Eliciting truthful answers to sensitive questions is an age-old problem in survey research. Respondents tend to underreport socially undesired or illegal behaviors while overreporting socially desirable ones. To combat such response bias, various techniques have been developed that are geared...

The Clinical Data Interchange Standards Consortium (CDISC) is a globally relevant nonprofit organization that defines standards for handling data in clinical research. It produces a range of standards for clinical data at various stages of maturity. One of the most mature standards is the Study...

Multilevel analysis is the statistical modeling of hierarchical and nonhierarchical clustered data. These data structures are common in social and medical sciences. Stata provides the xtmixed, xtmelogit, and xtmepoisson commands for fitting multilevel models, but these are only relevant for...

Researchers typically spend significant amounts of time cleaning and labeling data files in preparation of analyses of survey data. Computer-assisted personal interviewing (CAPI) gives the ability to automate this process. First, consistency checks can be run during the interview so that only...

Any analysis with incomplete data makes untestable assumptions about the missing data, and analysts are therefore urged to conduct sensitivity analyses. Ideally, a model is constructed containing a nonidentifiable parameter d, where d = 0 corresponds to the assumption made in the standard...

In this talk we describe how to fit structural mean models (SMMs), as proposed by Robins, using instrumental variables in the generalized method of moments (GMM) framework using Stata's gmm command. The GMM approach is flexible because it can fit overidentified models in which there are more...

Stata already has an extensive range of built-in and user-written commands for analyzing xt (cross-sectional time-series) data. However, most of these commands do not take into account important features of the data relating to their time-series properties or cross-sectional dependence. This...

Measurement error in exposure variables can lead to bias in effect estimates, and methods that aim to correct this bias often come at the price of greater standard errors (and so, lower statistical power). This means that standard sample size calculations are inadequate and that, in general,...

Multiple imputation (MI) is known as an effective method for handling missing data. However, it is not clear that the method will be effective when the data contain a high percentage of missing observations on a variable. This study examines the effectiveness of MI in data with 10% to 80%...

Econometricians have begun to devote more attention to spatial interactions when carrying out applied econometric studies. In part, this is motivated by an explicit focus on spatial interactions in policy formulation or market behavior, but it may also reflect concern about the role of omitted...