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. TheatreMusicOperaDanceArtScience crate54.33%84.33%87.00%86.87%58.80%74.73% Error18.2510.167.618.7317.0474.73 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