Who Cares About the Arts?

1. Who Cares About the Arts? Predicting Formal Arts Participation from Survey Data Angela Han ECE 539 December 2005

2. Project Objective • Apply pattern classifier neural network to arts marketing survey data • Use neural network as a predictive model to identify potential arts patrons

3. Data • Americans and the Arts (1992) • Telephone survey administered February 1992 • 1500 adults across the United States • Asked ~160 questions about participation, opinions, socialization, and support of the arts • Data stored on The Cultural Policy and the Arts National Data Archive www.cpanda.org

4. Inputs • 6 categories of survey questions • Demographics • Opinions on art, leisure, and artists • Participation in arts and leisure activities • Barriers to participation • Arts on TV • Arts socialization • Support for arts and culture • Not all questions were used as inputs – only measurable ones (33 out of ~160 questions)

5. Inputs • Sample questions used for inputs: • What is the last grade or level of school you completed? • Approximately how often did you go to the movies in the past 12 months? • How often would you estimate you buy compact discs, tapes, records, or recordings of classical music – do you buy classical music or recordings frequently, every once in a while, only occasionally, or almost never? • Over the past 12 months, have you personally or your immediate family contributed any money to an arts organization or an arts fund, or not?

6. Outputs • 6 survey questions were modified to become yes/no outputs for neural network: Not counting any performances given by your children in connection with school or classes, approximately how many times in the past 12 months did you go to… • live theater performances • live classical music performances • live performances of opera or musical theater • live performances of dance (ballet, modern, folk/ethnic, or jazz) • art museums or art galleries • science, or natural history museums or a history museum

7. The Model • A back propagation multilayer perceptron model was developed using the bp.m Matlab program from the course • The following parameters were used: • L=2, hidden layer=10 neurons • alpha=0.01, mu=0.8 • epoch size=100, max epochs=2000.

8. The Results • bp.m was run for each of the six questions – data set was the same except for the outputs. Crate and error were calculated. • These are not the most ideal results!

9. Discussion • The data is possibly flawed • Example 1: respondents were asked if they purchased classical music recordings “frequently”, “every once in a while”, “only occasionally”, “almost never”, and “never.” • All of these choices are subjective, similar purchasing habits could be placed in different categories. • Example 2: respondent could respond positively to attending classical music concerts in the past 12 months when he/she had actually been attending jazz concerts, or could respond to attending an art museum when it was really a natural history museum.

10. Discussion • Data is not properly adjusted • mean, variance, correlation not adjusted for • further linear transformation of feature vectors may be necessary • further transformations my be necessary to adjust for categorical nature • SVD may eliminate more features

11. Next Steps • Further analysis of data • Further adjustments to MLP structure • Examine other pattern classifiers • K-nearest neighbor most intuitive • Compare with marketing research regression models