  Proper statistical modeling incorporates domain theory about how concepts
relate and details of how data were measured. However, data analysts currently
lack tool support for recording and reasoning about domain assumptions, data
collection, and modeling choices in an integrated manner, leading to mistakes
that can compromise scientific validity. For instance, generalized linear
mixed-effects models (GLMMs) help answer complex research questions, but
omitting random effects impairs the generalizability of results. To address
this need, we present Tisane, a mixed-initiative system for authoring
generalized linear models with and without mixed-effects. Tisane introduces a
study design specification language for expressing and asking questions about
relationships between variables. Tisane contributes an interactive compilation
process that represents relationships in a graph, infers candidate statistical
models, and asks follow-up questions to disambiguate user queries to construct
a valid model. In case studies with three researchers, we find that Tisane
helps them focus on their goals and assumptions while avoiding past mistakes.
