Open Access: How Are Publishers Reacting?

Abstract

Open Access (OA)—defined simply as “free, unrestricted access (to primary research articles) for everyone”—exists in various forms. Authors can achieve OA either by self-archiving their articles on the Web or by publishing in an OA journal. OA journals themselves may adopt a model of delayed OA, partial (or hybrid) OA, or full, immediate OA. But for any of these alternative models of cost recovery to work, it is necessary to know what the real costs are. More research is needed to begin to evaluate the financial and nonfinancial effects of Open Access on all those involved.     

Has It Really Been Demonstrated That Most Genomic Research Findings Are False?

In a widely cited article, Ioannidis argued that most published research findings are false; particularly discovery research involving massive testing, genomics being a typical example. However, his argument ignores adjustment for multiple testing and thus should be taken with a large grain of salt. This is a potential example for statistics courses that concentrate on problem formulation.     

SAVE: Robust or not?

Abstract

Sliced average variance estimation (SAVE) is one of the best methods for estimating central dimension-reduction subspace in semi parametric regression models when covariates are normal. In recent days SAVE is being used to analyze DNA microarray data especially in tumor classification but most important drawback is normality of covariates. In this article, the asymptotic behavior of estimates of CDR space under varying slice size is studied through simulation studies when covariates are non normal but follows linearity condition as well as when covariates slightly perturbed from normal distribution and we observed that serious error may occur under violation normality assumption.     

Unemployed and their caseworkers: should they be friends or foes?

Summary.  In many countries, caseworkers in public employment offices have dual roles of counselling and monitoring unemployed people. These roles often conflict, which results in important caseworker heterogeneity: some consider providing services to their clients and satisfying their demands as their primary task. However, others may pursue their own strategies, even against the will of the unemployed person. They may assign jobs and labour market programmes without the consent of the unemployed person. On the basis of a very detailed linked jobseeker–caseworker data set for Switzerland, we investigate the effects of caseworkers' co-operativeness on the probabilities of employment of their clients. Modified statistical matching methods reveal that caseworkers who place less emphasis on a co-operative and harmonic relationship with their clients increase their chances of employment in the short and medium term.     

Journal Access Initiatives: Where Are They? Why the World Health Organization? and What Is HINARI?

Abstract

The move from print to online journal publishing has allowed the proliferation of journal access programs aimed at poor countries. These programs offer access to online journals on very favorable terms to developing country institutions and readers and are based on the premise that developing world scientists can contribute significantly to ameliorating the conditions of life in their countries. The authors give a brief overview of the environment in which these programs emerged, discuss different orientations of the major programs, examine the case of the Health InterNetwork Access to Research Initiative (HINARI), consider why the World Health Organization (WHO) runs a journal access program for developing countries, and conclude with the accomplishments of HINARI.     

Econometrics exams and round numbers: Use or misuse of indirect estimation methods?

The use of Monte Carlo methods to generate exam datasets is nowadays a well-established practice among econometrics and statistics examiners all over the world. Its advantages are well known: providing each student a different data set ensures that estimates are actually computed individually, rather than copied from someone sitting nearby. The method however has a major fault: initial “random errors,” such as mistakes in downloading the assigned dataset, might generate downward bias in student evaluation. We propose a set of calibration algorithms, typical of indirect estimation methods, that solve the issue of initial “random errors” and reduce evaluation bias. Ensuring round initial estimates of the parameters for each individual dataset, our calibration procedures allow the students to determine if they have started the exam correctly. When initial estimates are not round numbers, this random error in the initial stage of the exam can be corrected for immediately, thus reducing evaluation bias. The procedure offers the further advantage of rounding markers’ life by allowing them to check round numbers answers only, rather than lists of numbers with many decimal digits¹.     

Should Macroeconomic Forecasters Use Daily Financial Data and How?

We introduce easy-to-implement, regression-based methods for predicting quarterly real economic activity that use daily financial data and rely on forecast combinations of mixed data sampling (MIDAS) regressions. We also extract a novel small set of daily financial factors from a large panel of about 1000 daily financial assets. Our analysis is designed to elucidate the value of daily financial information and provide real-time forecast updates of the current (nowcasting) and future quarters of real GDP growth.     

High wage workers and low wage firms: negative assortative matching or limited mobility bias?

Summary.  In the empirical literature on assortative matching using linked employer–employee data, unobserved worker quality appears to be negatively correlated with unobserved firm quality. We show that this can be caused by standard estimation error. We develop formulae that show that the estimated correlation is biased downwards if there is true positive assortative matching and when any conditioning covariates are uncorrelated with the firm and worker fixed effects. We show that this bias is bigger the fewer movers there are in the data, which is 'limited mobility bias'. This result applies to any two-way (or higher) error components model that is estimated by fixed effects methods. We apply these bias corrections to a large German linked employer–employee data set. We find that, although the biases can be considerable, they are not sufficiently large to remove the negative correlation entirely.     

Robust statistical inference: weighted likelihoods or usual m-estimation?

We review the weighted likelihood estimating equations methodology introduced by Markatou, Basu and Lindsay (1995). and Basu, Markatou and Lindsay (1995) and compare it, in the case of symmetric and asymmetric contamination, with Huber's M-estimators of location. The simulation study shows that the weighted likelihood estimating equations estimator is at least as competitive as Huber's M-estimators in the case of symmetric contamination. In the case of asymmetric contamination it may be superior than Huber's M-estimators     

ML estimation for factor analysis: EM or non-EM?

To obtain maximum likelihood (ML) estimation in factor analysis (FA), we propose in this paper a novel and fast conditional maximization (CM) algorithm, which has quadratic and monotone convergence, consisting of a sequence of CM log-likelihood (CML) steps. The main contribution of this algorithm is that the closed form expression for the parameter to be updated in each step can be obtained explicitly, without resorting to any numerical optimization methods. In addition, a new ECME algorithm similar to Liu’s (Biometrika 81, 633–648, 1994) one is obtained as a by-product, which turns out to be very close to the simple iteration algorithm proposed by Lawley (Proc. R. Soc. Edinb. 60, 64–82, 1940) but our algorithm is guaranteed to increase log-likelihood at every iteration and hence to converge. Both algorithms inherit the simplicity and stability of EM but their convergence behaviors are much different as revealed in our extensive simulations: (1) In most situations, ECME and EM perform similarly; (2) CM outperforms EM and ECME substantially in all situations, no matter assessed by the CPU time or the number of iterations. Especially for the case close to the well known Heywood case, it accelerates EM by factors of around 100 or more. Also, CM is much more insensitive to the choice of starting values than EM and ECME.     

Glass Ceilings or Glass Doors? Wage Disparity Within and Between Firms

We investigate whether immigrant and minority workers’ poor access to high-wage jobs—that is, glass ceilings—is attributable to poor access to jobs in high-wage firms, a phenomenon we call glass doors. Our analysis uses linked employer–employee data to measure mean and quantile-wage differentials of immigrants and ethnic minorities, both within and across firms. We find that glass ceilings exist for some immigrant groups, and that they are driven in large measure by glass doors. For some immigrant groups, the sorting of these workers across firms accounts for as much as half of the economy-wide wage disparity they face. This article has the supplement materials online.     

Are Blogs Here to Stay?: An Examination of the Longevity and Currency of a Static List of Library and Information Science Weblogs

The chronological entries in Weblogs or blogs record musings, opinions, news or other information supplied by individuals or groups. The nature of blogs is ephemeral in that the content is closely tied to the time period of the posting. The author examines the library and information science blogs listed on Susan Herzog’s “BlogBib: Select Librarian/Library Blogs” to see if they were being updated thirteen months after Herzog stopped maintaining the Web site.¹ Active, inactive, ceased, and blogs with changed URLs were recorded. Extra content was noted.     

Ethnic ghettos in Britain: a fact or a myth?

A common claim is that Britain's minorities live isolated parallel lives in inner cities that have become ethnic ghettos. Does the evidence confirm such dangerous lack of integration? Ludi Simpson and Nissa Finney find a basic misunderstanding about population change—a misunderstanding that makes changing the ethnic mix of neighbourhoods an unrealistic policy goal.     

What works?: selectivity models and meta-analysis

What works seeks to identify rehabilitative treatments which are successful in reducing the likelihood that offenders will reoffend. A large number of small case–control studies have been reported in the literature, but with conflicting results. Meta-analysis has been used to reconcile these findings, but again with conflicting results. We reanalyse one of the published meta-analyses in the corrections literature and argue the importance of specifically modelling heterogeneity and selection bias. A sensitivity approach is advocated, suggesting lower average effects and substantially increased measures of uncertainty. The method is tested on a medical example where independent confirmation from a large controlled trial is also available.     

How are journal impact,prestige and article influence related? An application to neuroscience

The paper analyzes the leading journals in neurosciences using quantifiable research assessment measures (RAM), highlights the similarities and differences in alternative RAM, shows that several RAM capture similar performance characteristics of highly cited journals, and shows that some other RAM have low correlations with each other, and hence add significant informational value. Alternative RAM are discussed for the Thomson Reuters ISI Web of Science database (hereafter ISI). The RAM that are calculated annually or updated daily include the classic 2-year impact factor (2YIF), 5-year impact factor, immediacy (or zero-year impact factor), Eigenfactor score, article influence score, C3PO (citation performance per paper online), h-index, Zinfluence, PI-BETA (papers ignored by even the authors), 2-year and historical self-citation threshold approval ratings, impact factor inflation, and cited article influence (CAI). The RAM are analyzed for 26 highly cited journals in the ISI category of neurosciences. The paper finds that the Eigenfactor score and PI-BETA are not highly correlated with the other RAM scores, so that they convey additional information regarding journal rankings, that article influence is highly correlated with some existing RAM, so that it has little informative incremental value, and that CAI has additional informational value to that of article influence. Harmonic mean rankings of the 13 RAM criteria for the 26 highly cited journals are also presented. Emphasizing the 2YIF of a journal to the exclusion of other informative RAM criteria is shown to lead to a distorted evaluation of journal performance and influence, especially given the informative value of several other RAM.     

Gender and income inequality in the UK 1968–90: the feminization of earnings or of poverty?

This paper uses data from the Family Expenditure Survey for five selected years between 1968 and 1990 to examine trends in the income distribution in the UK, highlighting the role of women's labour force participation and earnings. The increased labour force participation of married women (especially mothers of young children in the 1980s) made a greater contribution to the decline of the 'traditional' male breadwinner family than the increased number of lone parents. The lower half of the distribution of weekly earnings became increasingly dominated by women. Though women's weekly earnings remained low relative to men's, the increase in their participation meant that, over the period, an increased share of family income came from women's labour market income: in 1990 nearly a quarter of the income of families with children came from women's earnings. Women's earnings were an important factor in keeping families out of poverty. There was no trend towards increasing feminization of poverty over the sample period. Adult women were somewhat more likely to be poor than adult men were, but female-headed families were very much more likely to be in poverty, and much more dependent on state benefits, than male-headed families were. Women's increased role in the labour market affected those in male-headed families more than those in female-headed families. Alongside a broad tendency for women's earnings to reduce poverty and inequality, there is evidence that the female population has become more economically polarized.     

Mixed models and unbalanced data: wherefrom,whereat and whereto?

A brief history of the early years (1820-1947) of random effects models and the estimation of variance components is followed by a personal evaluation of M.L, REML and MINQUE estimation. A method is suggested for combining ML estimator obtained from subsets of a large data set, and comments are made on the need for simulation studies to assess the degree of approximation in using asymptotic properties of ML-type estimators as if they were exact for finite-sized unbalanced data sets.     

Zero-inflated and overdispersed: what's one to do?

Zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB) models are recommended for handling excessive zeros in count data. For various reasons, researchers may not address zero inflation. This paper helps educate researchers on (1) the importance of accounting for zero inflation and (2) the consequences of misspecifying the statistical model. Using simulations, we found that when the zero inflation in the data was ignored, estimation was poor and statistically significant findings were missed. When overdispersion within the zero-inflated data was ignored, poor estimation and inflated Type I errors resulted. Recommendations on when to use the ZINB and ZIP models are provided. In an illustration using a two-step model selection procedure (likelihood ratio test and the Vuong test), the ZIP model was correctly identified only when the distributions had moderate means and sample sizes and did not correctly identify the ZINB model or the zero inflation in the ZIP and ZINB distributions.