuable research and intervention directions to enhance adolescent growth potential.

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Acknowledgements

The authors thank Conceição Martins and Filomena Covas for their help in assessing methodological options and text revision and Rita Fonseca and Sandra Torres for their advice regarding English accuracy.

This work was supported by the FCT — Fundação para a Ciência e a Tecnologia, IP, within the scope of the UIDEF — Unidade de Investigação e Desenvolvimento em Educação e Formação, under the reference UID/CED/04107/2020.

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NC and FV worked on the protocol and methodological design of the review. NC carried out the research, analyzed the studies, and presented the initial text for the results and their discussion. FV oversaw the conceptualization, research, and analysis of the studies. Both authors read and approved the final manuscript.

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de Carvalho, N.A., Veiga, F.H. Psychosocial Development Research in Adolescence: a Scoping Review. Trends in Psychol. 30 , 640–669 (2022). https://doi.org/10.1007/s43076-022-00143-0

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Effects of Positive Psychology Interventions on the Well-Being of Young Children: A Systematic Literature Review

Valérie benoit.

1 Department of Special Education, University of Teacher Education, 1014 Lausanne, Switzerland

Piera Gabola

2 Department of Development from Childhood to Adulthood, University of Teacher Education, 1014 Lausanne, Switzerland; [email protected]

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Data are available on request.

Over the last 20 years, the effectiveness of positive psychology interventions for the development of the well-being of children and adolescents and the moderation of high levels of anxiety and depression in this population has been largely demonstrated. Emphasis has been placed on the promotion of well-being and prevention of mental health problems in the school context in order to foster, through positive psychology, the cognitive and socio-emotional development of primary and secondary students, e.g., by strengthening positive relationships, positive emotions, character strengths, optimism, and hope. However, little is known about the impact of these interventions on young children. This systematic review aims at examining the effects of positive psychology interventions on the well-being of early childhood children (<6 years old), both in the preschool education context with educators or teachers and also in the family context with parents. Several electronic databases were searched, and the findings systematically reviewed and reported by the PRISMA guidelines. Very few studies met the inclusion criteria (n = 3), highlighting the need for further research in this area. Indeed, all of the selected studies demonstrated the importance of positive psychology interventions with young children to promote positive aspects of development, such as gratitude, positive emotions, life satisfaction, accomplishment, positive relationship, or self-esteem. Limitations in the field are discussed.

1. Introduction

Children’s well-being is often associated with objective aspects such as demographic and socio-economic characteristics [ 1 ]. In particular, poverty (lack of basic material resources, limited access to health care, and lack of family time) is known to affect, as are other environmental and social issues, youth’s physical and mental health (see, e.g., [ 2 ]). It could also affect happiness, another indicator of well-being, in multiple ways by directly or indirectly involving the child [ 3 ] (p. 336). In addition to objective aspects, well-being can also be defined more broadly as a subjective experience, i.e., the perception that individuals have of how well their lives are going (see, for example, [ 4 ], p. 295). In the field of positive psychology, Seligman [ 5 ] indeed defines well-being as “the positive evaluation that people make of their lives” and it “includes positive emotion, engagement satisfaction, and meaning” [ 6 ] (p. 1). Positive psychology considers well-being from two perspectives: first, hedonic or subjective well-being is related to positive affect (e.g., happiness), experiences of negative emotions (e.g., trauma and loss of a person), and life satisfaction [ 7 ]. Second, eudemonic or psychological well-being is operationalized with positive relationships, autonomy, and purpose in life [ 8 ]. Theses perspectives have implications in terms of measuring, despite the most popular measures of well-being are those that assess overall satisfaction or happiness in life [ 9 ]. Although it is rare to find studies on the development of programs that include subjective or eudemonic well-being for children at the school or preschool level, positive psychology emphasizes the importance of creating constructive living environments (e.g., families or institutions) throughout the life cycle. The promotion of healthy environmental systems, particularly healthy school environments, is essential to promoting the well-being and mental health of children and young children [ 10 ]. Aspects of social and emotional developmental are no less important for children’s well-being and mental health. Thus, even before entering school, skills such as self-confidence, developing positive relationships, and expressing emotions effectively are essential for the future development of young children [ 11 ]. A study including 52 systematic reviews and meta-analyses endorses the importance of promoting well-being and preventing mental health problems in schools through early intervention with young children, especially in areas that develop emotional and social skills [ 12 ].

Currently, there are many interventions aimed at developing and adjusting emotional and social skills in school, such as social and emotional learning (SEL) programs (see e.g., [ 13 ]) or positive youth development (PYD) interventions (see e.g., [ 14 ]). However, there seems to be a need to integrate or add other programs whose primary aim is to improve young children’s mental health by targeting well-being as a motivating factor to feel competent in future learning, reducing stress and increasing life satisfaction. Research in the field of positive psychology, the science of well-being and the study of optimal human functioning [ 15 ], aims in particular at evaluating practices that can improve well-being in the human life cycle [ 16 ].

The research community in positive psychology focuses specifically on positive emotions and positive character traits to improve mental health and promote well-being. The experience of positive emotions contributes to the development of social, physical, intellectual, and psychological resources [ 17 ] in adults and adolescents, and also in children. Indeed, the foundations of emotional development are set during childhood and influence the emergence of social and cognitive processes during this period [ 18 ]. Positive character traits such as optimism [ 19 ] are a quality in youth and adults, and also a potentially beneficial quality for child development [ 20 ]. Teaching optimism to children can prevent anxiety and depression problems [ 21 ]. Research has also shown that children with higher levels of hope, another positive trait, are more positive about themselves and less depressed than children with lower levels of hope are [ 22 ].

In order to test practices that can improve well-being, researchers in positive psychology use placebo-controlled trials of positive psychology interventions (PPIs). These are programs based on practices consistent with positive psychology theory to promote sustainable well-being [ 23 ]. Positive psychology interventions are often based on Seligman’s [ 24 ] PERMA model, which refers to five conditions: positive emotions, which refer to feelings that motivate human actions, such as happiness, pleasure, and optimism; engagement , which refers to participation and concentration (flow state) in learning activities; relationships , which refers to the perception of having positive and secure relationships, receiving support and appreciation; perceived meaning , which involves being able to use one’s strengths to accomplish a goal that is important to oneself; personal accomplishment , which refers to the feeling of having achieved personal goals. This model has been adapted to the educational context with the incorporation of a sixth condition, health , generating the PERMA(H) model [ 25 ]. Health refers to optimal physical, emotional, and psychological health, which are crucial aspects of well-being, especially in creating good habits at an early stage for long-term health benefits [ 25 , 26 ]. According to this model, well-being consists of fulfilling one or more of these dimensions, measured independently of each other, which are essential not only for well-being but also for fostering positive affect, the development of secure relationships, life satisfaction, and reducing the risk of mental health problems, including in young children. While the use of these dimensions is a useful and beneficial way to explore well-being, it is also true that while much has been done to depict the dimensions of well-being, there is little to no consensus in the literature on the definition of well-being [ 27 , 28 , 29 , 30 ]. “Well-being lacks definition, both as a concept and in practice. Thus there emerges a range of factors identified as inherent in it or against which it is recognizable and/or measurable” [ 29 ] (p. 183). In order to go beyond the dimensions and descriptions of well-being in previous research, Dodge et al. [ 30 ] proposed a new definition of well-being, which is intended to be simple, universal, and as close as possible to the need for equilibrium or homeostasis of any individual. Thus, they define “well-being as the balance point between an individual’s resource pool and the challenges faced” (p. 230). Another strength of this definition is its optimism, which links it to positive psychology. In this sense, individuals are perceived as agents of their happiness or well-being, who can act on their resources and challenges to maintain a certain state of balance [ 5 , 30 ]. This self-management can be learned and taught, and this is also what positive psychology intervention programs aim to do when they seek to develop or promote well-being, particularly from elements such as those represented in the PERMA(H) model.

1.1. Background on the Effects of PPIs

Several meta-analyses conducted with adult samples [ 31 , 32 , 33 , 34 , 35 , 36 ], with younger people, mainly from 8 to 18 years old [ 37 , 38 , 39 ], or with both [ 40 ] have reported PPIs’ effect on participants’ via increased levels of well-being and decreased levels in depressive, anxiety and stress symptoms compared to the control group. These meta-analyses were conducted with clinical and nonclinical samples, in Western and non-Western countries. Significant small-to-medium effects of PPIs on increasing strengths and quality of life were reported by Carr et al. [ 40 ]. Some of these meta-analyses [ 31 , 32 , 38 ] also reported that findings remained significant after follow-up (two to twelve months), such as decreased depressive symptoms or increased well-being in adults as well as in young people. In terms of moderator impacts (e.g., age, clinical status, and program duration), Carr et al. [ 40 ] found notably that PPIs’ effects were higher on quality of life within younger samples and on well-being within older samples. Likewise, Sin and Lyubomirsky [ 35 ] reported that age was one of the factors that influenced the effectiveness of the intervention, along with depression status, self-selection, and format and duration of the interventions. In line with Bolier et al. [ 31 ] and Sin and Lyubomirsky [ 35 ], Carr et al. [ 40 ] also showed greatest advantages when PPIs were provided in a long-term individual or group therapy format in clinical population. On the contrary, in a nonclinical population, in an educational context or in another context and with a group or individual, findings indicated that brief PPIs were more effective [ 40 ]. Some of these results need to be considered in light of certain limitations, primarily the bias associated with small sample sizes. Indeed, White et al. [ 41 ] reanalyzed two highly cited meta-analyses that examined the effectiveness of PPIs on well-being and depression, namely, Bolier et al. [ 31 ] and Sin and Lyubomirsky [ 35 ]. By taking into account the small-sample-size bias, the results of White et al. [ 41 ] indicated that the effectiveness of PPIs on well-being was smaller than the effects found, in particular, by Bolier et al. [ 31 ] and, to a lesser extent, by Sin and Lyubomirsky [ 35 ], but still significant. In contrast, their results also indicated that “the effects of PPIs on depression were variable, dependent on outliers, and generally not significant” (p. 1). In line with other researchers arguing for preliminary power analysis to establish an appropriate sample size (e.g., [ 32 , 40 ]), White et al. [ 41 ] called for more PPI research with a larger sample size.

While positive psychology emerged in the 2000s, its application in the form of positive education and positive intervention in schools arrived later [ 42 ]. PPIs in schools are many and different, but they share a common goal: to improve the developmental trajectory of young people and prevent possible future difficulties by teaching positive behaviors [ 43 ]. Benefits of positive psychology interventions in schools have been demonstrated notably through systematic meta-analyses [ 43 ]. For instance, Brunwasser et al. [ 38 ] indicated significant effects of a resilience intervention (Penn Resiliency Programme, PRP) on young people’s levels of depressive symptoms (but not on depressive diagnoses), i.e., lower levels, especially at the post-test follow-up measurement point. On the contrary, the meta-analysis by Bastounis et al. [ 37 ], conducted to evaluate the effectiveness of a resilience intervention (PRP) to prevent depression and anxiety in students aged 8–17 years, showed that there is no evidence that these programs and derivatives reduce depression or anxiety. Likewise, although the meta-analysis by Renshaw and Olinger Steeves [ 39 ] provided evidence that gratitude is an indicator of subjective well-being in young people (e.g., gratitude is positively associated with positive affects or negatively associated with depression), it also reported that gratitude-based interventions are poorly effective. Despite these mixed results, positive psychology interventions have, as with prevention programs [ 14 , 44 ], the advantage of focusing on all students, children or young children, and not just on those with problems. McCabe et al. [ 45 ], referring to Meyers and Meyers [ 46 ], pointed out that “exercises in positive psychology to teach concepts such as happiness, gratitude, and life satisfaction can be implemented in schools as measures of primary prevention to promote individual growth as well as positive interactions among all students, not just those at risk” (p. 180). Similarly, enjoyment of school work seems to play a more important role on motivation than the role of anxiety [ 47 ]. The school context is therefore seen as a key criterion for assessing the well-being of children. Children’s quality of life is also an important indicator reported in international OECD surveys, as poor schooling has later consequences for the adult life course [ 48 ]. Finally, a positive school climate has an impact on children’s happiness and well-being at school [ 45 ].

1.2. Study Objective

With the exception of the meta-analysis by Carr et al. [ 40 ], where 20% of the selected studies were conducted on samples of children and adolescents (<18 years old), previous meta-analyses were mainly based on the effects of PPIs in adults. Although three meta-analyses have been conducted on the effects of PPIs in young people [ 37 , 38 , 39 ], most of the samples are from late childhood or adolescence rather than from early childhood. Given the interest of positive psychology in preventing mental health problems by improving the well-being and quality of life of children, adolescents, and adults, research on this topic targeting children in early childhood seems crucial in terms of promoting healthy living environments, positive habits, as well as in an inclusive perspective [ 49 ]. Indeed, early childhood is a fundamental period of emotional and cognitive development. It is therefore considered to be a particularly favorable time for fostering future well-being in life [ 45 ]. However, little is known about the benefits of PPIs for young children. Thus, the purpose of this systematic literature review is to examine studies that have demonstrated the effectiveness of positive psychology interventions on the well-being of young children (<6 years old), both in the context of preschool education with educators or teachers and in the family context with parents. It is essential that parents, educators, and teachers have knowledge and understanding of such programs (PPIs) and their effectiveness in order to encourage, in a promotional and preventive perspective, the development of well-being in early childhood. Indeed, “increases in well-being are likely to produce increases in learning, the traditional goal of education”, but also increases in life satisfaction and decreases in depression [ 42 ] (p. 294).

2. Materials and Methods

The search process adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [ 50 ]. Literature searches of original studies published from 2000 to 2020 (as of 22 October 2020) were conducted in Web of science (Core Collection), OVID, and PubMed electronic databases. Various keywords were used in the search, including terms describing (1) the research area and the application (e.g., positive psychology intervention or positive psychology program), (2) the evaluation (e.g., intervention effect or efficacy), (3) elements associated with the PERMA(H) model and possible outcomes (e.g., well-being, quality of life, mental health, positive emotion, engagement, meaning, relationship, or achievement), and (4) the target population or setting (e.g., young children, toddler, preschool, kindergarten, or parent).

The inclusion criteria allowed for the selection of only (1) peer-reviewed studies (2) published from 2000 to 2020, (3) written in English, and (4) measuring the effects of (5) a positive psychology intervention (PPI) on (6) the well-being of (7) young children (<6 years old). More specifically, participants could be male or female children, with or without disabilities, with or without physical or mental health issues, and from community or clinical samples. Studies with samples of preschool and older children were also retained. In addition, the interventions were to aim to improve children’s well-being through activities related to positive psychology theory (i.e., PPIs). There were no restrictions on the professional experience of those conducting the interventions, the contexts in which the programs were carried out, or the mode of implementation. Only studies reporting data both before and after the intervention (with or without a follow-up measure) using self-report or clinician/researcher-administered psychometric instruments were retained. Studies with no alternative control groups, as well as studies without control groups were taken into account. Well-being was considered in its broad definition and was associated with at least one dimension of the PERMA(H) model [ 24 , 25 ]. No other limitations in terms of outcomes were imposed.

Exclusion criteria were (1) study sample over 6 years old only (e.g., primary school children, adolescents, or adults), (2) absence of PPI, (3) absence of outcomes, outcomes not reported or qualitative results only, (4) no pre-/post-test assessment, (5) no variable linked to PERMA(H) model, (6) literature review or books, and (7) grey literature (e.g., dissertations or conference proceedings). Studies on positive parenting programs (i.e., Triple P) have also been excluded as their efficacy has already been widely demonstrated (e.g., [ 51 , 52 ]). The screening process was conducted in Excel sheets; no automation tools were used. For the entire process, the two authors discussed discrepancies between their respective independent evaluation until they reached consensus.

In line with inclusion criteria, data extraction was carried out using the PICOTS method [ 53 ] and performed in a double-blind manner by listing, in an Excel sheet, the following categories: authors, year of publication, country, study objective, population characteristics (age, gender, and developmental characteristics), intervention (type and description), comparator (e.g., control group), method used, assessment instruments, time, setting, and outcomes in terms of effects of positive education intervention programs on children’s well-being. Thus, outcomes were subdivided with the PERMA(H) model dimensions (i.e., positive emotions, engagement, relationships, meaning, accomplishment, and health) [ 24 , 25 ]. The health dimension has been subdivided into five categories: mental health (i.e., behavior problems), emotional well-being, global well-being, self-esteem, and life satisfaction. The discrepancies were discussed and recoded by both authors in order to reach full agreement.

3.1. Identification of Eligible Studies

As presented in the PRISMA flow diagram ( Figure 1 ), the initial literature searches returned 762 papers, and 715 results remained after removing duplicates. Title and summary screening led to the removal of 685 articles not meeting the inclusion criteria, mostly due to population ineligibility (>6 years old), nonrelevant content and form (e.g., conference paper, review, language other than English, or incorrect reference). A total of 30 studies were included by the title and abstract screening for text eligibility. Discrepancies in eligibility were discussed between the authors until a consensus was reached. Finally, 27 publications did not fit the inclusion criteria (e.g., population characteristics, absence of PPI, review or theoretical, or no English language), and only three studies remained for review.

PRISMA flow diagram. * Reasons for exclusion included ineligible population (n = 15), ineligible intervention (n = 9), outcomes not reported (n = 3), other (irrelevant content, n = 1; ineligible language, n = 1; inadequate methodology, n = 1). Adapted from Moher et al. [ 54 ].

The following sections present the detailed results according to the PICOTS method for intervention studies reviews [ 53 ]. The population of the studies is described first, followed by the characteristics of the interventions, including information on comparator, time, and setting. This is followed by details on key methods of the studies (e.g., research design and assessment instrument). Finally, results in terms of the effects of positive psychology interventions on young children’s well-being are reported using the variables related to the PERMA(H) model.

3.2. Populations of the Studies Reviewed

Table 1 provides an overview of the selected studies in terms of country, sample, and intervention characteristics. The selected studies were conducted in three different countries (Netherlands, the USA, and Israel) and published in the last decade (2013 to 2020). Sample characteristics indicate that only one study included a specific sample of young children (<6 years; 315 children aged 3–6 years [ 55 ]). The other two included a broader age range of 4–12 years [ 56 , 57 ]. Results of Elfrink et al. [ 56 ] are discriminated for children from grade 1 (4–5 years) to grade 3 (6–7 years), with n = 32 remaining after parental permission, but not for preschoolers in grades 1 and 2 only. No such distinction is provided in the study by Owens and Patterson [ 57 ]. This finding reflects the current paucity of research focusing solely on preschoolers. Only one study included other populations in its sample, namely, parents and school staff [ 56 ]. In all selected studies, the ratio of girls to boys is well balanced (see Table 1 ). Finally, all three studies used a community sample without mentioning or focusing on children with special educational needs.

Overview of the positive psychology intervention programs.

3.3. Interventions, Comparators, Time and Research Settings

The positive psychology interventions of the three selected studies are very different in terms of content, duration, target population, and settings. An overview of the PPI characteristics is described in Table 1 . Their commonalities are highlighted further below.

In terms of setting, and as they did not target a unique preschool population, Owens and Patterson [ 57 ] recruited participants from suburban schools of middle- to lower-middle-class population. Elfrink et al. [ 56 ] selected two schools (rural/urban) in Netherlands in line with previous recommendations of “whole-school approach to positive education for primary schools” (p. 216). Shoshani and Slone [ 55 ] selected 12 demographically similar preschool classrooms in a central city in northern Israel (same geographic area). Regarding duration, one study [ 57 ] was conducted over a few weeks, comprising four to six intervention sessions. The other two studies [ 56 , 57 ] were conducted over a school year, with a consequently larger number of sessions.

Although only two studies [ 55 , 56 ] explicitly refer to the PERMA(H) Seligman’s [ 24 ] model, all three examined how PPIs are applied to the educational context. We can argue that the dimensions studied in each study can fit into one or more areas of the PERMA(H) model. The emotional dimension is taken into account in most studies as an essential aspect of young children’s well-being. As shown in Table 1 , the positive emotion of empathy is developed in one program [ 55 ] and the positive emotion of gratitude in two programs [ 55 , 57 ]. Engagement was worked on in two of the three programs considered [ 55 , 56 ], positive relationships in one program [ 55 ] and accomplishment in two [ 55 , 57 ]. Meaning has not been addressed in these programs.

3.4. Key Methods of the Reviewed Studies

With the aim of examining the effects of an intervention, the selected studies all used a longitudinal research design (see Table 2 ). All included at least two measurement points, pre- and post-intervention, at intervals of a few weeks [ 57 ] or several months within the same school year [ 55 , 56 ]. No study included an additional midpoint measurement or a delayed post-test follow-up measurement point. Two of the selected studies opted for a quasi-experimental design. The study by Shoshani and Slone [ 55 ] compared 160 children in the intervention condition to 155 children in demographically similar control classes. Owens and Patterson [ 57 ] compared three groups: an intervention group (gratitude condition), an alternative intervention group (best possible selves’ condition), and a control group. Elfrink et al. [ 56 ] did not include a control group. However, it is the only study to adopt a whole-school approach and to present a mixed-method research design by adding qualitative interviews with parents and school staff to quantitative measures.

Methodological characteristics, measures, and outcome summary of positive psychology interventions on children’s well-being.

To measure outcomes associated with children’s well-being through variables related to the PERMA(H) model, a number of reliable assessment instruments were used ( Table 2 ), either self-reports in individual interviews with the researchers or parent report: The Positive and Negative Affect Scale for Children (PANAS-C [ 58 , 59 ]) [ 55 , 57 ], a modified version of the Brief Multidimensional Students’ Life Satisfaction Scale (BMSLSS [ 60 ]), the Affective Situations Test for Empathy (FASTE [ 61 ]), the Perceived Competence Scale for Children (PCS-C [ 62 ]) to measure self-esteem, and the Kiddy Health-Related Quality of Life for children questionnaire (KINDL-R [ 63 ]) to measure children’s well-being. Shoshani and Slone [ 55 ] also assessed children behavioral regulation with the Head-to-Toes task (HTKS [ 64 ]) as well as children’s learning behaviors with the Approaches to Learning Scale (ALS [ 65 ]). Some instruments were modified in several ways to be completed by children, particularly preschoolers (e.g., items were read aloud; wording was simplified; the response scale was reduced; the Likert scale was pictured; see, e.g., [ 55 ]). The student–teacher relationship was completed by teachers in Elfrink et al.’s [ 56 ] study with the Leerkracht Leerling Relatie Vragenlijst (LLRV [ 66 ]). Finally, two of the selected studies [ 55 , 56 ] also assessed outcome variables related to the health dimension, namely, children’s emotional and behavioral functioning (or mental health), measured with the Strength and Difficulties Questionnaire (SDQ [ 67 ]) parent report form.

3.5. PPIs’ Effects on Preschool Children’s Well-Being

The results of the selected studies are summarized in Table 2 . They are presented according to the PERMA(H) model, with the exception of the dimension meaning, as none of the selected studies reported outcomes on it.

3.5.1. Positive Emotions

Two studies examined the impact of PPIs on positive and negative affect [ 55 , 57 ]. Their results differ slightly. Owens and Patterson [ 57 ] found no effect on positive affect in any condition (i.e., gratitude, best possible selves condition, or control condition). However, they showed that children as young as 5 or 6 years old were able to experience and to express gratitude for a variety of people or events (i.e., through drawing and explaining what they were grateful for).

Although neither Owens and Patterson [ 57 ] nor Shoshani and Slone [ 55 ] found any effects of the PPIs on negative emotions, Shoshani and Slone [ 55 ] found a significant increase in positive emotions after the intervention in the experimental group, especially empathy, as reported by the children themselves and their parents.

3.5.2. Engagement

The positive impact of the PPIs on children’s engagement in classroom activities is supported by qualitative evidence reported by teachers in the study by Elfrink et al. [ 56 ]. Teachers also reported how the program led them to improve their ability to observe and support students’ engagement. Similarly, teachers in the study by Shoshani and Slone [ 55 ] reported a significant increase in children’s engagement in the intervention group (medium effect size) but not in the control group. However, they did not find a significant impact of the PPI on children’s self-regulation.

3.5.3. Relationships

Shoshani and Slone [ 55 ] reported a significant increase of pro-social behaviors in the intervention group, but not in the control group. Although pro-social behaviors can lead to more positive relationships, no significant change in the overall teachers’ perception of his or her relationship with the students was found between pre- and post-intervention in Elfrink et al.’s [ 56 ] study. However, subscale scores revealed that such PPIs lead to significant improvements in teachers’ closeness with students, including experiences of enhanced affection, warmth, and communication. In addition, in this study, parents reported a positive impact of the intervention on the overall school climate (large effect size), with a significant improvement in subscale scores such as “supportive cooperation and active learning”, “forbidding physical punishment and violence”, “not tolerating bullying, harassment and discrimination” and “promoting equal opportunities and participation in decision-making”.

3.5.4. Accomplishment

This dimension is examined very differently in two of the selected studies. Elfrink et al. [ 56 ] reported that the PPIs improved teachers’ awareness of students’ skills and talents, which are known to be critical for achievement. In Shoshani and Slone’s [ 55 ] study, teachers reported improved preschool functioning in the intervention group (i.e., positive learning behaviors: enthusiasm for learning, attention, autonomy, and persistence).

3.5.5. Health—Well-Being

Several studies measured the PPIs’ effect on children’s overall well-being. Elfrink et al. [ 56 ] reported a positive impact on children’s self-report of well-being, with a larger effect for younger children on health-related quality of life (p. 224). Owens and Patterson [ 57 ] as well as Shoshani and Slone [ 55 ] measured children’s well-being through life satisfaction. Unlike Owens and Patterson [ 57 ], who found no significant effect of the intervention on life satisfaction (regardless of condition), Shoshani and Slone [ 55 ] found a significant increase in children’s life satisfaction in the intervention group but not in the control group. Regarding self-esteem, which is often acknowledged as a predictor of later health condition, Owens and Patterson [ 57 ] found a significant increase over time in the best possible selves condition, but not in the gratitude or control condition.

Two studies also examined the effect of PPIs on well-being in terms of mental health (i.e., behavior problems) measured with the SDQ. In this case, results also differ. Elfrink et al. [ 56 ] noted a positive impact of the intervention on behavior problems, including hyperactivity, emotional problems, and relationship problems. However, although the total difficulty score showed a significant decrease between pre- and post-intervention (medium to large effect size), no significant changes were found in the subscales. These quantitative results were confirmed by qualitative evidence from teachers about the positive impact of PPI on children’s positive behavior. Parents also reported a decrease in behavioral difficulties. On the contrary, Shoshani and Slone [ 55 ] found no change in mental health difficulties after the intervention. They found a significant effect of time in both the experimental and control groups i.e., a decrease in mental health difficulties.

3.5.6. Moderator Effects

All selected studies controlled for moderator effects. Among different variables (e.g., age, educational level, gender, family, and socio-economic status), only age and gender had significant effects. Shoshani and Slone [ 55 ] reported that age was the only variable found to be significantly correlated with one outcome variable (conduct problems). They also found that children’s age was positively related to higher subjective well-being and lower mental health problems at the beginning of their study. As for Owens and Patterson [ 57 ], their results indicated age effects on the frequency of realistic possible selves responding (i.e., declining with age). Finally, Elfrink et al. [ 56 ] reported larger effects of the PPI on quality of life (health-related variable) in younger children than in older ones.

In Owens and Patterson’s [ 57 ] study, gender was found to have significant effects in that girls reported realistic best possible selves more frequently than boys did. Without having a significant effect on the outcome variables, Shoshani and Slone [ 55 ] also found a gender difference, with boys having more behavior problems and fewer positive and negative emotions than girls.

4. Discussion

The aim of this review was to examine the effects of positive psychology interventions on the well-being of young children (<6 years), both in the context of preschool education with educators or teachers and in the family context with parents. Our review produced only three articles that met our criteria. However, the results of the selected studies provide preliminary support for the usefulness of PPIs in improving the well-being of young children. Before discussing these main findings in relation to the PERMA(H) model, we first present a general discussion based on the descriptive characteristics of the selected studies. The main limitations of our study will then be discussed, and implications for practice and future research will be highlighted.

4.1. Overall Discussion

Descriptive characteristics from the three selected studies indicate firstly that empirical interest in the effects of PPIs on young children is relatively recent, with the first published study dating from 2013. More importantly, these studies are extremely rare and have been conducted exclusively in school or after-school settings, but not in family settings. Moreover, the selected studies only included typically developing young children aged 3–6 years, leaving out children aged 0–2 years and children with special educational needs. This is consistent with previous meta-analyses of the effects of PPIs, indicating that positive psychology interventions appear to be most applicable with adults and youth in late childhood and adolescence, as well as in the educational context, and less so in the family context. In a related area, Renshaw and Olinger Steeves [ 39 ] also reported a very small number of studies examining the effects of gratitude-based interventions among young people (i.e., n = 5). However, several explanatory hypotheses deserve to be addressed.

First of all, one of the necessary conditions for implementing PPIs in schools and other educational settings (e.g., daycare) is to have trained educators, notably because they can be the connection between school and families [ 68 ]. For parents, being trained allows them to contribute and reinforce at home the message that children learn at school [ 12 ]. However, positive psychology is not yet well developed in the initial training of teachers or educators, implying that researchers in this field have to train participants, which takes time and resources. The intervention design of two of the selected studies [ 55 , 56 ] in our review included teacher and/or parent training in positive psychology or related area. Training in positive psychology prior to or concurrent with the intervention led to several advantages. Elfrink et al.’s [ 56 ] qualitative results indeed indicated that both teachers and parents positively assessed the professional workshops they attended. In particular, teachers reported the importance of training focusing on practical strategies, guidelines, and activity-based resources to support the roll out (and the perpetuation) of positive psychology interventions (p. 225). The results of this pilot study also showed that over the course of the school year, the positive education program was gradually integrated into daily school activities and teachers were better able to understand their role in the program and to continually provide children with positive psychology-based activities. This highlights the importance of ongoing training for adults in such programs as well as the continued integration of positive psychology practices into daily school activities and routine educational practices, as has already been demonstrated for SEL programs [ 69 , 70 ]. However, more than specific knowledge and teaching strategies acquisition, there is a strong need for changes in educators’ attitudes, beliefs, and values [ 12 ].

Secondly, the lack of research on the effect of PPIs on the well-being of young children may be because preschools and kindergartens place a disproportionate emphasis on the cognitive aspects of being ready for school, while it would also be important to focus on well-being aspects (e.g., social and emotional aspects) from an early age [ 71 ].

Thirdly, this may also be due to the fact that measuring the effects of PPIs involves the administration of self-report questionnaires before and after the intervention. Yet, as reported by Park and Peterson [ 3 ], self-report questionnaires are a limitation in research with very young children due to their level of language development and cognitive maturation. Similarly, the aspects of “meaning” are difficult to include in interventions with young children [ 72 ] if researchers are to apply all of the five conditions of the PERMA model to assess well-being.

Gratitude interventions are also questionable in young children, despite Owens and Patterson [ 57 ] finding that children under 7 years of age are cognitively mature enough to experience and express it. Indeed, gratitude appears to be a process that is developing over several years and consolidating by mid-childhood [ 73 ], making the effectiveness of gratitude interventions unclear in the scientific literature, particularly for younger children [ 39 ].

Finally, another possible explanation for this lack of studies on the effects of positive psychology interventions on young children’s well-being is that positive psychology is a recent scientific discipline (emerging in the early 2000s). Chodkiewicz and Boyle [ 43 ] also point out that “it will not be until the discipline has matured and researchers are able to carry out more comprehensive and longitudinal research studies, along with extensive meta-analyses, that the research field will begin to see the full potential of school-based positive psychology programmes” (p. 72). In line with this hypothesis, the paucity of PPIs for young children can be due to the already widespread use of SEL programs [ 70 ], which also provide systematic training in preschool settings on how to support children’s social-emotional development and improve their self-regulation skills.

After these initial considerations, the following discussion is intended as a reasoned interpretation of the results obtained due to the main limitations of the study outlined hereafter.

4.2. PERMA(H) Outcomes

As mentioned in the introduction, the difficulty in finding a consensus definition of well-being can present significant barriers to the implementation and evaluation of programs aimed at improving well-being in schools (see, for example, [ 74 ]). Although the dimensions of the PERMA(H) model do not define well-being per se, they nevertheless represent constituent and measurable elements of well-being [ 30 , 75 ]. Indeed, these dimensions allowed us to not only select articles related to both positive psychology and well-being, but also to organize and aggregate research findings in this area.

Our findings on the PERMA(H) model outcomes point out that all different kinds of programs lead to positive effects on children’s overall well-being. Specifically, with regard to positive emotions, two studies [ 55 , 57 ] examined the impact of PPIs on positive and negative emotions. Yet, an increase in positive emotions in the experimental group was found in only one study [ 55 ], highlighting the potential of these programs to promote positive emotions in young children, but also its inconsistency. A possible explanation for this increase in positive emotions in the study by Shoshani and Slone [ 55 ] but not in Owens and Patterson’s [ 57 ] study relates to the different duration of the intervention, with the former lasting much longer (months) than the latter (weeks). While this is not consistent with the findings of Carr et al. [ 40 ] that, in general, short PPIs are more effective particularly in educational settings, it is consistent with the findings and recommendations of Weare and Nind [ 12 ] regarding school-based interventions of all kinds that promote child well-being. Another explanation for this discrepancy may lie in the nature or the format of the intervention. In the study by Shoshani and Slone [ 55 ], the intervention was run by preschool teachers, equipped with a manual containing practical and theoretical material on four modules addressing different themes, including a specific one on positive emotions. This is interesting considering that, as Villarreal et al. [ 76 ] reported, knowledge of the theory and usefulness behind a program positively influences teachers’ engagement in its implementation. In contrast, in Owens and Patterson’s [ 57 ] study, the intervention was managed by a research assistant. It could therefore be that teachers who are in constant contact with the pupils and have a strong relationship with them can influence students’ positive emotions more than a research assistant who barely knows the children can. Furthermore, teachers have the opportunity to be in contact with students even on school days when the intervention is not taking place, thus promoting the skills acquired during the intervention on an ongoing basis [ 77 ]. Finally, no significant effect on negative emotions was observed in the study by Shoshani and Slone [ 55 ]. According to the authors, this result can be explained by the duration of the program, which does not seem to be sufficient to learn how to manage negative emotions for such young children. Indeed, at this stage of life, the regulatory system is not yet developed. These are probably the same reasons why, in Owens and Patterson’s [ 57 ] study, gratitude interventions did not influence children’s negative affect.

Regarding the dimension of engagement, results from two studies showed that children’s engagement in school was reported by teachers to be improved by the intervention [ 55 , 56 ]. Although Elfrink et al. [ 56 ] did not find results using a control group, their findings are consistent with those of other studies that have also shown how implementing a school-wide positive education approach has positive effects on improving school engagement, as well as achievement and health [ 2 , 78 ]. This effect of PPIs is interesting considering (1) that preschoolers’ positive engagement promotes better attention and impulse control [ 79 , 80 ] and (2) that pupils’ cognitive and behavioral skills at the time of school entry can predict school engagement some years later [ 81 , 82 ]. Moreover, a continued focus on engagement allows children’s resources to be valued over their limitations, thereby improving their academic performance [ 83 ].

In terms of (positive) relationships , Elfrink et al. [ 56 ] found no significant improvement in the overall teacher–student relationship, but they did report a positive impact of the program on one subscale, namely, teachers’ closeness to children. Children’s behavior is notably predicted longitudinally by the quality of the teacher–child relationship [ 84 ]. Specifically, teachers who establish positive emotional connections with children create an environment that is conducive to children’s ability to self-regulate their behavior. The demands and supports available (e.g., provided by the teacher) within a preschool classroom also influence children’s ability to regulate their behavior, emotions, and thoughts [ 84 ]. Furthermore, Elfrink et al. [ 56 ] found a positive impact of the intervention on improving supportive cooperation, promoting equal opportunities, and participation in decision-making, elements that promote positive relationships. Their intervention also led to improvements in behaviors such as the prohibition of physical punishment and violence and the nontolerance of bullying, harassment, and discrimination.

Findings on accomplishment were found in two studies [ 55 , 56 ]. In the school context, this dimension includes achievement in different school areas [ 25 ]. Elfrink et al. [ 56 ] reported that teachers were more aware of students’ talents. Teachers in Shoshani and Slone’s [ 55 ] study reported greater positive approaches to learning (e.g., enthusiasm for learning, attention, persistence, and autonomy) among children in the experimental group than in the control group. Yet, attention and perseverance would have a mediating effect on the relationship between cognitive flexibility and school readiness [ 85 ]. According to Shoshani and Slone [ 55 ], a better preschool functioning “forms the base for a sense of achievement and acquisition of personal goals” and “lays the foundation for learning skills and engagement with learning, which are important qualities that will influence subsequent academic success” (p. 8). Similarly, these strengths are expected to enhance students’ flourishing in school, thereby increasing their satisfaction with school [ 86 ]. Achievement through the implementation of these positive psychology programs from an early age can be developed by working and reinforcing children’s talents (e.g., in music, drawing, or sport), thus paying more attention to the potential than to limitations of the pupils.

With regard to the health dimension, PPIs target health promotion by addressing quality of life and life satisfaction already in young children. In this respect, one of the selected studies showed significant increases in children’s self-report of life satisfaction [ 55 ] and another found a positive impact on younger children’s self-report of health-related quality of life [ 56 ]. In our view, these results enhance the likelihood that children who have participated in a positive psychology intervention will have positive emotional and social development as adults. This is also in line with the findings of Weare and Nind [ 12 ], who recommend that school-based interventions that promote mental health and prevent problems in schools start early with the youngest children, notably in order to develop social and emotional skills.

4.3. Key Limitations

Despite the large number of studies screened, the main limitation is the small number of studies that were ultimately selected (n = 3). This obstacle could have been overcome by conducting the literature search with other, less usual electronic databases, by including grey literature, or by using more creative search terms [ 87 ] (p. 239). However, as pointed out for meta-analyses [ 87 ], we argue that a very small number of studies selected for a systematic review can transparently indicate the empirical status of a research area. Indeed, it definitely highlights the need for further research on this topic, with young children (<6 years old) and especially in family and day-care settings. Moreover, when very few studies are selected and these have very different characteristics, the synthesis of results may be undefendable [ 87 ] (p. 241). Although this may be the case for our review, the numerous and precise inclusion and exclusion criteria and the use of a theoretical model (PERMA(H)) in the conduct of the literature review and in the extraction of data helped to present interesting results in a most transparent manner.

Another limitation is due to methodological concerns in the selected studies. For instance, one study did not use a control group, some have small sample size, and none of them used a delayed post-test follow-up measurement point. We can also point out the lack of assessment tools validated within children samples. In addition, studies including preschool and older children did not clearly identify preschool subsamples (<6 years old), which would allow the presentation of results differentiated by children’s age. Indeed, the effects of PPIs on young children may differ from those of older children, depending on the former’s ability to understand certain concepts (e.g., gratitude; [ 57 ]) or their ability to see things in a systematically positive way [ 56 , 63 ].

4.4. Practical Implications

The diversity of PPIs implemented in the three selected studies allows us to present some useful practical recommendations. Firstly, according to Elfrink et al. [ 56 ] and as discussed above, the implementation of PPI in educational settings needs to be combined with training for educators in order to have a synergistic effect on children. This training should address a number of issues, such as the explicit link between theory and practice or the possibility for school staff to “live” the tenets of positive education, i.e., the skills taught within a PPI [ 25 ] (p. 151). Thus, the training should provide educators with activity-based strategies and resources to implement theoretical concepts based on positive psychology in daily practice and to integrate them in the curriculum [ 25 ]. Training should also be ongoing in schools, especially in response to the specific needs of educators.

A whole-school framework, as in the study by Elfrink et al. [ 56 ], is also recommended to improve student well-being [ 12 , 31 , 42 , 78 ] and to embed positive education throughout the school system [ 2 , 25 ]. Indeed, this approach not only considers the curriculum and classroom setting, but also shapes the whole school, including its organization, relationships, physical environment, curriculum, and teaching practices [ 88 ].

Another practical implication concerns the duration of the intervention. Despite the paucity of selected studies and contrary to the findings of Carr et al. [ 40 ], longer interventions appear to be more effective, as has been shown for adult samples [ 31 , 35 ], for other school-based mental health interventions [ 12 ] or for intervention programs to improve well-being in the family context [ 89 ].

Finally, creative methods like drawing, role-playing, or child-to-adult dictation should be explored as means to implement PPIs [ 57 ]. For young children in particular, creative ways to develop character strengths can pave the way for promoting well-being [ 25 ].

4.5. Future Research

Taken together, our findings suggest that this topic is very poorly studied within young children. Further studies using robust research methods to explore the effectiveness of PPIs with large samples are therefore particularly needed [ 41 , 55 , 56 ], especially given the importance of schools benefiting from such programs to improve the emotional and social well-being of young children (see also [ 12 ]). Moreover, future research could benefit from building on the dimensions of the PERMA(H) model, as they provide a useful and valid conceptual framework not only for implementing practices (PPIs) that promote children’s (and more broadly teachers’ and school community’s) well-being, but also for evaluating their effectiveness, as demonstrated, for example, in Shoshani and Slone’s [ 55 ] study.

Authors also called for immediate impacts of PPIs as well as long-term effects on children, as well as on teachers and the school as a whole [ 25 , 56 ]. Monitoring the long-term effects of the seeds of positive education sown in early childhood with longitudinal studies is therefore a necessity [ 55 ] (p. 9). However, demonstrating the long-term benefits of PPIs cannot take place without greater monitoring of children’s well-being and increased research support for planned PPIs in educational settings, notably preschool. Thus, research in this area could also follow the example of research on the effects of SEL programs not only on general well-being but also on academic performance to help convince school policymakers and stakeholders to implement PPIs. Indeed, they should be able to see how well-being can benefit the development of academic skills [ 90 ] and see it as a complementary rather than a competitive goal [ 25 ].

In addition, all three selected studies used a community sample without focusing on children with special educational needs, despite the current international trend towards inclusive education. Although the PPI in Shoshani and Slone’s [ 55 ] study is integrative in nature and therefore relevant to children with diverse needs and backgrounds, future research could explore the impact of PPI also on young SEN children explicitly, including those with behavioral difficulties, using community and clinical samples. In comparison, the effects of social and emotional learning (SEL) programs in school, and notably among high-risk children, are already well demonstrated in a variety of areas such as achievement, misbehavior, and mental health (see e.g., [ 12 , 69 ]).

Moreover, it seems essential to construct and validate appropriate measurement tools for young children. In this regard, the adaptations made by Shoshani and Slone [ 55 ] are typical examples of avenues to be explored and validated. Finally, given that our results are only from three countries, it would be relevant to explore the effectiveness of these programs on young children’s well-being in other educational settings.

5. Conclusions

This article summarized 20 years of research on the impact of positive psychology interventions on the well-being of young children (<6 years). To our knowledge, this is the first systematic review of the literature to examine the effects of these programs on the well-being of such a young population. Despite encouraging results in various areas (e.g., positive emotions, social competency, and positive relationships), our review demonstrated the paucity of research examining positive psychology interventions for preschool children. As mentioned earlier, we believe that it is essential to integrate these interventions into existing programs (e.g., SEL) at an early stage so that they can play a positive role in children’s developmental trajectories. Indeed, early childhood is a time of major acquisitions in many areas, including social-emotional skills [ 91 ], which are known to support school readiness, help prevent later mental health issues [ 92 , 93 , 94 ], and to promote well-being, which in turn should be an indicator of school success [ 95 ]. Certainly, talking about well-being in terms of positive psychology (i.e., focusing on children’s resources rather than their limitations, [ 56 ]) is a shift in perspective. We are aware that there is much to be done, such as replicating and confirming the few existing studies, before clarifying the findings in the field of positive early childhood psychology [ 43 ] (p. 78).

Author Contributions

Conceptualization, V.B. and P.G.; methodology, V.B. and P.G.; software, Excel; validation, P.G. and V.B.; formal analysis, P.G. and V.B.; investigation, V.B. and P.G.; data curation 22 October 2020; writing—original draft preparation, V.B. and P.G.; writing—review and editing, V.B. and P.G.; visualization, P.G. and V.B.; supervision, V.B. and P.G.; project administration, V.B. and P.G. All authors have read and agreed to the published version of the manuscript.

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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50+ Research Topics for Psychology Papers

How to Find Psychology Research Topics for Your Student Paper

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Steven Gans, MD is board-certified in psychiatry and is an active supervisor, teacher, and mentor at Massachusetts General Hospital.

• Specific Branches of Psychology
• Topics Involving a Disorder or Type of Therapy
• Human Cognition
• Human Development
• Critique of Publications
• Famous Experiments
• Historical Figures
• Specific Careers
• Case Studies
• Literature Reviews
• Your Own Study/Experiment

Are you searching for a great topic for your psychology paper ? Sometimes it seems like coming up with topics of psychology research is more challenging than the actual research and writing. Fortunately, there are plenty of great places to find inspiration and the following list contains just a few ideas to help get you started.

Finding a solid topic is one of the most important steps when writing any type of paper. It can be particularly important when you are writing a psychology research paper or essay. Psychology is such a broad topic, so you want to find a topic that allows you to adequately cover the subject without becoming overwhelmed with information.

I can always tell when a student really cares about the topic they chose; it comes through in the writing. My advice is to choose a topic that genuinely interests you, so you’ll be more motivated to do thorough research.

In some cases, such as in a general psychology class, you might have the option to select any topic from within psychology's broad reach. Other instances, such as in an  abnormal psychology  course, might require you to write your paper on a specific subject such as a psychological disorder.

As you begin your search for a topic for your psychology paper, it is first important to consider the guidelines established by your instructor.

Research Topics Within Specific Branches of Psychology

The key to selecting a good topic for your psychology paper is to select something that is narrow enough to allow you to really focus on the subject, but not so narrow that it is difficult to find sources or information to write about.

One approach is to narrow your focus down to a subject within a specific branch of psychology. For example, you might start by deciding that you want to write a paper on some sort of social psychology topic. Next, you might narrow your focus down to how persuasion can be used to influence behavior .

Other social psychology topics you might consider include:

• Prejudice and discrimination (i.e., homophobia, sexism, racism)
• Social cognition
• Person perception
• Social control and cults
• Persuasion, propaganda, and marketing
• Attraction, romance, and love
• Nonverbal communication
• Prosocial behavior

Psychology Research Topics Involving a Disorder or Type of Therapy

Exploring a psychological disorder or a specific treatment modality can also be a good topic for a psychology paper. Some potential abnormal psychology topics include specific psychological disorders or particular treatment modalities, including:

• Eating disorders
• Borderline personality disorder
• Seasonal affective disorder
• Schizophrenia
• Antisocial personality disorder
• Profile a  type of therapy  (i.e., cognitive-behavioral therapy, group therapy, psychoanalytic therapy)

Topics of Psychology Research Related to Human Cognition

Some of the possible topics you might explore in this area include thinking, language, intelligence, and decision-making. Other ideas might include:

• False memories
• Speech disorders
• Problem-solving

Topics of Psychology Research Related to Human Development

In this area, you might opt to focus on issues pertinent to  early childhood  such as language development, social learning, or childhood attachment or you might instead opt to concentrate on issues that affect older adults such as dementia or Alzheimer's disease.

Some other topics you might consider include:

• Language acquisition
• Media violence and children
• Learning disabilities
• Gender roles
• Child abuse
• Prenatal development
• Parenting styles
• Aspects of the aging process

Do a Critique of Publications Involving Psychology Research Topics

One option is to consider writing a critique paper of a published psychology book or academic journal article. For example, you might write a critical analysis of Sigmund Freud's Interpretation of Dreams or you might evaluate a more recent book such as Philip Zimbardo's  The Lucifer Effect: Understanding How Good People Turn Evil .

Professional and academic journals are also great places to find materials for a critique paper. Browse through the collection at your university library to find titles devoted to the subject that you are most interested in, then look through recent articles until you find one that grabs your attention.

Topics of Psychology Research Related to Famous Experiments

There have been many fascinating and groundbreaking experiments throughout the history of psychology, providing ample material for students looking for an interesting term paper topic. In your paper, you might choose to summarize the experiment, analyze the ethics of the research, or evaluate the implications of the study. Possible experiments that you might consider include:

• The Milgram Obedience Experiment
• The Stanford Prison Experiment
• The Little Albert Experiment
• Pavlov's Conditioning Experiments
• The Asch Conformity Experiment
• Harlow's Rhesus Monkey Experiments

Topics of Psychology Research About Historical Figures

One of the simplest ways to find a great topic is to choose an interesting person in the  history of psychology  and write a paper about them. Your paper might focus on many different elements of the individual's life, such as their biography, professional history, theories, or influence on psychology.

While this type of paper may be historical in nature, there is no need for this assignment to be dry or boring. Psychology is full of fascinating figures rife with intriguing stories and anecdotes. Consider such famous individuals as Sigmund Freud, B.F. Skinner, Harry Harlow, or one of the many other  eminent psychologists .

Psychology Research Topics About a Specific Career

​Another possible topic, depending on the course in which you are enrolled, is to write about specific career paths within the  field of psychology . This type of paper is especially appropriate if you are exploring different subtopics or considering which area interests you the most.

In your paper, you might opt to explore the typical duties of a psychologist, how much people working in these fields typically earn, and the different employment options that are available.

Topics of Psychology Research Involving Case Studies

One potentially interesting idea is to write a  psychology case study  of a particular individual or group of people. In this type of paper, you will provide an in-depth analysis of your subject, including a thorough biography.

Generally, you will also assess the person, often using a major psychological theory such as  Piaget's stages of cognitive development  or  Erikson's eight-stage theory of human development . It is also important to note that your paper doesn't necessarily have to be about someone you know personally.

In fact, many professors encourage students to write case studies on historical figures or fictional characters from books, television programs, or films.

Psychology Research Topics Involving Literature Reviews

Another possibility that would work well for a number of psychology courses is to do a literature review of a specific topic within psychology. A literature review involves finding a variety of sources on a particular subject, then summarizing and reporting on what these sources have to say about the topic.

Literature reviews are generally found in the  introduction  of journal articles and other  psychology papers , but this type of analysis also works well for a full-scale psychology term paper.

Topics of Psychology Research Based on Your Own Study or Experiment

Many psychology courses require students to design an actual psychological study or perform some type of experiment. In some cases, students simply devise the study and then imagine the possible results that might occur. In other situations, you may actually have the opportunity to collect data, analyze your findings, and write up your results.

Finding a topic for your study can be difficult, but there are plenty of great ways to come up with intriguing ideas. Start by considering your own interests as well as subjects you have studied in the past.

Online sources, newspaper articles, books , journal articles, and even your own class textbook are all great places to start searching for topics for your experiments and psychology term papers. Before you begin, learn more about  how to conduct a psychology experiment .

What This Means For You

After looking at this brief list of possible topics for psychology papers, it is easy to see that psychology is a very broad and diverse subject. While this variety makes it possible to find a topic that really catches your interest, it can sometimes make it very difficult for some students to select a good topic.

If you are still stumped by your assignment, ask your instructor for suggestions and consider a few from this list for inspiration.

• Hockenbury, SE & Nolan, SA. Psychology. New York: Worth Publishers; 2014.
• Santrock, JW. A Topical Approach to Lifespan Development. New York: McGraw-Hill Education; 2016.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Write down your thoughts and shred them to relieve anger, researchers say

Writing negative reactions on paper and shredding it or scrunching and throwing in the bin eliminates angry feelings, study finds

Since time immemorial humans have tried to devise anger management techniques.

In ancient Rome, the Stoic philosopher Seneca believed “my anger is likely to do me more harm than your wrong” and offered avoidance tips in his AD45 work De Ira (On Anger).

More modern methods include a workout on the gym punchbag or exercise bike. But the humble paper shredder may be a more effective – and accessible – way to decompress, according to research.

A study in Japan has found that writing down your reaction to a negative incident on a piece of paper and then shredding it, or scrunching it into a ball and throwing it in the bin, gets rid of anger.

“We expected that our method would suppress anger to some extent,” said Nobuyuki Kawai, lead researcher of the study at Nagoya University. “However, we were amazed that anger was eliminated almost entirely.”

The study, published in Scientific Reports on Nature , builds on research on the association between the written word and anger reduction as well as studies showing how interactions with physical objects can control a person’s mood. For instance, those wanting revenge on an ex-partner may burn letters or destroy gifts.

Researchers believe the shredder results may be related to the phenomenon of “backward magical contagion”, which is the belief that actions taken on an object associated with a person can affect the individuals themselves. In this case, getting rid of the negative physical entity, the piece of paper, causes the original emotion to also disappear.

This is a reversal of “magical contagion” or “celebrity contagion” – the belief that the “essence” of an individual can be transferred through their physical possessions.

Fifty student participants were asked to write brief opinions about an important social problem, such as whether smoking in public should be outlawed. Evaluators then deliberately scored the papers low on intelligence, interest, friendliness, logic, and rationality. For good measure, evaluators added insulting comments such as: “I cannot believe an educated person would think like this. I hope this person learns something while at the university.”

The wound-up participants then wrote down their angry thoughts on the negative feedback on a piece of paper. One group was told to either roll up the paper and throw it in a bin or keep it in a file on their desk. A second group was told to shred the paper, or put it in a plastic box.

Anger levels of the individuals who discarded their paper in the bin or shredded it returned to their initial state, while those who retained a hard copy of the paper experienced only a small decrease in their overall anger.

Researchers concluded that “the meaning (interpretation) of disposal plays a critical role” in reducing anger.

“This technique could be applied in the moment by writing down the source of anger as if taking a memo and then throwing it away,” said Kawai.

Along with its practical benefits, this discovery may shed light on the origins of the Japanese cultural tradition known as hakidashisara ( hakidashi sara refers to a dish or plate) at the Hiyoshi shrine in Kiyosu, just outside Nagoya. Hakidashisara is an annual festival where people smash small discs representing things that make them angry. The study’s findings may explain the feeling of relief that participants report after leaving the festival, the paper concluded.

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April 2, 2024

Eclipse Psychology: When the Sun and Moon Align, So Do We

How a total solar eclipse creates connection, unity and caring among the people watching

By Katie Weeman

Students observing a partial solar eclipse on June 21, 2020, in Lhokseumawe, Aceh Province, Indonesia.

NurPhoto/Getty Images

This article is part of a special report on the total solar eclipse that will be visible from parts of the U.S., Mexico and Canada on April 8, 2024.

It was 11:45 A.M. on August 21, 2017. I was in a grassy field in Glendo, Wyo., where I was surrounded by strangers turned friends, more than I could count—and far more people than had ever flocked to this town, population 210 or so. Golden sunlight blanketed thousands of cars parked in haphazard rows all over the rolling hills. The shadows were quickly growing longer, the air was still, and all of our faces pointed to the sky. As the moon progressively covered the sun, the light melted away, the sky blackened, and the temperature dropped. At the moment of totality, when the moon completely covered the sun , some people around me suddenly gasped. Some cheered; some cried; others laughed in disbelief.

Exactly 53 minutes later, in a downtown park in Greenville, S.C., the person who edited this story and the many individuals around him reacted in exactly the same ways.

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When a total solar eclipse descends—as one will across Mexico, the U.S. and Canada on April 8—everyone and everything in the path of totality are engulfed by deep shadow. Unlike the New Year’s Eve countdown that lurches across the globe one blocky time zone after another, the shadow of totality is a dark spot on Earth that measures about 100 miles wide and cruises steadily along a path, covering several thousand miles in four to five hours. The human experiences along that path are not isolated events any more than individual dominoes are isolated pillars in a formation. Once that first domino is tipped, we are all linked into something bigger—and unstoppable. We all experience the momentum and the awe together.

When this phenomenon progresses from Mexico through Texas, the Great Lakes and Canada on April 8, many observers will describe the event as life-changing, well beyond expectations. “You feel a sense of wrongness in those moments before totality , when your surroundings change so rapidly,” says Kate Russo, an author, psychologist and eclipse chaser. “Our initial response is to ask ourselves, ‘Is this an opportunity or a threat?’ When the light changes and the temperature drops, that triggers primal fear. When we have that threat response, our whole body is tuned in to taking in as much information as possible.”

Russo, who has witnessed 13 total eclipses and counting, has interviewed eclipse viewers from around the world. She continues to notice the same emotions felt by all. They begin with that sense of wrongness and primal fear as totality approaches. When totality starts, we feel powerful awe and connection to the world around us. A sense of euphoria develops as we continue watching, and when it’s over, we have a strong desire to seek out the next eclipse.

“The awe we feel during a total eclipse makes us think outside our sense of self. It makes you more attuned to things outside of you,” says Sean Goldy, a postdoctoral fellow at the department of psychiatry and behavioral sciences at Johns Hopkins University.

Goldy and his team analyzed Twitter data from nearly 2.9 million people during the 2017 total solar eclipse. They found that people within the path of totality were more likely to use not only language that expressed awe but also language that conveyed being unified and affiliated with others. That meant using more “we” words (“us” instead of “me”) and more humble words (“maybe” instead of “always”).

“During an eclipse, people have a broader, more collective focus,” Goldy says. “We also found that the more people expressed awe, the more likely they were to use those ‘we’ words, indicating that people who experience this emotion feel more connected with others.”

This connectivity ties into a sociological concept known as “collective effervescence,” Russo and Goldy say. When groups of humans come together over a shared experience, the energy is greater than the sum of its parts. If you’ve ever been to a large concert or sporting event, you’ve felt the electricity generated by a hive of humans. It magnifies our emotions.

I felt exactly that unified feeling in the open field in Glendo, as if thousands of us were breathing as one. But that’s not the only way people can experience a total eclipse.

During the 2008 total eclipse in Mongolia “I was up on a peak,” Russo recounts. “I was with only my husband and a close friend. We had left the rest of our 25-person tour group at the bottom of the hill. From that vantage point, when the shadow came sweeping in, there was not one man-made thing I could see: no power lines, no buildings or structures. Nothing tethered me to time: It could have been thousands of years ago or long into the future. In that moment, it was as if time didn’t exist.”

Giving us the ability to unhitch ourselves from time—to stop dwelling on time is a unique superpower of a total eclipse. In Russo’s work as a clinical psychologist, she notices patterns in our modern-day mentality. “People with anxiety tend to spend a lot of time in the future. And people with depression spend a lot of time in the past,” she says. An eclipse, time and time again, has the ability to snap us back into the present, at least for a few minutes. “And when you’re less anxious and worried, it opens you up to be more attuned to other people, feel more connected, care for others and be more compassionate,” Goldy says.

Russo, who founded Being in the Shadow , an organization that provides information about total solar eclipses and organizes eclipse events around the world, has experienced this firsthand. Venue managers regularly tell her that eclipse crowds are among the most polite and humble: they follow the rules; they pick up their garbage—they care.

Eclipses remind us that we are part of something bigger, that we are connected with something vast. In the hours before and after totality you have to wear protective glasses to look at the sun, to prevent damage to your eyes. But during the brief time when the moon blocks the last of the sun’s rays, you can finally lower your glasses and look directly at the eclipse. It’s like making eye contact with the universe.

“In my practice, usually if someone says, ‘I feel insignificant,’ that’s a negative thing. But the meaning shifts during an eclipse,” Russo says. To feel insignificant in the moon’s shadow instead means that your sense of self shrinks, that your ego shrinks, she says.

The scale of our “big picture” often changes after witnessing the awe of totality, too. “When you zoom out—really zoom out—it blows away our differences,” Goldy says. When you sit in the shadow of a celestial rock blocking the light of a star 400 times its size that burns at 10,000 degrees Fahrenheit on its surface, suddenly that argument with your partner, that bill sitting on your counter or even the differences among people’s beliefs, origins or politics feel insignificant. When we shift our perspective, connection becomes boundless.

You don’t need to wait for the next eclipse to feel this way. As we travel through life, we lose our relationship with everyday awe. Remember what that feels like? It’s the way a dog looks at a treat or the way my toddler points to the “blue sky!” outside his car window in the middle of rush hour traffic. To find awe, we have to surrender our full attention to the beauty around us. During an eclipse, that comes easily. In everyday life, we may need to be more intentional.

“Totality kick-starts our ability to experience wonder,” Russo says. And with that kick start, maybe we can all use our wonderment faculties more—whether that means pausing for a moment during a morning walk, a hug or a random sunset on a Tuesday. In the continental U.S., we won’t experience another total eclipse until 2044. Let’s not wait until then to seek awe and connection.