Nothing.

The basic aim of this course is to provide an essential knowledge about marketing research methods, which is a scientific way to gather information for strategic and tactical marketing decision making. The quality of the information depends on a rigorous research planning (problem definition, research design, data collection method, questionnaire design, measurement, sampling), efficient implementation of the process (research management) a rigorous data analysis (qualitative and statistical methods), and finally reporting (clear conclusions). Both, the quantitative and qualitative approaches will be emphasized. OBJECTIVES Upon the successful completion of this course, the attendants should have demonstrated an understanding of and an ability to apply the following: 1. Develop an understanding of standard marketing research methods avoiding the most common pitfalls, through learning the material in the text and the lectures, and completing group assignments. 2. Develop the ability to use basic statistical analysis in a marketing analysis context. Specifically commonly used procedures will be reviewed, in the framework of SPSS software. 3. Use the marketing research procedures to complete research projects and written reports.

Lecture 1: INTRODUCTION TO SOCIAL & MARKET RESEARCH - Welcome & Introduction to the Course Structure; - Introduction to social & market research: Main Concepts. - The Research Process. - Practice 1: Everything starts with a briefing!. Readings: - Guide on distinguishing market research from other data collection activities. ESOMAR world research codes & guidelines. - How to commission research. ESOMAR world research codes & guidelines - ICC / ESOMAR international code. Lecture 2: OVERVIEW ON SOCIAL & MARKET RESEARCH - Information Sources: Primary/Secondary, Internal/External. - Quantitative & Qualitative Research. - Case Studies. - Review of Business Plan. Lecture 3: QUALITATIVE RESEARCH: Introduction to qualitative techniques: practical overview - Classic qualitative research techniques - Online qualitative research techniques - Practice 2: The research proposal Lecture 4: QUANTITATIVE RESEARCH I: Commonly used methods for quantitative research - Classic data collection systems - New trends in quantitative data collection - Practical session on basic data analysis using IBM SPSS Reading: - SPSS Survival Manual (Julie Pallant) Lecture 5: QUANTITATIVE RESEARCH II: SAMPLING: Sampling and survey error. - Sampling. Calculating sampling error and sample size. - Sources of non-sampling error. - Weighting. - Practical session: exercises on sampling. Lecture 6: QUESTIONNAIRE DESIGN. - Writing a questionnaire. - Types of questions and types of measurement scales. - Practical session on transforming survey questions into dataset variables using IBM SPSS. Reading: - Brace, I. (2018). Questionnaire design: How to plan, structure and write survey material for effective market research. Kogan Page Publishers Fundamentals of questionnaire writing (author). Lecture 7: STATISTICAL ANALYSIS I. - Exploring statistical relationships between variables. - Practical session on performing Chi2 test, correlations and analyses of variance using IBM SPSS. - Practice 3: Reporting data Lecture 8: STATISTICAL ANALYSIS II. - Advance Statistical Analysis; Multivariate Analysis. - Practical session on performing multivariate analysis using IBM SPSS. - Practice 3: Reporting data (session with real data). Lecture 9: TURNING DATA INTO FINDINGS. - Writing a report. Creating tables and charts. - Presenting research findings. - Practical session using IBM SPSS. - Practice 4: The presentation Lecture 10: PRESENTATION OF THE REPORT. - Presentation of your own Research Findings

This course will be a mixture of lectures, class discussion, assignments, and examinations. The course is addressed to managers, the final users of marketing research, and will be oriented to show the applications to strategic marketing. There will be a project related to material being covered in this course. The students should work on a research briefing, plan a research proposal, analyze the data and report valid conclusions. In this course, students can use AI tools in assignments and exercises, but always citing them properly.