Exploring University Choice Factors Among School Leavers in Selected Sri Lankan Districts: A Second-Order Confirmatory Factor Analysis

Abstract

Abstract

Aim/Purpose: This study explored key factors influencing school leavers’ selection of public universities in rural Sri Lankan districts. Grounded in Chapman’s Model of Student College Choice and relevant economic decision-making theories, the variables that shape university selection were identified. It aims to provide policymakers and educational institutions with actionable insights to enhance enrollment strategies and improve access to higher education.

Introduction/Background: Higher education plays a crucial role in socio-economic development; however, Sri Lanka’s Gross Enrollment Ratio remains low compared to that of other middle-income Asian nations, limiting human capital growth. Although public universities have expanded open and distance learning programs, rural enrollment remains low due to sociocultural, economic, and institutional barriers.

Methodology: We conducted a cross-sectional survey to analyze the factors influencing university choice among school leavers in four economically disadvantaged districts: Badulla, Matale, Monaragala, and Rathnapura. The target population consisted of school leavers who had completed the General Certificate of Education Advanced Level examination. A stratified random sampling method was used to ensure representation across different socioeconomic backgrounds. A total of 300 self-administered questionnaires were distributed, of which 239 were fully completed. After data screening, 201 valid responses were retained, with 38 responses excluded due to missing data or response biases (e.g., extreme uniformity in unrelated questions). An Exploratory Factor Analysis was conducted using a statistical software package to identify latent constructs underlying the observed variables, retaining factors with loadings above 0.50. Confirmatory Factor Analysis was performed using AMOS Version 23 to confirm the factor structure, assess model fitness, and establish construct validity. Structural Equation Modeling was then employed to test hypothesized relationships between latent constructs and observed variables.

Findings: Exploratory Factor Analysis was conducted to identify key underlying factors influencing university selection. Out of 26 initial variables, eight were excluded because their factor loadings were below 0.50, leaving 18 variables retained for further analysis. The Kaiser criterion (eigenvalues > 1.0) and Principal Component Analysis with Varimax rotation were employed to extract and interpret the factors. This analysis identified five key constructs underlying university selection: Student Characteristics, University Image, Fixed University Characteristics, University Communication Efforts, and Influence of Significant Persons. Structural Equation Modeling provided further support for these findings, demonstrating that Student Characteristics had the strongest effect on university selection (γ = 0.95, p < 0.001). This construct included factors such as a student’s interest in higher education, career aspirations, and expectations of future job opportunities. Notably, nearly 50% of the surveyed students belonged to lower-middle-income households, highlighting the critical role of higher education in providing socioeconomic mobility and influencing university selection decisions.

The University Image construct also played a significant role (γ = 0.50, p < .01), with 83% of respondents preferring public universities due to their perceived reputation and better career prospects compared to private institutions. Fixed University Characteristics—including factors such as location, transportation costs, and cost of living—exerted a moderate effect on university choice (γ = 0.34, p < .05). These logistical and financial concerns were particularly relevant for students from rural areas, where accessibility remains a critical barrier to higher education.

Additionally, University Communication Efforts (γ = 0.34, p < .05) were found to be an important determinant of university selection. Outreach efforts such as open days, social media engagement, and career guidance seminars played a crucial role in bridging the informational gap, particularly in underprivileged districts where students have limited exposure to higher education opportunities. Lastly, the Influence of Significant Persons (e.g., peers, teachers, and family members) had a relatively minor impact (γ = 0.14, p = .08), suggesting that while external influences shape initial perceptions, students' intrinsic motivations and institutional factors are more decisive in final university selection.

Contribution/Impact on Society: This study provides empirical evidence on the key factors influencing university choice in economically disadvantaged districts of Sri Lanka, offering valuable insights for policymakers, universities, and education stakeholders.  A key contribution of this study is its emphasis on socioeconomic mobility through education. With nearly 50% of surveyed students from lower-middle-income households, higher education plays a vital role in breaking cycles of poverty and fostering long-term economic development. Furthermore, the study underscores the underutilization of open and distance learning systems, despite their potential to increase accessibility for students in remote areas.

Recommendations: By implementing data-driven policies based on these findings, Sri Lanka can improve its Gross Enrollment Ratio, strengthen its skilled workforce, and drive national progress in the global knowledge economy.

Research Limitation: This study was limited to four rural districts, which may affect its findings’ generalizability. Survey distribution challenges, language barriers, and non-response bias could have influenced the results.

Future Research: Future studies should expand their samples to include urban and rural areas for broader applicability. Longitudinal research tracking students' university and career outcomes would provide deeper insights. Investigating technology adoption in open and distance learning and assessing financial aid and career counseling programs could offer practical policy recommendations.