analysis of musical preferences and orchestral music
TRANSCRIPT
-
8/13/2019 Analysis of Musical Preferences and Orchestral Music
1/59
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
Analysis of Musical Preferences and OrchestralProgramming
Max Candocia
January 5, 2014
Orchestral preferences analysis MC 1
-
8/13/2019 Analysis of Musical Preferences and Orchestral Music
2/59
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
Motivation
Whenever I go to an orchestra concert, I notice a few things:1 The audience is mostly people with gray hair.2 The repetoire almost always includes Baroque, and almost never
includes anything written after 1950.3 The musicians are very talented.
This was especially the case after I watched a CSO performance at myschool, after which the students were promised a meet & greetwith the
musicians. Instead, the students talked to each other in a tent withrefreshments, while the musicians talked to the donors inside theperforming arts center.
Orchestral preferences analysis MC 2
-
8/13/2019 Analysis of Musical Preferences and Orchestral Music
3/59
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
Motivation (cont.)
After this small disappointment, I began wondering about theprogramming more. Here, one of the best orchestras in the world cameto one of the best concert halls in the world and decided to play Vivaldi,
Mozart, and Beethoven. Was this a good choice, or could they havechosen a better selection to appeal to a wider audience? My intuitiontold me that, as a premier orchestra, theyve probably done marketresearch on this before and their choice of music appeals to their donors.
The main question of this study then, is, What makes people go to or avoid an orchestra concert?
Orchestral preferences analysis MC 3
-
8/13/2019 Analysis of Musical Preferences and Orchestral Music
4/59
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
Description of Survey
Last April, I decided to make a survey to ask questions about peoplesmusical preferences and what types of changes to programming thatwould make them more likely to go to a concert. Additionally, I addedsome marketing questions, such as how much people would pay for aticket and how they hear about concerts they go to.
I posted the survey on Facebook and Reddit, and I also sent it in a chainemail to family and friends. I collected 1,654 responses total, which is
enough to run tests on various hypotheses. Here is a link to the originalsurvey. The survey may still be taken, but I will not be focusing onanalyzing any new data in the near future.
Orchestral preferences analysis MC 4
https://docs.google.com/forms/d/1LoeOSpyZxdQqiHZuH7vqomOjW_lCJnARmjTaUlBT5H4/viewformhttps://docs.google.com/forms/d/1LoeOSpyZxdQqiHZuH7vqomOjW_lCJnARmjTaUlBT5H4/viewform -
8/13/2019 Analysis of Musical Preferences and Orchestral Music
5/59
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
Sources of Bias
Now, before I get hundreds of messages along the lines of ...but did youtake X into account?, I should admit that the data is not 100%bias-free. The main source of bias is that most of my data was collectedon Reddit, a website with young Internet-users who are not an accurate
representation of the general population, or even their own age group.Additionally, the chain email spanned a relatively small, but olderdemographic. There were many who are members of the NorthshoreConcert Band, which is somewhat problematic for statistical purposes.My solution to this is to use where I posted the survey as a controlvariable, so that effects which are unique to specic sources are not aspronounced. Additionally, I also controlled for region and stratiedethnicity and source in the main part of the report.
Orchestral preferences analysis MC 5
O i D hi Q i P i C G M k i T i i l E d
-
8/13/2019 Analysis of Musical Preferences and Orchestral Music
6/59
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
Disclaimers
I should note that I am in no way affilitated with any of the externallinks posted in this report, and all the video links in the composerssection are meant to give people a good representation of the variouscomposers listed.
Additionally, the testimonials near the end do not necessarily representmy opinions. They are there to represent a pattern of complaints andexperiences I saw as well as to provide food for thought for the readers.
Lastly, I do not intend to denigrate the CSO with the example I gaveearlier this section. It was simply the event that piqued my interest inthis topic.
Orchestral preferences analysis MC 6
O i D hi Q ti P i C G M k ti T ti i l E d
-
8/13/2019 Analysis of Musical Preferences and Orchestral Music
7/59
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
Demographics
As you can see in Figure 1, the agedistribution is heavily peaked around20, with females being slightly olderon average. In Figure 2 and 3 on
the next page, you can see theregions and ethnicities representedby survey. It primarily consists of people from the United States andof Caucasian descent. Thepercentages of males and femalesoverall are 76.7% and 23.3%,respectively.
0.00
0.02
0.04
0.06
0.08
20 40 60 80Age
d e n s
i t y Sex
FemaleMale
Age Distribution of Survey respondents
Figure 1: Age density plot by gender.
Orchestral preferences analysis MC 7
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
-
8/13/2019 Analysis of Musical Preferences and Orchestral Music
8/59
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
Demographics (cont.)
67.2%
18.6%
8.7% Region
United States
EuropeOther North America
Australia
Asia
Africa
South America
Other
Responses by Region
Figure 2: Regional breakdown of survey respondents
80.6%
5.4 %
5.4%5.7%
Ethnicity
Ca ucasia n (nonHispanic)
Black (nonHispanic)
Hispanic/Latino
Native American/Alaskan
Other/Mixed
Asian/Pacific Islander
Prefer not to say
Responses by Ethnicity
Figure 3: Ethnic breakdown of survey respondents
Orchestral preferences analysis MC 8
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
-
8/13/2019 Analysis of Musical Preferences and Orchestral Music
9/59
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
Demographics (cont.)
Here is the breakdown of the surveyby different sources. While /r/Musicis the largest source, it provides alarge amount of data for a broaderaudience than many of the othersources. The Generalcategoryrefers to the copy of the survey sentout via email, which I had lessoversight over.
6.8%5.1%
11.1%
53.8%
5.3%
source
r/SampleSize
Facebook
GeneralNot Categorized
r/ClassicalMusic
r/GameMusic
r/Music
r/MusicGeeks
r/Orchestra
r/RepublicOfMusic
r/ShamelessPlug
r/WeAreTheMusicMakers
Responses by source
Figure 4: Breakdown of survey sources
Orchestral preferences analysis MC 9
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
-
8/13/2019 Analysis of Musical Preferences and Orchestral Music
10/59
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
General Modications
Each of these was rated on a scale from 1 to5, where 1 indicates a person would most likelyavoid a concert due to the change, and 5means they would most likely attend theconcert.
Adding preconcert lectures to describe
the music to be played.Changing ensemble to a wind orchestra,which is mostly brass, woodwind, andpercussion instruments.Playing popular music.Allowing food in the performance area.Allowing informal wear. Technicallythere isnt a strict rule against this inmost places, but oftentimes its expectedto dress nicely.
Playing shorter pieces, rather thanlonger ones.Going to a concert with friends. This ismore of a decision that a person makesthan a programming change.Playing more modern classical music.Playing music with more percussion.Advertising towards a younger audience.Advertising the mood of the music to beplayed.
Playing video game music.
Orchestral preferences analysis MC 10
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
-
8/13/2019 Analysis of Musical Preferences and Orchestral Music
11/59
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
Composer Programming
Each person was asked how thepresence of a piece by a certaincomposer would affect their likelihoodto go to a concert. There were only 3
options, being less likely, no change,and more likely.VivaldiMozartBeethovenGustav MahlerJohannes Brahms
Pyotr TchaikovskyGeorge Gershwin
Sergei RachmaninovIgor StravinskyLeonard BernsteinPhilip GlassSteve ReichJohn Williams
Orchestral preferences analysis MC 11
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
-
8/13/2019 Analysis of Musical Preferences and Orchestral Music
12/59
g p Q g g p g
Other Questions
Favorite genre of music and second most favorite genre of music.Whether or not the respondent listens to classical music.How often the respondent goes to an orchestra concert.The respondents favorite section of an orchestra.
Whether or not the person recognizes at least 10 of the composers for whichpreferences were asked.The maximum price the respondent would pay for an orchestra ticket.How the respondent normally hears about concerts they go to.Whether or not the respondent played a musical instrument in a high schoolband/orchestra.
Word-association questions with music the respondent listens to and what they think of classical music. I will go into more detail near the end of this report.A free response for any bad experiences the respondent has had with classical music.
Orchestral preferences analysis MC 12
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
-
8/13/2019 Analysis of Musical Preferences and Orchestral Music
13/59
g p g g p g
Overview of Analysis Methods
The absolute simplest method would be to simply analyze theaverages for each group. While straightforward, this methodsuffers when you want to take a large number of categoriesinto account, because it requires you to have a large numberof responses in each possible combination of categories.
The next simplest approach one uses to nd relationships indata is linear regression . Essentially, you try to t a linebased on the data you have to the outcome you want topredict. For example, if I want to predict how much money aperson spends based on their income, I might t a line to agraph, as you can see in Figure 5. In this case, the realformula for spending is 2 + 0 . 8 income , which is prettyclose to the value estimated by linear regression.
0 20 40 60 80 100
0
2 0
4 0
6 0
8 0
1 0 0
Spending vs. Income
Income in thousands of dollars
S p e n
d i n g
i n t h o u s a n
d s o
f d o
l l a r s
Slope: 0.79
Intercept: 2.49
Figure 5: A line of best t superimposed on randomly generated data, along with the estimated slope (how steep the line is) and intercept (the value of the line whenincome is zero).
Orchestral preferences analysis MC 13
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
-
8/13/2019 Analysis of Musical Preferences and Orchestral Music
14/59
Overview of Analysis Methods (2)Looking at Figure 6, it is obvious that this data is hard tointerpret. Literally, these results imply that someone at age20 would mark an average preference of 1. 462 20 0. 047 = 0 . 522 (or someone at age 0 has apreference of 1.462, but that makes no sense consideringteenagers are the youngest people under consideration), andfor each year older a person is, their predicted preference is0.047 of a category lower.
One other option for analyzing this data is using multinomiallogistic regression 1, which measures the probability someoneis in a category given their responses, which does not takeinto account the ordered nature of the responses (e.g.,dislike < no opinion < like ). While some information is lost,it is somewhat simpler in its interpretation, as its easier tomake sense out of the results.
+
+
++
+
+
+
+
+
+
++
+
+
+
+++
+
++
++
+
+ ++
+
++
++
+
+
+
+
+
+
+
++
++
+
+ +
++
+
+
+
+
+
++
+
+
+
+
+
+
++
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ +
+
+
+
+
+
+
++
++
+
+
+
+
+
+
+
+
+
+ +
+
+
+
+
++
++ +++
+
+
+
+
+
+
+
+
+
+
+
+
++
+
+ +
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
++
+ +
+
+
+
++
+
+ ++
+
+
+
+
+
+
+
+
+
+
+
+
+
+
++ ++
+
+
+
+
+
++
+
++
+
+
+
+
++
++
+
+
+
+
+
++
+
++
+
+
+
+
+
+
++
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
++++
+ +
+
+
+
+
+
++
+
+
+
++
+
+
++
+
++
++
+
+
+
++
+
+
+
+
+
+ +
+
+
+
+
+
+
+
++
+
+ +
+
+
+
+
+
+
+
++
+
+
+
+
+
+
+
++
+
+
+
+++
+
++
+
+
+
+
+
+
+
++
+ +
+
+
+
+
+
+
+
+
+ +
+
+
+
+ +
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
++
+
+
+
+
+
+
+
+
+
+
+
+
+
++
+
+
+
+
+
+
+
+
++
+
+
+
+
+
++
+
+ +
+
+
+
+
+
+
++
+
+
+ +
+
+ +
+
+
+
+
+
+
+
+
+
+
++
+
+
+
+
+
++
++
+
+
+
+
++
+
+
+
+
++ ++
++
+
+
++
+
+
+
+
+
+
+
+ ++
+
+
+
+
+
+
+
+ +
+
+
+
+
+
+
+
+
+ ++
+
+
+
+
+
++
+
+
++
+
+
+
+
+
+
++
+
+
+
+
+
++
+ +
+
+
+
++
++
+
+
+
+
++
+
++
++
++
+
+++
+
++
+
+++
+
+
+
+
+
+ +
++
+
+
+
+
+
+
++
+
+
+
++ +
+
+
+
+
+
+
+
+
++
+
+
+
+
++
+
++
+
+
+
+
+
+
+
+++
+
++
+
+
++
+
+ +
+
+
++++
+
+
+
+
+
+
+
+
+
+
++
+
+
+ +
++
+
+
++++
+
+
+
+
+
+
+
+
+
++
+
++
++
+
+
++
+
+
+
++
+
+
++ +
+
++
+
+
+ +
++ +
+
++
+
+
+
+
+
+
+
+
+
+
+ +
+
+
+
+
+
+
+
+++
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ +
++
+
+
+
+
+
+
+ +
+++ +
+
++
+
+
+
+
+
+
+
+ ++
+
+
+
++
+
+
+
+
+
++
+
+
++
++
+
+
+
+
+
+
+
+ +
+
+++ ++
+++
+
++
++
+
+
+
+
+
+
+
+
+
+
++
+
+ +
+
+
++ +
+
+++
+
+
+
+
+++
+
++
++
++
+++
++
+
+
+
+
+
+
+
++
+
+
+
+
+
+
+
+
+
+
+
+
+
++
+
+
+
++
+
++
+
+
++ +
+
+
+
+
+
++
+
+
+
+
+
+
+
+
+
++
+
+
+
++
+
+
+
+
+ +++
+
+
+
+
+
+
++
+
+
+
++
+
++
+
++
+
+ +
+
+
++
+
+++
+++
+
+
++ +
+
+
+
+
+++ ++
+
+
+
+
+
++
+
+
+
++
+
+
+
+
+
+
++
+
+
+
+
+ +
+
+
+
++
+
++
+
+ +
+
+++
+
+
+
+
++ ++
+++
+
+
+
+
+
+
++
+
+
+
+++
+
+
+
+
+
+
+
+ +
+
+
+
+
+
+
+
+
+
+
+
++
+
+
+
+
+
+
+
+
++
+
++
+
++
+
+
+
+
++
+
+
+
+
+
+
+
+
+
+
+
+
+
+
++ +
+
+
+
+
+
+
+
++ +
+
++
+
+
+
+
+
+
+
++
+ +
++
+
+
+
++
+
+++
+
+
+
+ +
+
+
+
+
+
+
+
+
+
+ ++
+
+
+
+
+
+
+
+
+
+
+
++
+
+
+
+
+
+
+ +
+
+
+
+
+
++
+
+
+
+ +
+
+
+
++
+
+
+
++
+
++ +
+
++
+
+
+
++ +
+
++
+
+
+
+
+
+
++
+
+
+ + +
+
+
++
+
+
+ +
+
+
+
+
+
+
+
+
+
++
+
+
++
+++
+
++
++
++
+
+ ++
+
+
+
++ +
+ +
+
+
+
+
+
++
+
+
+
+
+
+
+
+
+
+ +
+
++
+
+
+
+
+
+
+
+
+
++
+
++
+
+
+
+ +
+
+ ++
++
++
+
+
+
+
+
++
+
+
+
+
+
+
+
+
+
+
+
+
++
+
+
+
+
+
+
+
+
+
+ +
++
+
+
+
+
+
++
+
+ +
+
+
+
+
+
++
+ +
++
+ +
+
+
+
++
+
+
+ ++ + +
+
+
+
+
+
++
+
+ +
+
+
+
+
+
+
++
+
+
++
+
+
+
+
+
+
+
+
++
+
++
+
+
+ ++
+
+
+
+
+
+
+
+
+
+
++
++
+
+
++
++
+
+
+
+ +
+
+
++
+
+
+
++
++
+
+
+
+
+ +
++
++
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ +
+
+ +++ +
+
+
+
+
+
+ +
+
+
+
+
++
+
+
++
+
+
+
+
++
+
+
+
+
+
+
++
+
++
+
+
++
+
+
+
+ +
+
+
+
+
+
+
+++
+
+
+
+
+
+
+
+
+
+
+
++
++
+
++
++
+
+
++
+
+
+ +
+
+
++
+
+
+
+
+
+
+
++
+
+
++
++
+
++ +
+
+
++
+
+
+ ++
++
+
+ +
+
+ ++
++ +
+
+
+
+ ++
+
+
+
+++
+ +
++
+
+++
+ ++
+
+++
+
+
+
+
+
+
++
+
+
++
+
+
+
+
+
+++
+ +
+
+
+
+
+
+
+
++
+
20 30 40 50 60 70 80
2
1
0
1
2
Video Game Music Response vs. Age
Age
R e s p o n s e ,
j i t t e r e
d
Slope: 0.047
Intercept: 1.462
Figure 6: Attempt at linear regression of discrete data. The points are jittered so that you can see the density of points more easily, but they only take the values of -2,-1, 0, 1 and 2 on the y-axis. Note that this data is shifted down by 3 from the original values of 1, 2, 3, 4 and 5.
1 To further improve these models, I use stepwise selection, adding and removing terms from the model, until a model with the optimal AIC (forprogramming changes) or BIC (for composer results) is found. AIC and BIC are information criteria which reward models for having better ts andpenalizes them for using more terms. AIC is less restrictive, and the exact penalty term is just 2 p , where p is the number of predictors in the model.Note that for non-numeric variables, p increases by how many different values a variable can have minus 1. e.g., I keep track of 7 ethnic categories, so
adding ethnicity to the model would increase p by 6.
Orchestral preferences analysis MC 14
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
-
8/13/2019 Analysis of Musical Preferences and Orchestral Music
15/59
Analysis Methods - Technical Details
A few details important to the model are how I controlled for various sources of bias, such asethnicity, source, and region of respondents. For source and ethnicity, I used 500 stratiedsamples to construct the probability data. This means that in addition to the variables you seeon the following pages, I assigned a probability of 69.1%, 12.6%, 16.3%, 5%,0.9%, and 7.9%to Caucasian, Black, Hispanic, Asian/Pacic Islander, Native American, and other/mixedgroups, respectively, to control for any effects from ethnicity, and probabilities of 30%, 35%,25%, and 10% from /r/SampleSize, /r/Music, General, and Facebook, respectively. Theregion was xed to the United States for all of the probabilities. The values for the ethnicitiesare based on results from the 2010 US census . The values for the sources were chosensomewhat randomly so that the average of the sources would more closely resemble the USpopulation. This process was repeated to a total of 200 times so I could average theprobabilities you see on the following pages.
The original models I start out with include sex, source, age group, ethnicity, orchestraconcert frequency, and interaction effects between those variables. The model is then shrunkas terms are eliminated because they do not add signicantly to the model.
Orchestral preferences analysis MC 15
http://www.census.gov/prod/cen2010/briefs/c2010br-02.pdfhttp://www.census.gov/prod/cen2010/briefs/c2010br-02.pdfhttp://www.census.gov/prod/cen2010/briefs/c2010br-02.pdf -
8/13/2019 Analysis of Musical Preferences and Orchestral Music
16/59
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
-
8/13/2019 Analysis of Musical Preferences and Orchestral Music
17/59
Pre-concert lecturesPre-concert lectures arelectures usually given by
the conductor of anorchestra before a concertbegins about the music tobe played. The graph onthe right suggests that thepre-concert lectures aremore enjoyed by men andmore enjoyed by peoplewho attend more orchestra
concerts to begin with.They are also less popularwith the youngest agegroups, with the exceptionof those who already attendmany concerts a year.
Male Female
0.000.250.500.75
1.00
0.0 00.250.500.751.00
0.000.25
0.500.751.00
0.000.250.500.751.00
0.000.250.500.751.00
0.000.250.500.75
1.00
N ev er
S el d om
On c e / y r
2 6 / y r
7 1 2 / y r
1 3 + / y r
< 1 8
1 8
2 0
2 1
2 4
2 5
2 9
3 0
3 4
3 5
4 4
4 5
5 9
6 0 +
< 1 8
1 8
2 0
2 1
2 4
2 5
2 9
3 0
3 4
3 5
4 4
4 5
5 9
6 0 +
Age Group
p r o b a b i l i t y
Effect is...
Most likely
More likelyUnchanged
Less likely
Least likely
Probabilities of effects of having a preconcert lectures by sex and orchestra concert attendance
Figure 8: This graph shows the probability of changes to an individuals attendance of anorchestra concert due to the inclusion of pre-concert lectures.
Orchestral preferences analysis MC 17
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
-
8/13/2019 Analysis of Musical Preferences and Orchestral Music
18/59
Pop orchestraSometimes orchestras playpopular music, ranging
from soundtrack music topopular hip-hop and rocksongs. This is one of themore controversial changesto orchestral programming,as many people who go toorchestra concerts enjoythem for the type of musicthey play as well as the
sounds of the instruments.
To the right the graphsuggests many things.Generally, younger peoplewill have a warmerresponse to popular music,and women seem to bemore receptive to it.
However, the 30-34 femaleage group appears to be astrong exception to thattrend, whereas the 45-59female age group seems tohave a much more positiveresponse to them thantheir male counterpartsregardless of concertattendance frequency.
Male Female
0.000.250.500.75
1.00
0.0 00.250.500.751.00
0.000.25
0.500.751.00
0.000.250.500.751.00
0.000.250.500.751.00
0.000.250.500.75
1.00
N ev er
S el d om
On c e / y r
2 6 / y r
7 1 2 / y r
1 3 + / y r
< 1 8
1 8
2 0
2 1
2 4
2 5
2 9
3 0
3 4
3 5
4 4
4 5
5 9
6 0 +
< 1 8
1 8
2 0
2 1
2 4
2 5
2 9
3 0
3 4
3 5
4 4
4 5
5 9
6 0 +
Age Group
p r o b a b i l i t y
Effect is...
Most likely
More likelyUnchanged
Less likely
Least likely
Probabilities of effects of playing pop music by age group, sex, and orchestra concert attendance
Figure 9: This graph shows the probability of changes to an individuals attendance of anorchestra concert due to popular music being played.
Orchestral preferences analysis MC 18
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
-
8/13/2019 Analysis of Musical Preferences and Orchestral Music
19/59
Allowing foodAllowing food is anothercontroversial change that has
been suggested. Whileconsumption of food duringconcerts is more common incabaret-style settings, it is oftenconsidered informal and rude toeat food during a concert.
Looking at the graph to the right,it appears as if food being allowedhas the most positive effects onpeople who attend concerts lessfrequently. It also seems to havea negative effect on the older agegroups. This is especially so forthose ages 60+ who attendconcerts 7+ times per year.
0.000.250.50
0.751.00
0.000.250.500.751.00
0.000.250.500.751.0 0
0.000.250.500.751.00
0.000.250.500.751.00
0.000.250.500.751.00
N
ev er
S el d om
On c e / y r
2 6 / y r
7 1 2 / y r
1 3 + / y r
< 1 8
1 8
2 0
2 1
2 4
2 5
2 9
3 0
3 4
3 5
4 4
4 5
5 9
6 0 +
Age Group
p r o b a b i l i t y
E ffec t is...
Most likely
More likely
UnchangedLess likely
Least likely
Probabilities of effects of allowing food by age group and orchestra concert attendance
Figure 10: This graph shows the probability of changes to an individuals attendance of anorchestra concert due to the allowance of food in the concert hall or other place of
performance. The graph is faceted by how often one attends orchestra concerts.Orchestral preferences analysis MC 19
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
-
8/13/2019 Analysis of Musical Preferences and Orchestral Music
20/59
Suggesting informal attireWhile almost every concert halldoesnt have a dress code more strict
than that of a family restaurant,many often feel pressure to dress upin more formal wear at concerts. Theway the original question was worded(using required instead of suggested) may skew some of theseresults.
Looking at the graph to the right, itappears that sex and orchestraconcert attendance are the primaryexplanatory variables. Generally,males slightly prefer this and femalesare more indifferent. Those whoattend concerts more frequently aremore indifferent to this, and thereappears to be no strong presence of anegative effect anywhere.
Male Female
0.000.250.500.75
1.00
0.000.2 50.500.751.00
0.000.250.500.751.00
0.000.250.500.751.00
0.000.250.500.751.00
0.000.250.500.751.00
N ev er
S el d om
On c e / y
r
2 6 / y r
7 1 2 / y r
1 3 + / y r
p r o
b a b
i l i t y
Effect is...
Most likely
More likelyUnchanged
Less likely
Least likely
Probabilities of effects of suggesting informal attire by age group, attendance and sex
Figure 11: This graph shows the probability of changes to an individuals attendance of an orchestra concert due to less formal wear being required/suggested. The graph is faceted by sex and how often one attends orchestra concerts. There is no change among age groups.
Orchestral preferences analysis MC 20
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
-
8/13/2019 Analysis of Musical Preferences and Orchestral Music
21/59
Playing shorter piecesAnother common complaint is thatorchestra pieces are too long. In my
experience, this is usually in regardsto a piece of music where there aremultiple movements, i.e., sections of the piece which can last anywherefrom a few minutes to over 20minutes.
The effects of shortening piecesseems pretty straightforward tointerpret from the probabilities on theright. Most people are indifferent,although the second most commonresponse is positive for those whoattend concerts 6 times or less peryear and slightly negtive for thosewho attend concerts more often.Women are also slightly morereceptive to shorter pieces, althoughthis is a relatively small effect.
Male Female
0.000.250.500.75
1.00
0.000.2 50.500.751.00
0.000.250.500.751.00
0.000.250.500.751.00
0.000.250.500.751.00
0.000.250.500.751.00
N ev er
S el d om
On c e / y r
2 6 / y r
7 1 2 / y r
1 3 + / y r
p r o
b a b
i l i t y
Effect is...
Most likely
More likelyUnchanged
Less likely
Least likely
Probabilities of effects of playing shorter pieces by age group, sex and attendance
Figure 12: This graph shows the probability of changes to an individuals attendance of an orchestra concert due to shorter pieces being performed. The graph is faceted by sex and how often one attends orchestra concerts, but there is no change among age groups.
Orchestral preferences analysis MC 21
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
-
8/13/2019 Analysis of Musical Preferences and Orchestral Music
22/59
Attending a concert with friendsWhile not a programming change, Ithought it would be interesting to seehow bringing a friend might affectones likelihood to attend anorchestra concert.
On average, it seems like age groupis the primary determining factor,with positive effects all around,especially for those outside the 35-59age group. Males tend to have aslightly higher overall preference, butthere are very few differencesbetween men and women.
Male Female
0.00
0.25
0.50
0.75
1.00
< 1 8
1 8
2 0
2 1
2 4
2 5
2 9
3 0
3 4
3 5
4 4
4 5
5 9
6 0 +
< 1 8
1 8
2 0
2 1
2 4
2 5
2 9
3 0
3 4
3 5
4 4
4 5
5 9
6 0 +
Age Group
p r o b a b i l i t y
Effect is...
Most likely
More likely
UnchangedLess likely
Least likely
Probabilities of effects of going to concert with friends by age group and sex
Figure 13: This graph shows the probability of changes to an individuals attendance of a concert because that persons friend(s) will also be attending. The values vary across age group and gender.
Orchestral preferences analysis MC 22
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
-
8/13/2019 Analysis of Musical Preferences and Orchestral Music
23/59
Adding modern classical music to the programMost concerts Ive been to play music writtenin the rst half of the 20th century or earlier.Many people wish that orchestras would play
music more recently written, while otherscounter that much of that music is evenmore esoteric to the average person, andwould only make them want to attend less.
Overall, there is a tendency for a slightpositive effect in the groups that attendconcerts less frequently and a much strongerpositive preference in those who attendconcerts more frequently. As a side note, Iam curious to know what people think of when they think of modern classical music.Ive attended such concerts where even myclassical-music-loving friends said theydnever ever go to it because the music isreally difficult to listen to since its atonal.Example: Schoenberg, 6 Little Pieces . Othertimes people think of modern pieces writtenby composers like John Adams, whose piecesare not usually as harsh on the ear as theSchoenburg piece from the example I linked
to above. Example:Short Ride in a Fast Machine .
0.000.250.500.751.00
0.000.250.500.751.00
0.000.250.500.751.0 0
0.000.250.500.751.00
0.000.250.500.751.00
0.000.250.500.751.00
N ev er
S el d om
On c e / y r
2 6 / y r
7 1 2 / y r
1
3 + / y r
< 1 8
1 8
2 0
2 1
2 4
2 5
2 9
3 0
3 4
3 5
4 4
4 5
5 9
6 0 +
Age Group
p r o b a b i l i t y
E ffec t is...
Most likely
More likelyUnchanged
Less likely
Least likely
Probabilities of effects of playing modern classical music by age group and attendance
Figure 14: This graph shows the probability of changes to an individuals attendance of a concert due to modern classical music being played.
Orchestral preferences analysis MC 23
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
https://www.youtube.com/watch?v=YWQAw7XSkDYhttps://www.youtube.com/watch?v=YWQAw7XSkDYhttps://www.youtube.com/watch?v=YWQAw7XSkDYhttps://www.youtube.com/watch?v=Pi4A9bPDvTchttps://www.youtube.com/watch?v=Pi4A9bPDvTchttps://www.youtube.com/watch?v=YWQAw7XSkDYhttps://www.youtube.com/watch?v=Pi4A9bPDvTchttps://www.youtube.com/watch?v=YWQAw7XSkDY -
8/13/2019 Analysis of Musical Preferences and Orchestral Music
24/59
Adding more percussion-focused music to programmingWhile this is somewhat closelyrelated to modern classical music due
to how pieces are orchestrated withmore percussion as of late, the lackof percussion instruments in commonorchestral repetoire often leavespeople wanting.
About 24% nd this changefavorable, whereas about 11% nd itunfavorable. While this change
doesnt seem to have much of aneffect on most people, it couldcertainly draw in more people thanbefore, although there doesnt appearto be any strong relationshipsbetween adding percussion and sex orfrequency of concert attendance.
0.00
0.25
0.50
0.75
1.00
p r o
b a
b i l i t y
Effect is...
Most likely
More likely
Unchanged
Less likely
Least likely
Probabilities of effects of using more percussion on attendance of an orchestra concert
Figure 15: This graph shows the probability of changes to an individuals attendance of a concert due to the addition of more percussion-focused music. There does not appear to be any relationship between this and sex, age group, or frequency of concert attendance.
Orchestral preferences analysis MC 24
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
-
8/13/2019 Analysis of Musical Preferences and Orchestral Music
25/59
Advertising towards younger audiencesOne idea to bring in youngeraudiences is to advertise towards
them. One aw with this question isthat advertising towards a particulardemographic isnt always explicit, andthat can have an effect on howpeople are actually affected by it.
The probabilities to the rightsuggests that advertising (perhapsexplicit advertising, at least) towards
a younger demographic will have anet negative effect on people. I doquestion the validity of this based onhow I asked the question, but it maybe a useful inference.
0.00
0.25
0.50
0.75
1.00
p r o
b a
b i l i t y
Effect is...
Most likely
More likely
Unchanged
Less likely
Least likely
Probabilities of effects of advertising towards a younger audience
Figure 16: This graph shows the probability of changes to an individuals attendance of a concert due to the advertising of the concert targeting a younger audience.Surprisingly, the effect does not change across sex, age group, or frequency of orchestraconcert attendance.
Orchestral preferences analysis MC 25
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
-
8/13/2019 Analysis of Musical Preferences and Orchestral Music
26/59
Advertising the mood of a concertAnother idea I came up with isadvertising the mood of a concert.
Classical music contains a widevariety of music with every possiblemood you can imagine, and Itheorized that it might get peoplewho are unfamiliar with the music togo to the concert. One problem withthis is that asking people directlyabout advertising might not result intheir answer being the same asreality. Also, Im sure that the moodof the music would denitelyinuence whether or not these groupswould attend a concert, so moreresearch would have to be done inthis area to get any denitive results.
Looking at the plot on the right, itappears as if there is an overallpositive effect, especially for femalesand especially for people who dontgo to concerts very frequently.
Male Female
0.000.250.500.75
1.00
0.000.2 50.500.751.00
0.000.250.500.751.00
0.000.250.500.751.00
0.000.250.500.751.00
0.000.250.500.751.00
N ev er
S el d om
On c e / y r
2 6 / y r
7 1 2 / y r
1 3 + / y r
p r o
b a b
i l i t y
Effect is...
Most likely
More likelyUnchanged
Less likely
Least likely
Probabilities of effects of advertising the mood of a concert versus sex and attendance
Figure 17: This graph shows the probability of changes to an individuals attendance of a concert due to the mood being advertised. The graph is faceted by sex and frequency of orchestra concert attendance, but there is no change between age groups.
Orchestral preferences analysis MC 26
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
-
8/13/2019 Analysis of Musical Preferences and Orchestral Music
27/59
Playing video game musicOne last non-composer change toorchestral programming I looked into
was the playing of video game music.This is something that has beenbecoming more popular recently, andthere are many arguments for andagainst it that are similar to thearguments for and against orchestrasplaying popular music.
Surprisingly (at least for me), there isno signicant correlation betweenhow often one attends orchestraconcerts and how much they wouldlike to see video game music played.Only sex and age group aresignicant. Males tended to have amore positive response towards videogame music, although the older agegroups have highly negativeresponses to the music.
Male Female
0.00
0.25
0.50
0.75
1.00
< 1 8
1 8
2 0
2 1
2 4
2 5
2 9
3 0
3 4
3 5
4 4
4 5
5 9
6 0 +
< 1 8
1 8
2 0
2 1
2 4
2 5
2 9
3 0
3 4
3 5
4 4
4 5
5 9
6 0 +
Age Group
p r o b
a b i l i t y
Effect is...
Most likely
More likely
UnchangedLess likely
Least likely
Probabilities of effects of playing video game music at a concert versus sex and age group
Figure 18: This graph shows the probability of changes to an individuals attendance of a concert due to video game music being played. The graph is faceted based on sex,and there is a strong negative reaction for older age groups.
Orchestral preferences analysis MC 27
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
-
8/13/2019 Analysis of Musical Preferences and Orchestral Music
28/59
Composers and going to orchestra concerts
One thing I thought would be worthwhile to look into was how people felt about composersand their willingness to go to concerts featuring the composers. I selected several differentcomposers from different eras and measured the responses towards them on a scale of 1 to 3(in retrospect, 1 to 5 would have been much better).
One thing in particular which is difficult with measuring the meaningfulness of peoplesresponses with this model is taking into account whether or not people are familiar with thecomposers or not. In this case, it is meaningful to also look at that value, as well. While itcan be demonstrated that more regular concert attendence is correlated with the familiarity of composers, its still useful to treat it as its own group, particularly for the sake of people whomay be familiar with the music but do not go to concerts very often.
Because of the added variable, I will change the age group divisions to allow for a moreaccurate analysis of those groups. 1 Additionally, I use a more restrictive model selection
criteria so that more complicated models are highly unfavorable.2
1Specically, I need to increase the size of the population of each group to allow for dividing those groups by sex, concertattendance, and composer familiarity without having too many small groups biasing results.
2BIC, or Bayesian Information Criterion, penalizes a model based on both the sample size of the data and the number of explanatory variables in the model. Specically, the penalty term is p log(n), where n is the sample size and p is the numberof predictor variables. Note that including orchestra concert frequency in the model increases p by 5 because there are 6
levels (possible values) for that variable.Orchestral preferences analysis MC 28
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
-
8/13/2019 Analysis of Musical Preferences and Orchestral Music
29/59
Sample sizes of subgroups
Figure 19: This mosaic plot represents all of the different subgroups by four different variables. The size of each rectangle corresponds to the sample size of the subgroups. The colors, though not necessary for understanding the following models,indicate that there is at least one correlation between different groups (e.g., young people who go to concerts are less likely to be familiar with composers). Dots indicate empty groups. Generally, the results for groups that are represented by a larger sample size are more reliable.
Orchestral preferences analysis MC 29
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
-
8/13/2019 Analysis of Musical Preferences and Orchestral Music
30/59
Composer - Bach & How to read the plotsFor the composer probabilities, I always tooknote of how composer familiarity (that is,whether or not an individual was familiar
with at least 10 out of the 15 composerslisted here) affected preferences. Thesecharts are similar to the previous ones,except there are only 3 levels for the response(which was a mistake on my part), and asemi-transparent barplot indicates that theindividuals are less familiar with composers.Note that the estimates for older people whoare unfamiliar with the composers in generalare highly innaccurate (particularly for higherconcert attendance) because there were veryfew such people sampled.For music by J.S. Bach, a German Baroque 1era composer, the responses are generallypositive. Apart from older age groups, whoserepresentative sample is much smaller, thereisnt too strong of an effect.
Example of his music:Brandenburg Concerto No. 3
Male Female
0.000.250.50
0.751.00
0.0 00.250.500.751.00
0.000.250.500.751.0 0
0.000.250.500.75
1.00
0.000.250.500.751.00
0.000.250.500.751.00
N ev er
S el d om
On c e / y r
2 6 / y r
7 1 2 / y r
1 3 + / y r
< 1 8
1 8
2 0
2 1
2 4
2 5
2 9
3 0
4 4
4 5 +
< 1 8
1 8
2 0
2 1
2 4
2 5
2 9
3 0
4 4
4 5 +
Age Group
p r o
b a b i l i t y
Effect is...
More likely
No effect
Less likely
Fam.w/10+ composers?
Familiar
Unfamiliar
Probabilities of effects of adding Bachto a concert program
Figure 20: This graph shows the probability of changes to an individuals attendance of a concert due to music by J.S. Bach being programmed.
1 The Baroque era of music took place from roughly 1600-1750. It is characterized by smaller orchestras and old instruments, such as the harpsichord.
Orchestral preferences analysis MC 30
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
http://hhttps//www.youtube.com/watch?v=Xq2WTXtKurkhttp://hhttps//www.youtube.com/watch?v=Xq2WTXtKurk -
8/13/2019 Analysis of Musical Preferences and Orchestral Music
31/59
Composer - VivaldiFor music by AntonioVivaldi, an ItalianBaroque-era composer, theresponses are generallypositive. There arent anystrong relationships betweensexes or age groups.
Example of his music:Four Seasons - Spring
0.00
0.25
0.50
0.75
1.00
Familiar UnfamiliarComposer Familiarity
p r o b
a b i l i t y
Effect is...
More likely
No effect
Less likely
Fam.w/10+ composers?
Familiar
Unfamiliar
Probabilities of effects of adding Vivaldito a concert program
Figure 21: This graph shows the probability of changes to an individuals attendance of a concert due to music by Vivaldi being programmed, based on the individuals familiarity with at least 10 composers out of 15. There is no signicant difference between age groups.
Orchestral preferences analysis MC 31
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
https://www.youtube.com/watch?v=TKthRw4KjEghttps://www.youtube.com/watch?v=TKthRw4KjEghttps://www.youtube.com/watch?v=TKthRw4KjEg -
8/13/2019 Analysis of Musical Preferences and Orchestral Music
32/59
Composer - MozartFor music by WolfgangAmadeus Mozart, anAustrian composer of theClassical1 era, responses aregenerally very favorable,moreso than Vivaldi. Thereare no signicantcorrelations between
responses and age group,sex, or frequency of orchestra concertattendance.
Example of his music:The Magic Flute - Overture
0.00
0.25
0.50
0.75
1.00
Familiar UnfamiliarComposer Familiarity
p r o
b a
b i l i t y
Effect is...
More likely
No effect
Less likely
Fam.w/10+ composers?
Familiar
Unfamiliar
Probabilities of effects of adding Mozartto a concert program
Figure 22: This graph shows the probability of changes to an individuals attendance of a concert due to music by Mozart being programmed.
1 The Classical era roughly took place from 1750-1820. The music from this era is characterized by larger ensembles and less complex music than that of the Baroque era. Additionally,
the harpsichord became less popular as the piano became more prominent.Orchestral preferences analysis MC 32
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
https://www.youtube.com/watch?v=h018rMnA0pMhttps://www.youtube.com/watch?v=h018rMnA0pM -
8/13/2019 Analysis of Musical Preferences and Orchestral Music
33/59
Composer - Beethoven
For music by Ludwig van
Beethoven, a Germancomposer of the Classicalera, responses are generallyvery favorable, moreso thanVivaldi. There are nosignicant correlationsbetween responses and age
group, sex, or frequency of orchestra concertattendance.
Example of his music:Symphony No. 5 - Mvt. I
Male Female
0.000.250.500.75
1.00
0.0 00.250.500.751.00
0.000.250.500.751.0 0
0.000.250.500.751.00
0.000.250.500.751.00
0.00
0.250.500.751.00
N ev er
S el d om
On c e / y r
2 6 / y r
7 1 2 / y r
1 3 + / y r
< 1 8
1 8
2 0
2 1
2 4
2 5
2 9
3 0
4 4
4 5 +
< 1 8
1 8
2 0
2 1
2 4
2 5
2 9
3 0
4 4
4 5 +
Age Group
p r o b a b i l i t y
Effect is...
More likely
No effect
Less likely
Fam.w/10+ composers?
Familiar
Unfamiliar
Probabilities of effects of adding Beethovento a concert program
Figure 23: This graph shows the probability of changes to an individuals attendance of a concert due to music by Beethoven being programmed.
Orchestral preferences analysis MC 33
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
https://www.youtube.com/watch?feature=player_detailpage&v=N6K_IuBsRM4#t=20https://www.youtube.com/watch?feature=player_detailpage&v=N6K_IuBsRM4#t=20https://www.youtube.com/watch?feature=player_detailpage&v=N6K_IuBsRM4#t=20 -
8/13/2019 Analysis of Musical Preferences and Orchestral Music
34/59
Composer - MahlerFor music by GustavMahler, an Austriancomposer of the Romantic 1era, there is an overall trendthat more regularconcertgoers have astronger preference for hismusic. There is no apparent
difference between agegroups or sexes.
Example of his music:Finale of Symphony No. 1 , Titan
0.000.250.500.751.00
0.000.250.500.751.00
0.000.250.500.751.0 0
0.000.250.500.751.00
0.000.250.500.751.00
0.000.25
0.500.751.00
N ev er
S el d om
On c e / y r
2 6 / y r
7 1 2 / y r
1 3 +
/ y r
Familiar UnfamiliarComposer Familiarity
p r o
b
a b i l i t y
Effect is...
More likely
No effect
Less likely
Fam.w/10+ composers?
Familiar
Unfamiliar
Probabilities of effects of adding Mahlerto a concert program
Figure 24: This graph shows the probability of changes to an individuals attendance of a concert due to music by Mahler being programmed.
1 The Romantic era, roughly 1810-1920, was characterized by music focusing on nature, spirituality, nationalism, and a rejection of formulaic music composition.
Orchestral preferences analysis MC 34
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
https://www.youtube.com/watch?v=IIykYnoKKt8https://www.youtube.com/watch?v=IIykYnoKKt8https://www.youtube.com/watch?v=IIykYnoKKt8https://www.youtube.com/watch?v=IIykYnoKKt8 -
8/13/2019 Analysis of Musical Preferences and Orchestral Music
35/59
Composer - Brahms
For music by Johannes
Brahms, a Germancomposer of the Romanticera. Overall reception of hismusic seems to improvewith frequency of orchestraconcert visits. For somereason, females in the
Never and Seldomclasses for orchestra concertattendance seem to haveunusually high probabilitiesfor being less likely to go,provided they are familiarwith composers in general.
Example of his music:Hungarian Dance No. 5
Male Female
0.000.250.500.75
1.00
0.0 00.250.500.751.00
0.000.250.500.751.0 0
0.000.250.500.751.00
0.000.250.500.751.00
0.00
0.250.500.751.00
N ev er
S el d om
On c e / y r
2 6 / y r
7 1 2 / y r
1 3 + / y r
< 1 8
1 8
2 0
2 1
2 4
2 5
2 9
3 0
4 4
4 5 +
< 1 8
1 8
2 0
2 1
2 4
2 5
2 9
3 0
4 4
4 5 +
Age Group
p r o b a b i l i t y
Effect is...
More likely
No effect
Less likely
Fam.w/10+ composers?
Familiar
Unfamiliar
Probabilities of effects of adding Brahmsto a concert program
Figure 25: This graph shows the probability of changes to an individuals attendance of a concert due to music by Brahms being programmed.
Orchestral preferences analysis MC 35
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
h k k
https://www.youtube.com/watch?v=3X9LvC9WkkQhttps://www.youtube.com/watch?v=3X9LvC9WkkQhttps://www.youtube.com/watch?v=3X9LvC9WkkQhttps://www.youtube.com/watch?v=3X9LvC9WkkQhttps://www.youtube.com/watch?v=3X9LvC9WkkQ -
8/13/2019 Analysis of Musical Preferences and Orchestral Music
36/59
Composer - Tchaikovsky
For music by Pyotr
Tchaikovsky, a Russiancomposer of the Romanticera. With those familiarwith composers in general,the overall opinion seemsvery high. Even for thosewho dont know many
composers, there seems tobe a good opinion of hisname.
Example of his music:Piano Concerto No. 1
0.00
0.25
0.50
0.75
1.00
Familiar UnfamiliarComposer Familiarity
p r o
b a
b i l i t y
Effect is...
More likely
No effect
Less likely
Fam.w/10+ composers?
Familiar
Unfamiliar
Probabilities of effects of adding Tchaikovskyto a concert program
Figure 26: This graph shows the probability of changes to an individuals attendance of a concert due to music by Tchaikovsky being programmed.
Orchestral preferences analysis MC 36
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
C S i k
https://www.youtube.com/watch?feature=player_detailpage&v=0uoR76XEVPY#t=61https://www.youtube.com/watch?feature=player_detailpage&v=0uoR76XEVPY#t=61 -
8/13/2019 Analysis of Musical Preferences and Orchestral Music
37/59
Composer - StravinskyFor music by IgorStravinsky, a Russiancomposer of the Modern 1era. While there appears tobe a generally decentresponse to his inclusion inconcert programs, it is lowerthan that of Tchaikovsky.
Example of his music:Firebird - Infernal Dance
0.00
0.25
0.50
0.75
1.00
Familiar UnfamiliarComposer Familiarity
p r o
b a
b i l i t y
Effect is...
More likely
No effect
Less likely
Fam.w/10+ composers?
Familiar
Unfamiliar
Probabilities of effects of adding Stravinskyto a concert program
Figure 27: This graph shows the probability of changes to an individuals attendance of a concert due to music by Stravinsky being programmed.
1 The Modern era of music can be roughly dened as 1890-1930. During this time drastic changes to music included deviations from common rhythms in music and atonality.
Incidentally, Stravinsky became more of a neoclassical composer for a few decades, composing music that resembled that of the Classical era.Orchestral preferences analysis MC 37
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
C R h i
https://www.youtube.com/watch?v=6Vj8ow8iC4shttps://www.youtube.com/watch?v=6Vj8ow8iC4s -
8/13/2019 Analysis of Musical Preferences and Orchestral Music
38/59
Composer - Rachmnaninov
For music by Sergei
Rachmaninov, anotherRussian composer of theModern era. The overallresponse is somewhatneutral, although thereappears to be a noticeableincrease in his dislike in the
younger females.
Example of his music:Piano Concerto No. 2
Male Female
0.000.250.500.75
1.00
0.0 00.250.500.751.00
0.000.250.500.751.0 0
0.000.250.500.751.00
0.000.250.500.751.00
0.00
0.250.500.751.00
N ev er
S el d om
On c e / y r
2 6 / y r
7 1 2 / y r
1 3 + / y r
< 1 8
1 8
2 0
2 1
2 4
2 5
2 9
3 0
4 4
4 5 +
< 1 8
1 8
2 0
2 1
2 4
2 5
2 9
3 0
4 4
4 5 +
Age Group
p r o b a b i l i t y
Effect is...
More likely
No effect
Less likely
Fam.w/10+ composers?
Familiar
Unfamiliar
Probabilities of effects of adding Rachmaninovto a concert program
Figure 28: This graph shows the probability of changes to an individuals attendance of a concert due to music by Rachmaninov being programmed.
Orchestral preferences analysis MC 38
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
C Sh t k i h
https://www.youtube.com/watch?v=KgPXOW5bpZkhttps://www.youtube.com/watch?v=KgPXOW5bpZkhttps://www.youtube.com/watch?v=KgPXOW5bpZk -
8/13/2019 Analysis of Musical Preferences and Orchestral Music
39/59
Composer - Shostakovich
For music by Dmitri
Shostakovich, anotherRussian composer of theModern era. The overallresponse also more neutral,although the popularityseems high for females whoare regular concertgoers
(7+ concerts per year).
Example of his music:Symphony No. 5, Mvt. IV
Male Female
0.000.250.500.75
1.00
0.0 00.250.500.751.00
0.000.250.500.751.0 0
0.000.250.500.751.00
0.000.250.500.751.00
0.00
0.250.500.751.00
N ev er
S el d om
On c e / y r
2 6 / y r
7 1 2 / y r
1 3 + / y r
< 1 8
1 8
2 0
2 1
2 4
2 5
2 9
3 0
4 4
4 5 +
< 1 8
1 8
2 0
2 1
2 4
2 5
2 9
3 0
4 4
4 5 +
Age Group
p r o b a b i l i t y
Effect is...
More likely
No effect
Less likely
Fam.w/10+ composers?
Familiar
Unfamiliar
Probabilities of effects of adding Shostakovichto a concert program
Figure 29: This graph shows the probability of changes to an individuals attendance of a concert due to music by Shostakovich being programmed.
Orchestral preferences analysis MC 39
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
C G h i
https://www.youtube.com/watch?v=YarFI7r2shYhttps://www.youtube.com/watch?v=YarFI7r2shYhttps://www.youtube.com/watch?v=YarFI7r2shY -
8/13/2019 Analysis of Musical Preferences and Orchestral Music
40/59
Composer - Gershwin
For music by George
Gershwin, an Americancomposer who wrote musicwith elements of both jazzand classical music, theoverall response seems fairlypositive. However, for the30-44 male group, there
seems to be a slightly morenegative response toGershwin.
Example of his music:Rhapsody in Blue
Male Female
0.000.250.500.751.00
0.0 00.250.500.751.00
0.000.250.500.751.0 0
0.000.250.500.751.00
0.000.250.500.751.00
0.00
0.250.500.751.00
N ev er
S el d om
On c e / y r
2 6 / y r
7 1 2 / y r
1 3 + / y r
< 1 8
1 8
2 0
2 1
2 4
2 5
2 9
3 0
4 4
4 5 +
< 1 8
1 8
2 0
2 1
2 4
2 5
2 9
3 0
4 4
4 5 +
Age Group
p r o b
a b i l i t y
Effect is...
More likely
No effect
Less likely
Fam.w/10+ composers?
Familiar
Unfamiliar
Probabilities of effects of adding Gershwinto a concert program
Figure 30: This graph shows the probability of changes to an individuals attendance of a concert due to music by Gershwin being programmed.
Orchestral preferences analysis MC 40
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
Composer Bernstein
https://www.youtube.com/watch?feature=player_detailpage&v=uj158c_4e0M#t=40https://www.youtube.com/watch?feature=player_detailpage&v=uj158c_4e0M#t=40https://www.youtube.com/watch?feature=player_detailpage&v=uj158c_4e0M#t=40 -
8/13/2019 Analysis of Musical Preferences and Orchestral Music
41/59
Composer - Bernstein
For music by LeonardBernstein, an Americancomposer who wrote musicfor a wide variety of works,including West Side Story ,there seems to be anincreasing level of popularity with orchestral
concert attendance. For theolder age groups in themale category (30+), thereseems to be more of anegative effect for theregular concertgoers (7+times/year).
Example of his music:Mambo!
Male Female
0.000.250.500.751.00
0.0 00.250.500.751.00
0.000.250.500.751.0 0
0.000.250.500.751.00
0.000.250.500.751.00
0.00
0.250.500.751.00
N ev er
S el d om
On c e / y r
2 6 / y r
7 1 2 / y r
1 3 + / y r
< 1 8
1 8
2 0
2 1
2 4
2 5
2 9
3 0
4 4
4 5 +
< 1 8
1 8
2 0
2 1
2 4
2 5
2 9
3 0
4 4
4 5 +
Age Group
p r o b
a b i l i t y
Effect is...
More likely
No effect
Less likely
Fam.w/10+ composers?
Familiar
Unfamiliar
Probabilities of effects of adding Bernsteinto a concert program
Figure 31: This graph shows the probability of changes to an individuals attendance of a concert due to music by Bernstein being programmed.
Orchestral preferences analysis MC 41
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
Composer Reich
https://www.youtube.com/watch?feature=player_detailpage&v=NEs8yqhavtI#t=10https://www.youtube.com/watch?feature=player_detailpage&v=NEs8yqhavtI#t=10 -
8/13/2019 Analysis of Musical Preferences and Orchestral Music
42/59
Composer - ReichFor music by Steve Reich, anAmerican composer who is well
known for his minimalist music, theredoes not seem to be a very goodresponse to his music. While thegraph does suggest that there is aslight bias in favor of Reichs music,my experience with his music wouldlend me to believe that among thepeople who are less likely to go to aconcert with his music, there are
quite a few people who wouldabsolutely avoid it, because the factthat he has composed pieces thathave gained notoriety due to theirhighly minimalistic nature.
Example of a notorious piece of his:Four Organs
Another example of his music:Vermont Counterpoint
0.00
0.25
0.50
0.75
1.00
Familiar UnfamiliarComposer Familiarity
p r o b
a b i l i t y
Effect is...
More likely
No effect
Less likely
Fam.w/10+ composers?
Familiar
Unfamiliar
Probabilities of effects of adding Reichto a concert program
Figure 32: This graph shows the probability of changes to an individuals attendance of a concert due to music by Reich being programmed.
Orchestral preferences analysis MC 42
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
Composer Glass
https://www.youtube.com/watch?v=TYqs3NHCrlEhttps://www.youtube.com/watch?v=2SEHaB_ZITQhttps://www.youtube.com/watch?v=2SEHaB_ZITQhttps://www.youtube.com/watch?v=TYqs3NHCrlE -
8/13/2019 Analysis of Musical Preferences and Orchestral Music
43/59
Composer - Glass
For music by Philip Glass,another American composerwho is well known for hisminimalist music and musicin soundtracks, responsesseem to be more positivethan Reichs, although itstill suggests that the
minimalistic nature of hismusic is offputting tocertain audiences.
Example of his music:Symphony No. 8, Mvt. I
0.00
0.25
0.50
0.75
1.00
Familiar UnfamiliarComposer Familiarity
p r o
b a
b i l i t y
Effect is...
More likely
No effect
Less likely
Fam.w/10+ composers?
Familiar
Unfamiliar
Probabilities of effects of adding Glassto a concert program
Figure 33: This graph shows the probability of changes to an individuals attendance of a concert due to music by Glass being programmed.
Orchestral preferences analysis MC 43
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
Composer Williams
https://www.youtube.com/watch?v=HjQA6rMwDlEhttps://www.youtube.com/watch?v=HjQA6rMwDlE -
8/13/2019 Analysis of Musical Preferences and Orchestral Music
44/59
Composer - Williams
For music by John Williams,an American composer verywell-known for his music insoundtracks of movies suchas Harry Potter and Star Wars , responses aregenerally positive. There isa signicant probability for
a negative response forthose who are familiar withcomposers, which is similarto the responses of Reichand Glass in that regard.
Example of his music:Duel of the Fates 0.00
0.25
0.50
0.75
1.00
Familiar UnfamiliarComposer Familiarity
p r o
b a
b i l i t y
Effect is...
More likely
No effect
Less likely
Fam.w/10+ composers?
Familiar
Unfamiliar
Probabilities of effects of adding Williamsto a concert program
Figure 34: This graph shows the probability of changes to an individuals attendance of a concert due to music by Williams being programmed.
Orchestral preferences analysis MC 44
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
Genre Analysis - Overview
https://www.youtube.com/watch?v=J1gH_cjdb60https://www.youtube.com/watch?v=J1gH_cjdb60 -
8/13/2019 Analysis of Musical Preferences and Orchestral Music
45/59
Genre Analysis - Overview
The heavy part of the analysis is nished, and now I will presentsome more visual results. These are focused around peoples favorite(and second-most favorite) genres, as well as other statistics, includingsome word-association data that was collected.
Note: This section is not intended to be representative of any givenpopulation other than the sample its from. Unlike the previoussections, I did not control for source, ethnicity, etc.
Orchestral preferences analysis MC 45
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
Favorite Genres - Correlations
-
8/13/2019 Analysis of Musical Preferences and Orchestral Music
46/59
Favorite Genres Correlations
You can see the correlation matrix 1 between favoritegenre and second-most favorite genre in the gure onthe right. Correlation indicates how frequently twovalues are associated with each other, and ranges from-1 to 1. In the case of data like this, the diagonals (i.e.when the favorite genre is the same as second-mostfavorite genre) do not mean anything, and the mostinteresting part is the higher correlation between peoplewho list classical music as their favorite genre and jazzas their second. Hip-hop and rap as well as rock andmetal also share a somewhat positive correlation. Noneof these values is particularly high, although thecorrelation between classical (favorite) and jazz(second-most favorite) is particularly notable.
0 + + + + + + +
0 + + + + + + + +
+ 0 + + + +
+ 0 + + + + + +
+ + 0 + +
+ 0 + + + + +
+ + + 0 + +
+ + + 0 + +
+ + + 0 + +
+ + + + 0 +
+ + + + 0 +
+ + + + 0 + + + + + + + + 0
Classical
Alternative
Country
Electronic
Folk
Hiphop
Indie
Jazz
Metal
Pop
Punk
Rap
Rock
C l a s s
i c a
l
A l t e r n a
t i v e
C o u n
t r y
E l e c
t r o n
i c
F o
l k
H i p
h o p
I n d i e
J a z z
M e
t a l
P o p
P u n
k
R a p
R o c
k
Favorite Genre
S e c o n d
m o s t
F a v o r i
t e G e n r e
0.0
0.1
0.2
Correlation
Correlations between first and secondmost favorite genre
Figure 35: The correlation matrix between second-most favorite genres and favorite genres among the sample population. Orange indicates higher positive correlations, dark blue indicates more negative. The signs of each entry are plotted in the cells.
1 I used Pearsons correlation to generate this pot. The reasoning is that a) it is unbiased and b) it is easier to show to a general audience. Additionally, I set the diagonals to 0 so thatthe greatest negative correlations could be seen, since they would otherwise be on the diagonal.
Orchestral preferences analysis MC 46
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
Favorite Genre & Classical Listening
-
8/13/2019 Analysis of Musical Preferences and Orchestral Music
47/59
Favorite Genre & Classical ListeningLooking at what peoples favorite genres are, youcan see that there are differences among variousgenres in terms of whether or not people who
identify it as their favorite also listen to classicalmusic. There was one response for classical that wasNo, and I didnt want to exclude the possibilitythat someone who doesnt listen to a lot of musicmarked that answer down. Below is a table of theexact values for the responses.
Yes NoAlternative 76 133
Classical 243 1Country 5 10
Electronic 52 114Folk 18 21
Hip-hop 18 45Indie 63 132Jazz 43 25
Metal 56 94Other 38 35
Pop 23 36Punk 19 36
Rap 8 8Rock 116 186
Table 1: Total number of responses ineach category for favorite genre and classical listening.
0.00
0.25
0.50
0.75
1.00
H i p
h o p
E l e c t r o n i c
I n d i e
C o u n t r y
P u n k
A l t e r n a t i v e
M e t a l
R o c k
P o p
F o l k
R a p
O t h e r
J a z z
C l a s s i c a l
Favorite Genre
p r o p o r t i o n
Classical Listener
YesNo
Classical listening by favorite genre
Figure 36: Stacked barplot indicating how often people with a particular favorite genre tend to listen to classical music.
Orchestral preferences analysis MC 47
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
Word Association - Clustering
-
8/13/2019 Analysis of Musical Preferences and Orchestral Music
48/59
Word Association ClusteringAfter asking people whatwords they associated withboth classical music and
music they listen to, Iclustered each of theresponses by how often theywere associated with eachother 1 . On the right are twosets of dendrograms (tree-likediagrams), each of whichshow how close differentwords are to each other.Labels preceded by Classicalrefer to what adjectivespeople associated classicalmusic with, and labelspreceded by Listen refer toadjectives people used todescribe music they listen to.
To read one of thedendrograms, look at howlong the closest junctionbetween two adjacent
adjectives are. Thoseadjectives are the two mostcommonly associated witheach other. They form acluster with 2 members, andthen you can compare thatcluster with other clustersusing the same logic.
Clustering of words Classical listeners
List e n Loud
Listen Boring
Listen Complicated
Listen Cool
Listen Emotional
Listen Epic
Listen Ethereal
Listen Exciting
Listen Fast Listen Happy
Listen New Listen Old
Listen Repetitive
Listen Sad Listen Simple
Listen Soft Listen Slow
Listen Wild
Classical Loud
Classical Boring
Classical Complicat
Classical Cool
Classical Emotional Classical Epic
Classical Ethereal
Classical Exciting
Classical Fast
Classical Happy
Classical New
Classical Old
Classical Repetitive
Classical Sad
Classical Simple
Classical Soft
Classical Slow
Classical Wild
Figure 37: Clustering of words for classical listeners.
Clustering of words Nonclassicallisteners
Listen Loud
Listen Boring
Listen Complicated
Listen Cool
Listen Emotional
Listen Epic
Listen Ethereal
Listen Exciting
Listen Fast
Listen Happy
Listen New Listen Old
Listen Repetitive
Listen Sad
Listen Simple
Listen Soft Listen Slow
Listen Wild
Classical Loud
Classical Boring
Classical Complicat
Classical Cool
Classical Emotional
Classical Epic
Classical Ethereal Classical Exciting
Clas sic al Fast Classical Happy
Classical New
Classical Old
Classical Repetitive
Classical Sad
Classical Simple
Classical Soft Classical Slow
Classical Wild
Figure 38: Clustering of words for non-classical-listeners.
1 Specically, I used Jaccard dissimilarity for the distance function and Wards method as the clustering method.
Orchestral preferences analysis MC 48
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
High School Musical Experience
-
8/13/2019 Analysis of Musical Preferences and Orchestral Music
49/59
High School Musical Experience
Out of all the responses, 942 (57%) of respondents stated that they played
a musical instrument in their high school band or orchestra, and 712 (43%)stated they did not. On this page I will show a few different sets of statistics contrasting these two groups. The two tables below show thepercentage of responses from each group towards their favorite genre of music.
0%
5%
10%
15%
20%
A l t e r n a t i v e
C l a s s i c a l
C o u n t r y
E l e c t r o n i c
F o l k
H i p
h o p
I n d i e
J a z z
M e t a l
O t h e r
P o p
P u n k
R a p
R o c k
P e r c e n t a g e
Participation
Yes
No
Favorite Genre by HS Band/Orchestra Participation
Figure 39: Favorite genres by participation in a high school band or orchestra.
Looking at the statistics for favorite instrument section, there is a notable
difference between the two groups. In particu