Complex functional brain network analyses have exploded over the last decade, gaining traction due to their profound clinical implications. The application of network science (an interdisciplinary offshoot of graph theory) has facilitated these analyses and enabled examining the brain as an integrated system that produces complex behaviors. While the field of statistics has been integral in advancing activation analyses and some connectivity analyses in functional neuroimaging research, it has yet to play a commensurate role in complex network analyses. Fusing novel statistical methods with network-based functional neuroimage analysis will engender powerful analytical tools that will aid in our understanding of normal brain function as well as alterations due to various brain disorders. Here we survey widely used statistical and network science tools for analyzing fMRI network data and discuss the challenges faced in filling some of the remaining methodological gaps. When applied and interpreted correctly, the fusion of network scientific and statistical methods has a chance to revolutionize the understanding of brain function.

While household panel surveys are longitudinal in nature cross-sectional sampling weights are also of interest. The computation of cross-sectional weights is challenging because household compositions change over time. Sampling probabilities of household entrants after wave 1 are generally not known and assigning them zero weight is not satisfying. Two common approaches to cross-sectional weighting address this issue: (1) “shared weights” and (2) modeling or estimating unobserved sampling probabilities based on person-level characteristics. We survey how several well-known national household panels address cross-sectional weights for different groups of respondents (including immigrants and births) and in different situations (including household mergers and splits). When a new person moves into a household, both “shared weights” and “modeling” lead to reduced individual weights of pre-existing household members, but differences due to the approach arise elsewhere. The implementation of “shared weights” is problematic when the panel contains households without a household member already present in wave 1. Panels also differ in the treatment of immigrants, household merges, and sometimes on how weights are assigned to children born to wave 1 panel members.