MEASURING ACADEMIC RESEARCH IN CANADA:ALEX USHERHIGHER EDUCATION STRATEGY ASSOCIATES

IREG-7

Warsaw, 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 Professor

University 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 1

British Columbia 2

McGill 3

Alberta, McMaster, Montreal, Waterloo 2nd tier

Dalhousie, 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 1.197

2 Toronto – St. G 1.504 12 Trent 1.160

3 Montreal 1.500 13 Scarborough 1.153

4 McGill 1.327 14 Manitoba 1.057

5 Simon Fraser 1.306 15 Trois-Rivieres 1.054

6 Waterloo 1.257 16 Alberta 1.026

7 Ottawa 1.254 17 Western 0.996

8 York 1.208 18 Concordia 0.992

9 Queen’s 1.200 19 Laval 0.989

10 Rimouski 1.200 20 UQAM 0.967

Arts H-Index

Rank Institution Score Rank

Institution Score

1 UBC 1.927 11 Concordia 1.244

2 Toronto – St. G 1.647 12 Trent 1.238

3 McGill 1.629 13 Mississauga 1.219

4 Queen’s 1.533 14 Scarborough 1.192

5 Alberta 1.370 15 Carleton 1.162

6 McMaster 1.364 16 Manitoba 1.130

7 York 1.331 17 Montreal 1.096

8 Guelph 1.320 18 Calgary 1.070

9 Simon Fraser 1.312 19 Saskatchewan 1.054

10 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 Income

Rank Institution Score Rank

Institution Score

1 UBC 1.640 11 Guelph 1.250

2 Ottawa 1.623 12 McMaster 1.230

3 Montreal 1.572 13 Waterloo 1.229

4 Alberta 1.465 14 Queen’s 1.216

5 Toronto- St. G 1.447 15 Simon Fraser 1.206

6 Calgary 1.359 16 Scarborough 1.187

7 Rimouski 1.295 17 Carleton 1.139

8 Saskatchewan 1.292 18 Western 1.093

9 McGill 1.281 19 Sherbrooke 1.011

10 Laval 1.272 20 Chicoutimi 0.969

Arts Income

Rank Institution Score Rank

Institution Score

1 McGill 2.258 11 Calgary 1.305

2 UBC 2.206 12 Dalhousie 1.263

3 Montreal 1.944 13 Laval 1.263

4 Guelph 1.901 14 Queen’s 1.105

5 Alberta 1.895 15 Ottawa 1.090

6 McMaster 1.799 16 Waterloo 1.065

7 Toronto – St. G 1.733 17 Carleton 0.991

8 York 1.615 18 Rimouski 0.971

9 Concordia 1.582 19 Scarborough 0.953

10 Simon Fraser 1.372 20 Western 0.951

Science-Engineering Total

Rank Institution Score Rank

Institution Score

1 UBC 100 11 Queen’s 76.85

2 Montreal 97.63 12 Scarborough 74.40

3 Toronto – St. G 93.97 13 Calgary 73.26

4 Ottawa 91.05 14 Laval 71.55

5 McGill 83.05 15 Saskatchewan 70.15

6 SFU 80.04 16 Guelph 66.88

7 Rimouski 79.24 17 Western 66.34

8 Waterloo 79.14 18 York 65.97

9 Alberta 78.67 19 Carleton 62.01

10 McMaster 77.18 20 Concordia 59.67

Arts Total

Rank Institution Score Rank

Institution Score

1 UBC 98.84 11 Queen’s 64.25

2 McGill 92.26 12 Waterloo 57.03

3 Toronto – St. G 81.83 13 Calgary 56.65

4 Alberta 77.52 14 Dalhousie 54.09

5 Guelph 76.35 15 Carleton 51.27

6 Montreal 75.32 16 Scarborough 51.26

7 McMaster 75.22 17 Trent 48.36

8 York 70.29 18 Western 47.42

9 Concordia 67.15 19 Mississauga 47.15

10 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 7

2 Toronto-St. G 3 12 McMaster 10

3 Montreal 2 13 Queen’s 11

4 SFU 6 14 York 18

5 McGill 5 15 Guelph 16

6 Ottawa 4 16 Saskatchewan 15

7 Alberta 9 17 Manitoba 27

8 Waterloo 8 18 Trent 21

9 Laval 14 19 Western 17

10 Calgary 13 20 Concordia 20

SSHRC Scores (revised)

New Rank

Institution Old Rank

New Rank

Institution Old Rank

1 McGill 2 11 Concordia 9

2 UBC 1 12 Calgary 13

3 Toronto-St.G 3 13 Waterloo 12

4 Guelph 5 14 Laval 21

5 Alberta 4 15 Ottawa 20

6 McMaster 7 16 Dalhousie 14

7 Montreal 6 17 UQAM 43

8 Queen’s 11 18 Trent 17

9 Simon Fraser 10 19 Carleton 15

10 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