A central task for managers is to interpret and analyze data in order to make informed decisions that are beneficial for their firms. In order to make evidence-based decisions managers need to be able to collect, analyze and interpret data relevant to the problems their organizations face. The course thus introduces basic procedures for leveraging descriptive statistics and inference statistics to make business decisions.

At the completion of this course, students will be able to: Develop research for evidence-based managerial problem solving Compare strengths and weaknesses of different statistical procedures Illustrate findings from statistical analyses effectively Perform different types of data-analyses such as OLS regressions, Analysis of Variance and tests of mean differences Interpret results of multivariate data-analysis techniques (e.g., regression, factor analysis, ANOVA) Knowledge and understanding. The course examines the specific issues involved in developing and implementing a quantitative research to solve a managerial decision problem. Students will obtain computer skills such as SPSS for Statistics. Knowledge from this course can be also used for writing a master thesis. Students will understand how to structure a quantitative research using Qualtrics and how to test specific hypotheses using SPSS for Statitstics software. Applying knowledge and understanding. On the basis of the theoretical classes students will be able to understand given a managerial problem which statistical technique to use in order to solve it. In particular, students will be able to understand when is the case to use a regression, an ANOVA analysis or a Factor Analysis. Making Judgements. At the end if the course students will be able to support managerial decision making using reliable secondary and academic data, and more importantly to interpret statistical outputs aimed at providing trustworthy decisions in a managerial context characterized by uncertain environments. Communication Skills. Students will develop a statistical vocabulary that will allow them to better communicate meaningful advice to management decision making. In particular, students will be familiar with concepts as “p-values”, “significance”, “correlation”, “causation” etc. and will be able to effectively transfer their meaning in their working environments. Learning Skills. Will be provided, among others, team assignments in which they are called to apply the theoretical concepts discussed during the class to real business cases. In such a way students will develop organizational, time management and strategy development skills, among others.

The course introduces techniques for quantitative analysis and prepares the students for their research project.

Field, A. (2017). Discovering statistics using IBM SPSS statistics. London: Sage.  Hinton, P., McMurray, I., & Brownlow, C. (2014). SPSS explained. Routledge.   (available online through perlego)  Malhotra, N.K., “Marketing Research, An applied approach, Global Edition”, Seventh edition, ISBN-10:1292265639, ISBN-13: 978-1292265639, Pearson Prentice Hall 

The main teaching modalities used in this course are: Mix synchronous and asynchronous sessions TA consultations Team assignments Quizzes One-minute essays

Students will be evaluated on the following activities: 2 Quizzes and 2  Graded Team Assignments delivered throughout the course accounting for 75% of your final mark: Graded Quiz in Week 3 covering material from week 1 to week 3 (weight 15%) Graded Group Assignment to be submitted in week 6 (10%) Graded Group Assignment to be submitted in week 10 (10%) + On-Campus discussion of the work (10%) Final Quiz-Exam at the end of the course covering weeks 4-12 (30%) You can earn up to 1 bonus point throughout the semester for completing additional activities, which are not graded. The remaining 25% of your final mark will be earned through your research project (to be developed after the quantitative methods course ends)

High performance in research project assignment.

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