Quantitative methods in marketing

Course title
Quantitative methods in marketing
Course tag
11405
Semester
2
Course status
Mandatory
ECTS
6
Lectures
30
Practice
30
Independent work
120
Total
180
Teachers and associates
Msc Igor Kaluđer, Lecturer
Siniša Urošev, Instructor
The course aims
The aim of the course is to train students for independent application of quantitative analysis and modeling in marketing research. Students will acquire theoretical and practical knowledge of quantitative methods- They will learn to recognize certain types of problems and choose the appropriate method of analysis and modeling. It is especially important to develop skills of modeling real business issues in the domain of marketing through examples and exercises.
Content
Introduction to quantitative methods. The types of models. Process modeling. Defining the problem, objective and scope of research. Data collection. Primary and secondary sources. Measuring and scaling. The primary measurement scale. Comparative scaling. Specific scale. The design of the questionnaire. Sampling. Sampling techniques. Sampling. Distribution and confidence intervals. Descriptive statistics. Measures of average, dispersion and curvature. Non-parametric tests. Chi-square test. K-S. Wilcoxon. Correlation. Scatter. The correlation coefficient. Introduction to regression. Linear regression. Multiple regression. Analysis of the quality of the model. Factor analysis. Clustering.
Literature:
Malhotra, N.: Marketing Research: An Applied Approach, Prentice Hall, 2007.
Supplementary literature

Minimum learning outcomes

  1. Evaluate the basic concepts of quantitative modeling, data collection, sampling and measuring scale in terms of their applicability and usefulness in marketing analysis
  2. Choose and interpret the basic measures of central tendency and dispersion in terms of applicability, interpretability and usefulness for problems in the domain of marketing.
  3. Choose and interpret basic aspects of correlation and regression analysis
  4. Choose and interpret basic aspects of factor analysis and the difference from the regression analysis
  5. Choose and interpret basic aspects of clustering analysis in marketing

Preferred learning outcomes

  1. Choose the type of models and general modeling techniques for the problems in the domain of marketing. Choose the appropriate statistical tools. Choose the measuring scales and sampling techniques.
  2. Choose, measure and critically interpret relevant measures of descriptive statistics and non-parametric tests.
  3. Choose, measure and critically interpret the correlation and regression analysis, parameter estimation, standardized coefficients, significance, predictability and analysis of residuals
  4. Choose, measure and critically interpret the procedure of factor analysis, including the formulation of the problem, the correlation matrix, the choice of methods and the number of factors and rotation and interpretation of factors
  5. Choose, measure and interpret critical procedure for clustering analysis, including the formulation of the problem, the choice of distance measures, clustering algorithm and the number of clusters