The new multidimensional and user-driven higher education ranking concept of the European Union

Higher education rankings constitute a important but controversial topic due to the methodologies applied in existing rankings and to the use being done of these interpreting their results for purposes which they were not designed for. At present there is no international ranking can responds to the needs of all users and that is methodologically sound by considering the various missions of higher education institutions, mainly due to a narrow focus on research giving less importance to other missions in which higher education institutions can excel beyond research such as teaching quality, knowledge transfer, international orientation, regional engagement etc. The European Commission is currently involved in the implementation of a new higher education ranking methodology, characterised by taking into account a diversity of missions and the diversity of existing higher education institutions. The final aim is to create a tool allowing users to choose the performance indicators of their interest and providing them with a personalised ranking according to their interests. This paper describes the motivation for designing such a tool, the principles of the methodology proposed, as well as the steps foreseen to have it ready for end users by 2014.     

Appearance and Reality in The Philosophical Gourmet Report: Why the Discrepancy Matters to the Profession of Philosophy

This article is a data‐driven critique of The Philosophical Gourmet Report (PGR), the most institutionally influential publication in the field of Anglophone philosophy. The PGR is influential because it is perceived to be of high value. The article demonstrates that the actual value of the PGR, in its current form, is not nearly as high as it is assumed to be and that the PGR is, in fact, detrimental to the profession. The article lists and explains five objections to the methods and methodology of the report. Taken together, the objections demonstrate that the report is severely flawed, failing to provide the information it purports to and damaging the profession overall. Finally, the article explains how several modifications may improve the PGR so that it can more legitimately and equitably play the role it already plays.     

内隐的可接近信息对FOK判断等级和准确性的影响

该研究使用RJR研究范式,探讨内隐的可接近信息对FOK判断等级和判断准确性的影响。结果发现：FOK判断等级受内隐的可接近信息量的影响,二者呈非单调递增、递减的关系;FOK判断等级随内隐的可接近信息强度的增强而增高;FOK判断准确性随内隐的可接近信息量的增多而降低,并随内隐的可接近信息强度的增强而增高。     

Checking for Systematic Value Preferences Using the Method of Triads

Value preferences have long been central to research in political science and psychology. Despite their well‐established theoretical importance, however, their measurement is still an open question. Early research on values relied heavily on ranking instruments for data collection, but more recent work calls this measurement technique into question. Specifically, it is argued that traditional ranking instruments are (1) too long, (2) too complex, and (3) may force respondents to make ad hoc differentiations between values of similar importance, behind which there is no systematic preference. As a result, the reliability of the measure is called into question, and measurement error remains a concern. In this article, we discuss the method of triads—a technique used to gather rankings data that affords the researcher the opportunity to assess the extent to which random error affects preference rankings. Using the method of triads to collect preference data on five values central to American political culture, we find that Americans' value preferences are clearly structured and driven by systematic preferences, even when psychological theory suggests they may not. We also compare the predictive validity of the data collected with the method of triads against that of the data collected with traditional importance ratings. We show that models of ideology, party identification, presidential approval, and vote‐choice fit to “triads” data explain more variance than models fit to ratings data.     

Factor and ideal point analysis for interpersonally incomparable data

Interpersonally incomparable responses pose a significant problem for survey researchers. If the manifest responses of individuals differ from their underlying true responses by monotonic transformations which vary from person to person, then the covariances of the manifest responses tools such as factor analysis may yield incorrect results. Satisfactory results for interpersonally incomparable ordinal responses can be obtained by assuming that rankings are based upon a set of multivariate normal latent variables which satisfy the factor or ideal point models of choice. Two statistical methods based upon these assumptions are described; their statistical properties are explored; and their computational feasibility is demonstrated in some simulations. We conclude that is possible to develop methods for factor and ideal point analysis of interpersonally incomparable ordinal data.This research was begun in the supportive enviroment of the Survey Research Center at the University of California, Berkeley. Financial support was provided by Percy Tannenbaum, Director of the Center, by Allan Sindler, Dean of the Graduate School of Public Policy at Berkeley, by the Data Center of Harvard University, and by the National Science Foundation through grant number SES-84-03056. Chris Achen, Doug Rivers, and members of the Harvard-MIT econometrics seminar provided useful comments.     

Modeling within-subject dependencies in ordinal paired comparison data

This paper presents two probabilistic models based on the logistic and the normal distribution for the analysis of dependencies in individual paired comparison judgments. It is argued that a core assumption of latent class choice models, independence of individual decisions, may not be well-suited for the analysis of paired comparison data. Instead, the analysis and interpretation of paired comparison data may be much simplified by allowing for within-person dependencies that result from repeated evaluations of the same options in different pairs. Moreover, by relating dependencies among the individual-level responses to (in)consistencies in the judgmental process, we show that the proposed graded paired comparison models reduce to ranking models under certain conditions. Three applications are presented to illustrate the approach.This research was partially supported by NSF grant SBR-9409531. The authors are grateful to the reviewers, Alan Agresti and Herbert Hoijtink for their helpful comments on this research.     

Monte Carlo studies in nonmetric scaling

In response to Arabie several random ranking studies are compared and discussed. Differences are typically very small, however it is noted that those studies which used arbitrary configurations tend to produce slightly higher stress values. The choice of starting configuration is discussed and we suggest that the use of a principal components decomposition of the doubly centered matrix of dissimilarities, or some transformation thereof, will yield an initial configuration which is superior to a randomly chosen one.This research was supported by the National Research Council of Canada (Grant No. A8351) and by the National Institute of Mental Health (Grant Nos. MH10006 and MH26504). The authorship order has been determined by Monte Carlo methods.     

FOK判断与可接近信息之间的关系

通过三个实验,用语义联想集大小不同的中文单字词研究了靶项目联想集的大小与FOK判断等级和准确性的关系。实验一通过两种不同联想集大小的靶子,比较外显的、提取出的信息和内隐的、语义网络中固有的被激活信息量对FOK判断等级和FOK判断准确性是否有不同的影响。实验二和实验三通过不同的靶子呈现时间和学习遍数,比较不同的识记强度对不同大小联想集的靶子的FOK判断等级和FOK判断准确性的影响。实验结果表明：FOK判断等级随外显的、提取出的信息总量的增多而增高,随内隐的、语义网络中固有的被激活信息量的增多而降低。FOK判断准确性随靶子本身的激活强度的增强而增高,随内隐的、语义网络中固有的被激活信息的激活强度的增强而降低,FOK判断准确性取决于这两种激活强度的综合作用。     

Predicting Presidential Performance in the United States: Equation Replication on Recent Survey Results

Abstract

For more than 2 decades, researchers have tried to identify the variables that predict the overall performance of U.S. presidents. In 1986, there emerged a 6-variable prediction equation (D. K. Simonton, 1986c, 1987b) that has been replicated repeatedly. The predictors are years in office, war years, scandal, assassination, heroism in war, and intellectual brilliance. The author again replicated the equation on recent rankings of all presidents from George Washington through William Jefferson Clinton according to a survey of 719 experts (W. R. Ridings, Jr., & S. B. Mclver, 1997). The original 6-variable equation successfully predicted both the overall rankings as well as the 5 core components of the rankings (leadership qualities, accomplishment, political skill, appointments, character and integrity). The predictive value of the equation was illustrated for the presidencies of Ronald W. Reagan, George H. W. Bush, and Clinton.