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Prof. Simona Balzano

Course Title:

RESEARCH METHODS IN MANAGEMENT

instructor details

name: Simona Balzano

contact information: s.balzano@unicas.it

Term: (second semester)

 

Prerequisites: Although there aren’t formal prerequisites, it is strongly recommended to have passed the exam of Applied Statistics and to be familiar with statistical inference and modeling.

 

Language of Instruction: English

 

Class hours: 42

Credits (ECTS): 6

 

Learning Objectives

Cognitive / Knowledge skills

  • Reach an advanced knowledge of the statistical  
  • Understand the methodological aspects of data analysis methods
  • Deal with data coding according to data nature
  • Programming in R (or alternative data analysis tool) for the application methods
  • Writing the research report

Analytical / Critical Thinking Skills (Oral & Written)

  • Understand how to choice among the possible data analysis methods
  • Ability to define data analysis strategies, integrating different methods characteristics and properties
  • Ability to design new researches
  • Ability to find proper methodological solutions to management problems
  • Ability to use their knowledge in a multidisciplinary context

COURSE DESCRIPTION

This course aims at developing skills on using statistical tools for the analysis of data and on a proper interpretation of the results aimed at the adoption of solutions to organizational and management problems.

Attention is paid to both primary and secondary data, i.e. the case when new knowledge is needed and new data have to be collected and the case when data are available as a result of daily management activities and represent a hidden source of information.

After an overview on the how to conduct a survey and sampling designs, and on the basics of matrix algebra, the methods are presented according to the following classification:

-          Dependence analysis: Analysis of variance; Conjoint analysis

-          Interdependence analysis: Multidimensional data analysis (PCA;  MCA;  Multidimensional scaling, Structural equation model)

For all the statistical methods both methodological aspects and applications (by R software) are taught.

 

Instructional Format

The class will meet for 2 hours (gross of interclass break), twice a week, for a total of 9 sessions. After an introduction aimed at providing the needed background, participants are required to read the materials related to the class and to be prepared prior to coming to class.

Classes are organized such that lectures on methods alternates with R sessions for their applications on data.

 

 

workload expectations

All students are expected to spend at least 2,5 hours of time on academic studies outside of, and in addition to, each hour of class time.

 

Forms of Assessment

 

The instructor will use numerous and differentiated forms of assessment to calculate the final grade you receive for this course. For the record, these are listed and weighted below. The content, criteria and specific requirements for each assessment category will be explained in greater detail in class. Any questions about the requirements should be discussed directly with your faculty well in advance of the due date for each assignment.

The final grade you receive for this course depends on your performance in the essays listed and weighted below:

 

FORM OF ASSESSMENT

VALUE

Class participation

20%

Research report

20%

Oral exam

60%

 

 

ASSESSMENT OVERVIEW:

Class Participation:  This grade will be calculated to reflect your participation in class discussions, your capacity to introduce ideas and thoughts dealing with the texts, your ability to use language effectively, and to present your analysis in intellectual, constructive argumentation. If you cannot attend classes your participation can be shown by interacting with your instructor during office hours, i.e. by asking about specific subjects of the syllabus and discussing assignments.

Research report

The report should include applications of the methods the instructor explains during the course:

For each method you should find a suitable dataset, run the R script used in class (and available on the course website), print the output in a report.

You can look for data in any available source, such as books, internet, articles, questionnaire designed by you, etc.

Data have to be discussed with the instructor before proceeding with the analysis and writing the report

 

Oral exam

During the oral exam you should be able to discuss the output and describe the aim and characteristics of each method.

Your abilities will be tested in both the depth of your knowledge and your statistical language skills.

Class/instructor Policies

professionalism and communications: As a student, you are expected to maintain a professional, respectful and conscientious manner in the classroom with your instructors and fellow peers.

You are expected to take your academic work seriously and engage actively in your classes.. Advance preparation, completing your assignments, showing a focused and respectful attitude is expected of all students. Simply showing up for class or meeting minimum outlined criteria will not earn you a good grade in this course. Utilizing communications, properly addressing your faculty and staff, asking questions and expressing your views respectfully demonstrate your professionalism and cultural sensitivity.

 

attendance and Classroom behavior: Although attendance is not compulsory, it is highly recommended. All students must have a respectful attitude towards the professor as well as the classmates.

Arriving late / departing early from Class: Once they have decided to attend, students must behave consistently. Arriving late or leaving class early is disruptive and shows a lack of respect for instructor and fellow students.

 

make-up classes: The instructor reserves the right to schedule make-up classes in the event of an unforeseen or unavoidable schedule change. Make-up classes may be scheduled outside of typical class hours, as necessary. 

 

Missing Examinations: Examinations will not be rescheduled. Pre-arranged travel or anticipated absence does not constitute an emergency and requests for missing or rescheduling exams will not be granted.

 

Use of Cell Phones, Laptops and Other Electronic Devices: Always check with your instructor about acceptable usage of electronic devices in class. Inappropriate usage of your electronic devices will result in a warning and may lead to a deduction in participation grades. Use of a cell phone for phone calls, text messages, emails, or any other purposes during class is impolite, inappropriate and prohibited Faculty determines whether laptops will be allowed in class. The use of a laptop, tablets or of cell phones is prohibited during all tests and exams, unless otherwise specified by your instructor.

Required Readings

Listed below are the required course textbooks and additional readings. These are required materials for the course and you are expected to have constant access to them from the very beginning of the course for reading, highlighting and note-taking. It is required that you have unrestricted access to each. Access to additional sources required for certain class sessions may be provided in paper or electronic format consistent with applicable copyright legislation.

Required texts:

Mazzocchi M., Statistics for Marketing and Consumer Research, Sage (Chapters: 7, 10, 11, 13, 14, 15, Appendix)

Lebart, L., A. Morineau, K., H. Warwick: Multivariate descriptive statistical analysis. John Wiley & Sons

Recommended readings

Green E., Srinivasan V., Conjoint analysis in marketing: new developments with implications for research and practice, Journal of Marketing, 1990

Tenenhaus M, Esposito Vinzi V., Chatelin Y.M., Lauro N.C. (2005) PLS path modeling, Computational Statistics and Data Analysis, 48, 159-205

Sanchez, PLS Path Modeling, PhD tesi, chapter 3.

 

Online Reference & Research Tools

r-project.com

 

http://factominer.free.fr/classical-methods/index.html

 

[Ultima modifica: mercoledì 13 settembre 2017]