Abstract
Objective: Eyberg Child Behavior Inventory (ECBI) is one of the most frequently used tools for measuring behavioral problems; however, no research has been done to evaluate its psychometric properties in Iran.
Method : The present study sought for exploring the factor structure and psychometric properties of the F-ECBI in an Iranian sample. A total of 495 mothers (mean age = 33.83 years; SD = 4.78) who reported behavioral problems in their children aged 3 to 12 years (mean age = 6.36 years; SD = 2.66) were selected via convenience sampling in 2018-2019. The psychometric properties of F-ECBI, including reliability (Cronbach’s alpha) and validity (exploratory and confirmatory factor analysis, and convergent validity) were assessed using SPSS version 25 and LISREL 8.80.
Results: By performing EFA on the first sample part (n = 360), the examination of scree plot supported a 3-factor or 4-factor solution, and pattern matrix resulted in a 3-factor structure. The factors were called as “behavioral problems related to oppositional defiant”, “behavioral problems related to inattentive”, and “behavioral problems related to conduct”, according to their content and the research. CFA was performed on the second part of the sample (n = 135) to test the fitness of the 3-factor solution. According to CFI (0.91), NFI (0.91), NNFI (0.90), IFI (0.91), PNFI (0.77), GFI (0.75) AGFI (0.70), PGFI (0.62) and chi-square (540.31) indexes, the model had acceptable fitness. Cronbach's alpha was employed to measure the internal consistency and it revealed to be at a good to excellent level (behavioral problems related to oppositional defiant = 0.88; behavioral problems related to inattentive = 0.84; behavioral problems related to conduct = 0.74). The 3-factors subscales were associated with total difficulties, internalizing and externalizing SDQ, indicating the good convergent validity of F-ECBI.
Conclusion: The F-ECBI has good psychometric properties in Iran and can be employed as a useful instrument for measuring children's behavioral problems.
Key Words: Behavior Problems, Children, Eyberg Child Behavior Inventory, Iran, Psychometric Properties, Parent Reports
Psychological, emotional, and behavioral disorders at a young age are a serious concern in the field of public health (1-3). The suffer the patient and his family experience, limited ability to achieve the common goals of academic and social life, performing below the optimal level, and imposing heavy costs on the community due to the need for care all convey the high importance of these disorders (2). Prevention of mental, emotional, and behavioral disorders at an early age is an effective investment (2, 4, 5). Among the advantages are prevention, lower costs of treatment, less suffering, fewer premature mortality, a happier family, and more successful people.
As a result, practical action in this area will lead to these benefits (2).
High levels of behavioral problems in children are a serious risk factor with negative outcomes at a older ages (6, 7). Some examples related to behavioral problems at an early age are crime, violence, and fight (8-10), substance and alcohol abuse (8, 11), subsequent mental disorders (12, 13), problems in academic performance (14-17), job and employment problems (18), increased mortality (19), and poor social and intimate relationships (20).
Despite the prevalence of behavioral and emotional problems during childhood and its significant contribution to future problems, there is a potential for lack of diagnosis and timely treatment (21).
Behavioral measurements for screening these behavioral problems in children and more quickly identifying those children with problems for referring them to interventions and treatments can be useful. There is a large number of instruments for identifying children's behavioral problems, but only a few have acceptable psychometric properties (3). One of the most widely used and valid measurements of behavioral problems is the Eyberg Child Behavior Inventory (ECBI), created by Eyberg and Pincus (22) to track these difficulties in 2-16 year-old children (22). This questionnaire contains 36 items on behavioral problems that are filled out by parents on 2 scales of “intensity” and “problem.” The scale of intensity measures how often a certain behavior occurs on a Likert scale ranging from “never” to “always”, and the parent can response with a “Yes” or “No” on the problem scale to report if this behavior is problematic to his/her or not.
Parents' reports on children's behavioral problems can be highly important due to their observation in a variety of situations over a long period of time, making them one of the most important sources for assessing children's emotional-behavioral symptoms (23). There are several measurements for evaluating behavioral problems in Iran and the most commonly used are the Child Behavior Checklist (CBCL), the Strengths and Difficulties Questionnaire (SDQ), and the Conner’s Comprehensive Behavior Rating Scale (CBRS). Being lengthy and taking considerable time to respond, low age range, difficulty in administration and scoring, having several separate forms for different age groups, and not being specific to behavioral problems are some of the limitations of some behavioral problem evaluation questionnaires. Shortness, fast administration and scoring, simple interpretation, and usability in different contexts are the distinguishing features of ECBI compared with other tools, such as CBCL and SDQ, which has made it a widely used measurement (24). In early diagnosis and intervention, early screening of the child's behavioral problems is essential, and ECBI is one of the best tools for primary screening of symptoms. The tool is also able to monitor and detect behavioral changes over time and provide information about achieving therapeutic goals, which make it a suitable tool for use in therapeutic interventions (25).
There are several translations of ECBI in different languages and it has been employed in various countries. The validity of this questionnaire have been examined in countries such as the United States (26-28), the Netherlands (3, 24, 29), Norway (30, 31), Sweden (3), Finland (32), Australia (33), South Korea (34), Spain (35) and Taiwan (36). In examining the psychometric properties and factorial structure of this questionnaire in different cultures, different results have been obtained. In the Netherlands (24) and Finland (32), for example, they have achieved a one-dimensional structure by conducting exploratory factor analysis (EFA), as the original version of the questionnaire suggests. However, Burns and Patterson (26, 27) achieved a 3-factor structure in the United States, and several studies were designed and implemented to test this 3-factor model (3, 28, 30), by using EFA and CFA which confirmed their 3-factor model. Moreover, an 8-factor structure was found in South Korea (34) and a 2-factor in Australia (33). Cronbach's alpha values showed high values representing good internal consistency of the measurement in Dutch (24), Swedish (3), Finish (32), South Korean (34), and Norwegian (31) samples, ranging from 0.91 to 0.95, except for Spanish (35) sample with the Cronbach's alpha of 0.73. However, no research in Iran has been done so far to test the validity and reliability of this questionnaire. Therefore, the goal of this study was exploring the psychometric properties of ECBI in Farsi.
Materials and Methods
Participants
We tried to assess the psychometric properties of the ECBI in the Iranian children aged 3-12 years old (based on mother's report) and the children at this age residing in Tehran were the target population of this cross sectional study. Considering the cultural-geographical distribution and different regions of Tehran, 495 participants from kindergartens and schools in Tehran were selected by convenience sampling method in 2018-2019. Districts of 2, 5, 4, 8, 11, 12, 17, and 18 were considered and from each district a kindergarten and a school were selected for sampling. The entry criteria for this research were having a child aged between 3 and 12 years and willingness to participate in the study.
Procedure
The ECBI was translated into Farsi; then, it was back translated to English by 2 independent translators. Subsequently, an expert panel consisting 3 psychologists, discussed their views on the questionnaire and made some changes. In the next step for initial evaluation, this version of the questionnaire was administered to 20 mothers who had children aged 3 to 12 years and their views on the items were considered. At the beginning of the study, the participants were informed about the privacy and confidentiality regulations, voluntarily participation, research objectives, research field, provision of the results if desired, the average time needed to complete the measurement, and its application for only research purposes. Participants filled out the ECBI, Strengths and Difficulties Questionnaire (SDQ), and informed consent in written form.
Ethics
The study achieved the approval of the ethics committee of the University of Social Welfare and Rehabilitation Sciences of Tehran with approval number of IR.USWR.REC.1397.075 on 24.09.2018.
Statistical Analyses
Children’s age ranged from 3 to 12 (mean age = 6.36 years; SD = 2.66). The participants showed the following proportions: 3 years old (16.6%), 4 years old (15.2%), 5 years old (12.1%), 6 years old (13.1%), 7 years old (10.7%), 8 years old (9.3%), 9 years old (6.7%), 10 years old (7.1%), 11 years old (5.3%), and 12 years old (4%). Also, 41.2% of the children were girls and 58.8% were boys; 51.1% were the only child, 37% were the first child, 2% were the middle child, and 9.9% were last child. Mothers with an age range of 21 to 56 participated (mean age = 33.83 years; SD = 4.78). Of them, 71.9% were housewives and 28.1% employed; 3.8% were undergraduates, 22.4% had high school diploma, 11.9% associate degree, 42.8% bachelor’s degree, 16.4% master’s holder, and 2.6% PhD and above.
No missing data protocols were used for EFA (ie, 1:10; (37)) because no data were missing for F-ECBI for the first sample part of the participants (ie, n = 360). Bartlett’s tests of sphericity and the Kaiser-Meyer-Olkin measure of sampling adequacy were employed to check the factor structure of the ECBI items indicating 0.60 or higher values were appropriate for factor analysis (38). EFA with principal axis and promax rotation was performed to test the factor structure of the F-ECBI. The scree plot determined the number of factors (39, 40), an examination of the Kaiser-Guttman criterion (41, 42). Minimal factor loadings were set to 0.32, per recommendations (43, 44). For the second part of the participants (n = 135), confirmatory factor analysis (CFA) was used to test the fit of the standard F-ECBI structure (45). RMSEA values were less than about 0.08 or below, the CFI, values of about >0.95, with acceptable fit >0.90 (46). Moreover, internal consistency of the F-ECBI was calculated using Cronbach’s alpha coefficients. Internal consistency standard is α ≥ 0.70 (47, 48). Furthermore, with respect to convergent validity, we used Pearson correlating (PCC) to examine the relationship between the F-ECBI subscales with SDQ subscales. Accordingly, in terms of the general guidelines for the magnitude of correlations in behavioral sciences (49), r values equal to 0.10, 0.30, and 0.50 could be viewed as small, moderate, and large correlations, in turn. We used Skewness and Kurtosis to examine the normality of the F-ECBI scores. Analyses were conducted using SPSS v.25, except for the CFA, which were conducted using LISREL version 8.80. Statistical significance level was at p < 0.05; all tests were 2-tailed.
Results
Exploratory Factor Analysis. EFA using the principal axis factoring with promax rotation indicated that the F-ECBI items were appropriate for factor analyses for participants (Table 1). Bartlett’s test of sphericity was significant (χ2 = 6427.022, df = 630, p < 0.001) and the Kaiser-Meyer-Olkin was 0.89. Additionally, examination of eigenvalues and a 3-factor solution was proposed by scree plot (Figure 1). The pattern matrix indicated that the items loaded strongly onto 3 factors (loading of 0.40 or higher). The items in factor 1 (ie, behavioral problems related to oppositional defiant; 2, 3, 5, 6, 7, 8, 9, 11, 12, 13, 14, 17, 18, 28, 29), Factor 2 (ie, behavioral problems related to inattentive; 20, 30, 31, 32, 34), and factor 3 (ie, behavioral problems related to conduct; 23, 25, 27) were reported (Figure 2). Furthermore, the other items had cross loading or low factor loading.
Table 1.
Exploratory Factor Analysis of the Farsi Version of Eyberg Child Behavior Inventory (F-ECBI) Items
| Item |
Factor 1
Oppositional defiant |
Factor 2
Inattentive |
Factor 3
Conduct |
|---|---|---|---|
| 13 Has temper tantrums | 0.765 | -0.088 | 0.148 |
| 12 Get angry when doesn’t get own way | 0.758 | -0.047 | 0.017 |
| 7 Refuses to go to bed on time | 0.702 | -0.079 | -0.145 |
| 6 Slow in getting ready for bed | 0.696 | -0.058 | -0.159 |
| 17 Yells or screams | 0.673 | -0.146 | 0.266 |
| 18 Hits parents | 0.584 | -0.068 | 0.021 |
| 3 Has poor table manners | 0.581 | -0.030 | -0.029 |
| 14 Sasses adults | 0.543 | -0.086 | 0.225 |
| 2 Dawdles or lingers at mealtime | 0.527 | -0.084 | -0.226 |
| 11 Argues with parents about rules | 0.492 | 0.186 | 0.100 |
| 5 Refuses to do chores when asked | 0.476 | 0.215 | 0.094 |
| 9 Refuses to obey until threatened with punishment | 0.468 | 0.184 | 0.164 |
| 8 Does not obey house rules on own | 0.424 | 0.230 | 0.161 |
| 29 Interrupts | 0.423 | 0.269 | -0.132 |
| 28 Constantly seeks attention | 0.418 | 0.187 | 0.018 |
| 34 Has difficulty concentrating on one thing | -0.206 | 0.912 | 0.021 |
| 31 Has short attention span | -0.161 | 0.890 | 0.033 |
| 30 Is easily distracted | -0.072 | 0.810 | -0.009 |
| 32 Fails to finish tasks or projects | 0.097 | 0.643 | -0.012 |
| 20 Is careless with toys and other objects | -0.015 | 0.567 | 0.021 |
| 25 Verbally fights with sisters and brothers | -0.204 | -0.084 | 0.869 |
| 27 Physically fights with sisters and brothers | -0.210 | -0.025 | 0.855 |
| 23 Teases or provokes other children | 0.129 | 0.191 | 0.416 |
| 1 Dawdles in getting dressed | 0.422 | 0.200 | -0.351 |
| 10 Acts defiant when told to do something | 0.440 | 0.357 | -0.020 |
| 4 Refuses to eat food presented | 0.379 | -0.024 | -0.044 |
| 35 Is overactive or restless | 0.374 | 0.218 | 0.006 |
| 16 Cries easily | 0.357 | 0.018 | 0.012 |
| 15 whines | 0.334 | 0.230 | -0.005 |
| 19 Destroys toys and other objects | 0.072 | 0.371 | 0.062 |
| 22 Lies | -0.020 | 0.361 | 0.252 |
| 21 Steals | 0.044 | 0.117 | 0.222 |
| 24 Verbally fights with friends own age | 0.042 | 0.222 | 0.386 |
| 26 Physically fights with friends own age | 0.116 | 0.168 | 0.339 |
| 33 Has difficulty entertaining self alone | 0.222 | 0.396 | -0.162 |
| 36 Wets the bed | 0.094 | 0.108 | -0.012 |
| Eigenvalues | 10.28 | 1.85 | 1.34 |
| % Explained variance | 28.56 | 5.16 | 3.75 |
Factor loading and eigenvalues obtained using principal axis factoring with promax rotation. Factor loading ≥0.40 in boldface. Cross loading and low factor loading in Italic.
Figure 1.
Scree Plot of the Farsi Version of Eyberg Child Behavior Inventory (F-ECBI) Factors
Figure 2.
Factor Loading of the Farsi Version of Eyberg Child Behavior Inventory (F-ECBI) Factors in Confirmatory Factor Analysis (CFA)
Confirmatory Factor Analysis. Although RMSEA (ie, 0.09) was higher than 0.08, the 3-factor solution altogether using CFA provided relatively acceptable to the data (CFI = 0.91, NFI = 0.91, NNFI = 0.90, IFI = 0.91, PNFI = 0.77, GFI = 0.75, AGFI = 0.70, PGFI = 0.62, chi-Square (df) = 540.31 (227), p < 0.0001).
Internal Consistency and Intercorrelations. Cronbach’s alpha values ranged from 0.74 (Factor 3; Behavioral problems related to conduct) to 0.88 (Factor 1; Behavioral problems related to oppositional defiant) exhibiting good to excellent internal consistency of the F-ECBI. Furthermore, all correlations among the F-ECBI subscales were small to medium. Further, the data of F-ECBI scores were normal as Skewness and Kurtosis were between ±2 (Table 2 for more information).
Table 2.
Internal Consistency, Descriptive, Correlations, Skewness and Kurtosis among Farsi Version of Eyberg Child Behavior Inventory (F-ECBI) Subscales
| ECBI Subscales | Internal Consistency | Means (SD) | 1 | 2 | 3 |
|---|---|---|---|---|---|
| 1. Factor 1 Behavioral problems related to oppositional defiant |
0.88 | 48.17 (16.18) | |||
| 2. Factor 2 Behavioral problems related to inattentive |
0.84 | 13.60 (7.08) | 0.61*** | ||
| 3. Factor 3 Behavioral problems related to conduct |
0.74 | 5.11 (4.38) | 0.25** | 0.24** | |
| Skewness | 0.755 | 0.992 | 1.099 | ||
| Kurtosis | 0.235 | 0.286 | 0.398 |
p<0.001,
p<0.01
Convergent Validity. The Pearson correlation (PCC) indicated that the F-ECBI subscales were positively associated with total difficulties, and internalizing and externalizing SDQ scales (Table 3).
Table 3.
Pearson Correlations between Farsi Version of Eyberg Child Behavior Inventory (F-ECBI) and Strengths and Difficulties Questionnaire (SDQ) Subscales
| Total difficulties 1 | Externalizing | Internalizing | |
|---|---|---|---|
| Factor 1. Behavioral problems related to oppositional defiant | 0.67 | 0.63 | 0.54 |
| Factor 2. Behavioral problems related to inattentive | 0.69 | 0.70 | 0.47 |
| Factor 3. Behavioral problems related to conduct | 0.38 | 0.42 | 0.21 |
Note: All correlations are significant at p<0.0001.
. Summed externalizing and internalizing scores.
Discussion
The present work was carried out to evaluate the psychometric properties and factor structure of the Farsi version the Eyberg Child Behavior Inventory (F-ECBI). By performing EFA on the first sample part (n = 360), the pattern matrix of scree plot indicated that the items loaded strongly on 3 meaningful factors. The factors were named “behavioral problems related to oppositional defiant”, “behavioral problems related to inattentive”, and “behavioral problems related to conduct”, according to their content and the research. CFA was performed on the second part of sample (n = 135) to test the fitness of 3-factor solution. According to CFI, NFI, NNFI, RMSEA, and chi-Square indexes, CFA had acceptable fitness. To assess internal consistency, Cronbach’s alpha was calculated and the results indicated good to excellent values. Three factors of F-ECBI were associated with total difficulties, internalizing and externalizing SDQ subscales that indicated good convergent validity.
The analyses confirmed convergent validity and internal consistency for this instrument. These results were approved the findings of other researchers in other countries such as Norway (31), Sweden (3), Korea (34), Australia (33), Taiwan (36), and the Netherlands (24). The results of EFA indicated 3 significant factors (behavioral problems related to oppositional defiant, behavioral problems related to inattentive, and behavioral problems related to conduct), which were from a clinical viewpoint meaningful factors. In the CFA, these 3 factors were confirmed. The developer of this measure has considered its intensity subscale to be unidimensional (22); however, there are disagreements and various studies have achieved different results. Some studies have backed the unidimensional structure of this inventory (24, 32, 50, 51), while others found more than one factor in its structure (3, 26-28, 30, 33, 34). Having subscales for identifying behavioral problems in children enables us to select more homogenous sample of children with similar symptoms in each subscale. Another problem could be that if our aim is to choose the children for parenting interventions with high levels of oppositional defiant behavior, a higher score on behavioral problems related to oppositional defiant subscale is better than high scores in ECBI. Besides, behavioral problems related to conduct can be considered as severity of the problem. Compared to having just one total score, subscales would be more sensitive to interventions and can indicate changes on different domains of problems (27). In general, considering the 3-factor structure of this instrument, having 3 separate conceptual subscales can be more useful for diagnosis and comparison in research and intervention, compared to a general score of behavioral problems.
Limitations
Despite obtaining good reliability and validity of the F-ECBI in the present study, the current work also had some limitations. For example, since a part of the sample was not accessible for retesting, conducting a test-retest reliability was not possible in this study. Also, a high percentage of the sample was between the ages of 3 and 8, which requires further research on older children to achieve acceptable generalizability of the results. The assessment of the psychometric properties of the measurement in the clinical population and its comparison with the results of nonclinical population in further research can also be beneficial. This study was conducted on the sample of Tehran by using convenient sampling method, which is not representative of general population. Replication of this study in larger samples in Iran is also recommended.
Conclusion
The F-ECBI has good reliability and validity for use in Iran. Such research allows accessing instruments for measuring children's problems in cultural context. Besides, in reviewing the factor structure of this inventory, 3 subscales were explored, while the original version is unidimensional.
Acknowledgment
We thank all the mothers for their participation in this study.
Also, we appreciate Dr Mahmood Dehghani, Dr Forough Edrissi, and Dr Samaneh Ghafoori for reviewing the inventory and their comments.
Conflict of Interest
None.
References
- 1.Brauner CB, Stephens CB. Estimating the prevalence of early childhood serious emotional/behavioral disorders: challenges and recommendations. Public Health Rep. 2006;121(3):303–10. doi: 10.1177/003335490612100314. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.O’Connell ME, Boat T, Warner KE. Preventing mental, emotional, and behavioral disorders among young people: Progress and possibilities. Washington, DC: National Academies Press; 2009. [PubMed] [Google Scholar]
- 3.Axberg U, Johansson Hanse J, Broberg AG. Parents' description of conduct problems in their children - a test of the Eyberg Child Behavior Inventory (ECBI) in a Swedish sample aged 3-10. Scand J Psychol. 2008;49(6):497–505. doi: 10.1111/j.1467-9450.2008.00670.x. [DOI] [PubMed] [Google Scholar]
- 4.Cavaleri MA, Olin SS, Kim A, Hoagwood KE, Burns BJ. Family support in prevention programs for children at risk for emotional/behavioral problems. Clin Child Fam Psychol Rev. 2011;14(4):399–412. doi: 10.1007/s10567-011-0100-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Mejia A, Calam R, Sanders MR. A review of parenting programs in developing countries: opportunities and challenges for preventing emotional and behavioral difficulties in children. Clin Child Fam Psychol Rev. 2012;15(2):163–75. doi: 10.1007/s10567-012-0116-9. [DOI] [PubMed] [Google Scholar]
- 6.Burke JD, Waldman I, Lahey BB. Predictive validity of childhood oppositional defiant disorder and conduct disorder: implications for the DSM-V. J Abnorm Psychol. 2010;119(4):739–51. doi: 10.1037/a0019708. [DOI] [PubMed] [Google Scholar]
- 7.Reef J, Diamantopoulou S, van Meurs I, Verhulst F, van der Ende J. Predicting adult emotional and behavioral problems from externalizing problem trajectories in a 24-year longitudinal study. Eur Child Adolesc Psychiatry. 2010;19(7):577–85. doi: 10.1007/s00787-010-0088-6. [DOI] [PubMed] [Google Scholar]
- 8.Eklund JM, Klinteberg BA. Childhood behaviour as related to subsequent drinking offences and violent offending: a prospective study of 11- to 14-year-old youths into their fourth decade. Crim Behav Ment Health. 2003;13(4):294–309. doi: 10.1002/cbm.552. [DOI] [PubMed] [Google Scholar]
- 9.Murray J, Menezes AM, Hickman M, Maughan B, Gallo EA, Matijasevich A, et al. Childhood behaviour problems predict crime and violence in late adolescence: Brazilian and British birth cohort studies. Soc Psychiatry Psychiatr Epidemiol. 2015;50(4):579–89. doi: 10.1007/s00127-014-0976-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Vassallo S, Edwards B, Forrest W. Childhood behavior problems and fighting in early adulthood: What factors are protective? Journal of Criminal Justice. 2016;45:85–93. [Google Scholar]
- 11.Groenman AP, Janssen TWP, Oosterlaan J. Childhood Psychiatric Disorders as Risk Factor for Subsequent Substance Abuse: A Meta-Analysis. J Am Acad Child Adolesc Psychiatry. 2017;56(7):556–69. doi: 10.1016/j.jaac.2017.05.004. [DOI] [PubMed] [Google Scholar]
- 12.Kroes M, Kalff AC, Steyaert J, Kessels AG, Feron FJ, Hendriksen JG, et al. A longitudinal community study: do psychosocial risk factors and child behavior checklist scores at 5 years of age predict psychiatric diagnoses at a later age? J Am Acad Child Adolesc Psychiatry. 2002;41(8):955–63. doi: 10.1097/00004583-200208000-00014. [DOI] [PubMed] [Google Scholar]
- 13.Mesman J, Koot HM. Early preschool predictors of preadolescent internalizing and externalizing DSM-IV diagnoses. J Am Acad Child Adolesc Psychiatry. 2001;40(9):1029–36. doi: 10.1097/00004583-200109000-00011. [DOI] [PubMed] [Google Scholar]
- 14.Kremer KP, Flower A, Huang J, Vaughn MG. Behavior problems and children's academic achievement: A test of growth-curve models with gender and racial differences. Child Youth Serv Rev. 2016;67:95–104. doi: 10.1016/j.childyouth.2016.06.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Lewis GJ, Asbury K, Plomin R. Externalizing problems in childhood and adolescence predict subsequent educational achievement but for different genetic and environmental reasons. J Child Psychol Psychiatry. 2017;58(3):292–304. doi: 10.1111/jcpp.12655. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Loe IM, Feldman HM. Academic and educational outcomes of children with ADHD. J Pediatr Psychol. 2007;32(6):643–54. doi: 10.1093/jpepsy/jsl054. [DOI] [PubMed] [Google Scholar]
- 17.McLeod JD, Kaiser K. Childhood emotional and behavioral problems and educational attainment. American sociological review. 2004;69(5):636–58. [Google Scholar]
- 18.Erskine HE, Norman RE, Ferrari AJ, Chan GC, Copeland WE, Whiteford HA, et al. Long-Term Outcomes of Attention-Deficit/Hyperactivity Disorder and Conduct Disorder: A Systematic Review and Meta-Analysis. J Am Acad Child Adolesc Psychiatry. 2016;55(10):841–50. doi: 10.1016/j.jaac.2016.06.016. [DOI] [PubMed] [Google Scholar]
- 19.Scott JG, Giørtz Pedersen M, Erskine HE, Bikic A, Demontis D, McGrath JJ, et al. Mortality in individuals with disruptive behavior disorders diagnosed by specialist services - A nationwide cohort study. Psychiatry Res. 2017;251:255–60. doi: 10.1016/j.psychres.2017.02.029. [DOI] [PubMed] [Google Scholar]
- 20.Burke JD, Rowe R, Boylan K. Functional outcomes of child and adolescent oppositional defiant disorder symptoms in young adult men. J Child Psychol Psychiatry. 2014;55(3):264–72. doi: 10.1111/jcpp.12150. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Weitzman C, Wegner L. Promoting optimal development: screening for behavioral and emotional problems. Pediatrics. 2015;135(2):384–95. doi: 10.1542/peds.2014-3716. [DOI] [PubMed] [Google Scholar]
- 22.Eyberg S, Pincus D. Eyberg child behavior inventory (ECBI) & Sutter-Eyberg student behavior inventory-revised (SESBI-R) Professional manual Odessa, FL: Psychological Assessment Resources; 1999. [Google Scholar]
- 23.Howells Wrobel N, Lachar D. Validity of self‐and parent‐report scales in screening students for behavioral and emotional problems in elementary school. Psychology in the Schools. 1998;35(1):17–27. [Google Scholar]
- 24.Abrahamse ME, Junger M, Leijten PH, Lindeboom R, Boer F, Lindauer RJ. Psychometric Properties of the Dutch Eyberg Child Behavior Inventory (ECBI) in a Community Sample and a Multi-Ethnic Clinical Sample. J Psychopathol Behav Assess. 2015;37(4):679–91. doi: 10.1007/s10862-015-9482-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Rush Jr AJ, First MB, Blacker D. Handbook of psychiatric measures. American Psychiatric Pub; 2009. [Google Scholar]
- 26.Burns GL, Patterson DR. Factor structure of the Eyberg Child Behavior Inventory: Unidimensional or multidimensional measure of disruptive behavior? J Clin Child Adolesc Psychol. 1991;20(4):439–44. [Google Scholar]
- 27.Burns GL, Patterson DR. Factor structure of the Eyberg Child Behavior Inventory: a parent rating scale of Oppositional Defiant Behavior Toward Adults, Inattentive Behavior, and Conduct Problem Behavior. J Clin Child Psychol. 2000;29(4):569–77. doi: 10.1207/S15374424JCCP2904_9. [DOI] [PubMed] [Google Scholar]
- 28.Weis R, Lovejoy MC, Lundahl BW. Factor structure and discriminative validity of the Eyberg Child Behavior Inventory with young children. J Psychopathol Behav Assess. 2005;27(4):269–78. [Google Scholar]
- 29.Weeland J, van Aar J, Overbeek G. Dutch Norms for the Eyberg Child Behavior Inventory: Comparisons with other Western Countries. J Psychopathol Behav Assess. 2018;40(2):224–34. doi: 10.1007/s10862-017-9639-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Hukkelberg SS, Reedtz C, Kjøbli J. Construct validity of the eyberg child behavior inventory (ECBI) European Journal of Psychological Assessment. 2016 [Google Scholar]
- 31.Reedtz C, Bertelsen B, Lurie J, Handegård BH, Clifford G, Mørch WT. Eyberg Child Behavior Inventory (ECBI): Norwegian norms to identify conduct problems in children. Scand J Psychol. 2008;49(1):31–8. doi: 10.1111/j.1467-9450.2007.00621.x. [DOI] [PubMed] [Google Scholar]
- 32.Sorsa J, Fontell T, Laajasalo T, Aronen ET. Eyberg Child Behavior Inventory (ECBI): Normative data, psychometric properties, and associations with socioeconomic status in Finnish children. Scand J Psychol. 2019;60(5):430–9. doi: 10.1111/sjop.12541. [DOI] [PubMed] [Google Scholar]
- 33.Werba BE. Standardization and cross-cultural validity of the Eyberg Child Behavior Inventory with Australian preschoolers. Dissertation Abstracts International: Section B, The Sciences and Engineering. 2003;63:43–91. [Google Scholar]
- 34.Rhee ER, Rhee ES. Psychometric properties and standardization of the Korean version of the Eyberg Child Behavior Inventory. J Child Fam Stud. 2015;24(8):2453–62. [Google Scholar]
- 35.García-Tornel Florensa S, Calzada EJ, Eyberg SM, Mas Alguacil JC, Vilamala Serra C, Baraza Mendoza C, et al. [Eyberg inventory of child behavior. Standardization of the Spanish version and its usefulness in ambulatory pediatrics] An Esp Pediatr. 1998;48(5):475–82. [PubMed] [Google Scholar]
- 36.Chen YC, Fortson BL, Tiano JD. Establishing Norms on the Eyberg Child Behavior Inventory to Identify Disruptive Behaviors in Taiwanese Children. Child Fam Behav Ther. 2018;40(4):306–19. doi: 10.1080/07317107.2018.1522871. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Everitt BS. Multivariate analysis: the need for data, and other problems. Br J Psychiatry. 1975;126:237–40. doi: 10.1192/bjp.126.3.237. [DOI] [PubMed] [Google Scholar]
- 38.Tabachnick BG, Fidell LS, Ullman JB. Using multivariate statistics. Pearson Boston, MA: 2007. [Google Scholar]
- 39.Cattell RB. The Scree Test For The Number Of Factors. Multivariate Behav Res. 1966;1(2):245–76. doi: 10.1207/s15327906mbr0102_10. [DOI] [PubMed] [Google Scholar]
- 40.Floyd FJ, Widaman KF. Factor analysis in the development and refinement of clinical assessment instruments. Psychol Assess. 1995;7(3):286. [Google Scholar]
- 41.Guttman L. Some necessary conditions for common-factor analysis. Psychometrika. 1954;19(2):149–61. [Google Scholar]
- 42.Kaiser HF. The application of electronic computers to factor analysis. Educ Psychol Meas. 1960;20(1):141–51. [Google Scholar]
- 43.Tabachnick BG, Fidell LS, Ullman JB. Using multivariate statistics. Pearson Boston, MA: 2007. pp. 117–59. [Google Scholar]
- 44.Treiblmaier H, Filzmoser P. Exploratory factor analysis revisited: How robust methods support the detection of hidden multivariate data structures in IS research. Information & management. 2010;47(4):197–207. [Google Scholar]
- 45.Brown TA. Confirmatory factor analysis for applied research. Guilford publications; 2015. [Google Scholar]
- 46.Hu Lt, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct Equ Modeling. 1999;6(1):1–55. [Google Scholar]
- 47.George D, Mallery M. Using SPSS for Windows step by step: a simple guide and reference. 2003. [Google Scholar]
- 48.Nunnally JC. Psychometric Theory. 2d Ed. McGraw-Hill; 1978. [Google Scholar]
- 49.Cohen J. Statistical power analysis for the behavioral sciences. Hillsdale, NJ: Lawrence Earlbaum Associates; 1988. pp. 20–6. [Google Scholar]
- 50.Eyberg SM, Robinson EA. Conduct problem behavior: standardization of a behavioral rating. Journal of Clinical Child & Adolescent Psychology. 1983;12(3):347–54. [Google Scholar]
- 51.Robinson EA, Eyberg SM, Ross AW. The standardization of an inventory of child conduct problem behaviors. J Clin Child Adolesc Psychol. 1980;9(1):22–8. [Google Scholar]