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EMOS APPLICATION FORM 2018

1. APPLICANT INFORMATION

Please fill in the tables and mark the name of your University in the footer of the document.

Applying university:

Name of the University (in original language)Name of the University(in English)Acronym if commonly usedTownCountryWebsite of the InstitutionAdditional information (if necessary)

Contact information for EMOS related correspondence:

SurnameFirst nameFaculty/departmentPosition in the UniversityStreet addressP.O. boxPostal codeTownCountryE-mail address 1E-mail address 2Telephone numberMobile number

NAME OF THE UNIVERSITY of 13

Application form EMOS Call III 15 March – 30 June 2018

Information about the EMOS Master programme:

How do you qualify the EMOS programme in your proposal

☐ curriculum entirely dedicated to official statistics☐ add-on specialisation track on top of an underlying Master programme☐ other, please specify:

Title of the Master programme (in original language)Title of the Master programme(in English)Programme duration (in years)Number of ECTS creditsLink to the programme curriculum(if available – if not, please describe separately)Expected start date of the EMOS MasterTitle of degreeAdditional information (if necessary)

I confirm that the information given in this form is true, complete and accurate.

Programme Director:

Date: Signature_______________________________



2. MOTIVATION:

Please describe (max. 1000 characters):

your motivation to apply for the EMOS label what you think your proposal can bring to the network how you think the EMOS network can be beneficial for your institution any issue you think is relevant to bring to the attention to the assessment team



3. EMOS PROGRAMME DETAILS

Please describe (max. 1000 characters) your proposal for the EMOS programme

Objectives and structure If the EMOS proposal builds on an existing Master programme, please describe

which parts of the existing Master programme already support the EMOS learning outcomes and which are new elements



4. COURSES TO FULFIL EMOS LEARNING OUTCOMES - Please see EMOS Learning Outcomes (LO)

Explanatory notes

Course title: Give the title of the course contributing to the EMOS learning outcomesDescription: Brief description of the course and its expected resultsLeaning outcomes (LO) Mark the number of the learning outcome(s) the course fulfils:

1 a-b-c-d, 2 a-b-c, 3 a-b, 4 a-b-c, 5 a-b-c (see also Annex 2)ECTS Credits: Give the number of ECTS creditsComments: Add any other comments you may consider relevant below the table

Course title Description and expected results LO (1a-5c) No. of ECTS

1.

2.

3.

4.

5.

6.

Add as many rows as needed. Total number of ECTS:

Additional comments (please number your comments in the table above and list them below):



https://ec.europa.eu/eurostat/cros/system/files/emos_learning_outcomes.pdf

5. INTERNSHIPS AND MASTER THESES

Explanatory notes

Organisation: Name and contact details of the organisations supporting internships and/or mas-ter theses.Internships: If the organisation supports the Master with internships mark the duration in number of weeks.Master theses: If the organisation supports the Master theses, mark yes/no.Describe cooperation: Describe how the cooperation is implemented, list potential research topics.Number of students: Estimate the number of students the organisation would cover for supporting the EMOS Master.Comments: Add any other comments you may consider relevant.

Summary[Write a short summary or introduction. Explain how the respective part of the EMOS programme is organised – 10 lines max.]

Name of the organisation Name of the contact person,

function and e-mail

Internship (no. of weeks)

Master theses

(yes/no)

Describe cooperation, list potential research topics

No. of studen

ts

Comments

Add as many rows as needed.



6. TEACHING CAPACITY Explanatory notes

Name: The name of the teacherPosition/Level: Professional level (professor, lecturer, etc.) in your original language and the approximate English equivalentStatus: E.g. full-time employment at university, part-time employment, visiting teacher, NSI employee, etc.Courses taught: Give the title of the EMOS course taughtQualifications: Indicate the academic degree acquiredProfessional experience: Provide details for exposure to official statistics

Name Position/Level Status EMOS Courses taught Highest degree Experience & expertise in official statistics

[Examples]John Smith

Mary Jones

[Examples]Associate prof.

Guest lecturer

[Examples]Full-time at universityNSI employee

[Examples]Advanced Survey Methods

Official Statistics

[Examples]PhD

MSc

[Examples]Cooperation in several NSI projectsHead of standards section in the NSI for five years




7. QUALITY ASSURANCE

If your programme has established a quality assurance system, please describe below (max. 1000 characters)



8. COOPERATIONIf your programme has engaged in academic or vocational-oriented co-operation with institutions at the European or international level or through the creation of national networks, including an exchange of academic staff and students (at faculty/department level, but with identifiable goals for your pro-gramme or on its behalf), describe briefly the cooperation strategy, its objectives and achievements (max. 1000 characters)

Cooperation with Description of goals




Annex 1

NON-COMPULSORY TOOLS TO CHECK THE COMPLETENESS OF THE APPLICATION

LIST OF TABLES TO BE FILLED IN

No Table Done

1a Applicant information

2 Motivation

3 EMOS Programme details

4 Courses to fulfil EMOS learning outcomes

5 Internships and Master theses

6 Teaching Capacity

7 Quality assurance

8 Cooperation



OVERVIEW BY PPROPOSED COURSES vs LEARNING OUTCOMESCourse: Give the title of the course, add rows as needed Learning outcomes: Mark "X" if the competence in system of reference of learning outcomes in Annex 2 is fulfilled, leave blank if notECTS credits: Mark the total number of ECTS for the duration of the proposed EMOS programmeAdd rows as needed and mark at the end the total number of courses and ECTS

Course title

The system of official statistics

Production models and methods

Specific themes

Statistical methods

Dissemination of Official Statistics

No of ECTS over programme duration

1a 1b 1c 1d 2a 2b 2c 3a 3b 4a 4b 4c 5a 5b 5c

1.

2.

3.

4.

5.

6.

Add as many rows as needed. Total number of ECTS:



Annex 2

March 2018

Learning Outcomes of the EMOS programmes

I. Programme Profile

EMOS aims at providing students with an advanced training in the specific themes of statistics in general and official statistics in particular, supported by the complementary quantitative and statistical tools offered by the hosting university. The main objective of EMOS is to enhance the abilities of students to understand and to be able to analyse European official data at different levels: quality, production process, dissemination, and analysis in a national, European and international context.

This range of skills represents the ideal foundation for the development of professionals able to interpret the fast-changing official data production system of the 21st century. This is why it is important that the EMOS learning outcomes are included in a degree designed for students aiming at economical/social/statistical knowledge-intensive careers. It contributes to offer a solid foundation for those willing to pursue a preparation in the field of data collection on societal and economic facts or other professional activities characterised by a strong need for awareness about the development, production and dissemination as well as use of official statistics.

II. Learning Outcomes

Graduates who have successfully completed an EMOS programme will be able to demonstrate knowledge about:

1) The system of official statistics

a. To be aware of the relevance of official statistics as information infrastructure for the society and of its principles;

b. To master the organisation and role of the European Statistical System (ESS), the European System of Central Banks (ESCB) and other official data producers and their legal bases, includ-ing those referring to confidentiality;

c. To be aware of the main institutions operating at national and international level and their data sources (e.g. Eurostat, ECB, IMF, ILO, BIS, UN, OECD, World Bank);

d. To understand the statistical principles in the European Statistics Code of Practice (for the ESS)1 and the Public Commitment (for the ESCB) and how they apply to the different steps of data pro-duction and dissemination;

1 Laid down in Regulation (EC) No 223/2009 as amended as well as Council Regulation (EC) No 2533/98. NAME OF THE UNIVERSITY

of 13Application form EMOS Call III 15 March – 30 June 2018

2) Production models and methods

a. To understand and be able to use different kinds of data sources such as censuses, sample surveys – cross section, longitudinal –, administrative sources, big data, integrated sources, open linked data, experimental statistics as well as critic-ally evaluate pros and cons, also in terms of quality implications of the results;

b. To be able to design and manage data production processes, including the definition of the main dimensions of quality and how to monitor and evaluate them;

c. To be aware of different production models, including the business and enterprise architecture concepts applied to official statistics (e.g. metadata management, Generic Statistical Business Process Model, data archiving, mixed mode surveys, statistical standard classification);

3) Specific Themes

a. To be able to understand methodological issues related to some specific fields of official statistics and to interpret correctly official statistics in these fields and in evolving fields (e.g. general and regional statistics, economy and finance, population and social conditions, industry, trade and services, agriculture and fisheries, international trade, transport, environment and energy, sci-ence and technology etc.);

b. To be able to apply methods suitable to produce and analyse data in the specific field;

4) Statistical Methods

a. Knowledge of and ability to apply statistical methods such as sampling methods, small area es-timation, non-response adjustments and imputation, treatment of big data, time series analyses, index theory, multivariate statistics, econometrics, spatial statistics;

b. Critical capacity of framing analysis of statistical data within the context of editing, imputation, missing data problems, knowing the definition of metadata and paradata, data integration;

c. Ability to use statistical computer programmes such as SAS, R, SPSS or STATA;

5) Dissemination

a. Ability to present data in an effective way to different kinds of audience;

b. Understand confidentiality issues in the dissemination of official statistics and the main methods to ensure it (i.e. statistical disclosure control), especially when disseminating micro data;

c. To be aware of the different tools available for data and metadata dissemination and presentation of results (tables, charts in a static and dynamic web-based environment, data warehouses, ad-vanced visual graphics, etc.).



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