30280  APPLICATIONS FOR MANAGEMENT
Department of Social and Political Sciences
ARNSTEIN AASSVE
Class 15: MARTA ANGELICI, Class 16: NICOLO' CAVALLI, Class 17: ARNSTEIN AASSVE, Class 18: ARNSTEIN AASSVE
Synchronous Blended: Lessons in synchronous mode in the classroom (for a maximum of one hour per credit in remote mode)
Suggested background knowledge
PREREQUISITES
Mission & Content Summary
MISSION
CONTENT SUMMARY
 Introduction to Artificial Intelligence
 Concepts of causality and correlations
 Formulating the research questions, develpment of a research design, questionnaire design
 Sampling and data sources, sample selection, finding data for research projects.
 Regression analysis: multivariate regression, assumptions and properties, violation of assumptions and remedies.
 Factor analysis: model, extraction, rotation, interpretation.
 Scale construction and evaluation: reliability analysis and composite scores.
 Machine learning  Cluster analysis.
 Regression analysis revisited: regression analysis in combination with factor analysis and cluster analysis, binary response models.
Intended Learning Outcomes (ILO)
KNOWLEDGE AND UNDERSTANDING
 Become proficient use of AI as a tool for conducting research projects
 Select, clean and manage large and complex data sets to be used for applied research.
 Distinguish between different kinds of variables.
 Explain how variables can be used in standard statistical techniques.
 Explain linear regression, test for its underlying assumptions and make relevant interpretations.
 Explain the methods of cluster analysis and factor analysis.
APPLYING KNOWLEDGE AND UNDERSTANDING
 Distinguish between associations and causal effects in statistical analysis and assess their implications for policy.
 Apply statistical software to analyse and answer defined research questions and hypotheses.
 Formulate, analyse data, and write a structured applied research project.
 Confidently apply the methods of linear regression, cluster analysis and factor analysis.
Teaching methods
 Facetoface lectures
 Online lectures
 Guest speaker's talks (in class or in distance)
 Exercises (exercises, database, software etc.)
 Group assignments
DETAILS
Online Lectures:
 Outline and explain the process of implementing the research project assignment.
 One webinar to both explain and answer questions concerning the research project/assignment.
 One class is taught fully online.
Guest speaker:
 Introduction to Artificial Intelligence
Exercises:
 There are a set of meetings where we demonstrate and explain how to use the statistical software STATA, and where students are given tasks of answering and solving tasks related to the STATA and the use of large and complex data sets.
 Standard exercises concerning applied statistical calculations.
Group assignments:
 The assignments are applied research projects which you need to do with your designated group.
Assessment methods
Continuous assessment  Partial exams  General exam  


x  

x 
ATTENDING STUDENTS
Attendance is compulsory. You are strongly adviced to attend all classes. In order to count as an attending student, you must have attended at least 80% of classes.
Students will be working in groups throughout the course. The groups will have up to 4 members and will be assigned by the professors, based on individual objective characteristics of students in order to have balanced groups.
There will be three fourhour slots during the semester where the groups meet in presence and implement a task. These group projects will count 5 points each and count towards the final grade awarded in the course. In total, students can obtain max 15 points. During the fourhour group work, students will have any tools available – including AI.
At the end of the 4hour slots, each group must upload a written project that answers to the task given.
Grading: students will be given the same grade – i.e. individual grades will be that of the group grade.
Written exam
BBoard Test, with Respondus Lockdown Browser in class. The test consists of a number of questions of the following types:
1. Multiple Choice. Here you choose one (and only one) of the answers provided and only one of the provided options is correct (full score).
2. Multiple Answers. Here you can choose more than one of the answer options provided. Full score is achieved if all the correct options are selected. Note that choosing a wrong option will contribute negatively to your score. The sum of the negative scores for all the wrong options is always equal to the maximum score for the question. In other words, if you select all correct and incorrect options, the score will be zero. However, in no case will the score for the question be negative (e.g. even if you choose all the wrong options and no correct option, the score for the question will be zero).
3. Numerical Answers. Here you are required to provide an answer in numerical format, according to the required precision which is specified for each question. Please use the character “.” as decimal separator. With this format you may be required to perform calculations in order to derive your answer.
The written exam will have 16 questions and give a maximum of 16 points. This will be added to the points gained from the three group works described above.
Grading of written exam: The overall grade of the written exam is divided in two:
1) From the first eight questions, your grade will be the average of the individual exam scores of your group (including your own individual score).
2) The scores of the remaining eight questions are calculated from your own answers only (i.e. not dependent upon the individual scores of your group).
NOT ATTENDING STUDENTS
Written exam
BBoard Test, with Respondus Lockdown Browser in class. The test consists of a number of questions of the following types:
1. Multiple Choice. Here you choose one (and only one) of the answers provided and only one of the provided options is correct (full score).
2. Multiple Answers. Here you can choose more than one of the answer options provided. Full score is achieved if all the correct options are selected. Note that choosing a wrong option will contribute negatively to your score. The sum of the negative scores for all the wrong options is always equal to the maximum score for the question. In other words, if you select all correct and incorrect options, the score will be zero. However, in no case will the score for the question be negative (e.g. even if you choose all the wrong options and no correct option, the score for the question will be zero).
3. Numerical Answers. Here you are required to provide an answer in numerical format, according to the required precision which is specified for each question. Please use the character “.” as decimal separator. With this format you may be required to perform calculations in order to derive your answer.
The questions will cover theory, methods and interpretation of the results of applied research. The exam will cover all topics of the course.
The written exam counts 70% towards the overall grade.
The student is in addition required to write a research project that will count 30% towards the overall grade. The project should be no longer than 12 pages. It will have to be based on two or more rounds of the European Social Survey. It can be downloaded from www.europeansocialsurvey.com. The project has to be submitted on the same date of the exam.
Teaching materials
ATTENDING AND NOT ATTENDING STUDENTS
 Lecture notes online on the Bboard platform.
 P. NEWBOLD, W.L. CARLSON, B. THORNE, Statistics for Business and Economics and Student CD, Prentice Hall (International Edition), 2012, 8th edition (Note that this book is the one you have used for your previous Statistics course. The book is used in the lectures on Linear Regression and ANOVA).
 R. WARNER, Applied Statistics: From Bivariate through Multivariate Techniques, Los Angeles: Sage, 2008 (We only use chapter 4 from this book, which is made available online from the library).
 J.F. HAIR, R.L. TATHAM, R.E. ANDERSON, et al., Multivariate Data Analysis International Edition (Paperback), Pearson Education, International Edition, 5th or 6th edition,(ISBN10: 0139305874) (note that this book is used for Factor Analysis and Cluster Analysis (lectures from end of October onwards) – whereby chapter 3 (FA) and chapter 8 (CA) are made available online from the library).
 In this course we are using the software STATA. We introduce STATA through the lectures and tutorial office hours, but bear in mind that STATA is quite userfriendly and we expect you to learn STATA through “learningbydoing”. There are several books available for doing statistics with STATA (most of them available on Amazon). There is also a lot of online material on STATA.