analysis of musical preferences and orchestral programming
DESCRIPTION
"What makes people go to or avoid orchestra concerts?"During the spring of 2013, I posted a survey to Reddit and a few other sources to help answer this question. This report is a summary of results and statistics from the survey. A more rigorous analysis was done with asking what types of composers and programming changes users would like to see, while other, less rigorous, visualizations are also presented.I recommend downloading this and viewing it in a separate pdf viewer to take advantage of the navigation buttons at the top of each page.All of the visualizations used were generated in R programming language, largely with the help of the ggplot2 package.TRANSCRIPT
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Analysis of Musical Preferences and OrchestralProgramming
Max Candocia
January 5, 2014
Orchestral preferences analysis MC 1
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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 neverincludes 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 & greet”with themusicians. 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
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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, they’ve 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 oravoid an orchestra concert?”
Orchestral preferences analysis MC 3
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Description of Survey
Last April, I decided to make a survey to ask questions about people’smusical 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 isenough 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
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Sources of Bias
Now, before I get hundreds of messages along the lines of ”...but did you take X intoaccount?”, I should admit that the data is not 100% bias-free. The main source of bias is thatmost of my data was collected on Reddit, a website with young Internet-users who are not anaccurate representation of the general population, or even their own age group. Additionally,the chain email spanned a relatively small, but older demographic. There were many who aremembers of the Northshore Concert Band, which is somewhat problematic for statisticalpurposes.My solution to this is to use where I posted the survey as a control variable, so that effectswhich are unique to specific sources are not as pronounced. Additionally, I also controlled forregion and stratified1 ethnicity and source in the main part of the report.
1 This means that I calculated the probabilites for all the fixed groups that I want to show results for, such as different sexes and age groups, and then I randomly added ethnicities andsources to them to get the probabilities from models where ethnicities and source are significant. I do this a large number of times and take the averages of the results and use those.
Orchestral preferences analysis MC 5
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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
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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 onthe next page, you can see theregions and ethnicities representedby survey. It primarily consists ofpeople 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
dens
ity
Sex
Female
Male
Age Distribution of Survey respondents
Figure 1: Age density plot by gender.
Orchestral preferences analysis MC 7
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Demographics (cont.)
67.2%
18.6%
8.7% Region
United States
Europe
Other 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
Caucasian (non−Hispanic)
Black (non−Hispanic)
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
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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 ”General” categoryrefers 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
General
Not 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
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General Modifications
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 describethe 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 isn’t a strict rule against this inmost places, but oftentimes it’s 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
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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 3options, being ”less likely”, ”no change”,and ”more likely.”
Vivaldi
Mozart
Beethoven
Gustav Mahler
Johannes Brahms
Pyotr Tchaikovsky
George Gershwin
Sergei Rachmaninov
Igor Stravinsky
Leonard Bernstein
Philip Glass
Steve Reich
John Williams
Orchestral preferences analysis MC 11
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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 respondent’s 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 ofclassical 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
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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 find relationships indata is linear regression. Essentially, you try to fit 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 fit a line to agraph, as you can see in Figure 5. In this case, the ”real”formula for spending is 2 + 0.8 ∗ income, which is prettyclose to the value estimated by linear regression.
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0 20 40 60 80 100
020
4060
8010
0
Spending vs. Income
Income in thousands of dollars
Spe
ndin
g in
thou
sand
s of
dol
lars
Slope: 0.79
Intercept: 2.74
Figure 5: A line of best fit superimposedon randomly generated data, along with theestimated slope (how steep the line is) andintercept (the value of the line whenincome is zero).
Orchestral preferences analysis MC 13
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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 of1.462 − 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 regression1, 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 it’s easier tomake sense out of the results.
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20 30 40 50 60 70 80
−2
−1
01
2
Video Game Music Response vs. Age
Age
Res
pons
e, ji
ttere
d
Slope: −0.047
Intercept: 1.462
Figure 6: Attempt at linear regression ofdiscrete data. The points are jittered sothat you can see the density of points moreeasily, but they only take the values of -2,-1, 0, 1 and 2 on the y-axis. Note that thisdata is shifted down by 3 from the originalvalues 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 fits andpenalizes them for using more terms. AIC is less restrictive, and the exact penalty term is just 2p, 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, soadding ethnicity to the model would increase p by 6.
Orchestral preferences analysis MC 14
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
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 stratifiedsamples 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/Pacific 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 fixed 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 significantly to the model.
Orchestral preferences analysis MC 15
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
Wind orchestra concerts & How to read the probabilityplots
Each of the barplots in this graph arestacked so that one can comparetheprobabilities of effects across differentgroups. effects (responses) are howmuch more or less likely one is toattend an orchestra concert basedon the changes.
What one could conclude from thegraph on the right is that mostpeople’s likelihoods of concertattendance wouldn’t change based onwhether or not it was a windorchestra (i.e. mostly woodwind,brass, and percussion instruments)performing. As for other deductions,you could see that the somewhatregular concertgoers (1-12 times peryear) have a slight preference towardsthem , whereas other groups have amore mixed preference.
Never Seldom Once/yr 2−6/yr 7−12/yr 13+/yr
0.00
0.25
0.50
0.75
1.00
prob
abili
ty
Effect is...
Most likely
More likely
Unchanged
Less likely
Least likely
Probabilities of effects of using a wind orchestra by orchestra concert attendance
Figure 7: This graph of displaying the effect of wind orchestras shows a relativelyconstant set of probabilities with respect to age.
Orchestral preferences analysis MC 16
Overview Demographics Questions Programming Composers Genres Marketing Testimonials End
Pre-concert lectures
Pre-concert lectures arelectures usually given bythe 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 orchestraconcerts to begin with.They are also less popularwith the youngest agegroups, with the exceptionof those who already attendmany concerts a year.
Male Female
0.000.250.500.751.00
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Probabilities of effects of having a pre−concert lectures by sex and orchestra concert attendance
Figure 8: This graph shows the probability of changes to an individual’s attendance of anorchestra concert due to the inclusion of pre-concert lectures.
Orchestral preferences analysis MC 17
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Pop orchestra
Sometimes orchestras playpopular music, rangingfrom 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 thesounds 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
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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 individual’s attendance of anorchestra concert due to popular music being played.
Orchestral preferences analysis MC 18
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Allowing food
Allowing food is anothercontroversial change that hasbeen 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.
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Probabilities of effects of allowing food by age group and orchestra concert attendance
Figure 10: This graph shows the probability of changes to an individual’s attendance of anorchestra concert due to the allowance of food in the concert hall or other place ofperformance. The graph is faceted by how often one attends orchestra concerts.
Orchestral preferences analysis MC 19
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Suggesting informal attire
While almost every concert halldoesn’t have a dress code more strictthan 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.
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Probabilities of effects of suggesting informal attire by age group, attendance and sex
Figure 11: This graph shows the probability of changes to an individual’s attendanceof an orchestra concert due to less formal wear being required/suggested. The graph isfaceted by sex and how often one attends orchestra concerts. There is no changeamong age groups.
Orchestral preferences analysis MC 20
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Playing shorter pieces
Another common complaint is thatorchestra pieces are too long. In myexperience, this is usually in regardsto a piece of music where there aremultiple movements, i.e., sections ofthe 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.
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Probabilities of effects of playing shorter pieces by age group, sex and attendance
Figure 12: This graph shows the probability of changes to an individual’s attendanceof an orchestra concert due to shorter pieces being performed. The graph is faceted bysex and how often one attends orchestra concerts, but there is no change among agegroups.
Orchestral preferences analysis MC 21
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Attending a concert with friends
While not a programming change, Ithought it would be interesting to seehow bringing a friend might affectone’s 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.
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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 individual’s attendanceof a concert because that person’s friend(s) will also be attending. The values varyacross age group and gender.
Orchestral preferences analysis MC 22
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Adding modern classical music to the program
Most concerts I’ve been to play music writtenin the first half of the 20th century or earlier.Many people wish that orchestras would playmusic 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 ofwhen they think of ”modern classical music”.I’ve attended such concerts where even myclassical-music-loving friends said they’dnever ever go to it because the music isreally difficult to listen to since it’s 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 linkedto above. Example:Short Ride in a Fast Machine.
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Probabilities of effects of playing modern classical music by age group and attendance
Figure 14: This graph shows the probability of changes to an individual’s attendanceof a concert due to modern classical music being played.
Orchestral preferences analysis MC 23
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Adding more percussion-focused music to programming
While this is somewhat closelyrelated to modern classical music dueto how pieces are orchestrated withmore percussion as of late, the lackof percussion instruments in commonorchestral repetoire often leavespeople wanting.
About 24% find this changefavorable, whereas about 11% find itunfavorable. While this changedoesn’t seem to have much of aneffect on most people, it couldcertainly draw in more people thanbefore, although there doesn’t appearto be any strong relationshipsbetween adding percussion and sex orfrequency of concert attendance.
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Probabilities of effects of using more percussion on attendance of an orchestra concert
Figure 15: This graph shows the probability of changes to an individual’s attendanceof a concert due to the addition of more percussion-focused music. There does notappear to be any relationship between this and sex, age group, or frequency of concertattendance.
Orchestral preferences analysis MC 24
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Advertising towards younger audiences
One idea to bring in youngeraudiences is to advertise towardsthem. One flaw with this question isthat advertising towards a particulardemographic isn’t 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) towardsa 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.
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Probabilities of effects of advertising towards a younger audience
Figure 16: This graph shows the probability of changes to an individual’s attendanceof 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
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Advertising the mood of a concert
Another 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, I’m sure that the moodof the music would definitelyinfluence whether or not these groupswould attend a concert, so moreresearch would have to be done inthis area to get any definitive results.
Looking at the plot on the right, itappears as if there is an overallpositive effect, especially for femalesand especially for people who don’tgo to concerts very frequently.
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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 individual’s attendanceof a concert due to the mood being advertised. The graph is faceted by sex andfrequency of orchestra concert attendance, but there is no change between age groups.
Orchestral preferences analysis MC 26
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Playing video game music
One last non-composer change toorchestral programming I looked intowas 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 significant correlation betweenhow often one attends orchestraconcerts and how much they wouldlike to see video game music played.Only sex and age group aresignificant. Males tended to have amore positive response towards videogame music, although the older agegroups have highly negativeresponses to the music.
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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 individual’s attendanceof 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
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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 people’sresponses 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 ofcomposers, it’s 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 selectioncriteria so that more complicated models are highly unfavorable.2
1Specifically, 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 ofexplanatory variables in the model. Specifically, 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 6levels (possible values) for that variable.
Orchestral preferences analysis MC 28
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Sample sizes of subgroups
Figure 19: This mosaic plot represents all of the different subgroups by four different variables. The size of each rectanglecorresponds 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 tobe familiar with composers). Dots indicate empty groups. Generally, the results for groups that are represented by a largersample size are more reliable.
Orchestral preferences analysis MC 29
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Composer - Bach & How to read the plots
For the composer probabilities, I always tooknote of how composer familiarity (that is,whether or not an individual was familiarwith 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 Baroque1
era composer, the responses are generallypositive. Apart from older age groups, whoserepresentative sample is much smaller, thereisn’t too strong of an effect.
Example of his music:Brandenburg Concerto No. 3
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Probabilities of effects of adding Bach to a concert program
Figure 20: This graph shows the probability of changes to an individual’s attendanceof 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
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Composer - Vivaldi
For music by AntonioVivaldi, an ItalianBaroque-era composer, theresponses are generallypositive. There aren’t anystrong relationships betweensexes or age groups.
Example of his music:Four Seasons - Spring
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Figure 21: This graph shows the probability of changes to an individual’s attendanceof a concert due to music by Vivaldi being programmed, based on the individual’sfamiliarity with at least 10 composers out of 15. There is no significant differencebetween age groups.
Orchestral preferences analysis MC 31
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Composer - Mozart
For music by WolfgangAmadeus Mozart, anAustrian composer of theClassical1 era, responses aregenerally very favorable,moreso than Vivaldi. Thereare no significantcorrelations betweenresponses and age group,sex, or frequency oforchestra concertattendance.
Example of his music:The Magic Flute - Overture
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Probabilities of effects of adding Mozart to a concert program
Figure 22: This graph shows the probability of changes to an individual’s attendanceof 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
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Composer - Beethoven
For music by Ludwig vanBeethoven, a Germancomposer of the Classicalera, responses are generallyvery favorable, moreso thanVivaldi. There are nosignificant correlationsbetween responses and agegroup, sex, or frequency oforchestra concertattendance.
Example of his music:Symphony No. 5 - Mvt. I
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Probabilities of effects of adding Beethoven to a concert program
Figure 23: This graph shows the probability of changes to an individual’s attendanceof a concert due to music by Beethoven being programmed.
Orchestral preferences analysis MC 33
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Composer - Mahler
For music by GustavMahler, an Austriancomposer of the Romantic1
era, there is an overall trendthat more regularconcertgoers have astronger preference for hismusic. There is no apparentdifference between agegroups or sexes.
Example of his music:Finale of Symphony No. 1, ”Titan”
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Probabilities of effects of adding Mahler to a concert program
Figure 24: This graph shows the probability of changes to an individual’s attendanceof 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
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Composer - Brahms
For music by JohannesBrahms, a Germancomposer of the Romanticera. Overall reception of hismusic seems to improvewith frequency of orchestraconcert visits. For somereason, females in the”Never” and ”Seldom”classes 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
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Probabilities of effects of adding Brahms to a concert program
Figure 25: This graph shows the probability of changes to an individual’s attendanceof a concert due to music by Brahms being programmed.
Orchestral preferences analysis MC 35
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Composer - Tchaikovsky
For music by PyotrTchaikovsky, a Russiancomposer of the Romanticera. With those familiarwith composers in general,the overall opinion seemsvery high. Even for thosewho don’t know manycomposers, there seems tobe a good opinion of hisname.
Example of his music:Piano Concerto No. 1
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Probabilities of effects of adding Tchaikovsky to a concert program
Figure 26: This graph shows the probability of changes to an individual’s attendanceof a concert due to music by Tchaikovsky being programmed.
Orchestral preferences analysis MC 36
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Composer - Stravinsky
For music by IgorStravinsky, a Russiancomposer of the Modern1
era. 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
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Probabilities of effects of adding Stravinsky to a concert program
Figure 27: This graph shows the probability of changes to an individual’s attendanceof a concert due to music by Stravinsky being programmed.
1 The Modern era of music can be roughly defined 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
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Composer - Rachmnaninov
For music by SergeiRachmaninov, anotherRussian composer of theModern era. The overallresponse is somewhatneutral, although thereappears to be a noticeableincrease in his dislike in theyounger females.
Example of his music:Piano Concerto No. 2
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Probabilities of effects of adding Rachmaninov to a concert program
Figure 28: This graph shows the probability of changes to an individual’s attendanceof a concert due to music by Rachmaninov being programmed.
Orchestral preferences analysis MC 38
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Composer - Shostakovich
For music by DmitriShostakovich, 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
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Probabilities of effects of adding Shostakovich to a concert program
Figure 29: This graph shows the probability of changes to an individual’s attendanceof a concert due to music by Shostakovich being programmed.
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Composer - Gershwin
For music by GeorgeGershwin, an Americancomposer who wrote musicwith elements of both jazzand classical music, theoverall response seems fairlypositive. However, for the30-44 male group, thereseems to be a slightly morenegative response toGershwin.
Example of his music:Rhapsody in Blue
Male Female
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Probabilities of effects of adding Gershwin to a concert program
Figure 30: This graph shows the probability of changes to an individual’s attendanceof a concert due to music by Gershwin being programmed.
Orchestral preferences analysis MC 40
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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 ofpopularity with orchestralconcert 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
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Probabilities of effects of adding Bernstein to a concert program
Figure 31: This graph shows the probability of changes to an individual’s attendanceof a concert due to music by Bernstein being programmed.
Orchestral preferences analysis MC 41
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Composer - Reich
For music by Steve Reich, anAmerican composer who is wellknown 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 Reich’s 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 arequite 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
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Familiar UnfamiliarComposer Familiarity
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Probabilities of effects of adding Reich to a concert program
Figure 32: This graph shows the probability of changes to an individual’s attendanceof a concert due to music by Reich being programmed.
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Composer - Glass
For music by Philip Glass,another American composerwho is well known for hisminimalist music and musicin soundtracks, responsesseem to be more positivethan Reich’s, although itstill suggests that theminimalistic nature of hismusic is offputting tocertain audiences.
Example of his music:Symphony No. 8, Mvt. I
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Probabilities of effects of adding Glass to a concert program
Figure 33: This graph shows the probability of changes to an individual’s attendanceof a concert due to music by Glass being programmed.
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Composer - Williams
For music by John Williams,an American composer verywell-known for his music insoundtracks of movies suchas Harry Potter and StarWars, responses aregenerally positive. There isa significant probability fora 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
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Probabilities of effects of adding Williams to a concert program
Figure 34: This graph shows the probability of changes to an individual’s attendanceof a concert due to music by Williams being programmed.
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Genre Analysis - Overview
The ”heavy” part of the analysis is finished, and now I will presentsome more visual results. These are focused around people’s 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.
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Favorite Genres - Correlations
You can see the correlation matrix1 between favoritegenre and second-most favorite genre in the figure 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
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Jazz
Metal
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Correlations between first and second−most favorite genre
Figure 35: The correlation matrix between second-most favoritegenres and favorite genres among the sample population. Orangeindicates higher positive correlations, dark blue indicates morenegative. The signs of each entry are plotted in the cells.
1 I used Pearson’s 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
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Favorite Genre & Classical Listening
Looking at what people’s favorite genres are, youcan see that there are differences among variousgenres in terms of whether or not people whoidentify it as their favorite also listen to classicalmusic. There was one response for classical that was”No”, and I didn’t want to exclude the possibilitythat someone who doesn’t listen to a lot of musicmarked that answer down. Below is a table of theexact values for the responses.
Yes No
Alternative 76 133Classical 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 andclassical listening.
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Classical listening by favorite genre
Figure 36: Stacked barplot indicating how often people with a particular favoritegenre tend to listen to classical music.
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Word Association - Clustering
After asking people whatwords they associated withboth classical music andmusic they listen to, Iclustered each of theresponses by how often theywere associated with eachother1. On the right are twosets of dendrograms (tree-likediagrams), each of whichshow how ”close” differentwords are to each other.Labels preceded by ”Classical”refer 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 adjacentadjectives 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
Listen Loud
Listen Boring
Listen Complicated
Listen Cool
Listen Emotional
Listen Epic
Listen Ethereal
Listen Exciting
Listen FastListen Happy
Listen NewListen Old
Listen Repetitive
Listen SadListen Simple
Listen SoftListen Slow
Listen Wild
Classical Loud
Classical Boring
Classical Complicated
Classical Cool
Classical EmotionalClassical 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 classicallisteners.
Clustering of words − Non−classical−listeners
Listen Loud
Listen Boring
Listen Complicated
Listen Cool
Listen Emotional
Listen Epic
Listen Ethereal
Listen Exciting
Listen Fast
Listen Happy
Listen NewListen Old
Listen Repetitive
Listen Sad
Listen Simple
Listen SoftListen Slow
Listen Wild
Classical Loud
Classical Boring
Classical Complicated
Classical Cool
Classical Emotional
Classical Epic
Classical EtherealClassical Exciting
Classical FastClassical Happy
Classical New
Classical Old
Classical Repetitive
Classical Sad
Classical Simple
Classical SoftClassical Slow
Classical Wild
Figure 38: Clustering of words fornon-classical-listeners.
1 Specifically, I used Jaccard dissimilarity for the distance function and Ward’s method as the clustering method.
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High School Musical Experience
Out of all the responses, 942 (57%) of respondents stated that they playeda 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 ofstatistics contrasting these two groups. The two tables below show thepercentage of responses from each group towards their favorite genre ofmusic.
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Favorite Genre by HS Band/Orchestra Participation
Figure 39: Favorite genres by participation in a high schoolband or orchestra.
Looking at the statistics for favorite instrument section, there is a notabledifference between the two groups. In particular, those who played aninstrument in high school are more likely to prefer the woodwind or brasssection, whereas those who didn’t play an instrument are more likely toenjoy the string section or the piano more.
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Favorite Section by HS Band/Orchestra Participation
Figure 40: Favorite instrument sections by particpation in ahigh school band or orchestra.
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High School Musical Experiences (cont.)
It’s also somewhat interesting to see howoften people who played in a schoolensemble go to concerts now.
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Orchestra Concert Attendance by HS Band/Orchestra Participation
Figure 41: Orchestra concert attendance byparticipation in a high school band ororchestra.
Age group demographics are below, andthere aren’t too many alarming trends.However, the middle-aged group havinga higher proportion does suggest theremight be bias in prior results for thatage group.
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High School Band and Orchestra participation by Age Group
Figure 42: High school band andorchestra participation by age group.
Below are two tables, one describinghow listening to classical music isrelated to playing in a high school bandor orchestra (L=”Listens”,DNL=”Doesnot listen””), and the other describinghow the different sexes responded tothe high school question. Note that thecolumns add up to 100% for the firsttable and rows for the second.
Yes No
L 56.2% 35%DNL 43.8% 65%
Table 2: Classical listening andwhether or not one played in a band ororchestra in high school.
Yes No
Female 59.6% 40.4%Male 56.2% 43.8%
Table 3: Proportion of respondentswho played in a high school band ororchestra by sex.
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Marketing Analysis - Overview
The last part of this analysis is a few basic bits of marketing analysis.This means I will look at a few different variables, such as maximumticket price, how people hear about concerts, and how often they go toconcerts, and look at how decisions could be made from the data. I willkeep this section short and only analyze a couple aspects of the data, asit could get very lengthy otherwise.
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Ticket Pricing
Using the same statistical model as that of theprogramming changes and the composer responses, Imodeled how much people would be willing to pay for aconcert ticket based on how often they go to classicalconcerts and whether or not they listen to classical music.
The stacked barplots can be read from the bottom up,where the percentage of a group that would be willing to goto a concert for the ticket price can be found by looking atthe value at the lower border of a value. This represents thepercentage of people who would be willing to pay up to thatamount. The reason for this (except for the ”Over $100”,”Free”, and ”Never” values) is that the question asked peoplewhere the amount they would pay fits into a range. For thedarker blue color, that value essentially ranges from$0.01-$9.99. For the turquoise color, that means anywherefrom $10 to $25, and so on for the other ranges.
Looking at the figure to the right, it seems like there is asignificant increase in how much people are willing to paybased on how often they listen to classical music and howoften they go to concerts. However, that increase appearsto be most strongly represented at the high ends of theprice range. That means if you read downwards from thetop rows of the charts, you will notice that the turquoiseregion ends roughly around a constant area for anyone whohas been to a concert before, but the olive green (”Up to$75”) steadily grows, with a strange exception for the 2-6concerts/year group.
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Over $100
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Up to $75
Up to $50
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Maximum price one would pay for an orchestra concert by how often they go to orchestra concerts and
whether they listen to classical music or not
Figure 43: Stacked barplots indicating how much people would pay forconcert tickets by how often they attend concerts and whether or not theylisten to classical music.
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Advertising Sources
Here we look at which sourcespeople get information aboutconcerts they go to. I onlyinclude people who go toorchestra concerts at least twotimes per year, since those arethe types of concerts that areof particular interest. Below isa table with the size of eachage group in this sample.
Age.Group Size
<18 9618-20 13521-24 11025-29 7330-34 2135-44 2345-59 2960+ 12
Table 4: Sample sizes of thosewho go to orchestra concertsat least 2 times per year. Leftcolumn denotes age group.
33.3% 34.8% 46.4% 30.1% 23.8% 34.8% 51.7% 33.3%
38.5% 46.7% 47.3% 41.1% 42.9% 26.1% 13.8% 8.3%
76% 74.8% 64.5% 57.5% 57.1% 43.5% 51.7% 25%
37.5% 35.6% 37.3% 32.9% 23.8% 34.8% 17.2% 16.7%
42.7% 39.3% 49.1% 35.6% 33.3% 39.1% 17.2% 8.3%
12.5% 10.4% 17.3% 16.4% 14.3% 47.8% 31% 50%
6.2% 3.7% 0% 4.1% 4.8% 0% 3.4% 0%
2.1% 0.7% 2.7% 4.1% 19% 8.7% 0% 0%
19.8% 14.8% 21.8% 24.7% 33.3% 30.4% 27.6% 25%
1% 0.7% 0% 0% 0% 8.7% 0% 8.3%
39.6% 37.8% 44.5% 49.3% 38.1% 30.4% 3.4% 16.7%
13.5% 11.9% 10.9% 4.1% 4.8% 17.4% 24.1% 25%
Friends
Flyers
Posters
Direct Mail
Internet Radio
AM Radio
FM Radio
Satellite Radio
Other Internet
Other
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Sources from which people hear about concertsby age group
Figure 44: Grid of how prevalent different sources are in letting people know about concertsthey go to. Sample only includes those who go to orchestra concerts 2 or more times per year.
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TestimonialsSome people responded to the survey with complaints aboutorchestral music under the question phrased, ”Do you haveany bad experiences with classical music and/or itscommunity? If so, please describe them in detail.” I willpost select testimonials here, as they are relevant to thequestion of, ”What makes people go to or avoid anorchestra concert?” They will be followed by a word cloudof all testimonials.
While a broad statement, going to a college that was heavyin music majors, there was the occasional notion thatclassically-trained musicians that performed classical musichad an elitist attitudes towards more contemporary styles ofmusic. This resulted in the whole sub-culture to feel slightlyalienating towards one who wasn’t accustomed to the genre,when it came to either listening or performing it.
Expensive. I spent $80 for seats that weren’t even thatgreat. I went to a video game music concert for The Legendof Zelda, called Legend of the Goddess, or something... Itwas cute to see the cosplayers, but I didn’t like seeingpeople in flip-flops, jeans, and a t-shirt. The performancewas great though! This is the only concert I’ve ever been to.
Some classical fans and musicians are utterly intolerant of popular music.As someone who is a professional rock drummer, that turns me off. Notethat I don’t care for orchestral adaptations of pop. I really like classical(indeed pre-classical) music of many eras, from Palestrina to Varese andMorton Feldman. I occasionally have conversations with the snobbiestsubset of this crowd, whose tastes are straightjacketed in old world tastes.They are amazed to hear from a drummer who has something to sayabout, for instance, why Schnabel’s recordings of the Beethoven sonatas,while technically less facile, nevertheless have more soul than any numberof more recent versions. . . the very music they claim to love-Bach, Haydn,Mozart, Beethoven, Chopin, Wagner, Bartok, Stravinsky, Webern, and soforth-appeals to the very thing so many of them seem to lack: animagination!
Plenty. They mostly have to do with otherconcert goers that I find rude or behavinginappropriately: like talking or eating duringthe music, clapping in places you should notclap, and worst of all: coughing without end!
My only bad experience has been with theclassical community. I am a metalheadmostly and because of this, it’s assumed Ican’t appreciate classical music or ”pick upon the subtly of the nuances imbeddedwithin.”. . .
I actually love classical music, I just have adifficult time finding people to go with.
I don’t like arpeggios. Classical music soundsto me like constant repetitive arpeggios upand down, up and down, up and down, -monotonous - grating on the nerves. It’s likelistening to the worst singers for whom eachnote has to be stretched into a run of everynote. Same with most jazz. . .
It’s rare that I attend a classical musicconcert, but it’s mostly because I do notknow enough about when/where theyhappen, and if I do know, it’s usually amatter of affording to go to a classicalconcert when I could go to a contemporary(rock/electronic/etc) instead and have morefun and socialize more. Jazz is an amazingintermediate between classical music andcontemporary. . .
A Clockwork Orange introduced me toclassical music. Is that bad?
As a professional jazz musician I’ve had afair amount of interactions with classicalmusicians (mostly in college) and found anumber of them to be pretentious andhyper-competitive.
I quite dislike a lot of the more modernscores for movies and especially video games.99% of the time they’ll just rely on clicheemotion-stirring sounds, or bombasticover-the-top ’epicness’. They often seem tocompletely ignore - or seem to be unaware of- any of the early 20th century or modernadvances in the genre.
I feel that there is a stigma surrounding thesocioeconomic status of listeners of classicalmusic, particularly with concert attendance.I feel that it could be more popular with thegeneral public if it was more financiallyaccessible, rather than having a ”membershipfee” or having high ticket prices. I understandthat it costs money to run a show, butpeople definitely miss out on opportunities tohear great music if they cannot afford it.
Not particularly. Though the level ofhigh-donor patronage is still disconcerting.How I mean: when the highest donors aretreated like the ’owners’ of the orchestra,proper decision-making towards keeping saidorchestra relevant tends to go out thewindow.
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Testimonials (cont.)
Modern music, especially in the compositionfield. It seems like everyone’s just trying toout-weird everyone else. Experimentation isfine but just because something isexperimental doesn’t mean it’s worthanything or inherently good.
What annoys me most is the idea thatclassical music somehow attracts only rich,old, white people and that classical musicconcerts are used as some sort of badge ofelitism. This should not be the case and theonly people to blame are artistic directorswho continually opine for the classics, refuseto accept marketing techniques that targetyounger audiences, and refuse to lower ticketprices. Which leads me to my othergrievance: should we really call Philip Glass’smusic or Steve Reich’s music ”classical”?Why are we using this blanket term still? Iunderstand we can’t go around naming eachsonatina, each symphony or each form ofmusic, but it implies that this music isantiquated, despite its presence and influencein modern music. The term turns off peoplebefore they can actually listen to it. I guessthe problem though is coming up with areplacement. We can establish that Mozart’smusic and Nico Muhly’s music have somesort of vague connection to each other, butspecifically labeling them Baroque andcontemporary classical, or other pieces withvague labels, would become daunting to theaverage music enthusiast.
I find the position on ’new’ easy-listening classicalmusic is disheartening. Many in the general classicalmusic community feel that film music or game musicdoesn’t belong to the classical music genre. Whilst Iprefer watching say, a Mahler Symphony, over a concertfull of John Williams, I find that this lack of acceptancediscourages youth to listen to/get involved in classicalmusic. Worse, I think it alienates the generation. Whenmy Gran was in music college, she was forbidden tolisten to composers such as Tchaikovsky as it wasn’tseen as popper classical music - yet look where we aretoday. This slow acceptance to the ’new’, I feel, isslowly killing classical music. . . Although undoubtedlybrilliant, it is the John Williams, John Powell, HansZimmer, feel-good, easy-listening classical music that isgoing to engage them. People forget classical musictakes time to appreciate, you can’t expect someone todive into Bruckner 8 for the first time and love everymoment of it. You have to start somewhere simple andbuild upon that. I think orchestra’s forget this. I thinkmuch of the older generation forget this too. I’m sure intime I’ll forget this and then wonder why my future kidswill want me to turn off this boring Mozart in the car.Classical music, much like anything else that is worth alot in life, takes time. It’s hard to engage people, totransform them into thinking that putting in the time isworth it. We just need to take it one step at a time.
Too many orchestras play only classical era works. Thefuture of classical music relies on new works, most ofwhich are being written for wind ensemble, seeing aswind ensembles really embrace new music. Orchestrasshould focus their efforts on seeking out new works,and maybe even featuring works for wind band.
I’m an orchestral bass trombonist.
I am a professional symphony orchestra playerand the only bad experiences I’ve had withclassical music is when the orchestra board andupper administration tries to insert themselvesinto the artistic decision-making.
I am a former conductor/conducting student.IMHO, much of the orchestral music ”industry” isjust that, an industry. Constantly scared aboutmoney, and providing whatever attracts morepeople initially at the expense of stuff that is trueart: a communication. A communication aboutthe world, the nation, the people, God, love,death, the secret inner workings of thecomposer’s heart, and on and on. That’s part ofwhy I left. There is also a lot ofself-aggrandizement, which, to be fair, is notlimited to orchestral music.
My worst experience was actually working in theindustry, and the way the ”season pass” peopletreated those of us who worked at the concertvenue every weekend evening, and were glad todo it so that we could experience the symphonyand opera, at least a little bit.It was oftenassumed that we were only there for a paycheckand didn’t care about/understand whatever wason the program. I’m not sure whether this wasbecause we had low-paying jobs or because wewere generally young (college or late highschool). This elitism hurt my view of theindustry, and actually steered me away fromdoing music as a career.
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Testimonial Word Cloud
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Final Thoughts
As I mentioned in the beginning, the sources of data from which I collected were not necessarily representative of society as awhole, especially when taking older age groups into account. However, I do believe that the results of the survey providegood insight into many of the questions that were asked, and are at least somewhat accurate1, but many other things wouldhave to be done to further verify results, including,
Use the same scale for composers as I did for general modifications. It would provide a lot more insight, as there is abig difference between ”more likely” and ”almost certain to go”.
Keep track of which composers people are familiar with. While it was also useful to know if a person was familiarwith several composers or not, I think it would be more useful to know whether or not they were familiar with thecomposer in question.
Include a few more demographic options, including Internet usage, whether or not one is a musician, and other generaldemographics, which, when supplied with a larger sample size, can be used to make the results more representative ofa desired population. Also include an ”other” option for sex to avoid pigeonholing and increase response size.
Word the ticket pricing question more clearly, and perhaps include finer price intervals (e.g., $5 intervals).
Eliminate the word association problem, as it takes up a lot of space and did not provide too much interesting insight.
Keep a general ”other” option for favorite genre. It’s cumbersome to recategorize free-response data.
Additionally, from looking at the testimonials, many people appear to have problems with cultural disconnects, particularlywith elitism and snobbery. While I did not include a majority of the experiences respondents shared, a near-majority of themconsider this a major detractor from orchestral music.
1 I have no proof.2
2 Joking aside, I really do need a larger sample that does not use Reddit and consists a larger, older population. Especially one that does not use the Internet very often.
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Thanks
I would like to thank everyone who responded, including Reddit users, friends, and family, forall of their responses.
I would also like to thank my friend Andrew Lei for proofreading the first draft of this report.
I would also like to thank my friend James Balamuta for proofreading this report andproviding statistical input.
I would also like to thank my friends Steven, Eric, Eric, Michael, and Spencer for theirsuggestions.
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