1. Approaches to quantitative analysis on student performance
Dr Diego Bunge and Dr Daniel Uribe

2. Contents Institutional student data Issues with key variables
Ethnicity
Socioeconomic background
Entry scores
Cohort vs year by year
Cohort analysis and final outcome
Logistic regressions
Profiles

3. Institutional student data
UCAS data
Data at Programme level
DLHE data
LEO data
Data at Module level
Bursaries and Household Income data
SITS

4. Issues with key variables
Ethnicity
Socioeconomic background
Entry scores

5. Ethnicity Ethnicity Ethnic categories cultural heritage / identity
social or cultural characteristics
historical experience
White
Asian
Black
Arab
Chinese
Mixed
Other
BME

6. Socioeconomic background
Higher managerial & professional occupations
Lower managerial & professional occupations
Intermediate occupations
Small employers & own account workers
Lower supervisory & technical occupations
Semi-routine occupations
Routine occupations
No answer
Parental occupation
(NS-SEC)
Higher education
Not higher education
Information refused or no answer
Parental education
Zero income
Up to £13,500
£13,501 to £22,500
£22,501 to £31,000
£31,001 to £44,106
Over £44,106
Non means tested-refused
Not reported
Bursary recipients
Household income

7. Entry scores Tariff points (UCAS) Best 3 A levels Other
Main issue spread scores
Best 3 A levels
Main problem  exclusion of cases
Other
e.g. what grades do we accept students on?

8. degree classification
Cohort vs year by year
Year by year analysis
2014/15
2013/14
Institutions -e.g. the Higher Education Statistics Agency (HESA)
Academic studies e.g. McNabb et al, 2002; Connor et al, 2004; Naylor and Smith, 2004; Broecke & Nicholls, 2007
Looks at
degree classification

9. Cohort vs year by year Looks at final outcome Cohort analysis 2015/16
2012/13 entrants
2015/16
3-4 years
Looks at final outcome

10. Cohort vs year by year
Year by year analysis
Cohort analysis

11. Year by year and progression
Progression of new entrants
2012/13 entrants
2013/14 entrants
Compare progression rates
Progressed
Didn’t progress
Withdrew
2013/14
2014/15

12. Analysis of 2011/12 cohort Data source: SITS
In 2011/12, 1,674 students started a programme at Faculty X.
After 3 to 4 years, 1,393 (83%) obtained a degree: 262 achieved a 1st and 845 a 2.1; 199 scored 2.2, 45 a 3rd or a pass and 42 other qualifications.
On the other hand, 204 withdrew or failed and 77 remained with chances to complete a degree.
Data source: SITS
Note: Total numbers do not include students who changed school or those who still have chances to obtain a degree.

13. Logistic regression results
Factors increasing the odds of
Higher entry scores, state-funded school, living at parental home
High entry scores, being female, having attended state funded schools
Zero income reported, Clearing, living at parental home
Data source: SITS
Note: Total numbers do not include students who changed school or those who still have chances to obtain a degree.

14. Logistic regression results
Factors decreasing the odds of
BME, Zero income reported
Zero Income reported, Clearing
Higher entry scores, being female, state-funded school
Data source: SITS
Note: Total numbers do not include students who changed school or those who still have chances to obtain a degree.

15. Profiles
H&SS
S&E

16. Diego Bunge d.bunge@qmul.ac.uk and Daniel Uribe d.uribe@qmul.ac.uk
Thank you
Diego Bunge and Daniel Uribe