The determinants of academic outcomes in a competing risks approach: evidence from Italy

Clerici, Renata; Giraldo, Anna; Meggiolaro, Silvia

doi:10.1080/03075079.2013.878835

Cited by 29 publications

(31 citation statements)

References 33 publications

(43 reference statements)

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“…Several studies encompassed some meso-theoretical concepts, i.e. how the organizational context of the HE institutions influences how students proceed through HE (Clerici et al 2015;Ewert 2010;Giani 2015; Lassibille and Gómez 2009; Kalogrides and Grodsky 2011). For example, Meggiolaro et al (2017) argued that larger HE institutions provide more and better services to students.…”

Section: Overview Of the Literature Review Samplementioning

confidence: 99%

Students’ trajectories through higher education: a review of quantitative research

Haas

Hadjar

2019

High Educ

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With the increasing availability of high-quality longitudinal data on students in higher education, scholars' interest in how students proceed through higher education has risen. So far, the research field is diverse in theoretical perspectives and methodological approaches. Thus, based on 27 studies published in (higher) education research journals during the past two decades, this literature review provides an overview of the theoretical concepts, methodologies and main empirical findings in the study of students' trajectories in higher education. The results depict a US dominated research field. Most theoretical frameworks are based on student's decision-making. Across different country contexts and research designs-ranging from descriptions of student trajectories to studies predicting who engages in which types of trajectories to sequential trajectory reconstruction-we found that historically disadvantaged groups in higher education such as students from low social origin follow less linear and less smooth higher education trajectories. However, while the field of comparative education is growing steadily and may significantly contribute to explaining the link between the realization of students' opportunities and the way how higher education is designed and implemented both on the national and local level, there were no crosscountry comparison studies on higher education trajectories.

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Section: Overview Of the Literature Review Samplementioning

confidence: 99%

Students’ trajectories through higher education: a review of quantitative research

Haas

Hadjar

2019

High Educ

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“…For university outcomes, model (4) has been used by Scott and Kennedy (2005), Arias Ortis and Dehon (2011) and Clerici et al (2014), among others. Nonetheless, its use has some drawbacks.…”

Section: Proportional Odds Model For Competing Risks Datamentioning

confidence: 99%

“…Positive values of the cause-specific coefficients indicate that (at any time point) the hazard of the corresponding event increases with the associated covariate values and the effect of covariates on log odds is constant over time. For university outcomes, (5) has been used by Scott and Kennedy (2005), Arias Ortis and Dehon (2011) and Clerici et al (2014), among others. Nonetheless, its use has some drawbacks.…”

Section: Proportional Odds Model For Competing Risks Datamentioning

confidence: 99%

Bayesian Survival Modelling of University Outcomes

Vallejos

Steel

2016

Journal of the Royal Statistical Society Series A: Statistics in Society

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The aim of this paper is to model the length of registration at university and its associated academic outcome for undergraduate students at the Pontificia Universidad Católica de Chile. Survival time is defined as the time until the end of the enrollment period, which can relate to different reasons -graduation or two types of dropout -that are driven by different processes. Hence, a competing risks model is employed for the analysis. The issue of separation of the outcomes (which precludes maximum likelihood estimation) is handled through the use of Bayesian inference with an appropriately chosen prior. We are interested in identifying important determinants of university outcomes and the associated model uncertainty is formally addressed through Bayesian model averaging. The methodology introduced for modelling university outcomes is applied to three selected degree programmes, which are particularly affected by dropout and late graduation.

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“…(factor loadings, factor correlation, communalities) 11 . The advantages over traditional factor analysis are:…”

Section: Latent Class Factor Modelsmentioning

confidence: 99%

Self-Regulated Learning, Motivation and Emotions Towards University Study. A Latent Class Approach

Giraldo

Meggiolaro

2014

SSRN Journal

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Students' attitudes, motivations and emotions towards university study have a great influence on their university careers. In this work, we use a latent class factor analysis approach to reveal the underlying latent factors which summarise a series of items investigating students' positions in three domains: self-regulated learning, motivation, and emotions towards university study. Data come from a CAWI survey conducted in 2012 on a cohort of students enrolled in academic year 2006-07 at University of Padova, a large public university in north-east Italy. Results show that the underlying latent factors are in line with related psychological literature.

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The determinants of academic outcomes in a competing risks approach: evidence from Italy

Cited by 29 publications

References 33 publications

Students’ trajectories through higher education: a review of quantitative research

Students’ trajectories through higher education: a review of quantitative research

Bayesian Survival Modelling of University Outcomes

Self-Regulated Learning, Motivation and Emotions Towards University Study. A Latent Class Approach

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