How important are socioeconomic background and other factors to the university career vis-à-vis prior student performance: evidence from Australian longitudinal data

ABSTRACT

The literature on the relationship between socioeconomic background (SES) and university education is inconsistent. Some studies conclude SES is important to university entry and course completion, others find trivial SES effects, net of students’ prior performance, and a third group concludes that SES effects are important and policy relevant even when considering prior performance. Parallel arguments apply to demographic, school sector, and institutional differences in the university career, that is, are they unimportant when considering student performance? Using comprehensive and accurate measures of SES and student performance, and a statistical method that utilizes all non-missing data, this study quantifies the effects of socioeconomic, demographic, and institutional factors and prior student performance. SES has only weak effects on university entry and attrition, and no effects on course completion. Student performance has strong effects on entry and has moderate effects on attrition and completion. Demographic other differences mostly disappear when controlling for student performance.

KEYWORDS:

• PISA test scores
• tertiary entrance performance (ATAR)
• university participation
• university course attrition
• university course completion

Disclosure statement

No potential conflict of interest was reported by the author.

Notes on contributor

Gary N. Marks is an honorary principal fellow in the Department of Sociology, Social and Political Sciences at the University of Melbourne. He has publications in four main areas: educational outcomes (student achievement, university entrance performance, school completion, and early school leaving); labour market outcomes (e.g., employment, unemployment, occupational attainment, occupational mobility, income, and wealth); social outcomes (e.g., well-being, leaving home, and family formation); and political outcomes (voting behaviour). His work has a particular emphasis on changes over time and cross-national differences in social stratification, social inequality, and the contribution of genetics to educational and socioeconomic outcomes.

Notes

1 ATAR is a percentile ranking ranging from 30 to 99.95. All students with ranks 30 or below are assigned a rank of 30. The ranking is based on students’ performance in the last year(s) of secondary school adjusted for differences in the academic profiles of students taking easier and more difficult subjects.

2 Author’s calculation.

3 This claim is not true since only about 13% of the variance PISA test scores can be attributed to the OECD’s comprehensive SES measure, ESCS (OECD, 2019, p. 17).

4 For example, at present, entry to Victoria University, Federation University, and the University of Divinity does not require an Australian Tertiary Admissions Rank (https://universityreviews.com.au/atar-course-entry-scores). In addition, there are courses in other universities at which admission is not based solely on tertiary entrance rank, for example, art and the performing arts.

5 These attrition rates are based on a match process using universities' student identification number and the Commonwealth Higher Education Student Support Number (CHESSN). This provides a more accurate calculation of attrition as it identifies students at either the same or a different higher education institution (DET, 2015a).

6 The data analysed for this paper are available from Australian Data Archive (https://www.ada.edu.au/longitudinal/home).

7 The coding schemas can be found at http://www.lsay.edu.au/publications/2225.html

8 Universities Australia details the grouping of individual universities into the four networks (http://www.australianuniversities.com.au/directory/group-of-eight/).

9 A rough rule of thumb is: If the values of the mean plus or minus twice the standard error for two groups do not overlap, then the difference is statistically significant.

10 The odd ratios are the ratio of two odds. They are calculated as simply the exponent of estimates.$\text{Odds ratio}={\text{e}}^{b_{\mathrm{k}}}$ or $\text{odds ratio}=\text{exp}\left(b_{\mathrm{k}}\right)$.

11 These unexpected results for PISA test score controlling for ATAR in the attrition and completion analyses are not due to multicollinearity. In these data, ATAR and PISA score correlate at 0.58, and in the tests for multicollinearity the variance inflation factors are well below 10 and the lowest tolerance is a healthy 0.65.