Research about students’ affective outcomes (such as attitudes) in statistics courses has proliferated over the past three decades, but much of this work has focused only on one component of statistics courses: the students. While it has provided important contributions to the research literature, extant work has not been able to answer questions about the impact of instructors and the learning environment on student attitudes. In data science education, research about students’ attitudes is nascent. Without a reliable way to measure how characteristics of courses and institutions are related to student and instructor attitudes, we cannot identify barriers to student success in statistics and data science–much less dismantle those barriers. The MASDER team has developed separate surveys assessing student attitudes toward statistics and data science, and instructor attitudes toward teaching statistics and data science. Additionally, we have created inventories for data collection on the classroom/learning environment. By collecting data nationally and triangulating these data sources, we can develop a robust picture of the current state of statistics and data science education in the United States; these instruments will be made publicly available for use to support similar international efforts. This presentation will provide an overview of the MASDER project, the differences between and potential uses of the six instruments and the validity evidence supporting the use of these instruments. It will also include a first look at findings from our national (US) data collection of student attitudes toward statistics and concrete examples of ways to visualize and explore this data. These instruments should help researchers and practitioners seeking to foster more inclusive statistics and data science education spaces by providing them with tools for measuring the nexus of the student attitudes, instructor attitudes, and the learning environment supported by validity evidence.