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Higher Education Statistics – collection and processing
Discussion on the Swedish Case Anna Gärdqvist and Mats Haglund Statistics Sweden
Reflection on the Austrian CaseMichaela Schaffhauser-LinzattiUniversity of Vienna
May 11, 2006, Graz
Presentation
Sweden: students Austria: Overall Information
Reporting on performanceMonitoring of performanceMonitoring of data
Presentation
Paper• Data flow
• Organisational responsibilities
• General aspects of reporting on Higher Education
• Data flow
• Organisational responsibilities
• Selected repor- ting tools
• Possible answers from the Austrian experience
Data Flow
Public Universities
Vocational Universities (FH)
Private UniversitiesTheological UniversitiesOthers
Federal Ministry for Education,Science and Culture
FH-Council
Statistics Austria
University Report
Report on Higher Education
A:
Swedish National Board of Student Aid; Ministry of Education, Research and Culture; Statistics Sweden
S: Register of Higher Education
From Basic Data to Statistics
(Example: University of Vienna)
Administrativeregister
Register-statisti-cal processing
Statistical register
- Editing and coding
- Handling of missing objects and values
- Matching and selections
- Processing of time references
- Creating derived objects and variables
i3v Le Salaire SAP R/3
ETL
Data Warehouse
Reports
Federal Ministry
Sweden: Statistics Sweden Austria: Universities
Internal reports External reports
To the public To political decision-makers
Income and expenditure accounting
Others
Evaluation report
Performance Report
Intellectual Capital Statement
Financial Statement
Others
University Report
Report of the University Council
Others
External Reports (Public Universities)
according to UG 2002further: f.e. UniStEVO, BiDokV-Univ
Performance Report
• Aims– Report on the qualitative and quantitative development of the
university– Forecast on the future performance and financial situation – Basis for the university report
• Definition– Information on the performance agreement– Published annually by the rectorate– Including past two years, forecast for the following year
Performance Agreement• Aims
– Budgeting– Performance based controlling instrument
• Definition– Performance agreement by public law between each university and the
Federal Ministry– According to the aims of the university– Regulates budget allocation by the Federal Ministry on basis of the
performance of each university– For periods of 3 years– Indicators and details regulated by the order “Formelbudget-Verordnung”
Intellectual Capital Report (I)
• Aims– Measurement, evaluation und publication of
• Intellectual assets• Knowledge based processes• Knowledge based results
– Instrument for• Communication between university, Federal Ministry and the House of Parliament• Control for the university by separate bodies and through responsible bodies of
the state owner• Marketing of the university
Intellectual Capital Report (II)
• Definition– Demonstration of
• The university’s activities, social activities, and self-imposed objectives and strategies
• Intellectual capital, broken down into human, structural and relationship capital
• The processes set out in the performance agreement, including their outputs and impacts
– Published annually– Indicators and details regulated by the order “Wissensbilanz-
Verordnung”
Intellectual Capital Report Model
Visions, objectives, strategies
+
NarrativesAdditional topics
Intellectual Capital
Core processes
Resumee and perspectives
Narratives
Indicators
Indicators
Narratives
+Outcome and results of core processes Indicators
Contents
• Basis of data acquisition – Personalised
– Aggregated
• Dimension of indicators– Qualitative indicators
– Quantitative indicators• Monetary• Non-monetary
• Number of indicators– 53 “global” indicators
– Refer to 5 different reporting periods / key dates
– Structured into • Fields of Study• Curricula
Pros and Cons of the New Austrian Reporting System on Public Universities
• Pros– New reporting system with
international archetypes– Transparency– Communication
• External• Internal
– Management orientation• Comprehensive data
acquisition• Planning basis• Aid for decision making
• Cons – Overlappings of reports– Volume– Standardisation– Data validity– Costs of implementation,
preparation and auditing
Possible Consequences
• Performance orientation• Competition between the universities• Competition within the universities• New emphases of universities• New allocation of resources