This essay will review the information surveyed on current data from Theaterstatistik 2008/2009 collected by Deutscher Bühnenverein. The data that was given there consists of 112 German cities, number of inhabitants in these cities, capacity of theatres, i.e. seats in all
theatres that are available to visitors per 1000 inhabitants in each city. Moreover, there are 146 theatres given with the number of different types of performances produced in that particular theatre as well as the number of visitors presented in two ways: sold tickets to each type of the performance and regarding the type of a ticket.1
The aim of this essay is to summarize data about German theatres as well as to investigate interesting similarities and differences, do mean comparison and discuss the methodology used here.
Table of Contents
1 Introduction
2 Capacity
2.1 Minimum and maximum capacity in each group
2.2 Mean comparison
2.3 Kruskal-Wallis test of capacity
2.4 Mean capacity in each group
3 Performances
4 Visitors
4.1 Regarding the type of a ticket
4.2 Regarding the type of a performance
4.3 Mean comparison for visitors
4.3.1 Kruskal-Wallis test of opera visitors
4.3.2 Kruskal-Wallis test of puppet theatres
5 Methodology
6 Conclusion
Objectives and Topics
The primary objective of this research is to analyze data from the German "Theaterstatistik 2008/2009" to identify correlations between city size, theater capacity, performance types, and visitor behavior. By employing statistical methods, the study seeks to determine whether significant differences exist across various city categories (large, middle, and small).
- Statistical comparison of theater capacity across different city sizes.
- Analysis of performance frequency and relative popularity of genres.
- Examination of ticket sales distribution and preferences among different visitor demographics.
- Application of ANOVA and Kruskal-Wallis tests to validate statistical hypotheses.
- Critical review of data visualization techniques in economic reporting.
Excerpt from the Book
2 Capacity
After exploring data about inhabitants of the given German cities from the first table “Theaterunternehmen”, one could present the summary statistics about inhabitants of each city and capacity of its theatres. So, from 112 given German cities the maximum number of inhabitants is 3 431 675 (Berlin), minimum 11 455 (Dinkelsbühl) and the mean number is 531 533.1. For easier comparison of further data, the cities, their theatres and their numbers should be divided with regard to their size into three groups – large, middle size and small cities. Throughout this essay the term “large city” will be now used to refer to the group of cities that have more than 500 000 inhabitants, for example Berlin, Stuttgart, Bremen. The term “middle city” will be used for a group of cities that have between 500 000 and 100 000 inhabitants, e.g. Bonn, Halle, Ingolstadt. And last but not least, term “small city” will be used to refer to the group of cities with less than 100 000 inhabitants, for instance Dessau, Weimar, Eisleben.
Summary of Chapters
1 Introduction: This chapter introduces the dataset from the German Theaterstatistik 2008/2009 and outlines the research objective of investigating city theater structures.
2 Capacity: This chapter analyzes the seating capacity per 1000 inhabitants, categorizing cities into groups to perform mean comparisons using ANOVA and Kruskal-Wallis tests.
3 Performances: This chapter examines the distribution of various performance types, such as opera, dance, and plays, across different city sizes.
4 Visitors: This chapter investigates visitor behavior, focusing on ticket preferences and the popularity of specific genres among different city groups.
5 Methodology: This chapter discusses the statistical techniques employed, including the rationale for choosing non-parametric tests over ANOVA and the critical evaluation of graphical data representation.
6 Conclusion: This chapter summarizes the findings, highlighting the observed differences in theater capacity and visitor trends while acknowledging the limitations of the selected city groupings.
Keywords
Theaterstatistik, German theatres, statistical analysis, ANOVA, Kruskal-Wallis test, theater capacity, visitor behavior, performance genres, data visualization, urban economy, ticket distribution, quantitative analysis, city size, Theaterunternehmen, empirical research.
Frequently Asked Questions
What is the primary focus of this research paper?
The paper focuses on analyzing statistical data concerning German theaters for the 2008/2009 period, specifically looking at how theater capacity and audience behavior differ across cities of varying population sizes.
Which central topics are addressed in the analysis?
The core topics include the correlation between city size and theater seating capacity, the frequency of specific theater performance types, and the patterns observed in ticket sales and audience attendance.
What is the main objective of the study?
The aim is to conduct a mean comparison across defined city groups (large, middle, and small) to determine if there are statistically significant differences in how theater resources and productions are distributed.
Which statistical methods are utilized in the work?
The author primarily utilizes ANOVA for comparing means and, due to issues with unequal variances, employs the non-parametric Kruskal-Wallis test to ensure robust statistical inference.
What content is covered in the main body?
The main body systematically explores theater capacity, classifies performance types like opera and plays, and analyzes visitor ticket data, complemented by a methodological critique of the statistical tools used.
Which keywords characterize this paper?
Key terms include Theaterstatistik, German theatres, statistical analysis, Kruskal-Wallis test, theater capacity, visitor behavior, and data visualization.
Why did the author divide German cities into three specific groups?
The division into large, middle, and small cities serves to simplify the comparison of data and helps highlight disparities in theater infrastructure and visitor habits based on urban population density.
How does the author evaluate the use of pie charts in the methodology?
Following the recommendations of Edward Tufte and Stephen Few, the author argues that pie charts are generally poor design choices and that bar graphs are superior for comparing the magnitudes of different categories.
What was the conclusion regarding theater capacity in small cities?
The analysis revealed that the mean theater capacity per 1000 inhabitants is notably higher in small cities compared to middle or large cities, indicating a lack of uniformity across the regions.
Did the statistical tests confirm that all cities have the same theater performance distribution?
No, the Kruskal-Wallis tests rejected the null hypothesis in most cases, demonstrating that the distribution of performances and visitor behavior varies significantly depending on the city size group.
- Quote paper
- Marina Cuvilceva (Author), 2011, Explaining the situation of German theatres, Munich, GRIN Verlag, https://www.grin.com/document/197208
