C9164 - METHODS, ANALYTICS AND MACHINE LEARNING

Learning outcomes

By the end of the course, students - will know and understand the main research methods applied to work, organizational and personnel psychology - be able to identify appropriate measures, to use statistical tools for data analysis, and to present results - Manage and prepare data to apply supervised and unsupervised machine learning methods. - Evaluate and compare machine learning models using appropriate metrics and interpret and communicate results clearly and effectively, translating analytical outputs into organizational insights and actionable recommendations.

Course contents

The course is organized in the two modules #1 and #2 of 48 hours (Prof. Balboni) and 16 hours (Prof. Benassi), respectively

Module 1

− Research study features

− Steps of a research study

− Reliability and evidence of validity of psychological tests

− Rules for selecting and applying a statistical test for analytics

− Data analytics and presentation of results

Module 2

− Introduction to ML

− Probability and statistics for ML

− Basics of predictive performance evaluation

− Uncover hidden patterns in data using clustering methods.

Readings/Bibliography

Module #1 and #2

(1) Howitt, D., & Cramer, D. (2024). Research methods in psychology (7th Edition). Pearson.

(2) Lovett, B. J. (2023). Practical psychometrics. A guide for test users. The Guildford Press.

(3) Benassi, M. (Ed.), Advanced statistics for psychology: A practical handbook for fundamentals and application. Fondazione Bologna University Press. https://doi.org/10.30682/9791254777558

(4) Materials provided by the teacher, including the slides presented during the lectures, and made available on the virtual platform.

 

Suggested advanced readings

Landers, R. N., & Behrend, T. S. (2024). Research methods for industrial and organizational psychology (1st Edition). Routledge.

Wu, A. D., & Zumbo, B. D. (2008). Understanding and using mediators and moderators. Social Indicators Research, 87(3), 367–392. https://doi.org/10.1007/s11205-007-9143-1

 

Additional readings for students who do not meet the attendance requirement (80%)

Papers that will be defined based on the progress of the lessons.

 

The list of required and suggested chapters will be defined in the classroom based on the progress of the lessons. Both the course contents and the bibliography are subject to change based on the progress of the lessons.

Teaching methods

The course unit will adopt a learning-by-doing approach, with the teachers introducing each topic and then guiding the students in practical exercises, some of which are with statistical software.

The lectures will provide a more comprehensive examination of the topics presented in the texts and will include practical exercises that are essential for learning and cannot be replaced by studying the texts alone.

Given the interactive nature of the teaching methods adopted in this course, regular class attendance (at least 80% of scheduled classes) is considered an integral part of the learning process.

Students who do not meet the attendance requirement will be provided with supplementary reading and study materials to make up for the missed learning activities. During the oral exam, the materials will be investigated in addition to the scheduled material.

 

In view of the activities in the computer laboratory, the attendance of this course unit requires the prior participation of all students in Module 1, 2 [https://www.unibo.it/en/services-and-opportunities/health-and-assistance/health-and-safety/online-course-on-health-and-safety-in-study-and-internship-areas] on Health and Safety online.

Assessment methods

The final grade for the course is calculated as the weighted average of the grades for each module.

Each module grade is based on two types of performance: individual (e.g., an oral exam) and group (e.g., group assignments or presentations). The final grade for each module is the average of the grades obtained for both types of assignments. The grading scale for both individual and group assignments ranges from 0 to 30, with the possibility of receiving honors. The minimum passing grade is 18.

The oral exam is comprised of an oral discussion (approximately three questions for each module) that focuses on verifying the acquisition of the course unit topics and their application in novel contexts, as well as the assignment.
Students who do not meet the 80% attendance requirement will be provided with two additional questions for each module on the adjunct material.

All course unit topics may be examined, and each is given the same weight. During the exam, the use of books, notes, and electronic devices is prohibited, except for students with a Specific Learning Disorder certification and for students with disabilities who are permitted to utilize aids and support.

Details on the group assignments will be provided during the lectures and uploaded to Virtuale. A limited, transparent, and non-substantial use of AI tools is permitted only for support activities (e.g., summarising or rephrasing). Any use of AI must be explicitly declared.

 

The student is required to complete the online registration within the terms in order to be admitted to the exam. Students who are unable to enroll within the due date must inform the secretarial office promptly. The teachers will consider the request and decide about the admission.

Students with learning disorders and\or temporary or permanent disabilities: please, contact the office responsible (https://site.unibo.it/studenti-con-disabilita-e-dsa/en/for-students) as soon as possible so that they can propose acceptable adjustments. The request for adaptation must be submitted in advance (15 days before the exam date) to the lecturer, who will assess the appropriateness of the adjustments, taking into account the teaching objectives.

Teaching tools

PowerPoint presentations, paper-pencil instruments, guided discussions, teaching materials and databases provided by the teacher.

Policy on the Use of Technology in the Classroom

- Students may not use any handheld devices in the classroom (cell phones, cameras, etc.) without the explicit permission of the professor.

- Computers should be used for note-taking only.

- Students who need to use voice recorders for class lectures must receive the explicit permission of the professor in order to do so.

Office hours

See the website of Giulia Balboni

See the website of Mariagrazia Benassi