Syllabus database for doctoral courses
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Syllabus database for doctoral courses
SYLLABI FOR DOCTORAL COURSES
| Swedish title | Longitudinella forskningsmetoder: panel-, tillväxtkurv- och sekvensanalys |
|---|---|
| English title | Longitudinal research methods: panel, growth curve, and sequence analysis |
| Course number | 2965 |
| Credits | 1.5 |
| Responsible KI department | Institutet för miljömedicin |
| Specific entry requirements | Knowledge equivalent to ""Epidemiology I: Introduction to epidemiology"", ""Biostatistics I: Introduction for epidemiologists"" and ""Biostatistics II: Logistic regression for epidemiologists"" or corresponding courses. |
| Grading | Passed /Not passed |
| Established by | The Board of Doctoral Education |
| Established | 2016-09-27 |
| Purpose of the course | The course gives an introduction to methods for the analysis of register-based longitudinal data used in the social sciences, epidemiology, public health, and related research disciplines. |
| Intended learning outcomes | After successfully completing this course, the student is expected to be able to: - Explain the applicability of panel regression, growth curve analysis and sequence analysis to different research problems, which involve the use of longitudinal data. - Discuss the relationship between the above methods and other commonly used statistical methods in the light of their strengths and weaknesses for analysing different research questions. - Analyse a research problem that requires longitudinal data and identify the appropriate design and statistical method for analysing the data. - Perform panel regression, growth curve and elementary sequence analyses of longitudinal data. - Interpret and communicate the results from a longitudinal analysis. |
| Contents of the course | This course serves as an introduction to longitudinal research methods commonly used in register-based research. More specifically, we will cover regression analysis of panel (repeated measures) data, growth curve analysis, and sequence analysis. Longitudinal data, in which subjects are measured at multiple time points, are common both in the social and the medical sciences. The many uses of such data include controlling for unobserved confounding, and analysing change over time and the ways in which individual life course trajectories unfold. These features make longitudinal data an indispensable resource for answering a multitude of research questions central to both the social and the medical sciences. We will discuss the nature of longitudinal data and how the above-mentioned methods can be used to answer different types of research questions, demonstrate the use of these methods and practice their use within a specified research question. Both continuous and discrete outcome variables are discussed. The teaching is done through lectures, computer labs, as well as individual and group work. The statistical software used in the lectures and laboratory exercises is Stata. |
| Teaching and learning activities | Lectures, computer labs and individual and group work involving analysis of real-life research problems using longitudinal data and a statistical software (Stata). |
| Compulsory elements | Individual written examination (summative assessment). |
| Examination | To pass the course, the student has to show that the learning outcomes have been achieved. The assessment methods used in this course are individual and group assignments (formative assessment) and an individual take-home examination (summative assessment). The focus will be on application of longitudinal research methods to research problems, rather than mathematical detail. The examination is viewed as contributing to the development of knowledge, rather than a test of that knowledge. Students who do not obtain a passing grade in the first examination will be offered a second examination within two months of the final day of the course. |
| Literature and other teaching material | Suggested readings: AndreB, H-J., Golsch, K., & Schmidt, A.W. 2013. Applied Panel Data Analysis for Economic and Social Surveys. Heidelberg: Springer. Ch. 1-5.1 Cornwell, B. 2015. Social Sequence Analysis. Cambridge University Press. Ch 1-5, 8. Fitzmaurice, G.M., Laird, N.M. & Ware, J.H. 2011. Applied Longitudinal Analysis. Wiley. Ch 1-6, 8-9, 21-22. Additional literature as well as data will be provided during the course. |
Course responsible |
Karin Modig Institutet för miljömedicin 08-524 801 53 Karin.Modig@ki.se |
| Contact person |
Johanna Bergman Institutet för miljömedicin johanna.bergman@ki.se Nobels väg 13 17177 Stockholm |
