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Swedish title INTRODUKTION TILL FLERNIVÅANALYS
English title INTRODUCTION TO MULTILEVEL ANALYSIS
Course number 2254
Credits 1.5
Notes The course meets the requirements for a general science course.

Responsible KI department Institutionen för neurobiologi, vårdvetenskap och samhälle
Specific entry requirements
Grading Passed /Not passed
Established by The Board of Doctoral Education
Established 2011-02-14
Purpose of the course
Intended learning outcomes In this course, students will be able to conduct multilevel analyses in the field of medical research. They will leave the course with an understanding of the methodological and statistical issues involved in conducting multilevel analyses of medical research data. The course will also cover the limitations of such analyses.
By the end of the course, students will be able to: 1) understand the concept of hierarchically structured data and the need for multilevel modeling, 2) work with hierarchical databases, 3) understand the meaning of the terms ¿random¿ and ¿fixed¿ as used in mixed modeling, 4) perform analyses to study the relationships between variables observed at different levels in hierarchical structures, and 5) use statistical software to evaluate the results of the analyses and draw conclusions.
Additionally, upon completion of the course, participants will be able to critically examine and discuss scientific papers in medicine that use these methods. They will be able to identify appropriate literature and references from the many published epidemiologic, biostatistic, and mathematic materials on hierarchical data analysis. Course participants will be able to determine when more sophisticated statistical methods should be used and when collaboration with a statistician is necessary.
Contents of the course - Introduction to and overview of contextual models
- Mixed and random coefficient models
- Comparison of traditional regression analysis to random slope modeling
- Hierarchical linear methods for repeated measurements
- Generalization of discrete outcome variables
Teaching and learning activities Lectures will cover theoretical and statistical assumptions and definitions using examples from the fields of medicine and epidemiology. Basic principles and adoption will be discussed in non-technical terms. Students will undertake practical exercise (under supervision) using STATA 11 and SAS 9.22. Students may also choose to use other programs, such as SPSS, MLwiN, R, or HLM. All of these programs are available in the computer classroom. Students will also read and review scientific papers as homework and in seminars.
All the necessary instructions for the classroom exercises will be provided in the form of a course compendium; therefore, it is important that participants be thoroughly familiar with the basics of the software program they choose to use.
Compulsory elements Mandatory attendance at all lectures and exercises. Absence offset by additional assignments determined by the course leader.
Examination Each course segment will include individual computer exercises and theoretical questions that will be submitted every day. The examination consists of these exercises and questions. In the event of absence, written assignments that examine mandatory course elements will be submitted no later than 3 weeks after the course ends.
Literature and other teaching material At the beginning of the course, students will be given a reference list of relevant literature and articles. All necessary software (SAS, STATA, SPSS, R, and MLwiN) will be available in the computer classroom.
-- Alinaghizadeh Hassan, Course compendium (Multilevel analysis) I-III, 2011
-- Alinaghizadeh Hassan, Course compendium, MULTILEVEL ANALYSIS ON THE COMPUTER (SAS, STATA, SPSS, R, and MLwiN), 2011
-- Jos W. R. Twisk, Applied Multilevel Analysis, Cambridge, 2007, ISBN 9780521614986

Course responsible Farhad Alinaghizadeh
Institutionen för neurobiologi, vårdvetenskap och samhälle
52488745
0704840374
farhad.alinaghizadeh@ki.se

Alfred Nobels allé 12

141 83
Huddinge
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