Reliability and Validity Evidences of Tej Emotional and Behavioral Problem Checklist (TEJ-CL) for Child Mental Health Assessment in Nepal

Abstract

 Introduction: The Tej Emotional and Behavioral Problem Checklist (TEJ-CL) was developed in Nepalese context to aid assessment of childhood emotional and problems of children. This study aimed to evaluate TEJ-CL's factor structure, reliability, and validity evidences as an add-on and
symptom monitoring test.
Methods: This cross-sectional validation study included guardians of 320 children (age 5–17 years) from tertiary mental health centers in Kathmandu as referred group, along with 601 children from two schools (private and community) in Kathmandu as non-referred group. IRC was obtained ethical approval (ref: 183 (6-11-E)2/073/074 and ref: 776). TEJ-CL, an 89-item parent-reported questionnaire, served as the index test, while referral status acted as the reference standard. Factor structure, internal consistency, test-retest/ cross-informant correlations and criterion validity evidence was assessed using principal component analysis, coefficient alpha, spearman's rank correlation and
linear regression models, respectively.
Results: Analysis was done using 179 referred and 412 non-referred individuals based on non-missing data. Principal component analysis in referred sample reduced the number of items of questionnaire to 65 from 89 and indicated six factors: externalizing behavioral issues, anxiety/worries, upset/ sadness, somatic concern, miscellaneous syndrome, and severe issues with coefficient alpha ranging from 0.62 to 0.95. As criterion validity evidence, referred children showed significantly higher scores than non-referred children across composite and factor scores, except for anxiety/worries factor. Similarly, regression analyses within the referred group demonstrated significant associations
between factor scores and specific diagnoses.
Conclusions: Reliability and validity estimate of questionnaire is comparable to similar empirically based scales. Future research should focus on assessing the tool's generalizability and improving discriminatory indexes.