Multivariate analysis: applied factor and regression analysis
The aim of this course is to provide students with a good understanding of basic analytical tools with relevance for their Phd-work. Factor- and regression analysis are the main approaches covered, for the most part using the statistical program SPSS. When discussing mediation and moderation, the students will be introduced to Hayes' Process on top of SPSS. In addition, we will go into Confirmatory Factor analysis (CFA) and Structural Equation Modelling (SEM) via the Lisrel program, also with eye on the Mplus package. The course will also take up elements of logistic regression and multi-level analysis. Main focus will be on applications of the various techniques introduced.
The Phd-students/participants should have a general knowledge about Causal models and principles of Causal inference. The logic and results of Factor analysis and evaluation alternative factor models are central. Moreover, the student should have knowledge of Multivariate Regression analysis, specification of hypotheses and statistical inference, as well as combined uses of regression- and factor analysis. Participants should also know how to test for indirect and/or moderation effects, and how to make use of dummy-variable and interaction terms in more complex models. The students should become acquainted with how to set up a CFA model and a SEM model in Lisrel (or similar packages).
It is central to this course that the student can/should dig into a scientific problem/question at the international research frontier and analyze available data in a proficient manner, preferably with her own project as background. The student must also be competent in clarifying main assumptions of alternative analytical/statistical approaches, and to apply quantitative methods in a real research setting. Participants should have the skills needed to plan, evaluate, facilitate and carry out empirical/statistical studies with a multivariate framework, that could make up the basis for a publication at international level.
The student should be able to "translate" a research question into more precise hypotheses that could effectively be tested by the factor- or regression model, or more advanced approaches (CFA/SEM). It is central that the participant understands the results coming out from such analyses, and can provide meaningful interpretations of these, in addition to conveying such findings and conclusions to research colleagues at the national or international level.
Required prerequisite knowledge
Recommended previous knowledge
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