Measuring Usage: A Comprehensive Analysis of a Social Work Journal Collection

Abstract

This study examines what can be learned about a library’s electronic social work journal collection from usage statistics, survey data, faculty publications, and an examination of open access (OA) availability. A collections analysis was completed using data from two sources: a custom report by 1Science and results of a faculty survey on top journals for teaching. After creating a list of journals important to social work, top journals were identified by article downloads, faculty-authored publications, and references to faculty-authored papers. A publications analysis using faculty websites and author searches in Web of Science was also completed, to provide local, contextual data. SHERPA/RoMEO was used to determine the journals’ OA level and archiving policy. Library coverage for the journals was also included in the analysis. Results show that the McGill University Library has access to almost all of the journals identified as important to social work. Nearly one-third of publications authored by the McGill University School of Social Work since 2006 are OA, and more than half of the faculty in the school have at least one article published in an OA journal. While this is a good start for librarians who want to help faculty and students understand OA publishing and access, there is room for outreach in this area. While these results will aid librarians supporting faculty, students, and practitioners in the field of social work, a secondary aim of the study is to demonstrate a method that can be used by librarians undertaking similar analyses in other fields.     

Analysis of longitudinal data with drop-out: objectives, assumptions and a proposal

Summary. The problem of analysing longitudinal data that are complicated by possibly informative drop-out has received considerable attention in the statistical literature. Most researchers have concentrated on either methodology or application, but we begin this paper by arguing that more attention could be given to study objectives and to the relevant targets for inference. Next we summarize a variety of approaches that have been suggested for dealing with drop-out. A long-standing concern in this subject area is that all methods require untestable assumptions. We discuss circumstances in which we are willing to make such assumptions and we propose a new and computationally efficient modelling and analysis procedure for these situations. We assume a dynamic linear model for the expected increments of a constructed variable, under which subject-specific random effects follow a martingale process in the absence of drop-out. Informal diagnostic procedures to assess the tenability of the assumption are proposed. The paper is completed by simulations and a comparison of our method and several alternatives in the analysis of data from a trial into the treatment of schizophrenia, in which approximately 50% of recruited subjects dropped out before the final scheduled measurement time.     

Influence of human immunodeficiency virus infection on neurological impairment: an analysis of longitudinal binary data with informative drop-out

A study to investigate the human immunodeficiency virus (HIV) status on the course of neurological impairment, conducted by the HIV Center at Columbia University, followed a cohort of HIV positive and negative gay men for 5 years and assessed the presence or absence of neurological impairment every 6 months. Almost half of the subjects dropped out before the end of the study for reasons that might have been related to the missing neurological data. We propose likelihood-based methods for analysing such binary longitudinal data under informative and non-informative drop-out. A transition model is assumed for the binary response, and several models for the drop-out processes are considered which are functions of the response variable (neurological impairment). The likelihood ratio test is used to compare models with informative and non-informative drop-out mechanisms. Using simulations, we investigate the percentage bias and mean-squared error (MSE) of the parameter estimates in the transition model under various assumptions for the drop-out. We find evidence for informative drop-out in the study, and we illustrate that the bias and MSE for the parameters of the transition model are not directly related to the observed drop-out or missing data rates. The effect of HIV status on the neurological impairment is found to be statistically significant under each of the models considered for the drop-out, although the regression coefficient may be biased in certain cases. The presence and relative magnitude of the bias depend on factors such as the probability of drop-out conditional on the presence of neurological impairment and the prevalence of neurological impairment in the population under study.     

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.     

Graduate and undergraduate modular instruction

Undergraduate modular instruction at the University of Kentucky is at present designed to afford students every opportunity to complete successfully more of the material usually tought in un unter gratuvate 3-credit service course with minimum risk to themselves. Graduate modules enable students to design a statistics program better suited to their individual needs. In all cases, the price is a heavier load on faculty and administration, but the reward is a generally enthusiastic reception by the students     

Reducing the Costs of Course Materials

The University of California, Los Angeles (UCLA) and University of Toronto (U of T) describe their respective library-driven programs to reduce the cost of course materials for students—the Affordable Course Materials Initiative (ACMI) at UCLA and the Zero-to-Low-Cost-Course Program (ZTLCC) at U of T. With the same goal of reducing costs by leveraging existing library resources, each library approaches that cost reduction differently. UCLA uses financial incentives to help drive faculty participation in reducing costs, whereas U of T works to reduce overlap in licensing costs while encouraging use of these already-paid-for resources. The following provides an overview of each program along with guidelines for implementing a similar program.     

Streaming Film: How to Serve Our Users

Streaming video is an integral part of the curriculum for many academic courses. While faculty may have used DVDs in the classroom, the increase in online course instruction and the expectations of students comfortable with using streaming video in their personal lives has led to instructors making more requests of librarians to supply films—both instructional and feature—through streaming. Staff at the University Libraries of the University of North Carolina at Greensboro (UNCG) responded to this need as they worked across departments to create efficient workflows, worked with distributors and conducted a pilot project to learn more about the current streaming film environment, established policies, offered technical support, and shared information with faculty by a variety of means.     

大学生数学成绩影响因素的实证分析

伴随着近几年高校的扩大招生,部分大学生的数学成绩显著下降。用统计学软件SPSS 13.0对哈尔滨工程大学部分学生的高等数学成绩及其相关因素进行统计分析,考察学生的高考数学成绩、性别、生源所在地等因素对其大学数学成绩的影响。在给出了一些切实可行的对策和建议的同时,指出培养大学生学习的主动性和研究探索精神,已经成为当今高校教师和教育工作者培养高素质人才和提高教学水平的关键。     

Statistical analysis of performance indicators in UK higher education

Summary.  Attempts to measure the quality with which institutions such as hospitals and universities carry out their public mandates have gained in frequency and sophistication over the last decade. We examine methods for creating performance indicators in multilevel or hierarchical settings (e.g. students nested within universities) based on a dichotomous outcome variable (e.g. drop-out from the higher education system). The profiling methods that we study involve the indirect measurement of quality, by comparing institutional outputs after adjusting for inputs, rather than directly attempting to measure the quality of the processes unfolding inside the institutions. In the context of an extended case-study of the creation of performance indicators for universities in the UK higher education system, we demonstrate the large sample functional equivalence between a method based on indirect standardization and an approach based on fixed effects hierarchical modelling, offer simulation results on the performance of the standardization method in null and non-null settings, examine the sensitivity of this method to the inadvertent omission of relevant adjustment variables, explore random-effects reformulations and characterize settings in which they are preferable to fixed effects hierarchical modelling in this type of quality assessment and discuss extensions to longitudinal quality modelling and the overall pros and cons of institutional profiling. Our results are couched in the language of higher education but apply with equal force to other settings with dichotomous response variables, such as the examination of observed and expected rates of mortality (or other adverse outcomes) in investigations of the quality of health care or the study of retention rates in the workplace.     

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.     

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.

     

Dropping out of university: A statistical analysis of the probability of withdrawal for UK university students

From individual level data for an entire cohort of undergraduate students in the 'old' universities in the UK, we use a binomial probit model to estimate the probability that an individual will 'drop out' of university before the completion of their degree course. We examine the cohort of students enrolling full time for a 3- or 4-year degree in the academic year 1989–1990. We find evidence to support both the hypothesis that the completion of courses by students is influenced by the extent of prior academic preparedness and the hypothesis that social integration at university is important. We also find an influence of unemployment in the county of prior residence, especially for poorer male students. Finally, we draw conclusions regarding the public policy of constructing university performance indicators in this area.     

The Effect of Drop-Out on the Efficiency of Longitudinal Experiments

It is shown that drop-out often reduces the efficiency of longitudinal experiments considerably. In the framework of linear mixed models, a general, computationally simple method is provided, for designing longitudinal studies when drop-out is to be expected, such that there is little risk of large losses of efficiency due to the missing data. All the results are extensively illustrated using data from a randomized experiment with rats.     

Practical modeling strategies for unbalanced longitudinal data analysis

In practice, data are often measured repeatedly on the same individual at several points in time. Main interest often relies in characterizing the way the response changes in time, and the predictors of that change. Marginal, mixed and transition are frequently considered to be the main models for continuous longitudinal data analysis. These approaches are proposed primarily for balanced longitudinal design. However, in clinic studies, data are usually not balanced and some restrictions are necessary in order to use these models. This paper was motivated by a data set related to longitudinal height measurements in children of HIV-infected mothers that was recorded at the university hospital of the Federal University in Minas Gerais, Brazil. This data set is severely unbalanced. The goal of this paper is to assess the application of continuous longitudinal models for the analysis of unbalanced data set.     

Another view on the analysis of cardiovascular morbidity/mortality trials

In many morbidity/mortality studies, composite endpoints are considered. Although the primary interest is to demonstrate that an invention delays death, the expected death rate is often that low that studies focussing on survival exclusively are not feasible. Components of the composite endpoint are chosen such that their occurrence is predictive for time to death. Therefore, if the time to non-fatal events is censored by death, censoring is no longer independent. As a consequence, the analysis of the components of a composite endpoint cannot be reasonably performed using classical methods for the analysis of survival times like Kaplan-Meier estimates or log-rank tests. In this paper we visualize the impact of disregarding dependent censoring during the analysis and discuss practicable alternatives for the analysis of morbidity/mortality studies. In the context of simulations we provide evidence that copula-based methods have the potential to deliver practically unbiased estimates of hazards of components of a composite endpoint. At the same time, they require minimal assumptions, which is important since not all assumptions are generally verifiable because of censoring. Therefore, there are alternative ways to analyze morbidity/mortality studies more appropriately by accounting for the dependencies among the components of composite endpoints. Despite the limitations mentioned, these alternatives can at minimum serve as sensitivity analyses to check the robustness of the currently used methods.     

Handling drop-out in longitudinal clinical trials: a comparison of the LOCF and MMRM approaches

This study compares two methods for handling missing data in longitudinal trials: one using the last-observation-carried-forward (LOCF) method and one based on a multivariate or mixed model for repeated measurements (MMRM). Using data sets simulated to match six actual trials, I imposed several drop-out mechanisms, and compared the methods in terms of bias in the treatment difference and power of the treatment comparison. With equal drop-out in Active and Placebo arms, LOCF generally underestimated the treatment effect; but with unequal drop-out, bias could be much larger and in either direction. In contrast, bias with the MMRM method was much smaller; and whereas MMRM rarely caused a difference in power of greater than 20%, LOCF caused a difference in power of greater than 20% in nearly half the simulations. Use of the LOCF method is therefore likely to misrepresent the results of a trial seriously, and so is not a good choice for primary analysis. In contrast, the MMRM method is unlikely to result in serious misinterpretation, unless the drop-out mechanism is missing not at random (MNAR) and there is substantially unequal drop-out. Moreover, MMRM is clearly more reliable and better grounded statistically. Neither method is capable of dealing on its own with trials involving MNAR drop-out mechanisms, for which sensitivity analysis is needed using more complex methods.     

The Challenges in Developing an Online Applied Statistics Program: Lessons Learned at Penn State University

Numerous professional fields have an increasing need for individuals trained in statistics and other quantitative analysis techniques. Today there exists great potential to fulfill this need by providing opportunities through online learning. However, to provide a high-quality education for returning adult professionals seeking advanced degrees in applied statistics online, many challenges need to be overcome. Based on our experience developing Penn State University’s online program in applied statistics, we discuss the evolution of the program’s curriculum, recruitment and development of online faculty, and meeting the requirements of students as important areas that require consideration in the development of an online program. We also highlight program evaluation strategies employed to ensure innovation and improvement in online education as cornerstones to a program’s success.     

A uniform central limit theorem for neural network-based autoregressive processes with applications to change-point analysis

We consider an autoregressive process with a nonlinear regression function that is modelled by a feedforward neural network. First, we derive a uniform central limit theorem which is useful in the context of change-point analysis. Then, we propose a test for a change in the autoregression function which – by the uniform central limit theorem – has asymptotic power one for a large class of alternatives including local alternatives not restricted to the correctly specified model.     

Time-Varying Smooth Transition Autoregressive Models

Nonlinear regime-switching behavior and structural change are often perceived as competing alternatives to linearity. In this article we study the so-called time-varying smooth transition autoregressive (TV-STAR) model, which can be used both for describing simultaneous nonlinearity and structural change and for distinguishing between these features. Two modeling strategies for empirical specification of TV-STAR models are developed. Monte Carlo simulations show that neither of the two strategies dominates the other. A specific-to-general-to-specific procedure is best suited for obtaining a first impression of the importance of nonlinearity and/or structural change for a particular time series. A specific-to-general procedure is most useful in careful specification of a model with nonlinear and/or time-varying properties. An empirical application to a large dataset of U.S. macroeconomic time series illustrates the relative merits of both modeling strategies.