Table of contents

The course is connected to the following study programs

Teaching language

English

Prerequisites

PhD candidates. Applicants with relevant scientific background can be considered if the resources are available.

Recommended previous knowledge

It is assumed that students taking the course have knowledge of theory/conceptual framework development and have taken at least one course in statistics at the Master level. Students without prior knowledge in statistics are recommended to take SE414 (Introduction to Econometrics for Finance) or ME-423 (Research Methods). If students without basic knowledge of statistics are expected to invest more hours than those stipulated below in the workload.

Learning outcomes

On successful completion of the course, the students should be able to:

  • Critically evaluate the philosophical foundations underpinning various research designs.
  • Understand the principles of variable measurement, survey design, and survey administration.
  • Pre-register a research project on an open science platform.
  • Demonstrate proficiency in data analysis and determine appropriate techniques/methods for their own research.
  • Assess and critique the assumptions underlying statistical techniques.
  • Interpret and evaluate the outcomes of various statistical techniques.
  • Effectively communicate statistical analysis outcomes in a clear, accurate, and scientifically robust manner.
  • Apply statistical software (e.g., SPSS or STATA) for data analysis.

Contents

This course provides an overview of multivariate data analysis methods for research in the area of international management. The course will present the procedures for applying basic multivariate statistical techniques and provide hands-on experience with many of those techniques.

The primary objectives for this course are (1) to provide the student with the knowledge and confidence to identify appropriate data analysis tools for testing theories that are relevant to their field of research and (2) to equip the student with the background needed to develop deeper knowledge of techniques that are not explicitly covered in this course, or are not covered in sufficient detail for their particular research projects. The core topics and techniques introduced in this course are:

  1. Introduction

    1. Philosophy of Science and research methods
    2. The research process, research strategies
    3. Research ethics
    4. Open science and project pre-registration

  1. Descriptives and mean comparison tests

    1. Descriptive statistics, correlation, covariance
    2. Independent sample t-test, paired sample t-test, Mann–Whitney U test
    3. Chi-square tests
    4. ANOVA, Kruskal-Wallis test

  1. Measurement and Factor analysis

    1. Measurement scales, survey design and administration
    2. Exploratory factor analysis
    3. Confirmatory factor analysis

  1. Regression

    1. Bivariate and multivariate regression
    2. Hierarchical regression
    3. Dummy variable regression
    4. Moderated and mediated regression

  1. Time series analysis

    1. Stationarity, autocorrelation, co-integration
    2. Vector Autoregression (VAR)
    3. Neural networks

  1. Panel data models

    1. Fixed effect
    2. Random effects

Statistical packages

Statistical software SPSS/STATA will be used in the lab session.

Teaching and learning methods

A combination of lectures and in-class lab sessions. Lectures will usually be 3 hours followed by 3 hours laboratory exercises or a combination of the two.

Examination requirements

Assessment methods and criteria
The assessment is divided into two parts. (1) MCQ test after each lecture module. (2) Final course paper. The course paper counts 100% of the final grade. The course paper should be five (05) pages following IEEE IEEM conference paper template, using a method covered in the course and preferably on a topic related to PhD topic of the course participant.

Examinations

Assessment methods and criteria
The assessment is divided into two parts.

(1) multiple choice test (MCQ) after each lecture module.

(2) Final course paper.

The course paper counts 100% of the final grade. The course paper should be five (05) pages following IEEE IEEM conference paper template, using a method covered in the course and preferably on a topic related to PhD topic of the course participant.

Exam requirements: Pass MCQ tests with minimum 80% points.

Course paper is graded Pass/fail, where pass equals the letter grade B or better.

Last updated from FS (Felles studentsystem) Oct. 15, 2026 2:06:49 PM