GRA 2265 Introductory Multivariate Data Analysis
GRA 2265 Introductory Multivariate Data Analysis
This course gives an applied introduction to the most important statistical techniques for leadership and organizational psychology students. Students are given hands-on experience by working with data, using descriptive statistics to motivate models, and using models to turn data into actionable knowledge. The course will focus on “learning by doing”. The course will cover the theory and application of various multivariate statistical methods, as multiple and multivariate regression, classification methods, exploratory and confirmatory factor analysis and an introduction to structural equation modeling.
After completing the course, the students should be able to understand the concept of analyzing multivariate data and have an understanding of the link between multivariate techniques and corresponding univariate techniques. In addition, the students will have knowledge on how modern software can be used to analyze big and complex data matrices, and at the next level turn the results in to meaningful interpretation.
Upon completion of this course, the student should be able to: Analyze multivariate data, and apply suitable statistical techniques for exploratory as well as confirmatory analysis, use modern software and be able to understand and interpret the results. A central learning outcome is to be able to write and communicate the results in a scientific manner.
Being a mathematical tool, statistics are built on assumptions that are not always met in a real world setting. After completing the course students should be aware of these limitations, and be able to reflect upon how this can influence the final results in a research project, which is an encompassing goal of the course.
Introduction
- Dataset
- Software
- Sample
- Population
- Descriptive statistics
- Measurement levels
Variance, covariance, correlation
Review of probability and statistical inference
The linear regression model
- Simple regression
- Multiple regression
- Dummy variables (Anove and Ancovava)
Measurement level
Classification Analysis
- Logistic regression
- Discrimant analysis
Exploratory factor analysis and Principal Component Analysis
Confirmatory factor analysis
- Measurement Models
- Reliability
- MTMM Models
Structural Equation Modeling
- Multivariate Regression analysis
- Path Analytsis models
- Path Models with latent variables
- Inference for non-normal data and Likert-type data
- Model assessment and model modification
- Multi group models
Lectures and excercises.
Software: SPSS and R/lavaan (and/or Mplus).
Please note that while attendance is not compulsory in all courses, it is the student’s own responsibility to obtain any information provided in class.
All parts of the assessment must be passed in order to get a grade in the course.
All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.
Disclaimer
Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.
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| Assessments |
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Exam category: Submission Form of assessment: Submission PDF Exam/hand-in semester: First Semester Weight: 40 Grouping: Group/Individual (1 - 2) Duration: 1 Week(s) Exam code: GRA 22651 Grading scale: ECTS Resit: Examination when next scheduled course |
Exam category: Submission Form of assessment: Submission PDF Exam/hand-in semester: First Semester Weight: 60 Grouping: Group/Individual (1 - 2) Duration: 1 Week(s) Exam code: GRA 22652 Grading scale: ECTS Resit: Examination when next scheduled course |
All exams must be passed to get a grade in this course.
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.