In June 2019, Minnesota State set a critical goal: By 2030, eliminate the educational equity gaps at each of the 30 colleges and 7 universities that comprise the system. To achieve the goal of Equity 2030, and empower actors at every level of Minnesota State, system and campus leaders need to fully embrace data democratization. Without access to…

In this chapter, the author examines the extent to which community colleges invite students to share their sexual orientation and gender identity (SOGI) data, and the barriers to collecting such data. The author also underscores the important role that higher education professionals, particularly those in institutional research, can play in…

The increasing volume of information and the intense pace of its circulation are changing the ways universities access, use, analyze, and provide data. Many have championed the use of large-scale databases to track student admissions and retention, faculty productivity, student wellness, and other phenomena that shape our understandings of higher…

In this chapter, we explore the obligations for individuals and institutions that emerge from the newfound insights that are enabled through learning analytics. While ethical concerns are raised through learning analytics, a misplaced trend is a "do nothing" approach as a way to assure we "do no harm." We suggest that this is a…

As we become increasingly acquainted with the rich opportunities that analytics systems can provide, there is a commensurate need to consider the extent to which analytics tools are effectively integrated, with proper training, into the day-to-day functioning of higher education professionals. This chapter explores the extent to which predictive…

We discuss methods that can improve response rates in large-scale cross-sectional and longitudinal studies. Regardless of the specific study topic, sample member population, or method of contact, data collections should be designed with certain core principles in mind: legitimizing the study to prospective participants; presenting study…

In this chapter, we review the strengths of NCES survey data, provide an example of analyzing NCES survey data to explore the pathways between coursework in career and technical education in high school and postsecondary success, and offer suggestions for future data collection.

The federal government has collected data from colleges and universities for more than a century. However, how and what data are collected has evolved over time, as has the purposes for collecting those data. The most systematic collection of data happens through the Integrated Postsecondary Education Data System (IPEDS), which is required to be…

Dual enrollment is a complex policy issue and is increasingly of interest to state policymakers. Adequate data and data systems are critical to informing dual enrollment policy, yet the data at the federal level and many state data systems suffer from common challenges and lack the capacity to address dual enrollment policy. We review the…

Evidence-based decision-making requires access to good and reliable data. In recent years, there has been a tremendous increase of data available to institutions, from internal as well as external sources. With this increase in data and its analytics, new challenges and questions arise. One of the most pertinent of which is the ethics and best…

Over the past 20 years, many colleges and universities have made significant progress in building data warehouses and implementing business intelligence tools to provide better access to administrative data that were previously locked away in enterprise systems. However, the data architectures from the 1990s and 2000s are aging as new technologies…

This chapter provides examples of innovative methods and tools to collect, analyze, and report both quantitative and qualitative data in student affairs assessment.

Comprehensive data are essential to answer questions from prospective students, parents, and private and public entities about the cost of college and students' return on investment, as well as to demonstrate how colleges and universities are helping to prepare the future workforce. An evolutionary data-collection process, efforts to improve the…

This chapter will address the importance of intercampus involvement in reporting of gainful employment student-level data that will be used in the calculation of gainful employment metrics by the U.S. Department of Education. The authors will discuss why building relationships within the institution is critical for effective gainful employment…

This chapter provides institutional researchers a foundation to understand workforce data and how they can be accessed and used within an institutional research operating culture. Specific wage methodologies, earnings reporting, and future directions for using wage data are provided.