The Methods/Data section can be strengthened by reporting weighted descriptive statistics that profile the sample's socioeconomic status (parental education and income, respondent AFQT/ability), racial and ethnic composition, and geographic distribution (Census region, urbanicity), alongside unweighted sample sizes for the NLSY79 and NLSY97 cohorts. Analyses should incorporate NLSY survey weights and design variables to produce design-correct standard errors and enhance generalizability. To reduce confounding in estimates linking educational attainment to marriage and first-birth outcomes, the study can implement discrete-time hazard models for timing outcomes and logistic or multinomial logistic regression for marital status at first birth, adjusting in stages for demographics, family-of-origin characteristics, ability, and time-varying enrollment or employment. Interaction terms (e.g., Education Cohort, Education Race/Ethnicity) can be used to test heterogeneity across groups. Model diagnosticssuch as checks for multicollinearity using variance inflation factors, assessment of influential cases, and presentation of marginal effects or predicted probabilitiesshould be reported. Multiple imputation can address missing data, and robustness checks (such as alternative education groupings, alternate age windows, and multiple-comparisons control) can be added. These practices adhere to standard guidelines on confounding control, dummy variable coding, interaction modeling,

The Methods/Data section can be strengthened by reporting weighted descriptive statistics that profile the sample's socioeconomic status (parental education and income, respondent AFQT/ability), racial and ethnic composition, and geographic distribution (Census region, urbanicity), alongside unweighted sample sizes for the NLSY79 and NLSY97 cohorts. Analyses should incorporate NLSY survey weights and design variables to produce design-correct standard errors and enhance generalizability. To reduce confounding in estimates linking educational attainment to marriage and first-birth outcomes, the study can implement discrete- time hazard models for timing outcomes and logistic or multinomial logistic regression for tharital status at first birth, adjusting in stages for demographics, family-of-origin characteristics, ability, and time-varying enrollment or employment. Interaction terms (e.g., Education * Cohort, Education * Race/Ethnicity) can be used to test heterogeneity across groups. Model diagnosticssuch as checks for multicollinearity using variance inflation factors, assessment of influential cases, and presentation of marginal effects or predicted probabilitiesshould be reported. Multiple imputation can address missing data, and robustness checks (such as alternative education groupings, alternate age windows, and multiple-comparisons control) can be added. These practices adhere to standard guidelines on confounding control, dummy variable coding, interaction modeling, and inference for categorical outcomes in social research (Hanneman, 2012)