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Аспирантура 2020/2021

Кейс-метод в исследованиях

Статус: Курс по выбору
Направление: 38.06.01. Экономика
Когда читается: 1-й курс, 1 семестр
Формат изучения: без онлайн-курса
Язык: английский
Кредиты: 3

Course Syllabus

Abstract

The course aims to familiarize students with the case method, as possible options for a deeper research strategy. for conducting managerial and business studies in conditions when there are not enough copies of the object of study for statistical analysis and it is impossible to determine the boundaries and variables of occurring phenomena, the case study is the best tool. This discipline forms the skill of students in the effective search for options, non-linear business combinations and non-standard solutions.
Learning Objectives

Learning Objectives

  • to be able to find appropriate method for the research purposes
  • to execute several advanced methods of business research
  • to interpret results of estimation and analysis
  • to present obtained research results to the academics and business orally and in written format
  • to master research skills
Expected Learning Outcomes

Expected Learning Outcomes

  • Development of cognitive and soft skills: creativity and self-sufficiency
  • Development of research skills
  • Enhancing critical thinking and personal development skills
  • Extending theoretical knowledge
  • Learn how to use legal, regulatory, referential information and professional literature
  • Obtaining skills of efficient independent professional activities
  • Systemize theoretical knowledge received at lectures
Course Contents

Course Contents

  • Survey data
    Applied survey data analysis. Foundations and techniques for design-based estimation and inference. Descriptive analysis for continuous variables. Categorical data analysis. Linear regression models. Logistic regression and generalized linear models for binary survey variables. Generalized linear models for multinomial, ordinal, and count variables. Imputation of missing data.
  • Data parsing from digital sources
    Data mining process. Data mining techniques. Sources of data.
  • Clustering and factor analysis
    Basics of cluster analysis. Methods of cluster analysis. Exploratory factor analysis (EFA). Confirmatory factor analysis (CFA).
  • Parametric and non-parametric techniques
    Ordinary least squares (OLS). Two-Stage least squares (2SLS). Three-Stage least squares (3SLS), Data envelopment analysis (DEA), Stochastic frontier analysis (SFA).
  • Endogeneity and approaches to accomplish consistency
    Understanding the endogeneity. Panel-data analysis. Experimental techniques.
  • Structural models
    Fundamentals of structural equation modeling. Path analysis. Confirmatory factor analysis. Structural regression models. Latent change analysis. Softwae programs.
  • Panel-data analysis
    Advantages of panel data. Ussies, involved in utilizing panel data. Analysis of covariance. Fixed effects models. Random effects models. Fixed effects or Random effects. Tests for misspecification. Heteroscedasticity and autocorrelation. GMM.
Assessment Elements

Assessment Elements

  • non-blocking Problem-solving discussions
    All discussions will be based on the application of different techniques used for quantitative analysis. Sources for discussion are top publications in different fields where quantitative methods of research were used.
  • non-blocking Control work
    Students will have one control work during the course. Control work will be given at the end of the class, and it will take around 30 minutes. It will be based on students’ home reading and information obtained during the previous classes. The task will be focus on survey data topic.
  • non-blocking Exam
    Written examination (test) at the end of the course
  • non-blocking Group project
    Students will be given different data sets. They should develop a managerial research question and answer it by analyzing the given data. They should apply one of the techniques that would be covered within the course. Group project is carried out in the group of 2-3 students. Deadline of the whole project: the last class of the course according to the course schedule.
  • non-blocking Presentations of students’ projects
    During the course, students will do an analysis of real data about different industries and markets. They will use them to answer their research questions. Students will report results for each stage of their research and discuss their managerial implications.
  • non-blocking Workshops
    The course will include three workshops on data analysis techniques, in particular, cluster analysis and factor analysis, panel data, and structural equation modeling.
Interim Assessment

Interim Assessment

  • Interim assessment (1 semester)
    Final assessment: 50% exam assessment + 50% intermediate assessment Exam assessment: written examination at the end of the course Intermediate assessment: Control work (20%) Problem-solving discussions (20%) Workshops (30%) Teamwork task (30%) Grading policy: The assessment list with all students’ grades will be published in the LMS
Bibliography

Bibliography

Recommended Core Bibliography

  • Hegde, D. S. (2015). Essays on Research Methodology. New Delhi: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1001250

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
  • Raykov, T., & Marcoulides, G. A. (2006). A First Course in Structural Equation Modeling (Vol. 2nd ed). Mahwah, NJ: Routledge. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=188193