University Rankings, Diversity, and the New ... - Sense Publishers

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UNIVERSITY RANKINGS AND LEAGUE TABLES can provide much insight into the actual learning environments or even in many instances student outcomes from individual institutions. It is for this specific reason, that many rankings systems – perhaps most notably U.S. News and World Report – have taken the trouble of asking institutions themselves to provide information about themselves for the rankings. In the United States, the result of so many news organizations doing this led to the creation of what is known as ‘the Common Data Set’, which has become a sort of alternative performance indicator datasource complementing the more narrow data available from the Department of Education and its IPEDS system. The benefit of this approach is that one can – in theory – answer a number of questions about institutional quality and learning environments that cannot otherwise be answered. For instance, one might be able to glean from government statistics what the teacher to pupil ratio at a school is, but one would not know things like average class sizes or the number of classes where professors (as opposed to graduate students) are teaching first year classes unless one asked institutions to provide the data themselves. The main drawback is that there is absolutely no guarantee that institutions will actually report the data to the ranker on a consistent basis, as all have a clear incentive to manipulate data in a manner which will benefit them. Indeed, at some institutions in the United States, there are staff positions within institutional research offices which require the incumbent to do nothing but provide institutional data to the US News and World Report in a favourable light. And, of course, as Maclean’s recently discovered to its cost, over-reliance on institutional data sources leaves one vulnerable to institutional boycotts. Finally, there is survey data. Typically, surveys have been used to get the opinions of ‘experts’ – usually professors, administrators and employers – about the quality and reputation of various institutions. This data is scientific in the sense that it records observations accurately, but has been criticized for being misused or acting as a ‘lagging indicator’ of school quality. However, as Federkeil (2006) has shown, narrowly focussed reputation surveys can actually be very useful, as professor’s views about relative departmental quality is actually an extremely good proxy for research output. More recently, surveys of students have become an important source of data in rankings, notably in the German CHE, Canadian University Navigator and the two Dutch rankings. The increasing use of this type of data is creating a real shift in the kinds of topics which rankings are being used to assess. Usher and Savino (2006) showed that surveys were the least frequently-used source of data among the various rankings examined; this was largely because the older surveys tended to only have one indicator (i.e. reputation) out of twenty or thirty populated by survey data. The CHE, Elsevier, Keuzegids Hoger Onderwijs and the University Navigator rankings all use survey data to populate half or more of all their indicators, so this has changed slightly (in addition, while THES uses surveys to populate only one out of its five indicators, but this indicator is worth 50% of the entire score). Outside North America, third-party sources are by far the most heavily used sources of data: indeed, four of the 18 ranking schemes in this study use them exclusively. Of the remaining 14, third-party sources comprise a plurality of indicators in eight and university sources form a plurality in six. The predominance of 7
USHER AND MEDOW data from universities is most understandable in the cases of the Asiaweek and THES rankings, as their international scope significantly reduces the possibility of third-party sources providing data on a consistent trans-national basis. (The other two international rankings – Shanghai Jiao Tong, and universia.com – solved this problem by relying almost exclusively on research output measures such as scientific publications and citations.) In the cases of the US News and World Report, Maclean’s, the Guardian and Rzeczpospolita, the explanation seems to be that the editors’ definitions of ‘quality’ could not be measured using government administrative data. This may indicate a failure of government data collection in these countries, in the sense that information deemed important to quality measurement is not collected on a consistent and centralized basis; alternatively, it may indicate that the rankers’ views of what constitutes an indicator of quality is not shared by governments or the higher education community. WHAT LEAGUE TABLES MEASURE – A LOOK AT INDICATORS AND WEIGHTINGS It should come as no surprise to learn that different ranking systems use very different indicators in order to obtain a picture of ‘quality’. The number of individual indicators used in ranking systems worldwide runs well into the hundreds, making any kind of comparison grid too large to be useful. In order to look at indicators and weightings in a manageable way, Savino and Usher categorized them into seven larger headings, based in part on an existing model of institutional quality written by Finnie and Usher (2005) (the alternative of using the four categories used by Dill and Soo (2004) was rejected because it was felt these categories were too broad). The Finnie-Usher work was originally a conceptual framework for quality measurement based on the following four elements: Beginning characteristics, which represent the characteristics, attributes and abilities of incoming students as they start their programs. Learning inputs, which come in two main types: – resources, both financial and material, available to students and faculty for educational ends; and – staff, both in terms of the numbers but also the way in which they are deployed to teach and the learning environment they create, as measured by the amount of contact time students have with their teachers, the kinds of exams they face, etc. Learning outputs, which represent the ‘skill sets’ or other attributes of graduates, which culminate from their educational experiences, such as critical thinking, analytic reasoning and technical knowledge. They also include records relating to retention and completion. Final outcomes represent the ultimate ends to which the educational system may contribute, including not only such traditional measures as employment rates and incomes but also any other outcome deemed to be important to individuals and society, such as job satisfaction, being a ‘good citizen’, etc. 8
Page 1 and 2: G L O B A L P E R S P E C T I V E S
Page 3 and 4: GLOBAL PERSPECTIVES ON HIGHER EDUCA
Page 5 and 6: A C.I.P. record for this book is av
Page 7 and 8: TABLE OF CONTENTS Towards Unified a
Page 9 and 10: STENSAKER AND KEHM There is growing
Page 11 and 12: STENSAKER AND KEHM of higher educat
Page 13 and 14: STENSAKER AND KEHM perspective, the
Page 15 and 16: STENSAKER AND KEHM about their view
Page 17 and 18: STENSAKER AND KEHM according to Mar
Page 19 and 20: STENSAKER AND KEHM Second, and very
Page 22: PART I: METHODOLOGY
Page 25 and 26: USHER AND MEDOW WHAT ARE UNIVERSITY
Page 27: USHER AND MEDOW With only a few exc
Page 31 and 32: USHER AND MEDOW Table 1. League Tab
Page 33 and 34: USHER AND MEDOW Other regional patt
Page 35 and 36: USHER AND MEDOW RANKINGS WITHOUT LE
Page 37 and 38: USHER AND MEDOW currently being und
Page 39 and 40: USHER AND MEDOW National Accreditat
Page 41 and 42: FEDERKEIL REPUTATION INDICATORS IN
Page 43 and 44: FEDERKEIL In particular, reputation
Page 45 and 46: FEDERKEIL Discipline Reputation sco
Page 47 and 48: FEDERKEIL The relationship between
Page 49 and 50: FEDERKEIL 41% 40% percent of cases
Page 51 and 52: FEDERKEIL 100 THES 2005: repu
Page 53 and 54: FEDERKEIL universities. According t

University Rankings, Diversity, and the New ... - Sense Publishers

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