Module Overview

Market Research: Quantitative Methods

This module will develop the student’s skills and abilities for quantitative business research, statistical work, data mining, and analytical problem solving. It builds upon the Marketing Statistics module, and prepares the ground for further modules in later semesters.

Module Code

DMKT H3014

ECTS Credits


*Curricular information is subject to change

Survey Data

Research designs. Observational and Experimental studies. Pitfalls of designs. Ethics.Developing indicators for concepts. Validity. Reliability. Sampling techniques. The construction and administration of Questionnaires.Data coding and preparation for analysis. Factor analysis and scale creation.

Descriptive Statistics

Review of descriptive statistics for nominal, ordinal and interval variables.

Inferential Statistics

Review of hypothesis testing; the meaning and interpretations of Statistical significance, Type I and II errors and an overview of the concept of statistical Power. Calculation and interpretation of correlation coefficients for nominal, ordinal, interval and mixed variables. Sampling distributions and Inferential statistics for correlation coefficients. The calculation and importance of statistical and practical effect size estimates. Confidence intervals for correlation coefficients. The definition and interpretation of Cronbach’s alpha.The Central limit theorem and its importance for statistical testing. The properties, uses and assumptions of T tests: Single, Paired, Unpaired. Reporting the results of T-tests. Multiple comparison and familywise error rates.Effect sizes.


Analysis of Variance (ANOVA): its assumptions and techniques for assumption checking, execution and reporting. Post-hoc testing. Least significant differences.Review of experimental designs: Full and partial factorial designs and effect estimation, Main effects and interaction effects. Main effects and interaction plots.

Linear Regression

Linear and multiple regression: Assumptions and assumption checking, execution and reporting. Stepwise regression in exploratory analysis. The problems of collinearity. An overview of Principal components analysis and its applications.Linearization for non-linear variables.R2 and R2adj. Confidence and prediction intervals. The use of dummy/indicator variables.Tests on regression coefficients, and confidence intervals for regression coefficients.

Non-parametric Statistics

Inferential statistics for nominal and ordinal data: use of the Chi-square distribution in tests for independence. Confidence intervals for a proportion.Non-parametric testing: Single, Paired, Unpaired tests of medians.

Module Content & Assessment
Assessment Breakdown %
Other Assessment(s)100