Kaisa_2012_3_photo by Veikko Somerpuro

Welcome

This course covers the analysis of complex survey data.

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.

Study materials: 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.

Due to Coronavirus situation all teaching events are via Zoom. More information: Course Moodle are.

Enrol
10.2.2020 at 09:00 - 27.4.2020 at 23:59

Timetable

Here is the course’s teaching schedule. Check the description for possible other schedules.

DateTimeLocation
Mon 9.3.2020
10:15 - 14:00
Mon 16.3.2020
10:15 - 14:00
Mon 23.3.2020
10:15 - 14:00
Mon 30.3.2020
10:15 - 14:00
Mon 6.4.2020
10:15 - 14:00
Mon 20.4.2020
10:15 - 14:00
Mon 27.4.2020
10:15 - 14:00

Description

Optional course.

Master's Programme in Mathematics and Statistics is responsible for the course.

The course belongs to the Statistics and Social statistics module.

The course is available to students from other degree programmes.

Survey sampling

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.

Recommended time/stage of studies for completion: 1. or 2. year

Term/teaching period when the course will be offered: varying

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.

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.

Lectures and exercise classes

Exam and written assignment, Course will be graded with grades 1-5

Exam and written assignment