Modelling the factor structure of the Child Depression Inventory in a population of apparently healthy adolescents in Nigeria.

Background: Childhood and adolescent depression is common and often persists into adulthood with negative implications for school performances, peer relationship and behavioural functioning. The Child Depression Inventory (CDI) has been used to assess depression among adolescents in many countries including Nigeria but it is uncertain if the theoretical structure of CDI appropriately fits the experiences of adolescents in Nigeria. This study assessed varying theoretical modelling structure of the CDI in a population of apparently healthy adolescents in Benue state, Nigeria.

Methods: Data was extracted on CDI scale and demographic information from a total of 1, 963 adolescents (aged 10-19 years), who participated in a state wide study assessing adolescent psychosocial functioning. In addition to descriptive statistics and reliability tests, Exploratory Factor Analysis (EFA) and Confirmatory Factor analysis (CFA) were used to model the underlying factor structure and its adequacy. The suggested new model was compared with existing CDI models as well as the CDI's original theoretical model. A model is considered better, if it has minimum Root Mean Square Error of Approximation (RMSEA<0.05), Minimum value of Discrepancy (CMIN/DF<3.0) and Akaike information criteria. All analyses were performed at 95% confidence level, using the version 21 of AMOS and the R software.

Results: Participants were 14.7±2.1 years and mostly male (54.3%), from Monogamous homes (67.9%) and lived in urban areas (52.2%). The measure of the overall internal consistency of the 2-factor CDI was α = 0.84. The 2-factor model had the minimum RMSEA (0.044), CMIN/DF (2.87) and least AIC (1037.996) compared to the other five CDI models.

Conclusion: The child depression inventory has a 2-factor structure in a non-clinical general population of adolescents in Nigeria. Future use of the CDI in related setting may consider the 2-factor model.