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Course Syllabus

Master 2020/2021

Empirical Methods and Applications in Business

Category 'Best Course for Career Development'
Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'
Type: Elective course (Management and Analytics for Business)
Area of studies: Management
Delivered by: Department of Management
Where: St Petersburg School of Economics and Management
When: 2 year, 3 module
Mode of studies: offline
Instructors: Elena Shakina
Master’s programme: Management and Analytics for Business
Language: English
ECTS credits: 3
Contact hours: 24

Course Syllabus

Full SyllabusAsk Question

Abstract

This course introduces students to sources and analytical techniques of data commonly used in management and business studies. A conceptual part of the course is dedicated to the overview of appropriate data sources, indicators and statistical metrics, basic and advanced techniques for data analysis and econometrics Practical approach to learning is based on professional tools for data collection and processing and analysis – Stata and R.
Learning Objectives

Learning Objectives

  • to have knowledge of commonly used data sources, their benefits and limitations
  • to understand the meaning of various statistical indicators in principle fields of social science
  • be able to identify suitable statistical sources for a defined research problem
  • be able to run descriptive analysis using Stata and R
Expected Learning Outcomes

Expected Learning Outcomes

  • to have knowledge of commonly used data sources, their benefits and limitations
  • to understand the meaning of various statistical indicators in principle fields of social science
  • be able to identify suitable statistical sources for a defined research problem
  • be able to run descriptive analysis using Stata and R
Course Contents

Course Contents

  • Introduction into principles of collecting and using business data
    Research in business. Ethics in business research. Research questions and associated techniques. Screening data prior to analysis. Normality, linearity, and homoscedasticity. Data transformations.
  • Probability and statistics in advanced data analysis
    Exploratory data analysis. Data and sampling distributions. Statistical experiments and significance testing. Experimental and quasi-experimental techniques. Regression and prediction. Correlation vs causation. Endogeneity. Guide to entering, editing, saving, and retrieving large quantities of data using R and Stata.
  • Data visualization and reporting
    Presenting insights and findings. Written report: research report components, writings, presentation of statistics. Oral presentation: planning, organizing, supporting, visualizing, delivering.
Assessment Elements

Assessment Elements

  • non-blocking Exam
  • non-blocking Control work
  • non-blocking Problem-solving discussions
  • non-blocking Workshops
  • non-blocking Teamwork task
Interim Assessment

Interim Assessment

  • Interim assessment (3 module)
    0.1 * Control work + 0.5 * Exam + 0.1 * Problem-solving discussions + 0.15 * Teamwork task + 0.15 * Workshops
Bibliography

Bibliography

Recommended Core Bibliography

  • Tong, H., Huang, Y. X., & Kumar, T. K. (2011). Developing Econometrics. Hoboken: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=473846

Recommended Additional Bibliography

  • Lancaster, G. (2005). Research Methods in Management. Amsterdam: Routledge. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=195596