040501 KU Data Analysis for Marketing Decisions (MA) (2018S)

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 (e.g., NHST, Type I and II error, confidence intervals) as well as (b) different methods of data analysis (e.g., t-test, χ2 test, ANOVA, regression 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, master thesis) 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 theoretical discussions that are accompanied by several practical examples and hands-on exercises. Primarily, the lectures provide background knowledge on the statistical theory, the selection, and the understanding of various data analysis techniques. In addition, hands-on exercises introduce the SPSS environment and illustrate how to perform and interpret statistical data analyses. Note that successful completion of DAMD depends greatly on students’ effort to systematically review the material and suggested homework throughout the semester.