Quantitative methods in marketing research

The course combines two fields of study: market research and statistics. Students will get insights on how to gather and analyze data that reflect a firms’ internal and external situation by the means of quantitative methods of market research. The aim of this course is to familiarize students with statistical data analysis techniques used in the quantitative marketing research.

Methods of teaching and learning

Lectures, class discussion, case studies, teamwork assignments for case discussion, guided self study.

Lectures 1. Nature of marketing research.

2. Collecting, preparing, and checking data. The problem of measurement.

3-4. Data exploration. Detection of missing values and outliers.

5-6. Descriptive statistics in marketing research.

7. Sampling and inference.

8. Relationship among marketing variables.

9-10. Multivariate techniques in marketing research.

Classes 1-2. Marketing research process.

3-4. Collecting, preparing, and checking data. The problem of measurement. Measurement scales. Primary and secondary data. Primary data collection.

5-6. Data exploration. Detection of missing values and outliers.

7-8. Descriptive statistics in marketing research.

9. Sampling and inference. Probability sampling. Non-probability sampling.

10-11. Relationship among marketing variables. Correlation. Linear regression. Multiple regression.

12-14. Multivariate techniques in marketing research.

15. Subject assessment.

Subject statistic

1. Number of hours and ECTS credits - compulsory subject Hours: -; ECTS: -

2. Number of hours and ECTS credits - facultative subject Hours: 50; ECTS: 2

3. Total number of hours and ECTS credits, a student must earn by direct contact with academics (lectures, classes, seminars....) Hours: 25; ECTS: 1,0

4. Total number of hours and ECTS credits, a student earns in the course of a practical nature, such as laboratory, field trips and design classes Hours: 0; ECTS: 0,0

5. Expected personal workload (without or with academics participation during consultations) necessary for realization of subject objectives. Hours: 25; ECTS: 1,0

Aaker, D. A., Kumar., V., Day, G. S., Marketing research, John Wiley & Sons Inc., New York, 2000.

Cabena P., Hadjinian P., Stadler R., Verhees J., Zanasi A., Discovering data mining: From concept to implementation, Prentice Hall, Upper Saddle River, 1998.

Chakrapani, C., Statistics in market research. Oxford University Press Inc., New York , 2004.

Franses P.H., R.P.Paap, Quantitative Models in Marketing Research, Cambridge University Press, Cambridge, 2001.

Janssens W., De Pelsmacker P., Van Kenhove P., Wijnen K., Marketing research with SPSS, Pearson Education Ltd., Essex, 2008.

Ratner B, Statistical modeling and analysis for database marketing: effective techniques for mining big data, Chapman & Hall, Boca Raton, 2003.

Rees D. G., Essential statistics, Chapman & Hall, London, 1995.

Smith S.M., Albaum G.S., Fundamentals of Marketing Research, Sage Publications Inc., Thousand Oaks, 2005.

Knowledge

Student can prepare and analyze simple marketing data.

Student acquires knowledge about basic statistical techniques useful in marketing research.

Skills

Student can prepare data.

Student can analyze data.

Student can interpret outcome of the analysis marketing datasets.

Social competences

Student has the ability to gather and interpret relevant data.

Student can communicate information, ideas, and solutions.

Student is conscious of the necessity to enhance unceasingly knowledge.

Written examination.

Potential examination question topics will cover the entire subject curriculum.

Consisting of 6 questions from the entire range of the subject.

with four response options)

The marking system:

Four questions must be attempted; candidates will fail the written exam if three or more questions are not

attempted.

Each question is scored using a 11-point scale ranging from 0 through 10. Negative marking is not used.

The pass mark is 50%.

Grade E (2.0) Very low level of understanding , score below 50%

Grade D (3.0) Basic information about the lectures and lab labs, score 50-60%

Grade C (3.5) Ability to solve any problem at the intermediate level, score 61-70%

Grade B (4.0) Good understanding of the subject, ability to discuss different issues, score 71-80%

Grade B+ (4.5) Very good knowledge about the subject, score 81-90%

Grade A (5.0) Excellent understanding, full discussion, score more than 90%