Related Experiment Video
Updated: Sep 23, 2025

Author Spotlight: Exploring Microglial Interactions with Stress-Response Circuitry Using the Limited Bedding and Nesting Model
Published on: July 12, 2024
Regression to the mean in latent change score models: an example involving breastfeeding and intelligence
Kimmo Sorjonen1, Gustav Nilsonne2,3,4, Michael Ingre2,5,6
1Department of Clinical Neuroscience, Karolinska Institutet, 171 77, Stockholm, Sweden. kimmo.sorjonen@ki.se.
Background:
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. Earlier observational studies have identified a positive association between breastfeeding and child intelligence, even when adjusting for maternal intelligence.
Method:
In the present study, we investigate regression to the mean in the case of breastfeeding and intelligence of children. 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).
Results:
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.
Conclusions:
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.
More Related Videos
Related Concept Videos
Regression Toward the Mean
Environmental Influences on Intelligence
Biological Influences on Intelligence
Measures of Intelligence
Validity refers to how well a test measures what it claims to measure. An intelligence test should accurately assess intelligence rather than another characteristic, like anxiety. Criterion validity is one way to evaluate this;...
z Scores and Area Under the Curve
Multiple Regression
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...

