MEASURING ACADEMIC RESEARCH IN CANADA:ALEX USHERHIGHER EDUCATION STRATEGY ASSOCIATES

IREG-7Warsaw, Poland – May 17, 2013

The Problem

When making institutional comparisons, biases can occur both because of institutional size and distribution of fields of study

Can we find a way to compare institutional research output in a way that controls for size and field of study?

YES

Basic methodology Simple 2-indicator system: publication (H-

index) and research income (granting councils)

Data gathered at the level of the individual researcher, not institution

Every researcher given a score for his/her performance relative to the average of his/her discipline. Scores are then summed and averaged.

Publication Metric: H-Index

“A scientist has index h if h of his/her Np papers have at least h citations each, and the other (Np − h) papers have no

more than h citations each.” (i.e., the largest possible number N where a scientist has a total of N papers with

N or more citations)

Ex. 2Publication 1: 10 citationsPublication 2: 2 citationsPublication 3: 2 citationsPublication 4: 2 citations

Ex. 1Publication 1: 5 citationsPublication 2: 4 citationsPublication 3: 3 citationsPublication 4: 2 citations

H-Index: 3

H-Index: 2

H-Index (pros and cons)- Pros

- Discounts publications with little or no impact- Discounts sole publications with very high impact

Cons- Requires a large, accurate, cross-referenced database

(labour)- Age bias (less concern on aggregates)- Differences in publication cultures (can be fixed)- Not very useful in disciplines with low publication

cultures

The HiBar Database

Automated collection & calculation

Manual correction

Analysis

Faculty listsStandardized discipline

names

Example: Dr. Joshua Barker

Barker, Joshua D.Associate ProfessorUniversity of Toronto

Social cultural anthropology, violence & power, crime & policing, theories of modernity, anthropology of technology, nationalism, urban studies; Indonesia, South East Asia

• Simple automated search

129 (1000+ pubs)

• Add advanced filtering and Boolean logic

43 (800+ pubs)

• Manual elimination of false positives, excluded publication types, etc.

2 (5

pubs)

The Canadian Prestige Hierarchy

Institution ARWU/THE

Toronto 1British Columbia 2McGill 3Alberta, McMaster, Montreal, Waterloo 2nd tierDalhousie, Laval, Queen’s, Simon Fraser, Calgary, Western, Guelph, Manitoba, Ottawa, Saskatchewan, Victoria

3rd tier

Laval, Carleton, Quebec, UQAM, Concordia Other major institutions

Science-Engineering H-Index

Rank Institution Score Rank

Institution Score

1 UBC 1.509 11 McMaster 1.1972 Toronto – St. G 1.504 12 Trent 1.1603 Montreal 1.500 13 Scarborough 1.1534 McGill 1.327 14 Manitoba 1.0575 Simon Fraser 1.306 15 Trois-Rivieres 1.0546 Waterloo 1.257 16 Alberta 1.0267 Ottawa 1.254 17 Western 0.9968 York 1.208 18 Concordia 0.9929 Queen’s 1.200 19 Laval 0.98910 Rimouski 1.200 20 UQAM 0.967

Arts H-IndexRank Institution Score Ran

kInstitution Score

1 UBC 1.927 11 Concordia 1.2442 Toronto – St. G 1.647 12 Trent 1.2383 McGill 1.629 13 Mississauga 1.2194 Queen’s 1.533 14 Scarborough 1.1925 Alberta 1.370 15 Carleton 1.1626 McMaster 1.364 16 Manitoba 1.1307 York 1.331 17 Montreal 1.0968 Guelph 1.320 18 Calgary 1.0709 Simon Fraser 1.312 19 Saskatchewan 1.05410 Waterloo 1.289 20 Western 1.016

Medicine

We did not cover medical fields

Impossible to do so because manner in which certain institutions choose to list staff at associated teaching hospitals made it impossible to generate equivalent staff lists.

Research Income

Collected data on peer-evaluated individual grants (i.e. major institutional allocations for equipment, etc excluded) made by two main granting councils (SSHRC and NSERC) over a period of three years

Data then field-normalized as per process for H-Index.

Research Income (pros and cons)

- Pros - Publicly available, 3rd party data, with personal

identifiers- Based on a peer-review system designed to

reward excellence

Cons- Issues with respect to cross-institutional awards- Ignores income from private sources which may

be substantial

Science-Engineering IncomeRank Institution Score Ran

kInstitution Score

1 UBC 1.640 11 Guelph 1.2502 Ottawa 1.623 12 McMaster 1.2303 Montreal 1.572 13 Waterloo 1.2294 Alberta 1.465 14 Queen’s 1.2165 Toronto- St. G 1.447 15 Simon Fraser 1.2066 Calgary 1.359 16 Scarborough 1.1877 Rimouski 1.295 17 Carleton 1.1398 Saskatchewan 1.292 18 Western 1.0939 McGill 1.281 19 Sherbrooke 1.01110 Laval 1.272 20 Chicoutimi 0.969

Arts IncomeRank Institution Score Ran

kInstitution Score

1 McGill 2.258 11 Calgary 1.3052 UBC 2.206 12 Dalhousie 1.2633 Montreal 1.944 13 Laval 1.2634 Guelph 1.901 14 Queen’s 1.1055 Alberta 1.895 15 Ottawa 1.0906 McMaster 1.799 16 Waterloo 1.0657 Toronto – St. G 1.733 17 Carleton 0.9918 York 1.615 18 Rimouski 0.9719 Concordia 1.582 19 Scarborough 0.95310 Simon Fraser 1.372 20 Western 0.951

Science-Engineering TotalRank Institution Score Ran

kInstitution Score

1 UBC 100 11 Queen’s 76.852 Montreal 97.63 12 Scarborough 74.403 Toronto – St. G 93.97 13 Calgary 73.264 Ottawa 91.05 14 Laval 71.555 McGill 83.05 15 Saskatchewan 70.156 SFU 80.04 16 Guelph 66.887 Rimouski 79.24 17 Western 66.348 Waterloo 79.14 18 York 65.979 Alberta 78.67 19 Carleton 62.0110 McMaster 77.18 20 Concordia 59.67

Arts TotalRank Institution Score Ran

kInstitution Score

1 UBC 98.84 11 Queen’s 64.252 McGill 92.26 12 Waterloo 57.033 Toronto – St. G 81.83 13 Calgary 56.654 Alberta 77.52 14 Dalhousie 54.095 Guelph 76.35 15 Carleton 51.276 Montreal 75.32 16 Scarborough 51.267 McMaster 75.22 17 Trent 48.368 York 70.29 18 Western 47.429 Concordia 67.15 19 Mississauga 47.1510 Simon Fraser 64.44 20 Ottawa 46.06

Controversies (1)

The double-count issue. In an initial draft, we included a record count of staff rather than a head count (former is higher because of cross-appointments). Led to questions

The part-time professor issue. Many objected to our inclusion of part-time staff in the total. So we re-did the numbers without them…

NSERC Scores (revised)New Rank

Institution Old Rank

New Rank

Institution Old Rank

1 UBC 1 11 Rimouski 72 Toronto-St. G 3 12 McMaster 103 Montreal 2 13 Queen’s 114 SFU 6 14 York 185 McGill 5 15 Guelph 166 Ottawa 4 16 Saskatchewan 157 Alberta 9 17 Manitoba 278 Waterloo 8 18 Trent 219 Laval 14 19 Western 1710 Calgary 13 20 Concordia 20

SSHRC Scores (revised)New Rank

Institution Old Rank

New Rank

Institution Old Rank

1 McGill 2 11 Concordia 92 UBC 1 12 Calgary 133 Toronto-St.G 3 13 Waterloo 124 Guelph 5 14 Laval 215 Alberta 4 15 Ottawa 206 McMaster 7 16 Dalhousie 147 Montreal 6 17 UQAM 438 Queen’s 11 18 Trent 179 Simon Fraser 10 19 Carleton 1510 York 8 20 Western 18

The Philosophical Part

Who is a university?

Whose performance gets included in a ranking says something about who one believes embodies a university. Should it include:

FT faculty only? PT faculty? Emeritus faculty? Graduate students?

At the moment, most ranking systems decision driven by data collection methodology.

Do all subjects matter equally? Field-normalization implies that they do.

But is this correct? Are some fields more central to the creation of knowledge than others? Should some fields be privileged when making inter-institutional comparisons?

Does Size Matter?

Does aggregation of talent bring benefits of its own, independent of the quality of people being aggregated?

Where Does Greatness Lie?

On whose work should institutional reputation be based? Its best scholars, or all of its scholars?

Norming for size implicitly rewards schools with good average professors. Failure to norm more likely to reward a few “top” professors