Topics in Official Statistics: Demography and National Acounts, Sampling Data
| Title | ΕΙΔΙΚΑ ΘΕΜΑΤΑ ΣΕ ΕΠΙΣΗΜΕΣ ΣΤΑΤΙΣΤΙΚΕΣ: Δημογραφία και Εθνικοί Λογαριασμοί, Ανάλυση Δειγματοληπτικών Δεδομένων / Topics in Official Statistics: Demography and National Acounts, Sampling Data |
| Code | ΣΜ06 |
| Faculty | Sciences |
| School | Mathematics |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Spring |
| Coordinator | Vasileios Karagiannis |
| Common | No |
| Status | Active |
| Course ID | 600017507 |
Programme of Study: PMS Tmīmatos Mathīmatikṓn (2018-sīmera)
Registered students: 10
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| STATISTIKĪ KAI MONTELOPOIĪSĪ | Compulsory Course | 2 | 1 | 4 |
| Academic Year | 2019 – 2020 |
| Class Period | Spring |
| Instructors from Other Categories | |
| Weekly Hours | 1 |
| Class ID | 600153703
|
Course Type 2016-2020
- Scientific Area
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600153703
- eLearning (Moodle):
Language of Instruction
- Greek (Instruction, Examination)
Prerequisites
Required Courses
- 0749 Statistics and Decision Making
General Prerequisites
Probability Theory, Mathematical Staistics, Estimation Theory, Sampling Theory.
Learning Outcomes
1.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).
2.To understand and be able to use different kinds of data sources such as censuses, sample surveys – cross section, longitudinal –, administrative sources, big data.
3.To be able to understand methodological issues related to some specific fields of official statistics and to interpret correctly official statistics.
4.To be able to apply methods suitable to produce and analyse data in the specific field.
5.Knowledge of and ability to apply special statistical methods such as sampling methods, non-response adjustments and imputation.
6.Ability to use statistical computer programmes such as R or SPSS.
7.Ability to present data in an effective way to different kinds of audience.
8.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
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Make decisions
- Work autonomously
- Work in teams
- Work in an international context
- Work in an interdisciplinary team
Course Content (Syllabus)
Basic concepts in National Accounts. Basic concepts in Demography, definitions, research questions and framework of demography and population research. Introduction to techniques of demographic analysis; elements of period and cohort analysis; demographic rates, ratios and measures. Sources of data (census, vital statistics, sample surveys) and errors in demographic data. Population change and structure; analysis of migration data. Population pyramids, factors affecting age distribution of the population; socioeconomic and demographic implications of population aging. Population predictions and projections. Basic concepts, modern theory and practice of index numbers as a means of comparing prices and quantities.
Downloading data from European Demographic Databases (Big Data). Tidying data, manipulation of nonresponders and missing values using weights and multiple imputation methods, calculation of estimators, statistical analysis and hypothesis testing in sample survey data. The module “Complex Samples Procedures” of the IBM Statistics SPSS software and the libraries tidyr, stringr, lubridate, hmisc, mice, survey, nlme, lme4, dplyr, shiny, surveyoutliers, tabplot, survey, eurostat, rcmdrPlugin.sampling, cbsodataR, censusapi of the statistical software R in the RStudio environment. Presentation - Visualization of data and statistical results with the ggplot2 library.
Keywords
Big Data, Tidying data, nonresponders, missing values, multiple imputation, survey data, eurostat, R, RStudio, IBM Statistics SPSS
Educational Material Types
- Notes
- Slide presentations
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Laboratory Teaching
- Use of ICT in Communication with Students
Description
Computer, statistical software, www, europian survey data bases, free databases.
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 12 | 0.4 | |||
| Laboratory Work | 12 | 0.4 | |||
| Reading Assigment | 20 | 0.7 | |||
| Written assigments | 76 | 2.5 | |||
| Total | 120 | 4 |
Student Assessment
Description
Coursework assignment(s)
Student Assessment methods
- Written Exam with Short Answer Questions (Formative, Summative)
- Written Assignment (Formative, Summative)
- Labortatory Assignment (Formative, Summative)
Bibliography
Additional bibliography for study
1. Särndal, C., B. Swensson, and J. Wretman. 1992. Model Assisted Survey Sampling. New York: Springer-Verlag.
2. Lumley, T. 2010. Complex Surveys A Guide to Analysis Using R. John Wiley & Sons, Inc., Hoboken, New Jersey.
3. IBM SPSS Complex Samples 21. IBM Corporation, 1989 – 2012.
4. Rubin B. D. 1987. Multiple Imputation for Nonresponse in Surveys. John Wiley & Sons, Inc., Hoboken, New Jersey.
5. Stef van Buuren . 2012. Flexible Imputation of Missing Data. CRC Press,Taylor & Francis Group, Boca Raton.
6. Särndal, Lundström. 2005. Estimation in Surveys with Nonresponse. John Wiley & Sons Ltd, West Sussex, UK.
7. Bethlehem J., Cobben F., Schouten B. 2011. Handbook of Nonresponse in Household Surveys. John Wiley & Sons, Inc., Hoboken, New Jersey.
Last Update
14-03-2020
