No abstract

This study seeks to determine the association between gender role attitudes about childcare, utilization of parental leave policies and parental/infant preferences, on the one hand, and the distribution of childcare in the families of assistant professors with children under two on the other. Both utilization of paid parental leave policies by men and men's belief in non-traditional gender roles are associated with higher levels of participation in parenting tasks. However, even those male professors who take leave and believe in nontraditional gender roles do much less childcare relative to their spouses than female professors do. This result holds even when the male professor's wife works full time. Our results suggest that one reason why female professors do more childcare may be that they like it more than men do. The association of enjoyment of childcare with gender role attitudes or leave-taking status is not statistically significant, which suggests that sex differences in the enjoyment of childcare will not be easily changed by changes in policies or gender role ideology. Accordingly, when exploring the stickiness of gender roles with respect to infant and toddler care, it would seem prudent to consider biological and evolutionary explanations as well as those focusing on institutions and gender ideology.

These findings suggest that this may be an innovative and promising new approach to improving asthma outcomes in this difficult-to-reach population.

Experimental designs that randomly assign entire clusters of individuals (e.g., schools and classrooms) to treatments are frequently advocated as a way of guarding against contamination of the estimated average causal effect of treatment. However, in the absence of contamination, experimental designs that randomly assign intact clusters to treatments are less efficient than designs that randomly assign individual units within clusters. The current article considers the case of contamination processes that tend to make experimental and control subjects appear more similar than they truly are. The article demonstrates that, for most parameter values of practical interest, the statistical power of a randomized block (RB) design remains higher than the power of a cluster randomized (CR) design even when contamination causes the effect size to decrease by as much as 10%–60%. Furthermore, from the standpoint of point estimation, RB designs will tend to be preferred when true effect sizes are small and when the number of clusters in the experiment is not too large, but CR designs will tend to be preferred when true effect sizes are large or when the number of clusters in the experiment is large.

Policy analysts involved in quantitative research have many options for handling missing data. The method chosen will often greatly influence the substantive policy conclusions that will be drawn from the data. The most frequent methods for handling missing data assume that the data are missing at random (MAR). The current paper notes that an omnibus, nonparametric test of the MAR assumption is impossible using the observed data alone. Nonetheless various purported tests of the missingness mechanism (including tests of MAR) appear in the literature. The current paper clarifies that all of these tests rely on some assumption that cannot be tested from the data. The paper notes that tests of the missingness mechanism are frequently misinterpreted and it clarifies the appropriate interpretation of such tests. Policy analysts are encouraged not to develop the false impression that modern procedures for handling missing data in conjunction with tests of the missingness mechanism provide protection against the ill effects of missing data. Any justification for a particular approach to handling missing data must be come from substantive knowledge of the missingness process, not from the data.