Regression to the mean in latent change score models: an example involving breastfeeding and intelligence

Latent change score models are often used to study change over time in observational data. However, latent change score models may be susceptible to regression to the mean. In the present study, we investigate regression to the mean in the case of breastfeeding and intelligence of children. Earlier observational studies have identified a positive association between breastfeeding and child intelligence, even when adjusting for maternal intelligence. Here, we used latent change score modeling to analyze intergenerational change in intelligence, both from mothers to children and backward from children to mothers, in the 1979 National Longitudinal Survey of Youth (NLSY79) dataset (N = 6283). When analyzing change from mothers to children, breastfeeding was found to have a positive association with intergenerational change in intelligence, whereas when analyzing backward change from children to mothers, a negative association was found. These discrepant findings highlight a hidden flexibility in the analytical space and call into question the reliability of earlier studies of breastfeeding and intelligence using observational data.