How individual characteristics affect university students drop-out: a semiparametric mixed-effects model for an Italian case study

University drop-out is a topic of increasing concern in Italy as well as in other countries. In empirical analysis, university drop-out is generally measured by means of a binary variable indicating the drop-out versus retention. In this paper, we argue that the withdrawal decision is one of the possible outcomes of a set of four alternatives: retention in the same faculty, drop out, change of faculty within the same university, and change of institution. We examine individual-level data collected by the administrative offices of “Sapienza” University of Rome, which cover 117 072 students enrolling full-time for a 3-year degree in the academic years from 2001/2002 to 2006/2007. Relying on a non-parametric maximum likelihood approach in a finite mixture context, we introduce a multinomial latent effects model with endogeneity that accounts for both heterogeneity and omitted covariates. Our estimation results show that the decisions to change faculty or university have their own peculiarities, thus we suggest that caution should be used in interpreting results obtained without modeling all the relevant alternatives that students face.     

Bayesian modeling of university first-year students' grades after placement test

University first-year students grades are naturally correlated with the scores obtained at placement tests. Often this characteristic leads the university grades in the first exams to be asymmetrically distributed. Motivated by the analysis of grades of the basic Statistics examination of first-year students, we discuss informative priors for the shape parameter of the skew-normal model, a class of distribution which account for several degree of asymmetry. Our proposed prior leads to closed-form full-conditional posterior distributions, particularly useful in Markov Chain Monte Carlo simulation. A Gibbs sampling algorithm is discussed for the joint vector of parameters and the method is applied to a real data set from the School of Economics, University of Padua, Italy. Our analysis reveals that the correlation between the placement test and the grades of first-year students leads to a measurable positive skewness of the distribution of the university grades.     

A hazard model of the probability of medical school drop-out in the UK

Summary.  From individual level longitudinal data for two entire cohorts of medical students in UK universities, we use multilevel models to analyse the probability that an individual student will drop out of medical school. We find that academic preparedness—both in terms of previous subjects studied and levels of attainment therein—is the major influence on withdrawal by medical students. Additionally, males and more mature students are more likely to withdraw than females or younger students respectively. We find evidence that the factors influencing the decision to transfer course differ from those affecting the decision to drop out for other reasons.     

Bayesian binary quantile regression for the analysis of Bachelor-to-Master transition

The multi-cycle organization of modern university systems stimulates the interest in studying the progression to higher level degree courses during the academic career. In particular, after the achievement of the first level qualification (Bachelor degree), students have to decide whether to continue their university studies, by enrolling in a second level (Master) programme, or to conclude their training experience. In this work we propose a binary quantile regression (BQR) approach to analyse the Bachelor-to-Master transition phenomenon with the adoption of the Bayesian inferential perspective. In addition to the traditional predictors of academic outcomes, such as the personal characteristics and the field of study, different aspects of student's performance are considered. Moreover, the role of a new contextual variable, representing the type of university regulations experienced during the academic path, is investigated. The utility of the Bayesian BQR to characterize the non-continuation decision after the first cycle studies is illustrated with an application to administrative data of Bachelor graduates at the School of Economics of Sapienza University of Rome. The method favourably compares with more conventional model specifications concerning the conditional mean of the binary response.     

Publish or Perish: How Are Research and Reputation Related?

A study of twenty-seven fields in 350 highly ranked universities examines the relationship between reputation and rank. We find that many metrics associated with research prowess significantly correlate to university reputation. However, the next logical step– looking at the relationship that links different academic fields with the reputation of the university–did not always offer the expected results. The phrase “publish or perish” clearly has very different meanings in different fields.     

The Perpetual Student: Modeling Duration of Undergraduate Studies Based on Lifetime-Type Educational Data

It is important to educational planners to estimate the likelihood and time-scale of graduation of students enrolled on a curriculum. The particular case we are concerned with, emerges when studies are not completed in the prescribed interval of time. Under these circumstances we use a framework of survival analysis applied to lifetime-type educational data to examine the distribution of duration of undergraduate studies for 10,313 students, enrolled in a Greek university during ten consecutive academic years. Non-parametric and parametric survival models have been developed for handling this distribution as well as a modified procedure for testing goodness-of-fit of the models. Data censoring was taken into account in the statistical analysis and the problems of thresholding of graduation and of perpetual students are also addressed. We found that the proposed parametric model adequately describes the empirical distribution provided by non-parametric estimation. We also found significant difference between duration of studies of men and women students. The proposed methodology could be useful to analyse data from any other type and level of education or general lifetime data with similar characteristics.     

What are Research Expectations? A Comparative Study of Different Academic Disciplines

This paper is intended to assist professors, administrators, librarians and other members of university level committees that must consider research expectations and research quality in academic fields that they lack. While this is not a problem for field experts, it is a difficulty when people are asked to make decisions in areas of study other than their own. This is commonly the case for senior university professors, librarians and administrators in regards to university wide decisions. The paper investigates this gap, through a study of 27 academic fields in 348 highly regarded universities. We find that there are almost always statistically significant differences in activity between academic fields, regardless of the metric one considers. However, it is possible to understand these differences by comparing the distribution of a known academic field to that of a field that one is not familiar with. Tables and information are provided to assist in the comparison of different fields of study on metrics such as: departmental publications and researcher level metrics of publications, citations, H-index, and total number of co-authors. The information can also be used to support decisions associated with promotion to senior posts such as endowed chairs and professorships. Information regarding specific universities and researchers are included in the data supplement.     

Multilevel IRT models for the university teaching evaluation

In this paper, a generalization of the two-parameter partial credit model (2PL-PCM) and of two special cases, the partial credit model (PCM) and the rating scale model (RSM), with a hierarchical data structure will be presented. Having shown how 2PL-PCM, as with other item response theory (IRT) models, may be read in terms of a generalized linear mixed model (GLMM) with two aggregation levels, a presentation will be given of an extension to the case of measuring the latent trait of individuals aggregated in groups. The use of this Multilevel IRT model will be illustrated via reference to the evaluation of university teaching by students following the courses. The aim is to generate a ranking of teaching on the basis of student satisfaction, so as to give teachers, and those responsible for organizing study courses, a background of information that takes the opinions of the direct target group for university teaching (that is, the students) into account, in the context of improving the teaching courses available.     

Modelling the effect of subject-specific covariates in paired comparison studies with an application to university rankings

Preference decisions will usually depend on the characteristics of both the judges and the objects being judged. In the analysis of paired comparison data concerning European universities and students' characteristics, it is demonstrated how to incorporate subject-specific information into Bradley–Terry-type models. Using this information it is shown that preferences for universities and therefore university rankings are dramatically different for different groups of students. A log-linear representation of a generalized Bradley–Terry model is specified which allows simultaneous modelling of subject- and object-specific covariates and interactions between them. A further advantage of this approach is that standard software for fitting log-linear models, such as GLIM, can be used.     

Inter-university variations in undergraduate non-completion rates: A statistical analysis by subject of study

SUMMARY Non-completion of higher education degree courses is a considerable problem, incurring costs on the taxpayer, higher education institutions and the students who fail to complete. Closer examination of the data reveals that non-completion rates in higher education vary substantially across institutions and by subject of degree. The purpose of this paper is to investigate, within each of 13 broad subject categories, the potential determinants of inter-university variations in non-completion rates. Published data are used to compute university non-completion rates over four time periods and to construct corresponding explanatory variables which could potentially be related to non-completion rates. The explanatory variables measure the characteristics (both academic and socioeconomic) of students recruited by universities and the characteristics of the institutions themselves. The significance of the relationship between the possible explanatory variables and non-completion rates within each given subject is assessed using both weighted leastsquares and weighted logit analysis. The conclusions drawn from the results of each technique are identical, and, therefore, for interpretation reasons, only the results of the weighted least-squares analysis are reported. As expected, the academic quality of student entrants is an important determinant of non-completion rates in the majority of subjects, although the magnitude of the effect varies according to subject. Variables reflecting the age and gender mix of university entrants are generally not significantly related to noncompletion rates. The characteristics of institutions which are significantly related to non-completion rates in specific subjects include the staff student ratio and the length of the degree course     

Building up adjusted indicators of students’ evaluation of university courses using generalized item response models

This article advances a proposal for building up adjusted composite indicators of the quality of university courses from students’ assessments. The flexible framework of Generalized Item Response Models is adopted here for controlling the sources of heterogeneity in the data structure that make evaluations across courses not directly comparable. Specifically, it allows us to: jointly model students’ ratings to the set of items which define the quality of university courses; explicitly consider the dimensionality of the items composing the evaluation form; evaluate and remove the effect of potential confounding factors which may affect students’ evaluation; model the intra-cluster variability at course level. The approach simultaneously deals with: (i) multilevel data structure; (ii) multidimensional latent trait; (iii) personal explanatory latent regression models. The paper pays attention to the potential of such a flexible approach in the analysis of students evaluation of university courses in order to explore both how the quality of the different aspects (teaching, management, etc.) is perceived by students and how to make meaningful comparisons across them on the basis of adjusted indicators.     

Higher education outcomes, graduate employment and university performance indicators

Summary.  Official employment-related performance indicators in UK higher education are based on the population of students responding to the 'First destination supplement' (FDS). This generates potentially biased performance indicators as this population of students is not necessarily representative of the full population of leavers from each institution. University leavers who do not obtain qualifications and those who do not respond to the FDS are not included within the official analysis. We compare an employment-related performance indicator based on those students who responded to the FDS with alternative approaches which address the potential non-random nature of this subgroup of university leavers.     

Analysis of the bovine spongiform encephalopathy maternal cohort study, revisited

The bovine spongiform encephalopathy (BSE) maternal cohort study provided robust evidence of an enhanced risk of developing BSE for offspring of BSE-affected dams. We present for the first time, but in retrospect, an interim analysis of the BSE maternal cohort study and set it in historical context, some of which has only been revealed through the BSE inquiry. We also consider the implications for design of extending the BSE maternal cohort study once an enhanced risk to exposed calves had been established, to assess the risk to calves born further from the clinical onset of BSE in the dam than those in the original study. We demonstrate that, if a data monitoring committee had been established, conclusions similar to those based on the final results could have been drawn several years before the completion of the BSE maternal cohort study. Further, we conclude that an extension of the cohort study is unlikely to have been commissioned because of the substantial financial investment required, yet low power, and practical difficulties associated with implementation of any worthwhile extension.     

适应经济发展的高校学科结构优化模型

文章利用1994~2003年的分学科高校在校生资料,进行一阶差分,用净增人数建立灰色动态模型,对2008年和2010年的在校生进行中期预测。并把分学科在校生与人均GDP的关联度作为灰色线性规划的贡献率灰数,优化出各学科在校生的结构。结果显示,需要适度减缓哲学、历史学、法学、经济学的发展速度,增加教育学、工学、医学的培养人数。     

Testing for state dependence in binary panel data with individual covariates by a modified quadratic exponential model

We propose a test for state dependence in binary panel data with individual covariates. For this aim, we rely on a quadratic exponential model in which the association between the response variables is accounted for in a different way with respect to more standard formulations. The level of association is measured by a single parameter that may be estimated by a Conditional Maximum Likelihood (CML) approach. Under the dynamic logit model, the conditional estimator of this parameter converges to zero when the hypothesis of absence of state dependence is true. Therefore, it is possible to implement a t-test for this hypothesis which may be very simply performed and attains the nominal significance level under several structures of the individual covariates. Through an extensive simulation study, we find that our test has good finite sample properties and it is more robust to the presence of (autocorrelated) covariates in the model specification in comparison with other existing testing procedures for state dependence. The proposed approach is illustrated by two empirical applications: the first is based on data coming from the Panel Study of Income Dynamics and concerns employment and fertility; the second is based on the Health and Retirement Study and concerns the self reported health status.     

University teaching of econometrics a personal view

University teachers of economics have generally been keen to propound, discuss and criticise alternative points of view on ways of structuring courses in their discipline. Econometricians, by contrast, have in this respect been very neglectful - though their subject has been a university discipline for over a quarter of a century.

This paper appraises broadly the issues relevant to the design of an academic program in econometrics, at both undergraduate and graduate levels, and proposes ways in which a blended education in economics and econometrics may best be offered.     

University teaching of econometrics a personal view

University teachers of economics have generally been keen to propound, discuss and criticise alternative points of view on ways of structuring courses in their discipline. Econometricians, by contrast, have in this respect been very neglectful - though their subject has been a university discipline for over a quarter of a century.

This paper appraises broadly the issues relevant to the design of an academic program in econometrics, at both undergraduate and graduate levels, and proposes ways in which a blended education in economics and econometrics may best be offered.     

A network analysis of student mobility patterns from high school to master’s

Human migration involves the movement of people from one place to another. An example of undirected migration is Italian student mobility where students move from the South to the Center-North. This kind of mobility has become of general interest, and this work explores student mobility from Sicily towards universities outside the island. The data used in this paper regards six cohorts of students, from 2008/09 to 2013/14. In particular, our goal is to study the 3-step migration path: the area of origin (Sicilian provinces), the regional university for the bachelor’s degree, and the regional university for the master’s. Our analysis is conducted by building a multipartite network with four sets of nodes: students; Sicilian provinces; bachelor region of studies; and the master region of studies. By projecting the students’ set onto the others, we obtain a tripartite network where the number of students represents the link weight. Results show that the big Sicilian cities—Palermo, Catania, and Messina—have different preferential paths compared to small Sicilian cities. Furthermore, the results reveal preferential paths of 3-step mobility that only, in part, reflect a south-north orientation in the transition from the region of study for the bachelor degree to that for the master’s.

     

Ethical Numbers: Ethics Training in U.S. Graduate Statistics Programs, 2013–2014

As important members of research teams, statisticians bear an ethical responsibility to analyze, interpret, and report data honestly and objectively. One way of reinforcing ethical responsibilities is through required courses covering a variety of ethics-related topics at the graduate level. We assessed ethics requirements for graduate-level statistics training programs in the United States for the 2013–2014 academic year using the websites of 88 universities, examining 103 biostatistics programs, and 136 statistics degree programs. We categorized programs’ ethics training requirements as required or not required. Thirty-one (35.1%) universities required an ethics course for at least some degree students. Sixty-two (25.5%) degree programs required an ethics course for at least some students. The majority (77.4%) of required courses were worth 0 or 1 credit. Of the 177 programs without an ethics requirement, 19 (10.7%) listed an ethics elective. Although a single ethics course is insufficient for instilling an ethical approach to science, degree programs that model expectations through coursework point to the value of ethics in science. More training programs should prepare statisticians to consider the ethical dimensions of their work through required coursework. Supplementary materials for this article are available online.     

Enriching Students’ Conceptual Understanding of Confidence Intervals: An Interactive Trivia-Based Classroom Activity

Confidence intervals provide a way to determine plausible values for a population parameter. They are omnipresent in research articles involving statistical analyses. Appropriately, a key statistical literacy learning objective is the ability to interpret and understand confidence intervals in a wide range of settings. As instructors, we devote a considerable amount of time and effort to ensure that students master this topic in introductory courses and beyond. Yet, studies continue to find that confidence intervals are commonly misinterpreted and that even experts have trouble calibrating their individual confidence levels. In this article, we present a 10-min trivia game-based activity that addresses these misconceptions by exposing students to confidence intervals from a personal perspective. We describe how the activity can be integrated into a statistics course as a one-time activity or with repetition at intervals throughout a course, discuss results of using the activity in class, and present possible extensions. Supplementary materials for this article are available online.