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Lesson details

APPLIED MARKETING STATISTICS
2018-2019	EnIESEG School of Management ( IÉSEG )
Class code :	1819-IÉSEG-M1S1-MKT-MA-FE42UE	MARKETING

Level	Year	Period	Language of instruction
Master	1	S1	EnEnglish

Academic responsibility	N.HEUVINCK
Lecturer(s)	Nico Heuvinck (Lille) Yi Li (Paris)

IÉSEG > IESEG Degree - Programme Grande École > Semester 1 > 2,00 ECTS

Prerequisites

This course requires students to understand the basics of marketing research (going through all the steps of a Marketing Research Project unto data collection) and statistics.

Learning outcomes

This course enables students to analyze marketing data in general:

- Prepare data before starting with statistical analyses (including Descriptives, Factor Analysis & Cronbach's
Alpha)
- Choose the appropriate statistical test to provide an answer on a research question or testing hypotheses
(including correlation, linear & logistic regressions, T-tests, Chi-Square, ANOVA's, Moderation &
Mediation)
- Execute these statistical test(s)
- Interpret the findings, correctly reporting the results and drawing conclusions

Course description

This course will be divided into 6 sessions (extensive format).

The first session will provide a general introduction to the IBM SPSS 22 environment (the statistical software package that will be used throughout this course) as well as preparing data (including dealing with missing values, computing new variables, summating scales, and selecting subsets). The second session contains the descriptive analysis of data, Factor Analysis, and Cronach's Alpha. The first part of a real-life marketing analysis project will take place in session 3 where students will have to apply the knowledge gathered in sessions 1 & 2. The fourth session includes Correlational anlysis as well as both linear and logistic regressions. Session 5 deals with hypothesis testing by means of T-tests, Chi-Square, ANOVA's, moderation, and mediation. In the final session, the second part of the real-life marketing analysis project has to be analyzed by using the knowledge gathered in session 4 & 5.