The effects of sample selection bias on racial differences in child abuse reporting

Sheila Ards, Chanjin Chung, Samuel L. Myers

    Research output: Contribution to journalArticlepeer-review

    82 Scopus citations


    Objective: The aim was to examine whether design features of Wave 1, 1980 National Incidence Study (NIS) data resulted in sample selection bias when certain victims of maltreatment were excluded. Method: Logistic regression models for the probability of child abuse reports to the child protective services (CPS) were estimated using maximum likelihood methods for Black (n = 511) and White (n = 2499) child abuse cases. The models were estimated with and without correction for selection bias using a two-step procedure proposed by Heckman. Results: Substantial differences were found in the characteristics of Black and White victims by source of report and by type of maltreatment. Also found were sizeable differences within each racial group between sampled agencies and nonsampled agencies. Sample selection bias affected the estimation of both White and Black child abuse reporting rates. In the Black sample, however, the effect of sample selection bias was to reduce the statistical significance of the impacts of reporting agency and physical and sexual abuse on report rates. In the White sample, most significant factors in the basic model remained statistically significant with correction for selection bias. Conclusions: Selection bias was found to be caused by the exclusion of family, friends, and neighbors in the NIS sample design. Such exclusion has the effect of altering the interpretation of the determinants of child abuse reporting among Blacks, but not among Whites. Thus, conclusions about racial differences in child maltreatment must be reached cautiously, given the NIS study design.

    Original languageEnglish (US)
    Pages (from-to)103-115
    Number of pages13
    JournalChild Abuse and Neglect
    Issue number2
    StatePublished - Feb 1998


    • CPS reporting
    • NIS
    • Racial disparities
    • Sample selection bias
    • Survey design


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