Large-scale surveys typically exhibit data structures characterized by rich mutual dependencies between surveyed variables and individual-specific skip patterns. Despite high efforts in fieldwork and questionnaire design, missing values inevitably occur. One approach for handling missing values is to provide multiply imputed data sets, thus enhancing the analytical potential of the surveyed data. To preserve possible nonlinear relationships among variables and incorporate skip patterns that make the full conditional distributions individual specific, we adapt a full conditional multiple imputation approach based on sequential classification and regression trees. Individual-specific skip patterns and constraints are handled within imputation in a way ensuring the consistency of the sequence of full conditional distributions. The suggested approach is illustrated in the context of income imputation in the adult cohort of the National Educational Panel Study.
Background: Human populations differ in height. Recent evidence suggests that social networks play an important role in the regulation of adolescent growth and adult height. We further investigated the effect of physical connectedness on height.
Material and methods: We considered Switzerland as a geographic network with 169 nodes (district capitals) and 335 edges (connecting roads) and studied effects of connectedness on height in Swiss conscript from 1884–1891, 1908–1910, and 2004–2009. We also created exponential-family random graph models to separate possible unspecific effects of geographic vicinity.
Results: In 1884–1891, in 1908–1910, and in 2004–2009, 1st, 2nd and 3rd order neighboring districts significantly correlate in height (p < 0.01). The correlations depend on the order of connectedness, they decline with increasing distance. Short stature districts tend to have short, tall stature districts tend to have tall neighbors. Random network analyses suggest direct road effects on height. Whereas in 1884–1891, direct road effects were only visible between 1st order neighbors, direct road effects extended to 2nd and 3rd in 1908–1910, and in 2004–2009, also to 4th order neighbors, and might reflect historic improvements in transportation. The spatial correlations did not significantly change when height was controlled for goiter (1884–1889) and for median per capita income (2006), suggesting direct road effects also in goiter-allowed-for height and income-allowed-for height.
Conclusion: Height in a district depends on height of physically connected neighboring districts. The association decreases with increasing distance in the net. The present data suggest that people can be short because their neighbors are short; or tall because their neighbors are tall (community effect on growth). Psycho-biological effects seem to control growth and development within communities that go far beyond our current understanding of growth regulation.