Developing a Measure of Belonging in Higher Education

Feeling a sense of belonging has been described as a fundamental human need (Baumeister & Leary, 1955), and its presence has been linked with a range of positive outcomes for students engaged in higher education. These include improved academic performance (Goodenow & Grady, 1993), persistence in studies (Mtshweni, 2024), increased retention (Kelly et al., 2024), and improved wellbeing (Bernat & Resnick, 2009). Despite its clear importance, prior research suggests that the conceptualisation of belonging may differ depending on the student and the context in which belonging is experienced (Fernández et al., 2023). The purpose of this study is to develop and evaluate a multidimensional measure of student belonging in higher education. Using self-report survey data from a large sample of university students, the study will explore student’s experiences of belonging, including feelings of inclusion, acceptance, connection and support on their university course. The present study follows guidelines for scale development as outlined by Boateng et al (2018). Initial item generation was informed by previous scales, a Delphi study and previous qualitative findings. This led to a conceptual framework of 5 domains: Recognition and Inclusion; Fitting In; Ease and Security within the Community; Perceived Care and Support within the Community and finally Relational Cohesion. Participants then completed a domain mapping survey to assess content relevance and coverage. Cognitive interviews were subsequently conducted to evaluate content relevance, representativeness and technical quality., and alignment with students lived experiences, informing further item refinement. At the current stage, survey administration is underway, and the present preregistration specifies the planned analytic approach. Exploratory factor analysis will be used to examine the underlying structure of the refined item set, followed by confirmatory factor analysis conducted in an independent sample to evaluate the resulting measurement model. Exploratory factor analysis (EFA) will be used to identify the structure of the proposed set of items. Confirmatory factor analysis (CFA) will be used to evaluate the measurement model in an independent sample.