Social science fields have each taken their turn in the spotlight with instances of influential research that fell apart when scrutinized. Beyond deliberate fraud, there is growing evidence that much social science research features sloppy yet inadvertent errors, and a sense that many analyses produce statistically “significant” results only by chance. Due in part to a rising number of highly publicized cases, there is growing demand for solutions. A movement is emerging across disciplines towards greater research transparency, open science, and reproducibility. Researchers have developed new tools for combatting false positives and non-reproducible findings, as well as adapting approaches from medicine and other fields. For instance, more researchers are conducting meta-analyses, pushing to reform the journal peer review process to focus on good research design rather than on “sexy” results, and posting pre-specified hypothesis documents in public registries, all to curb rampant publication bias. New software tools make it easier to implement version control with dynamic documents that can reproduce an entire research workflow with a single mouse click, and data repositories are making it simple to download others’ data, encouraging the replication and extension of previous work. Our textbook is the first to fully synthesize these new methods. (The book is forthcoming with University of California Press, expected publication date: early 2019).