040501 KU Data Analysis for Marketing Decisions (MA) (2017W)

Sound knowledge of statistics and data analysis is an essential requirement for marketing research and managerial decision-making – more than one would expect! The present course discusses (a) key concepts of statistics and statistical inference as well as (b) different methods of data analysis in a series of lectures that combine theory with applied examples and hands-on exercises. The application of the analytical techniques is carried out using the IBM SPSS software package. However, this course is not a tutorial on SPSS but it rather focuses on the logic, the implementation, and the interpretation of statistical data analysis. Students who successfully complete DAMD will be equipped with the ability to effectively conduct research projects in their later academic (e.g., other university courses, dissertations) and/or professional career.

The major topics covered in the course are:
Theoretical introduction to basic research terms: data, variables, models, research process, sample, population, measurement scales, etc.
Introduction and familiarization with the statistical software SPSS
Clearing and preparing data for further analysis
Descriptive statistics: central tendency, variability, skewness, kurtosis
Testing statistical assumptions: normality, homogeneity of variance, homoscedasticity
Inferential statistics and hypothesis testing: parameter estimates, sampling error, confidence intervals, Type I and Type II errors, p-values, t-values
Performing comparisons: chi-square test, independent samples t-test, paired-sample t-tests, analysis of variance
Investigating relationships: bivariate correlation, partial correlation
Regression models: simple linear regression, multiple linear regression, logistic regression
Finding structures using Factor Analysis
Presenting, reporting and interpreting results
Identifying practical and theoretical implications drawn from statistical analyses

The classes involve a combination of formal theory lectures and practical lab sessions. Formal lectures primarily provide background knowledge on statistical inference and the selection of appropriate statistical techniques to analyse data. On the other hand, lab sessions and hands-on exercises introduce the SPSS environment and illustrate how to conduct and interpret different types of data analyses.