The Role of Gender Norms on Intimate Partner Violence Among Newly Married Adolescent Girls and Young Women in India: A Longitudinal Multilevel Analysis.

Gender norms have been posited to impact intimate partner violence (IPV), but there is scant evidence of the longitudinal association between community-level gender norms and IPV. Using longitudinal data on 3,965 married girls surveyed in India, we fitted mixed-effects ordinal and binary logistic regression models for physical IPV intensity and occurrence of sexual IPV. We found a 26% increase in the odds that women experience frequent physical IPV per one unit increase in greater community-level equitable gender norms.

Role of marriage, motherhood, son preference on adolescent girls' and young women's empowerment: Evidence from a panel study in India.

Background: Marriage is a key determinant of health and well-being of adolescent girls and young women (AGYW) in India. It is a key life event in which girls move to their marital households, often co-residing with their in-laws and begin childbearing. The change in the normative environment in conjunction with cultural norms surrounding son preference influences women's overall life course.

Diagnosing and Handling Common Violations of Missing at Random.

Ignorable likelihood (IL) approaches are often used to handle missing data when estimating a multivariate model, such as a structural equation model. In this case, the likelihood is based on all available data, and no model is specified for the missing data mechanism. Inference proceeds via maximum likelihood or Bayesian methods, including multiple imputation without auxiliary variables.

Ignoring Non-ignorable Missingness.

The classical missing at random (MAR) assumption, as defined by Rubin (Biometrika 63:581-592, 1976), is often not required for valid inference ignoring the missingness process. Neither are other assumptions sometimes believed to be necessary that result from misunderstandings of MAR. We discuss three strategies that allow us to use standard estimators (i.

The Role of Conditional Likelihoods in Latent Variable Modeling.

In psychometrics, the canonical use of conditional likelihoods is for the Rasch model in measurement. Whilst not disputing the utility of conditional likelihoods in measurement, we examine a broader class of problems in psychometrics that can be addressed via conditional likelihoods. Specifically, we consider cluster-level endogeneity where the standard assumption that observed explanatory variables are independent from latent variables is violated.