QUALITATIVE & QUANTITATIVE RESEARCH METHODS FOR MARKETING
Instructional goals
The course will examine the specific issues involved in developing and implementing marketing 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.
Attention will be focused on the following points:
• Explaining and discussing procedural and methodological factors that play a role when assessing marketing research projects
• Discussing the various procedures used for data collection
• Creating and explaining a questionnaire
• Explaining and discussing in detail the use and application of scales in marketing research
• Explaining the fundamentals of sampling in marketing research
• Explaining the techniques to check and describe data (e.g., frequency tables, cross-tabulations)
• Performing and interpreting parametric and non-parametric tests
• Explaining the right multivariate technique for data analysis given a marketing research problem
• Understanding objectives, use and interpretation of multivariate data-analysis techniques (e.g., regression, factor analysis, ANOVA)
• Understanding how to prepare a research report
Intended learning outcomes
• Knowledge and understanding: At the end of the course students will acquire knowledge on the statistical and analytical tools needed for solving specific marketing-related problems.
• Ability to apply knowledge and understanding: At the end of the course students will be able to design and manage the marketing research process, adopting data collection tools (Qualtrics) and software for statistical analysis (SPSS). Additionally, these capabilities will help students in developing their master thesis. They will be able to critically understand research papers and to propose their own research project.
• Independent judgment: At the end of the course, students will acquire the ability to analyze marketing problems and to identify the right insights for their solution. Specifically, critical thinking, problem solving, self-management, team-working, relationship and communication skills will be adequately developed, thus enhancing the disciplinary skills. These abilities will be acquired both through discussion during the lessons and through group work.
• Communication skills: At the end of the course students will acquire adequate skills and tools for data management and research reporting, both to specialists and non-specialists. These abilities will be acquired both through discussion during the lessons and through group work.
• Learning skills: The course mainly aims to develop methodological and analytical skills to develop a marketing research project on the basis of contextual needs. At the end of the course students will be able to autonomously deepen their knowledge. This ability will be acquired through the combination of the different teaching method.
Course Contents
The course will be divided into three main parts.
The first part discusses research process in depth to write a marketing research report. In particular, the main issues associated with quantitative research design, data collection, data collection techniques, questionnaire and experiment design are discussed.
In the second part, an overview of basic statistics and computer skills ‘SPSS’ is provided. Key topics covered in this part include exploratory data analysis, univariate and bivariate hypothesis testing.
In the third part, frequent and relevant marketing research problems, including profiling for predictive modelling, ad-copy testing, customer value analysis, and positioning are examined. For these problems, multivariate statistic techniques (e.g. regression, ANOVA, factor and reliability analyses) will be examined and indicated with SPSS.
Reference Books
Malhotra, N.K. (2019), “Marketing Research: An applied approach, Global Edition”, Seventh edition, Pearson Prentice Hall (Anche edizioni precedenti sono adeguate)
Slides will be uploaded on the website at the end of each lecture.
Additional material: Field, A. (2017) “Discovering Statistics using IBM SPSS Statistics”, Sage
Teaching Methods
1. Conventional lectures
2. Lab sessions
3. Teamwork
Assessment Method
Attending Students:
1. Written individual final exam: 30%
2. Two intermediate team assignments: 25%
3. Marketing research project (team project): 45%
Non Attending Students:
For non attending students the final grade will depend 100% on the final written exam. In order to be considered as non attending students have to be previously authorized by the Graduate School.
Thesis assignment criteria
Students are required to prepare a preliminary research proposal that will be carefully evaluated by the teaching team. More details will be discussed at the end of the course.
Week 1
Course introduction
Introduction to Research Design: Qualitative and Quantitative techniques
Malhotra, Chapters 1-4
Week 2
Survey and Experiments (Chapter 1-4)
Measurement and Scaling
Malhotra, Chapters 8-10
Week 3
Questionnaire Design
Malhotra, Chapters 13-15
Lab Session 1: Qualtrics - Preparing a survey
Week 4
Introduction to Descriptive Statistics and Exploratory Data Analysis: Data Preparation, Frequency Distribution, Cross-tabulation, summary statistics
Malhotra, Chapters 14,15
Lab Session 2: From Qualtrics to SPSS; Introduction to SPSS (open and save a file, checking/changing scale type, importing Qualtrics survey data into a SPSS file, syntax usage); Computing Descriptive Statistics on SPSS
Week 5
Scale Validity: Factor Analysis
Scale Reliability: Reliability Analysis
Malhotra, Chapter 9, 19
Lab Session 3: Factor and Reliability analyses
Week 6
Lab Session 4: Experiment Creation on Qualtrics, Creating Independent Variables on SPSS
The Experimental Design
Sampling Design and Procedures
Malhotra, Chapters 7, 11
Week 7
Introduction to Hypothesis; Testing differences between groups and relationship between variables (t-tests, chi-square)
Lab Session 5: t-tests, chi-square test
Malhotra, Chapter 15
Week 8
One-way Analysis of Variance (ANOVA); how to conduct a pre-test
Lab Session 6: one-way ANOVA
Malhotra, Chapter 16
Week 9
Two-way ANOVA and the Moderating Effect
Lab Session 7: Two-way ANOVA, Moderation Analysis with Categorical Moderator
Malhotra, Chapter 16
Week 10
Predictive Models: Regression and Correlation, Dummy Variables
Lab Session 8: Regression Analysis
Malhotra, Chapter 17
Week 11
Introduction to Mediation Analysis; Moderation and Mediation on PROCESS
Lab Session 9: Moderation Analysis with Continuous Moderator (PROCESS); Mediation Analysis (PROCESS)
Week 12
Course Recap – Q&A