Prenatal and Postnatal Predictors of Infant Emotion Regulation

Featuring variables from the iGrow study, including prenatal maternal distress, postnatal observed maternal sensitivity at 2 months, and observed infant positive and negative affect at 6 months. It also includes infant RSA at 6 months and covariates. The author is Yu Chen from Harvard Dataverse.

Use Cases

• Analyze the relationship between prenatal maternal distress and observed infant positive and negative affect at 6 months.
• Model the association between postnatal observed maternal sensitivity at 2 months and infant RSA at 6 months.
• Investigate how covariates moderate the link between prenatal predictors and infant emotion regulation outcomes.

Strengths

• Data includes longitudinal measures from prenatal to 6 months postnatal.
• Variables are based on observed behaviors (maternal sensitivity, infant affect) rather than self-report.
• The dataset is hosted on Harvard Dataverse, a reputable academic repository.

Limitations

• The sample size and number of rows are unknown, limiting assessment of statistical power.
• Specific column definitions and data formats are unavailable, complicating direct analysis.
• The geographic and demographic scope of the iGrow study is not specified, which may limit generalizability.

Provenance

Source

Harvard Dataverse

Collection Method

Data were drawn from the iGrow study.

Time Range

Data collection spans from prenatal period to at least 6 months postnatal.

Freshness

The dataset was last updated on 2026-02-20.