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Course 2023-2024 a.y.

20812 - APPLIED RESEARCH IN CULTURAL INDUSTRIES AND INSTITUTIONS - MODULE I (QUANTITATIVE METHODS FOR MANAGEMENT)

ACME
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


Advanced: Lessons in synchronous and asynchronous mode in the classroom and in remote mode

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 contribute to the commissioning and the interpretation of reports of business research, including surveys, market research and program evaluations; to prepare accurate and informative data summaries for inclusion in management reports; to make use of advanced statistical techniques to support management data-driven decision making.

CONTENT SUMMARY

The course focuses on multivariate statistical techniques widely used in business analytics. Students are taught how to set up the appropriate analyses, implement them through the use of a statistical software (SPSS) and give an interpretation to the obtained results.

 

The course is divided into four learning modules.

 

Module 1 lays the foundation for understanding multiple linear regression. Through practical examples and case studies, students learn how to assess and model the association between a numerical dependent variable and independent variables of any kind, how to evaluate the goodness of fit of the model, assess and compare the marginal impact of each independent variable, how to run appropriate model diagnostics so to detect assumptions violation and set-up appropriate remedies.

Module 2 delves deeper into advanced techniques in multiple linear regression. Students learn how to assess and model nonlinear relationships and how to detect and account for moderation effects. An introduction to causality analysis is provided as well, through path analysis. These techniques will empower students to uncover hidden relationships and causal mechanisms in the data, enabling to make informed decisions and develop effective strategies.

Module 3 shifts the focus to logistic regression, a powerful tool for modeling binary and multinomial outcomes. Students will learn how to analyze and interpret data with categorical dependent variables, such as customer preferences or audience segmentation.

Module 4 explores factor analysis and cluster analysis. Factor analysis allows to uncover latent variables and underlying dimensions in the data, providing valuable insights into individuals preferences and behavior. As an output of the analysis numerical scales can be generated to measure latent constructs and to be used in further analyses. Cluster analysis, on the other hand, helps identifying groups of objects or individuals based on their similarity, which can guide strategic planning, resource allocation, and problem-solving in various domains.

 

Throughout the course, we prioritize engagement and interaction. Students will actively participate in group discussions, collaborate on projects, and engage in role-plays that simulate real-world scenarios.


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 program evaluations.

Teaching methods
  • Face-to-face lectures
  • Online lectures
  • Exercises (exercises, database, software etc.)
  • Individual assignments
  • Group assignments
  • Interactive class activities on campus/online (role playing, business game, simulation, online forum, instant polls)
DETAILS

Online lectures

Online lectures are delivered for each Learning Module, as informative short video lectures that provide concise descriptions of the quantitative methods covered in the course. These lectures serve as a quick overview of the various techniques, accompanied by examples and real-world applications. Through these videos, students will gain a clear understanding of the quantitative methods being taught, their underlying principles, and how they can be applied to solve practical problems. 

 

Exercises

Exercises are Tests delivered through Bboard platform, to be used as self-assessment tests and to prepare for the in-class continuous assessment tests and the final test. They include only multiple choice questions with solutions provided as Feedbacks.

 

Assignments

Assignments are delivered through Bboard platform for E-Learning, as in-class work to evaluate students' ability to implement the learned methods using SPSS. These assignments involve performing simple analyses using SPSS and serve as checkpoints to assess students' proficiency in using the software and their understanding of the analytical techniques covered in the course.

 

Group assignment

The final project is delivered as group assignment through Bboard platform for E-Learning. The project requires students to apply the methods learned throughout the course to conduct a comprehensive analysis. The students are then expected to produce a report that includes a description of the analyses conducted and the findings obtained. The purpose of the final project is to provide students with an opportunity to apply what they have learned, and serves as preparation for their final thesis, helping them develop the necessary skills for the professional world. The evaluation grid as for the individual assignments is used.

 

Role-plays

The course incorporates a role-play activity for each module, where students work in groups and assume two different roles: client and consultant.  By simulating real-world scenarios, students have the opportunity to put their theoretical knowledge into practice and demonstrate their understanding of the subject matter. This hands-on approach allows students to bridge the gap between theory and application, preparing them for the final project or real-world challenges they may encounter in their professional careers.

 


Assessment methods
  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
  • x   x
  • Group assignment (report, exercise, presentation, project work etc.)
  • x   x
    ATTENDING AND NOT ATTENDING STUDENTS

    Five Partial Tests or a comprehensive General Exam Test, four in-class assignments and a final project.

     

    Five Partial Tests or a comprehensive General Exam Test

    Four tests are scheduled, one for each Learning Module to be taken in class, covering the respective module's content. The fifth test is taken at the scheduled General exam date. Each test consists of three multiple-choice questions, with each question worth one point. The final mark is given by the sum of the five tests marks.

    During the scheduled General exam, students have the option to choose a single test comprising 15 multiple-choice questions, with each question worth one point,  that cover the entire course curriculum. However, if they choose to take the comprehensive test, they will forfeit the points earned in the four in-class tests.

     

    The tests aim at assessing the students acquired knowledge on the content of the course, in particular with reference to the first and fourth Intended Learning Outcomes, as the ability to read reports and scientific articles that make use of basic and advanced statistical techniques and to contribute to the commissioning and interpretation of reports of business research, including market research and programme evaluations.

     

    In-class Assignments

    Delivered in class at the end of each Learning Module through Blackboard platform for E-Learning, during an applied session. Students are asked to run with SPSS the analysis of a provided dataset so to apply the techniques learnt in the corresponding Module. A brief report with the description of the results and comments needs to be posted in Bboard platform for E-learning.

     

    The in-class assignments let build piece by piece the skills related to the second and the third Intended Learning Outcomes, as to set up and run empirical analyses, that require the use of basic and advanced statistical techniques and the use a statistical software (SPSS) to implement multivariate statistical analyses to support management decision making.

     

    The in-class assignments 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 in-class assignment marks contributes to the final grade. An in-class assignment that is not handed in is marked 0.

     

    Final Project

    The final project consists in an analysis of a provided dataset based on a research question, set by the instructor. The project can be made individually or in group of at most 5 students. 1 point is awarded if the project is done in group. A report with the description of the results and comments needs to be posted in Bboard platform for E-learning. Students are required to post a commented script as well, with all commands of the analyses  run.

    The final project is graded with a maximum of 13 points.

     

    The final project achieves the second and the third Intended Learning Outcomes, as the ability to set up and run empirical analyses, that require the use of basic and advanced statistical techniques and the use a statistical software (SPSS) to run multivariate statistical analyses to support management decision making.

     

    The final grade is given by the sum of the Tests grade, the In-class Assignments grade and the Final Project grade. An extra point is awarded to students partecipating in all role-plays.


    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.
    • Video Lectures and Slides 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 06/06/2023 15:32