european master in official statistics: the emos module · to-face, web and mixed). second part...

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EUROPEAN MASTER IN OFFICIAL STATISTICS: THE EMOS MODULE EMOS constitutes a pillar in the parent study programme Master Studies in Statistics Objective: Education of statisticians specialized in Survey Statistics and Official Statistics Studies: The EMOS module structured within the parent study programme EMOS module (120 ECTS): Studies required for Master's degree in Statistics at the UH and studies required to award the EMOS label CURRICULUM

EMOS core module (12 ECTS)

Survey Sampling 7 credits 1st year o Part 1: Sampling and Estimation o Part 2: Topics in Official Statistics

Survey Methodology 5 credits 1st year EMOS semi-elective courses (minimum 30 ECTS)

Intro to Register-Based Research 5 credits 1st year

Data Analysis with SAS 5 credits 1st year

Demographic Analysis 6 credits 1st year

Register-Based Data Analysis 5 1st year

Small Area Estimation 8 credits 2nd year

Structural Equation Models 8 credits 2nd year

Generalized Linear Mixed Models 6 credits 2nd year

Other applicable courses EMOS elective courses (minimum 25 ECTS)

Analysis of Complex Surveys 5 credits 1st year

Applied Logistic Regression 5 credits 1st year

Environmental Statistics 5 credits 1st year

Robust Regression 6 credits 1st year

Analysis of Categorical Data 5 credits 2nd year

Statistical Demography 5 credits 2nd year

Discrete Markov Processes 5 credits 2nd year

Nonparametric and Robust Methods 6 credits 2nd year

Other applicable courses Master's Thesis (incl. internship) (40 ECTS)

Master's Thesis Project 40 credits 1st /2nd year Obligatory courses for Master's degree (10 ECTS)

Generalized Linear Models 5 credits 1 st year

Advanced Statistical Inference 5 credits 2nd year Other obligatory studies (3 ECTS)

Master seminar 2 credits 1st /2nd year

HOPS (Personal Study Plan) 1 credits 1st year

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ANNEX EMOS STUDIES

EMOS PROGRAMME DETAILS Master Studies in Statistics - University of Helsinki, Faculty of Social Sciences, Department of Social Research The aim of the study programme “Master Studies in Statistics” is to educate statisticians qualified in theories and methodologies of statistical science and capable to apply statistical methods in important and emerging areas of the society. Required entry qualification consists of an applicable Bachelor degree with studies in relevant disciplines (e.g. statistics, mathematics, economics, sociology, computer sciences). The study programme cooperates closely with the Centre for Research Methods of the Faculty of Social Sciences. The programme qualifies for doctoral studies in statistics. The objective of studies in "European Master in Official Statistics (EMOS)" is to educate statisticians specialized in methodological areas of official statistics. Studies are organized as an EMOS module structured within the study programme “Master Studies in Statistics”. The EMOS module consists of studies required to award the EMOS label and additional studies required for Master's degree in statistics at University of Helsinki. The EMOS studies are divided into the following parts: EMOS core module (obligatory) (Table 1) EMOS Semi-elective courses (Table 2) EMOS Elective courses (Table 3) Master's thesis (Table 4) Internship (Table 5) The detailed description of EMOS studies is given in Annex. Obligatory Master's studies in statistics that are described elsewhere (Tilastotieteen tutkintovaatimukset) are excluded.

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Table 1. EMOS MODULE

Summary

EMOS core module is compulsory. The core module includes key areas relevant to official statistics. The core module is implemented by two courses, “Survey Sampling” and “Survey Methodology”.

Course/module Hours

Credits Description and expected results Comments

Survey Sampling 30 hrs (one period course)

7 The course consists of Part 1 and Part 2. PART 1 (5 ECTS) Sampling and estimation Description Part 1 gives an overview on sampling methods and estimation under different sampling designs and the use of the methods in empirical research and statistics production. Methods include simple random sampling, systematic sampling, Bernoulli sampling, Poisson sampling, PPS sampling, stratified sampling and multi-stage sampling and the estimation of finite population parameters under the various sampling designs, including HT estimation, GREG estimation and calibration as well as variance estimation. Special emphasis is in methods to incorporate auxiliary information into sampling design and estimation design. Empirical

Implementation: Yearly Part 1 involves lecture sessions and PC training sessions Method of completion for Part 1 Course exam 5 ECTS Student can gain extra credit points (2 ECTS) by homework assignment Reading: Lehtonen R. and Pahkinen E. (2004) Practical Methods for Design and Analysis of Complex Surveys. 2nd Edition. Wiley. Lohr S.L. (2010) Sampling: Design and Analysis, 2nd Edition. Brooks/Cole.

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examples and case studies are given. Software are used (SAS, SPSS, R). Expected results Knowledge on

- Sample survey, register survey, data integration

- Sampling techniques - Use of auxiliary data in

sampling and estimation - Estimators for finite

population parameters (HT, GREG, calibration)

Ability to apply selected methods in practice PART 2 (2 ECTS) Topics in Official Statistics Part 2 is devoted to the EMOS students. Description Part 2 covers key materials required for the core module, including essentials on ESS and NSS as well as the necessary statistics quality and production process related topics. Expected results Knowledge on:

- Essentials of National Statistical System (NSS)

- Essentials of European

Method of completion for Part 2: Course exam 2 ECTS Selected reading from sources (1), (2) and (3) Reading: (1) Quality Guidelines for Official Statistics. 2nd Revised Edition. Statistics Finland, Handbooks 43B, 2007. http://www.stat.fi/meta/qg_2ed_en.pdf

(2) Martin M. E., Miron L. Straf and Constance F. Citro (2005) (ed), Principles and practices for a federal statistical agency; National Research Council. 3rd ed. Washington: National Academy Press. (3) Simpura J. and Melkas J. (2013): Tilastot käyttöön! Opas tilastojen maailmaan. Gaudeamus. (In Finnish) (Statistics in use! Guide to the world of statistics)

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Statistical System (ESS) - Code of Practice - Quality - Production Model - Statistical Disclosure Control - Data law - Classification - Evaluation and Monitoring

Survey Methodology 30 hrs (one period course)

5 Description The course gives an overview of methods and practices of statistical surveys. The first part of the course covers principles of questionnaire design and survey modes used in data collection (postal, phone, face-to-face, web and mixed). Second part covers weighting and reweighting methods and imputation methods for adjustment of unit and item nonresponse. Imputation methods are integrated with statistical data editing. Empirical examples and case studies using international data sources (e.g. European Social Survey, PISA) are given. Software tools are discussed (SAS, SPSS, R). Expected results Knowledge on:

- Survey data production procedure and survey data terms

- Meta data and para data - Questionnaire designing and

survey modes

Implementation: Yearly The course involves lecture sessions and PC training sessions Course exam: 5 ECTS Student can gain extra credit points (2 ECTS) by homework assignment Reading: Groves R.M., Fowler F.J., Couper M.P., Lepkowski J.M., Singer E., Tourangeau R. (2009). Survey Methodology. 2nd ed. Wiley. Bethlehem J.G. (2009). Applied Survey Methods: A Statistical Perspective. Wiley. Laaksonen S. (2013) Surveymetodiikka. 2nd Edition. BookBon. (In Finnish)

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- Treatment of unit and item nonresponse

- Editing and imputation - Sampling design data file - Weighting and reweighting

Ability to apply selected methods in practice

Total credits: 12

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Table 2. SEMI-ELECTIVE COURSES

Summary

Optional courses The semi-elective courses include a set of optional courses in general statistics and survey statistics that are relevant to official statistics. Student can select from a list of courses so that the limit of 30 ECTS is met. Table 2 extends to applicable courses on survey methodology, econometrics and time series analysis and computational statistics, given by Department of Social Research, Department of Political and Economic Studies and Department of Mathematics and Statistics of Faculty of Science (agreed on case basis to fit with EMOS requirements of semi-elective courses).

Course/module Hours


Register-Based Research 14 hrs (one period course)

3 Description The course provides introduction to the most widely used Finnish administrative registers, study designs suitable for register-based research and the principles of data pre-processing. Expected result The student gets an overview of the research process involving register-based data and related philosophic-methodological challenges, will be able to design register-based research and learns to critically evaluate studies utilizing register-based data.

Method of completion: Homework assignment

Data Analysis with SAS 21 hrs (one period course)

5 Description The course covers the essential data handling and data analysis tools implemented in selected SAS software (ver. 9.4) packages (BASE, STAT, GRAPH, IML). Expected result Knowledge on:


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- Data analysis by SAS tools - SAS programming - Data language - Macro language - Matrix algebra (IML language) - Main descriptive and analysis SAS

procedures - Interfaces (SAS/EG) - Graphics - SAS and R interface.

Ability to use SAS tools in data analysis practice

Demographic Analysis 30 hrs (one period course)

6 Description The evolution of human populations is given in terms of book-keeping equations, in which births, deaths and migration determine population change. Demographic measures describing such processes are defined, and their estimation by age and sex is described. Hands on experience using Statistics Finland population databases is emphasized. Expected result Student learns the basic approaches, methods, data sources and computational tools used in demographic analysis and is able to apply selected methods in typical real-world situations.

Method of completion: Homework assignments plus exam

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Linear Mixed Models 30 hrs (one period course)

6 Description The course provides an introduction to methods used in causal research, when the outcome of interest is continuous. The dependencies arise when experimental units form groups (such as members of a family, repeated measures from the same individual, or measures from close locations). The term “mixed” refers to the presence of both fixed and random effects in the linear mixed model. R package is used for computing. Expected result Student understands the rationale and scope of mixed modeling, knows the basic principles of their numerical estimation, and is able to apply selected methods using R in typical real-world analysis situations.


Small Area Estimation 30 hrs (one period course)

8 Description The course covers topics in modern statistical methods for the estimation of parameters for population subgroups or domains and small areas (Small Area Estimation, SAE). Topics include:

- Sampling design for SAE - Design-based model-assisted methods

(generalized regression estimation, calibration techniques)

- Model-based methods (synthetic, EBLUP and EBP estimators)

- Variance and MSE estimation

Method of completion: Course exam plus homework assignment Reading: Lehtonen R. and Veijanen A. (2009). Design-based methods for small area estimation. In: Rao C.R. and Pfeffermann D. (Eds.). Handbook of Statistics vol. 29B. Elsevier. Rao J.N.K. (2003). Small Area Estimation. Wiley.

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- SAS and R tools - real-world applications - Case studies

Expected result Student gets knowledge on approaches, methods, data sources and computational tools in the estimation for regions and other population subgroups and is able to apply selected methods in typical real-world analysis situations.

Structural Equation Models

42 hrs (one period course)

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Description The course gives an overview on approaches, methods and tools for structural equation modelling in social and behavioural sciences. Real-world applications are given. Mplus and R software are used as the computational tools. Expected result Student gets knowledge on approaches, methods and computational tools in structural equation modelling and is able to apply selected methods in typical real-world analysis situations.

Method of completion: Homework assignments weekly

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Generalized linear mixed models


6 Description The course provides an introduction to methods used in causal research, when the outcome of interest might be a binomial “yes/no” or a Poisson distributed count. The dependencies arise when experimental units form groups (such as members of a family, repeated measures from the same individual, or measures from close locations). The term “mixed” refers to the presence of both fixed and random effects in the generalized linear model. R package is used for computing. Expected result: Student understands the difference between linear and generalized linear mixed models, appreciates the implications of the added computational burden for the latter. Student will be able to use existing likelihood based and Bayesian software to estimate such models, and understands the numerical techniques that are needed. Student is able to to apply selected methods in typical real-world analysis situations.


Examples of other applicable courses

Stationary Time Series Analysis

5 Dept. of Mathematics and Statistics See Faculty of Science

Multidimensional Time Series Analysis

5 Dept. of Mathematics and Statistics See Faculty of Science

Computational Statistics 5 Dept. of Mathematics and Statistics See Faculty of Science

Total credits:

30 (Minimum)

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Table 3. ELECTIVE COURSES

Summary

Optional courses. The elective courses include a set of optional courses in general statistics and survey statistics. Student can select from a list of courses so that the minimum of 25 ECTS is met. Table 3 extends to applicable methodological and subject matter courses, given by Department of Social Research, Department of Political and Economic Studies and Department of Mathematics and Statistics of Faculty of Science (agreed on case basis to fit with the EMOS framework).

Course/module Hours


Analysis of Complex Surveys


5 Description The course covers topics in the analysis of complex survey data where the complexity arises from the complex (multi-stage) cluster sampling design. Complex sampling designs involve correlation between observations within clusters. Methods are needed that properly account for the complexities in the analysis phase. Methods covered include variance estimation by linearization, jackknife and bootstrap, design-based Wald tests of independence and homogeneity, linear and logistic regression and ANCOVA, and linear mixed modelling. Methods are applied with SAS and R tools to complex survey data collected by stratified cluster sampling. Expected result Student gets knowledge on approaches, methods and computational tools needed in the analysis of complex surveys and is able to apply the selected methods in typical real-world analysis situations.

Method of completion: Course exam plus homework assignment Reading: Lehtonen R. and Pahkinen E. (2004) Practical Methods for Design and Analysis of Complex Surveys. 2nd Edition. Wiley. Lohr S.L. (2010) Sampling: Design and Analysis, 2nd Edition. Brooks/Cole.

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Applied Logistic Regression


5 Description This course covers the statistical modelling of binary outcomes using regression techniques. In this setting the application of ordinary regression techniques is only partly justified. Among the many models that could be entertained, logistic regression is favored because of its mathematical tractability. R will be used for computation. Expected results Student understands the basic features of the probability models underlying logistic regression and their differences and similarities as compared to ordinary linear regression. Student understands the basic principles that are used in estimation, and is able to apply the methods in typical real-world analysis situations.


Environmental Statistics 30 hrs (one period course)

5 Description Environmental statistics aims to describe the state and changes in environmental domain. Special interest is in the interaction of humanity with the environment. Course concentrates to practical applications of statistical methods to environmental phenomena. Expected results Expected result is to provide students adequate skills and tools to apply appropriate methods to analyse environmental changes and challenges.


Robust Regression 34hrs (one period course)

6 Description In linear regression analysis, the ordinary least

Method of completion: Course exam

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squares estimation method is very sensitive to outliers (i.e. to observations that are distant from the main part of the sample data). In this course, we will study estimation methods which are robust against outlying observations. Expected results After this course the students 1) will understand the basic concepts of robust regression, 2) know several different robust estimation methods along with their strengths and weaknesses and 3) are able to use R for robust regression data analysis.

Analysis of Categorical Data


5 Description Likelihood based methods for analysis of categorical data are explored. These include confidence intervals and tests for proportions and differences of them, testing for independence, small sample adjustments and methods and logistic regression. McNemar’s test for matched pairs data is also presented. R software is used in the homework assignment. Expected results Theory of statistical inference becomes alive in students’ minds in the context of categorical data. Students become aware of potential exciting applications, develop a skill to do them themselves and a desire to learn more.

Method of completion: Course exam + homework assignment

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Discrete Markov Processes

30 hrs (one period course

5 Description Stochastic processes are probabilistic models for random phenomena that display some forms of dependencies over time or space. Markov processes are the most important subclass of such processes: they are mathematically tractable, yet they have a wide range of applications. The primary goal of the course is to provide an introduction to discrete state Markov processes, notably Bernoulli, Poisson, and Markov processes. The emphasis is on practical applications rather than on theoretical rigor. Illustrations include gambling problems, simple operations research, Markov chain Monte Carlo, and Google’s PageRank. R program may be used in practical calculations. Expected results Student gets familiar on discrete Markov processes and practical applications.


Statistical Demography 30 hrs (one period course)

5 Description Demographic processes are viewed from the perspective of statistical inference and time-series analysis. Classical stable population concepts are defined. An alternating special topic, such as mortality forecasting, measurement of nuptiality, or estimation based on incomplete data, is chosen each year for in depth study. Statistics Finland population databases, Human Mortality Database and other international sources of data are utilized. Expected results Student understands how mathematical and statistical models can be used to describe

Method of completion: Homework assignments plus small exam

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population processes, and knows how they can be fitted to actual data. Student appreciates the effect of erroneous or defective data in such modeling, and is able to apply the models in practice.

Nonparametric and Robust Methods


6 Description Statistical tests, estimates and confidence intervals based on signs and ranks. M-estimation method. Studying the properties of the methods using computer simulations. Student gets knowledge on nonparametric and robust methods and computational tools and is able to apply selected methods in typical real-world analysis situations.

Method of completion: Course exam

Total credits:

25 (Minimum)

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Table 4. MASTER THESIS

Summary

40 credits In Social Statistics, the preparation of the thesis is typically organized as a joint project with the university and a partner institute. Currently, the most important partner is Statistics Finland. The actors in the thesis project are student, scientific supervisor of the university and subject-matter supervisor of the partner institute:

University Student Partner institute (NSI)

Supply of courses, scientific supervision, scheduling, follow-up, project guidance, administration, co-funding

Advanced studies in statistics, studies in application area, research work, preparation of thesis

Subject-matter supervision, data provision, premises and equipment, project support, co-funding

The topic of the thesis is agreed jointly with the three actors. The topic must be scientifically valid and relevant for the NSI. Topics cover such areas as Survey sampling, sampling methods and estimation methods, methods of statistical demography, mixed-mode data collection, nonresponse adjustment (reweighting, imputation), statistical disclosure limitation methods, small area estimation, survey analysis, data integration, and quality of statistics. Statistical computing tools (SAS, R) are used. Data sources of the NSI are used.

Table 5. INTERNSHIP

Summary

Internship is obligatory. In Social Statistics, the EMOS internship scheme is the following. Students preparing their Master thesis are hired by the partner institute (Statistics Finland, other governmental institutions such as Social Insurance Institution, SYKE- Finnish Environment Institute) for a fixed time period to complete the thesis project.

Organisation of internships Length Credits Collaboration with NSI or other statistical authority

Comments

EMOS trainee at the partner institute

Varies (e.g. Statistics Finland: currently 6 months)

No extra credit points

Currently: Statistics Finland For EMOS students, Internship is included in the training period for completing Master’s thesis

european master in official statistics: the emos module · to-face, web and mixed). second part...

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