This article describes how a large Institutional Research and Decision Support (IRDS) office engaged in efforts to promote culturally responsive and inclusive institutional research (IR) practices that take into account the increasingly diverse student, faculty, and staff populations we serve. We describe how we developed a diversity, equity and inclusion (D.E.I.) strategic plan that addressed the following: (a) ensuring our Mission Statement reflects our commitment to diversity; (b) applying an equity lens to our work; (c) supporting campus-wide D.E.I. efforts while helping the campus live out a commitment to D.E.I.; (d) implementing internal office practices to support the recruitment and retention of a diverse team; and (e) ensuring IR professionals have access to and opportunities to learn about current trends in equity and inclusion. Our approach was guided by Kendi's conceptualization of antiracism, and we examine how we have opportunities to make antiracist decisions in various aspects of our work, including how we frame IR, analyze and label data, and interpret results given local contexts and structural inequities.
American community colleges are typically public 2-year higher education institutions with a history and mission of providing educational access to the people in the communities in which they are located and serve. Nationally community colleges continue to become more diverse, providing increased access especially for the socioeconomically disadvantaged, women, and other marginalized groups (Malcom, 2012). Since community colleges continue to become more diverse, many have focused their attention on diversity, equity, and inclusion (D.E.I.) efforts. These efforts are impacted by the local community (location and culture). In this article, the researcher used geography of college choice and Braddock's perpetuation hypothesis as the frameworks for examining the impact of the community on community college campus diversity, as well as D.E.I. efforts. The researcher also provides recommendations including how to use data to navigate the impact of the community on the community college's diversity and D.E.I. efforts.
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 data that can guide discovery of equity gaps within the context of their own work, faculty and staff lack critical information. When educators use data to guide inquiry and action, they can check any assumptions or biases they may have as well as understand if a change they are making is effective in closing the gap or addressing the need. Data security and ethical and responsible use must be emphasized, but data democratization calls for intentional examination of institutional policies to ensure people have the information they need to identify, implement, and evaluate changes in their approaches to create more equitable outcomes. To advance the work of Equity 2030, Minnesota State is working to review policies and practices around data sharing, specifically with faculty and staff, as well as working to create resources and training to support ethical and appropriate use of data to support student success. This article will establish the current state of efforts at the system office level, including an upcoming multi-year transition to a new system-wide enterprise resource planning platform and complications arising from the centralized collection and maintenance of institution-level data within the system office, as well as outline the strategic and tactical approach to democratizing data while maintaining security and confidentiality.
While many campuses have access to and publish equity-focused data, it can be difficult to share that data with the campus community in a way that engages multiple offices. This article outlines the Equity Liaison Initiative at the University of Wisconsin–La Crosse, which engages individuals from most offices on campus with equity-focused data to affect change in their individual departments. We provide an overview of the development of the Equity Liaison Initiative, some lessons we learned through the process, discuss potential directions for the program and identify future challenges we expect to encounter.
Institutional research/effectiveness offices are instrumental in advancing diversity, equity, and inclusion (D.E.I.) efforts. Ideally they have the knowledge, perspective, and network within an institution to impact practice, process, policy, and planning in order to guide the institution toward a goal of being fully inclusive. With an understanding of D.E.I. terms within the institution and higher education, IR offices can provide a cohesive perspective and focus through both qualitative and quantitative data that can lead to shared definitions, a collective mindset, and ultimately actionable change.
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 advocating for sexual minority students.
As improving equity becomes prioritized in higher education, Offices of Institutional Research (OIRs) find themselves in a central position to identify and address educational inequities faced by racially minoritized students. However, their potential to serve as a catalyst for organizational change has yet to be fulfilled. In this study, we present a critical discourse analysis of mission statements to understand how these OIRs describe their function and purpose in the California Community Colleges system. Results are based on 108 reviewed statements. These results reveal a limited discourse around race and equity. None of the statements in our sample included the word race or any words stemming from it such as racism or racial disparity. The majority (86%) of statements omitted equity from their purpose, failing to describe how OIRs can serve to improve equitable outcomes in community college. Our work prompts the field to reimagine their role within the community college they serve by becoming race-conscious and equity-minded in the ways they articulate their role and function as major hubs of institutional data.
Complex adaptive systems (CASs) theory provides a framework for understanding how systems of multiple, independent, and intelligent agents interact with each other in a nested and overlapping set of environments to create both a whole that has an identity apart from any of its individual components as well as a setting in which simple cause-and-effect relationships are rarely linear or predictable. CASs are resilient and ever-evolving, adapting both to internal and external stimuli according to both tacit and explicit rule structures to move continually toward the better realization of an ideal state as defined by those rules. Managing change within CAS requires capitalizing on moments of disruption, sometimes referred to as the edge of chaos, to guide the system toward a different, more desired state. Higher education institutions and systems can easily be described as meeting the criteria for CAS, containing multiple layers of both hierarchical and collegial networks that seem at times to stubbornly resist transformative change, while simultaneously adapting and evolving. This article will use Minnesota State's Equity 2030 strategic priority and efforts to implement a shared understanding of data democratization as examples to explain the application of CAS theory to higher education and discuss how applying transition management approaches from the CAS literature has the potential to lead to long-term and sustainable structural change.
As institutions seek to shift into more advanced analytics and data-based decision-support, many institutional research offices face the challenge of meeting the office's current demands while taking on more intricate and specialized work to support decision-making. Given the great need organizations have for information that supports real-time strategic decision-making, institutions must advance beyond traditional static data reporting offices to modern offices with regular predictive analytics use. The authors believe that institutional research offices should actively engage in contemporary analytical approaches and provide leadership in this area. The following chapter focuses on (a) why higher education should embrace analytics, (b) discusses areas where analytical advancements have occurred, (c) discusses areas where analytical growth is lacking, and (d) provides guidance on addressing cultural changes concerning institutional data use, policies, and practices.
AbstractData analytics is increasingly important to the operations and strategic growth of higher education institutions. In September 2019, three higher education professional associations issued a rare joint statement calling for the accelerated investment and intensified efforts to develop and deploy data analytics in support of campus decision making. COVID‐19 disrupted the momentum generated by the joint statement. However, during the pandemic, many universities recognized data analytics as a critical asset to their response to the crisis. In this case study of Ohio State University's COVID‐19 dashboard development process, the authors demonstrate that the principles advocated by the joint statement are exactly the reasons that a successful and nationally recognized dashboard effort was possible. The case study shows that senior leadership support, strategic investment in the right technologies, and close collaboration among campus data analytics professionals are among the key factors of success.
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 education. These changes have had major effects on analytics as well. This chapter outlines how analytics can assist the academic side of colleges and universities to best serve all academic stakeholders, including administrators, faculty, and staff. It also addresses the evolving roles of Institutional Research Offices in helping set institutional strategies, collaborating across university offices to develop analytics, and cultivating an institutional culture of informed decision making. IR Offices should consider various facets of the process of collecting, analyzing, and communicating information to serve academic departments. One priority is determining which software, tools, and approaches best align with institutional priorities and needs. Another is creating specific analytics that serve the academic missions of and strategic planning for the institution and representing data in accessible ways to serve academic administration's diverse purposes and roles. Finally, cultivating buy-in and support from administration and other stakeholders is essential. IR offices can be instrumental in fostering academic colleagues' use, interpretation, and valuing of data and in developing collaborative relationships with academic leadership, colleges and departments. The new data analytics landscape requires expanding the roles and institutional reach of IR offices, thus creating opportunities for IR involvement in strategic planning and vision at high levels of their institutions.
Institutions have many unique cultures and organizational structures that can strengthen or inhibit the planning and implementation of initiatives to enhance data-informed decision-making, build supporting infrastructure, and align the necessary human resources. This chapter discusses different strategies for aligning resources and organizing Institutional Research and Business Intelligence functions. Additionally, the authors present the strengths, challenges, and unintended impacts of varying alignment options.
Abstract The depths of the impact of COVID‐19 on nearly every aspect of our personal and professional lives was becoming painfully clear in May and June of 2020. While institutions of higher education had implemented crisis/emergency plans in the immediate onset of the pandemic, and largely extended those efforts into summer terms, the fall 2020 term loomed large. Institutional researchers were asked for myriad kinds of data and reports, ostensibly to inform executive leaders’ decision‐making regarding how to operate for the fall term. On its face, this fits with the current conceptualization of IR's work largely as decision support. Despite these requests, however, the question remains as to what was really driving decision‐making about the fall. This research examined a random, representative sample of public statements regarding fall operations, often released in the name of a president or chancellor, to determine what factors these leaders espoused publicly as informing their fall plans.
Abstract This article explores how the global Coronavirus (COVID‐19) pandemic impacted United States higher education over the course of the first year from Spring 2020–Spring 2021. Utilizing a case study methodology, blending proprietary, closely held, public use, and archival data, we find that political and institutional factors shaped the decision to return to in‐person instruction or remain online in fall 2020, which in turn, influenced the enrollment and financial health of institutions in that semester and beyond. Although nearly every institution of higher education was impacted, we find that those most adversely impacted are 2‐year public and 4‐year private baccalaureate institutions. Despite considerable challenges resulting from COVID‐19, we highlight several practices of how systems and institutions are innovating and how this work can be replicated to positively impact higher education for years to come.
Abstract This article outlines the impacts of COVID‐19 on higher education and provides a contextual assessment of how higher education systems can provide support to institutions and stakeholders through a formulated planning process which helps identify, plan, and achieve strategic goals in response to fluctuating priorities.
Colleges have access to more data than ever before, and to unlock its full potential, the right culture, processes, and technologies are needed. Given the pressures now facing campuses, the demand for evidence-based approaches to strategy have intensified. As a result, more attention has been placed on the importance of the relationship between Senior Leaders and IR, which, if fully harnessed, can help to ensure the viability of institutions nationwide. Yet, recent surveys and literature suggest that for this relationship to thrive, more must be done to ensure that IR is considered as an important functional area at a time when data-informed decision-making has become increasingly vital. To that end, the purpose of this chapter is to offer ideas for how executives can champion data-informed decision-making and the ways in which IR functions must take on greater leadership through an integrated and collaborative approach focused on analytics.
Information Technology (IT) and Institutional Research (IR) units working together with planning, a focus on collaboration, and a willingness to create partnerships can help accelerate their institution's efforts to improve the data landscape. This chapter includes an overview of the challenges facing these two functional areas, the benefits of a new way of operating together, and the steps leaders from both IR and IT can take to work together to build new relationships across units and their staff. This work derives from a focus on values, beliefs, and actions. Taken together, these strategies, if implemented with care, can establish a solid foundation to support the need to expand, improve, and mature IR's role in the data landscape.
Human resources (HR) represent the most significant investment on most of our college campuses in terms of time and financial resources. The importance of good data about the workforce begins with establishing a need for a new position. That need for data continues with recruiting, hiring, training, promoting, retaining (or not), and retiring faculty and staff. Understanding how institutional research (IR) can provide evidence and decision support for this process should generate considerable returns for campus leadership with increased transactional efficiencies and enhanced strategic decisions. Identifying best practices and new approaches for collaboration between the functions involving human resources and institutional research benefits the institution, their stakeholders, and, ultimately, the faculty and staff.
Building a culture of data governance at a higher education institution involves collaboration across the entire institution. Before the creation of formalized roles to perform data management and data governance functions at colleges and universities, these functions were performed by traditional institutional research personnel. Documentation and management of data for analysis have, for a long time, been part of the institutional researcher's forte. However, a concentrated effort in data management and governance is necessary to transform traditional reporting into a dynamic analytic landscape complete with adequate definitions and documentation, and often this goes beyond the scope of today's institutional research offices. The following article offers a framework for developing a data governance program in a higher education setting and highlights the critical role that institutional research offices can play in helping to build institutional capacity for a well-documented and managed data landscape. Support and rationale for developing a program at your institution are offered, and helpful tips and pitfalls to avoid are provided.
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.
