Background: Child maltreatment measurement has been a longstanding issue, with discrepancies across administrative records, parent-reports, and self-reports. One proposed solution is "triangulation," or integrating data from multiple reporters and sources. However, it remains unclear how best to operationalize this concept.
Objective: This study examines the concept of "triangulation" by employing different analytic methods to determine whether these methods reveal a common underlying construct of physical abuse and whether they predict adult depression.
Participants and setting: Data come from the Lehigh Longitudinal Study, a 40+ year prospective study that began in the 1970s with children ages 18 months to 6 years of age. Data were collected in early childhood, middle childhood, adolescence, and adulthood (ages 36 and 46, on average).
Methods: We applied five analytic approaches - network analysis, ordinary least squares (OLS) regression, structural equation modeling (SEM), latent profile analysis (LPA), and a cumulative index regression - to assess the relationships among multiple reporters of childhood physical abuse and adult depression.
Results: SEM best modeled the latent construct of physical abuse and significantly predicted adult depression, with adult self-reports playing a particularly strong role. Network analysis also highlighted strong intercorrelations among self-reports and meaningful links with depression.
Conclusion: SEM and network analysis were the most informative for triangulation and prediction of adult depression. Adult self-reports of abuse were most related and most predictive of adult depression.
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