Students are able to,

• understand and explain key statistical concepts,
  

• justify the relevance and necessity of statistical information processing for the economic context as well as the scientific and social process,
  

• describe and explain the basic ideas and characteristics of selected quantitative data processing methods,
  

• describe and explain basic concepts and methods of descriptive statistics and inferential statistics and explain their benefits and limitations conceptually and technically,
  

• apply basic and complex descriptive-statistical and inferential-statistical methods competently, taking into account the characteristics of the data,
  

• select adequate statistical methods to answer given content-related questions,
  

• evaluate data using suitable descriptive-statistical and inferential-statistical methods, present them with suitable graphics and interpret the results of the analyses.
  

• perform computer-aided quantitative data analysis using new technologies (e.g. AI-based tools) for the preparation and analysis of quantitative data.
  

• critically scrutinize the data and results on which empirical studies are based, including the interpretations derived from them, and evaluate their quality.
  

• identify limitations in the informative value of descriptive and inferential statistical data analyses for answering content-related questions.
  

none

• Basic concepts of descriptive statistics: characteristic values, measurability, scale levels, etc.
  

• Fundamentals, planning and practical implementation of selected methods for processing quantitative data, including possibilities for using new technologies (e.g. AI-based tools)
  

• Univariate descriptive-statistical methods: Frequencies and frequency distributions, graphical representation of data, measures of location and dispersion
  

• Bivariate descriptive-statistical methods: Correlation and regression analysis, contingency analysis
  

• Concept of probability, calculating with probabilities
  

• Probability distributions: Binomial distribution, normal distribution
  

• Parameter estimation, confidence interval, hypothesis testing
  

• Parametric inferential statistical methods (e.g. t-test, F-test)
  

• Non-parametric inferential statistical methods (e.g. U-test, W-test, Chi2-test)
  

• Practical implementation of quantitative data analyses using statistical software packages (e.g. MS Excel, SPSS, R, JAMOVI, ...)
  

• Possibilities of using new technologies (e.g. AI-based tools) for computer-aided quantitative data analysis
  

• current developments in the field of quantitative data analysis (e.g. new methods and tools in computer-aided and automated quantitative data analysis, ...)
  

Bortz, J./Schuster, C. (2010). Statistics for human and social scientists (7th ed.). Berlin: Springer.

Bühl A. (2016): SPSS 23: Introduction to modern data analysis. 15th, updated edition. Pearson Studium.

Dürr, W./Mayer, H. (1992): Wahrscheinlichkeitsrechnung und Schließende Statistik. Munich/Vienna: Carl Hanser Verlag.

Eid, M./Gollwitzer, M. and Schmitt, M. (2015). Statistics and research methods. Weinheim: Beltz.

Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th edition). London: Sage Publications Ltd.

Field, A./Miles, J./Field, Z. (2012). Discovering statistics using R. London: Sage Publications Ltd.

Kockelkorn, U. (2012). Statistics for users. Berlin/Heidelberg: Springer.

Mc Clave, J. T./Benson, P. G./Sincich, T. (2005). Statistics for Business and Economics (9th ed.). Upper Saddle River: Pearson.

Quatember, A. (2005): Statistics without fear of formulas. A textbook for economists and social scientists. Munich: Pearson Studium.

Rasch, B./Friese, M./Hofmann, W./Naumann, E. (2014a). Quantitative Methods 1. Introduction to Statistics for Psychologists and Social Scientists. Berlin: Springer.

Rasch, B./Friese, M./Hofmann, W./Naumann, E. (2014). Quantitative Methods 2. Introduction to Statistics for Psychologists and Social Scientists. Berlin: Springer.

Wewel, M.C. (2010). Statistics in the Bachelor's program in business administration and economics. Munich: Pearson Studium.

Lecture, group work, working on exercises, practical implementation of (computer-aided) data analyses

Cumulative module grade: The individual course grade of ILV "Quantitative Data Analysis" is incorporated into the overall module grade and is weighted in accordance with its assigned ECTS-Credits.
Assessment type: final assessment
Assessment method/s: Exercise sheets, presentation of exercises, written examination (written exam or challenge)