Data-Informed Collection Decisions Using R

ABSTRACT

Collection decisions can seem daunting to a librarian who is new to the job. In charge of many new-to-her subject fund codes, the author wanted to understand what had been previously purchased using these funds and how to effectively manage a custom ebook collection. The author used the R programming language to clean and merge purchase and usage data. This article will provide an overview of tasks in R that can be leveraged for making data-informed collection decisions and for sharing information with colleagues.     

Discipline-Specific Literature Bases: A View of the APS Model

Abstract

Librarians see the large quantities of literature and the complete writing/publishing/world as a complete system. Changes to one piece (e.g., making publishing or reading electronic) will not fundamentally change the process. The pieces of the system are there, not for technological reasons, but for reasons of authority, validation, and self-correction in the process of scholarly communication. Electronic publishing will cause great changes, but the changes will not eliminate roles that now exist. The changes will be to enhance our abilities to better do that which we do now, whether that be to edit, review, referee, write, or manage the information flow.     

Optimal Routing Among ⋅/M/1 Queues with Partial Information

Abstract

When routing dynamically randomly arriving messages, the controller of a high-speed communication network very often gets the information on the congestion state of down stream nodes only after a considerable delay, making that information irrelevant at decision epochs. We consider the situation where jobs arrive according to a Poisson process and must be routed to one of two (parallel) queues with exponential service time distributions (possibly with different means), without knowing the congestion state in one of the queues. However, the (conditional) probability distribution of the state of the unobservable queue can be computed by the router. We derive the joint probability distribution of the congestion states in both queues as a function of the routing policy. This allows us to identify optimal routing schemes for two types of frameworks: global optimization, in which the weighted sum of average queue lengths is minimized, and individual optimization, in which the goal is to minimize the expected delay of individual jobs.     

Reduced forms and weak instrumentation

ABSTRACT

This paper develops exact finite sample and asymptotic distributions for a class of reduced form estimators and predictors, allowing for the presence of unidentified or weakly identified structural equations. Weak instrument asymptotic theory is developed directly from finite sample results, unifying earlier findings and showing the usefulness of structural information in making predictions from reduced form systems in applications. Asymptotic results are reported for predictions from models with many weak instruments. Of particular interest is the finding that, in unidentified and weakly identified structural models, partially restricted reduced form predictors have considerably smaller forecast mean square errors than unrestricted reduced forms. These results are related to the use of shrinkage methods in system-wide reduced form estimation.     

The NISO Institutional Identifiers (I2) Working Group

Abstract

Recognizing the importance of the institution as a critical entity in any information model, from information creation to information sharing, NISO (National Information Standards Organization) has formed a working group to develop a standard (Z39.94) for identifying institutions. This installment of “Standards Update” will discuss the mission and objectives of that working group and provide some information about its timelines for developing the standard.     

Modeling Efficiency of Foreign Aid Allocation in Malawi

ABSTRACT

The Open Aid Malawi initiative has collected an unprecedented database that identifies as much location-specific information as possible for each of over 2500 individual foreign aid donations to Malawi since 2003. The efficient use and distribution of such aid is important to donors and to Malawi citizens. However, because of individual donor goals and difficulty in tracking donor coordination it is difficult to determine whether aid allocation is efficient. We compare several Bayesian spatial generalized linear mixed models to relate aid allocation to various economic indicators within seven donation sectors. We find that the spatial gamma regression model best predicts current aid allocation. While we are cautious about making strong claims based on this exploratory study, we provide a methodology by which one could (i) evaluate the efficiency of aid allocation via a study of the locations of current aid allocation as compared to the need at those locations and (ii) come up with a strategy for efficient allocation of resources in conditions where there exists an ideal relationship between aid allocation and economic sectors.     

Asymmetric generalizations of symmetric univariate probability distributions obtained through quantile splicing

Abstract

Balakrishnan et al. proposed a two-piece skew logistic distribution by making use of the cumulative distribution function (CDF) of half distributions as the building block, to give rise to an asymmetric family of two-piece distributions, through the inclusion of a single shape parameter. This paper proposes the construction of asymmetric families of two-piece distributions by making use of quantile functions of symmetric distributions as building blocks. This proposition will enable the derivation of a general formula for the L-moments of two-piece distributions. Examples will be presented, where the logistic, normal, Student’s t(2) and hyperbolic secant distributions are considered.     

Some New Results on the Multinomial Randomized Response Model

ABSTRACT

The randomized response technique is an effective survey method designed to elicit sensitive information while ensuring the privacy of the respondents. In this article, we present some new results on the randomization response model in situations wherein one or two response variables are assumed to follow a multinomial distribution. For a single sensitive question, we use the well-known Hopkins randomization device to derive estimates, both under the assumption of truthful and untruthful responses, and present a technique for making pairwise comparisons. When there are two sensitive questions of interest, we derive a Pearson product moment correlation estimator based on the multinomial model assumption. This estimator may be used to quantify the linear relationship between two variables when multinomial response data are observed according to a randomized-response protocol.     

Point estimation using tail modelling for right skew populations

ABSTRACT

We evaluate the merits of estimators for right-skewed data which are motivated by a distributional assumption for the tail of the population. Our aim is to make a coherent account of estimators of this type already existing in the literature as well as suggesting some modifications, and to compare their performance empirically to other conceivable alternatives. Our results indicate that making use of parametric models to derive the explicit form of estimators can be a fruitful approach for a wide range of right-skewed populations when auxiliary data is not available, especially for small samples.     

Tools of the Serials Trade

Abstract

The primary purpose of information services has always been and will always be to reduce to a minimum the amount of time required by local users to obtain access to that information they need to do their work.¹     

Digital Accessibility as a Core Competency for E-Resources Librarians

Abstract

This article will outline the electronic resources life cycle and identify key elements involved in e-resource management that exhibit potential for growth in terms of accessibility remediation. The aim of the article is to convey an understanding of the importance of digital accessibility to electronic resources librarians and the role they may play in making digital collections more available to users with impairments.     

MODELING AND ESTIMATING VARIANCES IN REGRESSION

ABSTRACT

It is well known that ignoring heteroscedasticity in regression analysis adversely affects the efficiency of estimation and renders the usual procedure for constructing prediction intervals inappropriate. In some applications, such as off-line quality control, knowledge of the variance function is also of considerable interest in its own right. Thus the modeling of variance constitutes an important part of regression analysis. A common practice in modeling variance is to assume that a certain function of the variance can be closely approximated by a function of a known parametric form. The logarithm link function is often used even if it does not fit the observed variation satisfactorily, as other alternatives may yield negative estimated variances. In this paper we propose a rich class of link functions for more flexible variance modeling which alleviates the major difficulty of negative variances. We suggest also an alternative analysis for heteroscedastic regression models that exploits the principle of “separation” discussed in Box (Signal-to-Noise Ratios, Performance Criteria and Transformation. Technometrics 1988, 30, 1–31). The proposed method does not require any distributional assumptions once an appropriate link function for modeling variance has been chosen. Unlike the analysis in Box (Signal-to-Noise Ratios, Performance Criteria and Transformation. Technometrics 1988, 30, 1–31), the estimated variances and their associated asymptotic variances are found in the original metric (although a transformation has been applied to achieve separation in a different scale), making interpretation of results considerably easier.     

Why is Getting Rid of P-Values So Hard? Musings on Science and Statistics

ABSTRACT

The current concerns about reproducibility have focused attention on proper use of statistics across the sciences. This gives statisticians an extraordinary opportunity to change what are widely regarded as statistical practices detrimental to the cause of good science. However, how that should be done is enormously complex, made more difficult by the balkanization of research methods and statistical traditions across scientific subdisciplines. Working within those sciences while also allying with science reform movements—operating simultaneously on the micro and macro levels—are the key to making lasting change in applied science.     

The Value of South and Southeast Asian Studies Journal Rankings

Abstract

This article ranks South and Southeast Asian Studies journals along three categories—quality, efficiency, and impact. The ranking summarizes the results of a survey sent to an international sample of South and Southeast Asian Studies scholars. This study is valuable in three ways: as a decision making tool for the South and Southeast Asian Studies academic community, as an evaluation of methodology in journal ranking studies, and as a discussion of the value of journal rankings studies to assessment and evaluation in higher education generally.     

Index

Abstract

A subject like “failure” seems an unusual focus for a journal. An e-journal by that name has made a niche for itself by examining some of the modern world's less successful ventures; its value comes from making the unsuccessful, interesting. Serials Review 2002; 28:53–56.     

A Proposed Hybrid Effect Size Plus p-Value Criterion: Empirical Evidence Supporting its Use

ABSTRACT

When the editors of Basic and Applied Social Psychology effectively banned the use of null hypothesis significance testing (NHST) from articles published in their journal, it set off a fire-storm of discussions both supporting the decision and defending the utility of NHST in scientific research. At the heart of NHST is the p-value which is the probability of obtaining an effect equal to or more extreme than the one observed in the sample data, given the null hypothesis and other model assumptions. Although this is conceptually different from the probability of the null hypothesis being true, given the sample, p-values nonetheless can provide evidential information, toward making an inference about a parameter. Applying a 10,000-case simulation described in this article, the authors found that p-values’ inferential signals to either reject or not reject a null hypothesis about the mean (α?=?0.05) were consistent for almost 70% of the cases with the parameter’s true location for the sampled-from population. Success increases if a hybrid decision criterion, minimum effect size plus p-value (MESP), is used. Here, rejecting the null also requires the difference of the observed statistic from the exact null to be meaningfully large or practically significant, in the researcher’s judgment and experience. The simulation compares performances of several methods: from p-value and/or effect size-based, to confidence-interval based, under various conditions of true location of the mean, test power, and comparative sizes of the meaningful distance and population variability. For any inference procedure that outputs a binary indicator, like flagging whether a p-value is significant, the output of one single experiment is not sufficient evidence for a definitive conclusion. Yet, if a tool like MESP generates a relatively reliable signal and is used knowledgeably as part of a research process, it can provide useful information.     

Developing Nations,the Digital Divide and Research Databases

Abstract

The digital divide affects many nations of the developing world. The term encompasses inadequate funding, a lack of necessary computer and Internet skills, and a lack of English-language proficiency that hinder expansion and use of digital information resources. Visionary individuals and organizations can assist these countries with their information needs. Aggregated full-text databases serve as a prominent tool in closing the digital divide because they provide important research resources. Participating organizations must demonstrate the value of these resources to users and supply essential training to ensure success with electronic resource initiatives.     

Investigating the role of orthogonal and non – orthogonal rotation in multivariate factor analysis,in regard to the repeatability of the extracted factors: A simulation study

ABSTRACT

Factor analysis (FA) is the most commonly used pattern recognition methodology in social and health research. A technique that may help to better retrieve true information from FA is the rotation of the information axes. The main goal is to test the reliability of the results derived through FA and to reveal the best rotation method under various scenarios. Based on the results of the simulations, it was observed that when applying non-orthogonal rotation, the results were more repeatable as compared to the orthogonal rotation, and, when no rotation was applied.