GRA 2265 Introductory Multivariate Data Analysis

GRA 2265 Introductory Multivariate Data Analysis

Course code: 
GRA 2265
Department: 
Economics
Credits: 
6
Course coordinator: 
Ulf Henning Olsson
Product category: 
Master
Portfolio: 
MSc - Core course
Semester: 
2020 Autumn
Active status: 
Active
Teaching language: 
English
Course type: 
One semester
Introduction

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.

Learning outcomes - Knowledge

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.  

Learning outcomes - Skills

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.

General Competence

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.  

Course content

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
Teaching and learning activities

Lectures and excercises.

Software tools
R
SPSS
Additional information

Software: SPSS and R/lavaan (and/or Mplus).

All parts of the assessment must be passed in order to get a grade in the course

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.

Qualifications

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.

Required prerequisite knowledge

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Exam categoryWeightInvigilationDurationGroupingComment exam
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA22651
Grading scale:
ECTS
Grading rules:
Internal and external examiner
Resit:
Examination when next scheduled course
40No1 Week(s)Group (2 - 2)
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA22652
Grading scale:
ECTS
Grading rules:
Internal and external examiner
Resit:
Examination when next scheduled course
60No1 Week(s)Group (2 - 2)
Exams:
Exam category:Submission
Form of assessment:Written submission
Weight:40
Invigilation:No
Grouping (size):Group (2-2)
Duration:1 Week(s)
Comment:
Exam code: GRA22651
Grading scale:ECTS
Resit:Examination when next scheduled course
Exam category:Submission
Form of assessment:Written submission
Weight:60
Invigilation:No
Grouping (size):Group (2-2)
Duration:1 Week(s)
Comment:
Exam code: GRA22652
Grading scale:ECTS
Resit:Examination when next scheduled course
Type of Assessment: 
Ordinary examination
Total weight: 
100
Sum workload: 
0

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.