• Introduction to quantitative research methods:
• Use of primary data sources such as in survey research
• Use of secondary data sources such as patent data bases and financial data bases, bibliometrics, etc
• Use of modeling and simulation
• Quantitative research methodology:
• Underlying assumptions of quantitative analysis
• Design and implementation of quantitative studies
• Reading and reporting quantitative research
• Validity and reliability issues
• Statistical analysis:
• Statistical inference, association and causation among variables and multivariate techniques
• Statistical packages such as SPSS and AMOS

• Describe the breadth of research approaches and data collection techniques available to a quantitative researcher in the field of Industrial economics and management
• Describe the basic ideas and underlying assumptions of quantitative analysis
• Describe basic elements of design and implementation of quantitative studies
• Understand how to read quantitative research in a critical way
• Understand how to report quantitative research in a publishable way
• Appreciate quality aspects in conclusions based on statistical reasoning
• Be familiar with basic statistical inference
• Be familiar with and able to test statistical association and causation among variables
• Be familiar with and able to use basic multivariate data analysis techniques
• Be familiar with the basics of widely used computer based statistical packages such as SPSS and AMOS

• Data processing.
• Introduction to SQL.
• Linear estimation methods.
• Methods for panel data estimation.
• Instrumental variable regressions.
• Difference-in-difference, matching and event studies.
• Choice modelling using multinomial frameworks.
• Special topics (individual choices).
• Transformation of models, tables and figures into scientific documents.

This is a course in practical application of advanced quantitative methods for
doctoral students. The course first introduces methods to collect and store data
from existing databases, to generate data by survey approaches and simulation
techniques, and to prepare the data for further analysis. Second, it provides
theoretical background to analyse data. Third, the course teaches how to use
methods from modern empirical tool-boxes to analyse data. Forth, the students
applies their skills in quantitative analysis by replicating research from a large
number of examples from existing studies, or developing own research-projects.
Finally, the course learn the students efficient methods to transform quantitative
analyses using different statistical softwares to tables, equations and figures
in a professional paper.

  Will be announced when course starts.

Students at KTH with a permanent disability can get support during studies from Funka:

Funka - compensatory support for students with disabilities

P, F

• TEN1 - Exam, 7.5 credits, Grading scale: P, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Mandatory to be present and participate in all modules

• All members of a group are responsible for the group's work.
• In any assessment, every student shall honestly disclose any help received and sources used.
• In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.