Course 2020-2021 a.y.

20149 - QUANTITATIVE METHODS FOR MANAGEMENT

Department of Decision Sciences

Course taught in English
Go to class group/s: 19
ACME (6 credits - I sem. - OB  |  3 credits SECS-S/01  |  3 credits SECS-S/06)
Course Director:
REBECCA GRAZIANI

Classes: 19 (I sem.)
Instructors:
Class 19: REBECCA GRAZIANI


Class-group lessons delivered in blended format (part online and part on campus)

Mission & Content Summary

MISSION

This course is designed to develop students' knowledge and skills as users of quantitative methods to support management decision making. After completing the course, students are able to prepare accurate and informative data summaries for inclusion in management reports; contribute to the commissioning and the interpretation of reports of business research, including surveys, market research and program evaluations; and be able to use the main statistical techniques to support management decision making.

CONTENT SUMMARY

The course focuses on multivariate statistical techniques widely used in business analytics. Through the course students are taught how to set up the appropriate analysis, implement it through the use of a statistical software (SPSS) and give an interpretation to the obtained results. The following techniques are discussed:

  • Multivariate linear regression.
  • Logistic regressions.
  • Factor analysis.

Intended Learning Outcomes (ILO)

KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...
  • Read reports and scientific articles that make use of basic and advanced statistical techniques.
  • Set up and run empirical analyses, that require the use of basic and advanced statistical techniques.

APPLYING KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...
  • Use a statistical software (SPSS) to run multivariate statistical analyses to support management decision making.
  • Contribute to the commissioning and interpretation of reports of business research, including market research and programme evaluations.

Teaching methods

  • Face-to-face lectures
  • Online lectures
  • Exercises (exercises, database, software etc.)
  • Individual assignments
  • Group assignments

DETAILS

Exercises are delivered through Bboard platform for E-Learning as in-class simulation of the exams. They are multiple choice questions, with solutions provided as Feedbacks.


Assignments are delivered through Bboard platform for E-Learning as takehome. Students are asked to run analyses of provided datasets with reports to be posted through Bboard platform for E-Learning. An evaluation grid is provided as well.

Group assignments. Students are asked to analyse a provided dataset and write a report with the interpretation of the analyses, to be posted through Bboard platform for E-Learning . The evaluation grid as for the individual assignments is used.

 


Assessment methods

  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
  x x
  • Individual assignment (report, exercise, presentation, project work etc.)
x    
  • Group assignment (report, exercise, presentation, project work etc.)
  x x

ATTENDING AND NOT ATTENDING STUDENTS

Two partial exams or a general general, four take-homes and a final assignment

 

Partial Exams and General Exam

 

The two partial exams are delivered asTests through Blackboard platform for E-Learning. The tests are made up by questions that can be both multiple choice questions and essay questions.

 

The general exam is delivered as Test through Blackboard platform for E-Learning. The test is made up of 12 questions, that can be both multiple choice questions and essay questions.

 

Partial tests and general test are graded out of 31. The arithmetic average of the partial tests marks or the general exam mark contribute by 50% to the final grade.

 

Take-homes

Delivered at the end of each Block of lectures through Blackboard platform for E-Learning. Students are asked to run with SPSS the analysis of a provided dataset so to apply the techniques learnt in the Block. A brief report with the description of the results and comments needs to be posted in Blackboard platform for E-learning, according to the following schedule.

 

Takehome Delivery date Due date (by midnight)
1 9/14 9/25
2 10/12 10/12
3 11/16 11/23
4 12/3 12/10

 

The take-homes can be made individually or in group of at most 5 students. Groups do not need to be the same.

The arithmetic average of the take-home marks contributes by 10% to the final grade. A take-home that is not handed in is marked 0.

 

Final Assignment

 

The assignment consists in an analysis of a provided dataset based on a research question, set by the instructor. The report with a description of the results and comments needs to be posted in Blackboard platform for E-Learning by the 21st of December 2020. The results are going to posted by the11th of January 2021.

 

Students who are registered for the exam held on the 16th of December 2020 and want to know the assignment's mark before need to post the assignment by the 9th of December 2020. In this case the mark is going to be communicated on the day of exam, before its start.

 

The final assignment can be made individually or in group of at most 5 students. 1 point is awarded if the assignment is done in group.

The final assignment is graded out of 31 and contributes 40% to the final mark.

 

Both the evaluation grid of the take-homes and the general exam is provided. 


Teaching materials


ATTENDING AND NOT ATTENDING STUDENTS

  • R. GRAZIANI, E. GREGORI, Lectures notes on Multivariate Statistical Analyses with SPSS, delivered through Bboard platform for E-Learning.
  • Slides of the course delivered through Bboard platform for E-Learning.
  • Additional Readings: TARLING, ROGER, Statistical Modelling for Social Researchers. Principles and practice, London and New York, Routledge, 2009. Bartholomew, D.J., Steele, F., Moustaki, I., Galbraith J.I. 2008. Analysis of Multivariate Social Science Data (second edition). Chapman & Hall/CRC, Warner, R.M. 2012. Applied Statistics. Sage
Last change 25/08/2020 12:29