This chapter reviews a broad emerging literature on research transparency and reproducibility. This recent literature finds that problems with publication bias, specification searching, and an inability to reproduce empirical findings create clear deviations from the scientific pillars of openness and transparency of research. These failings can also result in incorrect inferences.

Social science research is increasingly moving toward a model of open and accessible data. Accessibility opens possibilities of allowing secondary analysis, enhancing pedagogy, and supporting research transparency. This chapter argues that these benefits will accrue more quickly, and will be more significant and more enduing, if researchers make their data "meaningfully accessible," that is, when the data can be interpreted and analyzed by scholars far beyond those who generated them. Making data meaningfully accessible requires researchers to prepare data for sharing and to take advantage of a growing range of tools for publishing and preserving data.

New methods and tools have emerged over the past decade to address pervasive problems of publication bias, p-hacking, and lack of reproducibility. This chapter reviews some of these advances, considering the strengths and shortcomings of each. Meta-analysis, study registration, pre-analysis plans, improved disclosure policies, and open data are all considered.

Social psychology has undergone a crisis in which concerns about replicability have cast a specter of radical doubt over widely reported findings in the field. This chapter uses the crisis in social psychology as a case study to articulate some of the challenges surrounding replication that bedevil efforts to improve replicability more broadly in the social sciences. It does so with an eye toward policy implications, but with the caveat that research heterogeneity means that there are no simple prescriptions applicable to all fields or research methods.