The Audience
«
American Art Museums
J. Mark Davidson Schuster
NATIONAL
endowment
forW^the
Research Division Report #23 A R 1 S
The Audience for
American Art Museums
The Audience for
American Art Museums
J. Mark Davidson Schuster
Research Division Report # 23
National Endowment for the Arts
Seven Locks Press
Washington
The Audience for American Art Museums is Report #23 in a series on matters of
interest to the arts community commissioned by the Research
Division of the National Endowment for the Arts.
First printed 1991
Second printing 1992
Library of Congress Cataloging-in-Publication Data
Schuster, J. Mark Davidson, 1950-
The audience for American art museums / J. Mark Davidson Schuster
p. cm. — (Research Division report / National Endowment for
the Arts ; 23)
Includes bibliographical references.
ISBN 0-929765-00—1
1. Art museums — United States — Visitors. I. Title. II. Series:
Research Division report (National Endowment for the Arts. Research
Division) ; 23.
N510.S3 1990
708. 13— <Ic20 90-20253
CIP
Manufactured in the United States of America
Seven Locks Press
Washington, D.C.
1-800-354-5348
Table of Contents
Introduction: Who are the Visitors to Art Museums? 1
Part I: The Demographics of Participation Rates 4
Comparing Participation Rates: The Americans
and the Arts Studies 10
Comparing Participation Rates:
An International Perspective 12
Participation Rates Controlling for "Third" Variables 18
Part II: Socialization and Art Museum Attendance 27
Part III: Unsatisfied Demand and Barriers to Attendance 30
Unsatisfied Demand 30
Barriers to Attendance 32
Part IV: Profiles of the Museum Audience(s) 37
Conclusion 46
Notes 47
Appendix: Results from Three Logit Analyses 52
Acknowledgments 56
About the Author 57
Introduction: Who are the Visitors to Art Museums?
Until the mid-nineteenth century most museums were founded around pri-
vate collections, and access was restricted to an audience selected by the
collector, though few went to such great lengths as Sir Ashton Lever in 1773:
This is to inform the Publick that being tired out with the insolence
of the common People, who I have hitherto indulged with a sight
of my museum (at Alkrington), I am now come to the resolution
of refusing admittance to the lower class except they come
provided with a ticket from some Gentleman or Lady of my
acquaintance. And I hereby authorize every friend of mine to give
a ticket to any orderly Man to bring in eleven Persons, besides
himself, whose behavior he must be answerable for, according to
the directions he will receive before they are admitted. They will
not be admitted during the time of Gentlemen and Ladies being in
the Museum. If it happens to be inconvenient when they bring their
ticket, they must submit to go back and come some other day,
admittance in the morning only from eight o'clock till twelve.1
In the late eighteenth century, people who wished to visit the British
Museum had to present their credentials at the office and await word,
sometimes for months, as to whether they would receive an admission ticket.2
And it was not until 1960 that the Barnes Foundation in Philadelphia was
forced, in exchange for its status as a tax-free institution, to open its doors to
the general public, though admissions were still limited to 400 per week.3
In the nineteenth century, particularly in the United States, the distinction
between private and public museums began to fade. The motivation for
establishing a museum became not so much the need to house a collection
as the desire to provide an opportunity for the general edification of the
public.
In the last decade, with the rise of both public and private nonprofit
funding mechanisms that take a large part of their mandate to be increasing
the breadth of exposure of Americans to the arts, overall attendance figures
have become increasingly important for two reasons. First, museums are
finding that carefully documenting audience size helps them to make a more
persuasive case to government and private funders, whether or not they
actually consider broadening the range of their audience as one of their
primary goals. Second, museums need the revenue that comes from increased
attendance.
J. Mark Davidson Schuster
At the same time, more and more museums are becoming concerned with
the fine-grained detail of who attends art museums and who does not. For
these museums an understanding of their audience is a critical point of
departure for a wide variety of management decisions. Once a museum has
a profile of its audience, it can compare that profile with other demographic
profiles to help answer a number of interesting policy questions. Some of the
databases a museum might wish to use, along with the policy questions that
might be answered by such comparisons, are summarized on the next page.
Accordingly, this study constructs a series of profiles of the American
audience for art museums and galleries, and outlines a number of the
methodological issues that are involved in constructing such profiles.
There is a longer tradition of audience studies among art museums than
perhaps among any other type of arts institution. Evidence from a number of
museum audience studies, along with studies from the performing arts, was
brought together for the first time in 1977 by DiMaggio, Useem and Brown.4
Carefully aggregating the results of these diverse studies, DiMaggio et al.
summarized the demographic composition of the public for the arts in the
United States:
• The audience for the arts was more highly educated, was of higher
occupational status, and had a higher income than the population as
a whole.
• Women were slightly overrepresented in the arts audience.
• The median age of the arts audience was close to the median age of
the population at large but varied widely from audience to audience.
• Minorities were present in proportions smaller than their share of
relevant metropolitan populations.
And, with respect to the public for museums:
• Museum visitor populations were somewhat more representative
of the American public than were the performing arts audiences
surveyed.
• The museum surveys found smaller proportions of professionals and
the well educated, and lower median incomes than did studies of
performing arts audiences.
• The art museum visitor population was better educated, wealthier,
older, and composed of more professionals than visitors to history,
science, or other museums.5
Audience Profiles as Data Bases
for Answering Policy Questions
Audience Profiles
Policy Questions
The profile of the overall audience for
What portions of the population is the
art museums as reflected in SPPA '85
museum serving as compared to
or in similar studies.
museums on average?
The profile of the overall population
What segments of the population are
or of the population in the museum's
underrepresented in the museum's
local area.
audience?
The profile of the museum's target
Is the museum serving the segments
population.
of the population to which it has
targeted its activities?
The staff's impression of what the
How well does the museum staff
museum's audience profile is
understand the composition of the
currently.
current audience?
Is the programming promoted by the
staff meeting the needs of the actual
audience?
The audience profile of other similar
How does the museum's experience
museums.
compare to that of sister museums?
Is the museum more successful or less
successful than other museums at
attracting particular groups to the
museum?
The audience profile of other nearby
What are the other local educational
"attractions."
and leisure opportunities that
compete for the audience?
To what extent is the museum
attracting an audience that is different
from the audience attracted by others?
Is the museum competing for the
same audience?
Changes in the museum's audience
How has the audience profile
profile over time.
changed?
Has it changed because of things that
the museum has done differently or
because of external factors?
Has it changed in ways in which the
museum would like its audience to
change?
J. Mark Davidson Schuster
These results were not terribly surprising; they reinforced widely held
views on the composition of the arts audience. Yet, because these results are
based on a wide variety of studies that are not inherently comparable, they
are, at best, only suggestive of the audience profile of art museums. What
might an extensive, careful, cross- sectional survey of the entire American
adult population reveal about visitors to art museums?
In this study, the Survey of Public Participation in the Arts (SPPA) is
used to explore the composition of the audience for art museums and art
galleries in the United States. Sponsored by the National Endowment for the
Arts and conducted by the U.S. Bureau of the Census in collaboration with
the University of Maryland, the SPPA is the first major attempt to collect
coherent data on arts attendance and participation across the entire adult
population of the United States. The SPPA was first undertaken in 1982 and
repeated in 1985. This work relies on data from the 1985 SPPA, in which a
probability sample of the American adult population was taken and 13,675
adults were interviewed between January and June 1985. Because of the scale
of this survey and the care with which it was taken, its data present an
important opportunity to explore a variety of interesting questions concern-
ing the participation of American adults in artistic activities.
Part I: The Demographics of Participation Rates
When asked if they had visited an art museum or art gallery in the twelve
months preceeding their 1985 SPPA interview, 22 percent of the adult
American population said that they had. Two out of every nine adults?
This overall participation rate is a convenient base of comparison for
determining which subgroups of the population are more likely to be
museumgoers and which are less likely. Table 1 summarizes participation
rates across a variety of demographic variables, both in raw terms and as
percentages. The results for these variables are discussed below.
Care must be taken in interpreting these numbers. First, the key attendance question grouped
art museums and art galleries together, but there is considerable variation in the use of the
phrase "art gallery." In some places it refers only to shops selling art works, in others to "art
museums." If everyone who shopped in a gallery also attended a museum in the preceeding
year, then there is likely to be little bias; if not, there is a bias whose aggregate effect is
unknown. Second, the data are based on recollections of activities over the previous twelve
months, recollections that might not be entirely accurate. While these caveats may limit
one's confidence in the absolute numbers, they do not necessarily affect relative
demographic comparisons.
The Audience for American Art Museums
Presenting the findings as the raw number per 1,000 adults is necessary
because of the fundamental difference between the size of a percentage and
the size of the population base to which the percentage is to be applied. A
small percentage applied to a large base can still represent a large number of
people. For example, the SPPA data show that while 58 percent of attenders
would like to attend art museums more often, only 23 percent of non-attenders
would like to attend. Yet, out of every 1,000 adults, 307 would like to attend
more often and 179 of them — well over half — are currently non-attenders.6
Income: As income rises the participation rate rises, from 1 1 percent of
those with incomes between $5,000 and $10,000 to 45 percent of those with
incomes greater than $50,000. Thus, differences in income levels are par-
ticularly helpful in explaining the relative likelihood of attendance. Compar-
ing these figures with those per 1,000 adults, however, reveals that although
the participation rate is highest in the highest income group, more than a third
of the art museum audience actually comes from the $25,000-$49,999
income group, the largest income group in the adult population.
There is one exception to the general increase in the probability of
attendance with increases in income: a decrease from 16 percent to 1 1 percent
between the lowest and the next-lowest income categories. An important
component of this seeming anomaly is the fact that adults who are currently
students are disproportionately in the lowest income group, yet their atten-
dance pattern differs markedly from the non-students in the same income
group. However, the overall pattern is quite clear: Adults who are currently
students are much more likely to be attenders than are other adults.
Education: Educational level is clearly correlated with participation rate.
The rate rises from a low of 4 percent of adults with a grade school education
to a high of 55 percent of adults with some graduate school education, a
difference of 5 1 percentage points. This difference makes education the most
important predictive variable in this list of demographic variables. (For
income, the corresponding difference is 29 percentage points.) However, the
raw figures in Table 1 show that well over half the audience completed less
than a full college education. Again, this is because of the relative size of
these groups in the adult population.
To understand the full effect of education on participation rates, separat-
ing students from non-students is once again important. While understand-
ably there are very few current adult students in the first three education
categories (grade school, some high school, and high school graduate), there
are a number who are enrolled in college, and their participation rates are
Table 1
Attendance at Art Museums and Art Galleries, 1985
Question: During the last 12 months, did you visit an art gallery or art museum?
Per 1,000 Adults
Overall
Of all adults
Participation Number Number
Rate Attending in Category
22%
219
1,000
Income
Of adults whose
family income was
$4,999 or less
$5,000-$9,999
$10,000-$ 14,999
$!5,000-$24,999
$25,000-$49,999
$50,000 or more
16%
11%
15%
19%
28%
45%
13
14
21
47
85
42
82
126
143
247
308
94
222
1,000
Education
Of adults whose
Grade School
4%
4
110
highest education
Some High School
71%
8
118
level was
High School Grad
14%
53
376
Some College
29%
60
203
Four-year College Grad
45%
50
110
Graduate School
55%
45
82
220
1,000
Age
Adults whose
18-24 years
22%
35
161
age was
25-34 years
25%
61
238
35-44 years
26%
48
182
45-54 years
23%
30
132
55-64 years
18%
24
130
65-74 years
16%
16
97
75+ years
10%
6
220
59
1,000
Gender
Of adult
Female
23%
121
528
Male
21%
99
220
472
1,000
Race
Of adults
Black
11%
12
108
White
23%
203
873
Other
26%
5
220
19
1,000
Urbanization
Adults who
SMSAf and in Central City
25%
69
271
lived in
SMS A but not Central City
26%
107
413
Area outside an SMS A
14%
44
316
220
1,000
Table 1 (Continued)
Per 1,000 Adults
Participation
Number
Number
Rate
Attending
in Category
Region
Of adults who
Northeast
20%
42
209
lived in the
Midwest
21%
53
252
South
19%
64
344
West
31%
60
219
195
1,000
Subregion
Of adults who
New England
24%
13
54
lived in
Mid Atlantic
19%
29
155
East Northcentral
20%
37
182
West Northcentral
23%
16
70
South Atlantic
19%
35
180
East Southcentral
11%
7
66
West Southcentral
23%
23
98
Mountain
28%
13
46
Pacific
32%
47
220
149
1,000
Selected States*
Of adults who
California
32%
36
114
lived in
Florida
20%
9
46
Georgia
17%
5
29
Illinois
23%
11
48
Indiana
23%
5
22
Massachusetts
25%
6
24
Michigan
21%
9
43
New Jersey
16%
5
31
New York
21%
16
75
Ohio
14%
7
50
Pennsylvania
14%
7
49
Texas
26%
17
64
Virginia
30%
8
27
North Carolina
13%
4
32
Selected Occupations
Of adults whose
Professional
49%
44
89
occupation was
Managerial
37%
32
85
classified
Sales/Clerical
27%
64
240
Craftsman
14%
13
91
Operatives
10%
7
73
Laborers
10%
8
80
Service Workers
16%
17
108
Source: "Survey of Public Participation in the Arts," 1985.
Notes: The number who attended per 1,000 adults varies slightly across variables because
of missing values and rounding errors.
'SMS A stands for Standard Metropolitan Statistical Area.
4-
+These are the only states for which the U.S. Bureau of the Census has prepared separate
tabulations.
J. Mark Davidson Schuster
quite high: 38 percent for students with some college education, 37 percent
for college graduates, and a very high 67 percent for students in graduate
school. (This last participation rate is one of the highest found in this analysis.
Yet, with the introduction of additional "third" variables, it is possible that
this participation rate would become even higher.) As with income, once the
students are separated out of the adult population, the participation rates by
education for non-students differ very little from the overall distribution for
all adults.
Age: Participation rates are roughly constant (in the low to mid- twenty
percent range) until age 55 when they begin to tail off. The highest participa-
tion rate, 27 percent, occurs in the 35-44 year bracket, perhaps reflecting
increased attendance among families with children.
Gender: Women are slightly more likely to attend than are men. Coupled
with the fact that there are more women in the adult population than men,
this means that among visitors to art museums, women outnumber men by a
ratio of 6 to 5.
Race: Whites are roughly twice as likely to have visited an art museum
in the previous year as African- Americans. Much of this difference may be
attributed to differences in education level or income level. On average, other
racial and ethnic groups have a participation rate that is approximately the
same as that of whites.
Geographic Distribution: Adults who lived in a Standard Metropolitan
Statistical Area (SMSA), a U.S. Census Bureau designation used here as a
rough indicator of urbanization, had slightly higher than average participa-
tion rates whether or not they actually lived in the SMS A's central city (e.g.,
Boston as opposed to one of its suburbs). Adults who lived outside an SMSA
had a participation rate that was only two- thirds of the average. Roughly half
of the audience was made up of individuals who lived in an SMSA but not
in the central city.
An analysis of the population by region of the country shows interesting
variations.7 While the participation rates for the Northeast, Midwest and
South are all roughly 20 percent, the rate in the West is 31 percent. An
analysis by subregion shows that New England's participation rate is some-
what higher than average, but that the Mountain and Pacific states have
considerably higher participation rates.
High participation rates in the West are centered in the large metropolitan
8
The Audience for American Art Museums
areas. According to special Census Bureau tabulations for selected metro-
politan areas, the highest metropolitan-area participation rates are all in the
western states: 41 percent in the San Francisco Bay area, 28 percent in the
Los Angeles area, 38 percent in other central cities in SMS As in the West,
and 33 percent outside of the central cities in the same SMS As. In addition,
Boston has a participation rate of 26 percent, Baltimore/Washington, D.C.
26 percent, Chicago 27 percent, and cities in Texas 3 1 percent.
The available data for selected states reflect the regional figures in certain
cases a 32 percent participation rate in California and 27 percent in Mas-
sachusetts, for example. But the data also point out some less expected results
in light of the regional aggregates: 26 percent in Texas and 31 percent in
Virginia. One wonders if the high participation rate for Virginia is a function
of the easy accessibility of the national museums in Washington, D.C, to the
bulk of Virginia's urban population.
This, in turn, suggests an important possible explanation for the differ-
ences in participation rates for each of the geographic variables. Is the
variation in participation better explained by the geographic distribution of
museums than by geographic differences in the population? In other words,
to what extent is attendance a function of the supply of museums rather than
of the demand for museums inherent in the demographics of particular
populations?
Occupation: Participation rates across this variable range from a low of
9 percent for operatives (machine operators) to a high of 49 percent for
professionals. Both the managerial and professional categories show par-
ticipation rates well above the overall average. But because both categories
also have higher than average incomes and education levels, looking at
occupation by itself may mask the effect of these other important variables.
Up to this point, this analysis of the 1985 SPPA data has been a relatively
straightforward one, based on the demographic variables that are commonly
cited as important in analyzing audience participation in the arts and across
which significant differences in participation rates are, in fact, observed. But
this group of variables has a very interesting common property: they are all
variables over which neither the individual museum nor any arts funding
agency has any influence (except, perhaps, by actually moving the museum).
It is difficult, for example, to imagine the museum that would be in a
position to increase the level of formal education or income of its potential
audience in order to increase the local participation rate. We are left with the
impression that potential visitors are prisoners of their own demographics or
that museums are prisoners of the demographics of their potential local
audiences. While this may in a sense be true in the aggregate, it does not help
the individual decisions made by potential visitors in choosing whether or
not to attend a museum.
To be sure, a demographic analysis will help to document that the
audience is much larger than had been hoped or smaller than had been feared,
or that particular segments of the population are not being reached as much
as the museum might like. But its usefulness in suggesting how a museum
can go about changing its audience demographics is limited. It can indicate
if the overall demographics of the audience have changed over time, but
attributing those changes to specific interventions is difficult. Change in
audience composition is a slow, resistant process. A demographic analysis
of the audience is descriptive rather than prescriptive, and one should resist
the temptation to conclude that one knows more than one actually does about
audience behavior and motivations when armed with these demographic
results.
Comparing Participation Rates:
The Americans and the Arts Studies
In 1973, 1975, 1980, 1984, and 1987 the National Research Center of
the Arts, an affiliate of Louis Harris and Associates, conducted the Americans
and the Arts studies.8 These studies have received much visibility within the
arts advocacy community, particularly for their high estimates of attendance
at, and support for, artistic activities. How do their results compare with those
from the SPPA?
Table 2 presents a comparison of the key participation rates calculated
in the 1985 SPPA and the 1984 Harris study. Harris reports an overall art
museum participation rate of 58 percent, just slightly more than five adults
in nine, which is two and a half times the 1985 SPPA participation rate. Art
museums and the theatre are the two sectors that show the greatest dis-
crepancy between studies: 36 percentage points in the case of museums (a
58 percent participation rate in Harris versus 22 percent in the SPPA) and
37 percentage points for theatre (a 60 percent participation rate in Harris
versus 23 percent in the SPPA). Moreover, when the two studies' participa-
tion rates are compared for each separate educational level, the discrepan-
cies are very large at each level (27% v. 4% at the lowest reported levels).
What accounts for these large discrepancies?
John P. Robinson and his colleagues have carefully compared SPPA
10
The Audience for American Art Museums
results with the Harris figures.9 They point to several factors that help to
explain part of the difference:
• The placement and wording of the questions, in the Harris survey in
particular, may tempt respondents to give artificially high responses
so they will not appear to be "uncultured."
• In presenting aggregate figures, the Harris underweights the lowest
educational groups in proportion to their true weight in the population.
• Harris' use of telephone interviews with quota sampling and a lower
response rate than the Bureau of the Census achieved in SPPA
combine to suggest that there may have been selection biases that led
to respondents who were simply more likely to be attenders than a
random cross-section of the population.
From a technical standpoint the SPPA studies are considerably more
defensible, and, therefore, their results are to be taken more seriously. More-
over, it would be a mistake to focus too much on technique and lose sight of
common sense. Before the Americans and the Arts series began, the art
museum world dared not hope that it would one day discover it was already
reaching a substantial proportion of the adult population each year. When the
Harris studies suggested this possibility, the results were first treated with
gratified astonishment and then were gradually incorporated into the estab-
lished canon of arts policy "knowledge." The SPPA data indicate that the
initial skepticism had a lot more truth in it than arts advocates eventually came
to believe. The 58-percent participation rate is simply too high.
Comparing Participation Rates:
An International Perspective
Americans seem to have the view that attendance at artistic events is
much more ingrained in the culture of other countries, particularly in Western
Europe, than it is in the United States. How do American art museum
participation rates compare to those in other countries?
Although cross-national comparison in arts policy is plagued by the wide
variation in definitions and approaches across both countries and cultures,10
the variation in what is considered to be an art museum or an art gallery is
much smaller than similar variations might be within other artistic sectors.
Even so, important differences in both surveying procedures and definitions
of key demographic categories must be taken into account.
Table 3 compares participation rates from audience studies in Great
Britain, France, Sweden, and the Canadian province of Quebec to the results
11
J. Mark Davidson Schuster
Table 2
A Comparison of Participation Rates
1985 SPPA and 1984 Harris
1985 SPPA
Question: During the last 12 months did
you visit an art gallery or an art
museum?
Overall
22%
1984 Harris
Question: How many times, if any,
did you visit art museums that exhibit
paintings, drawings, sculpture, etc.,
during the past 12 months?
58%
Overall
Income
Income
$4,999 or less
16%
43%
$7,500 or less
$5,000-49,999
11%
53%
$7,501^$15,000
$10,000-$14,999 ■
15%
58%
$15,001-$25,000
$15,000-$24,999
19%
62%
$25,001-$35,000
$25,000-$49,999
28%
67%
$35,001-$50,000
$50,000 or more
45%
76%
$50,001 or more
Education
Education
Grade School
4%
27%
Eighth Grade
Some High School
11%
High School Grad
14%
46%
High School Grad
Some College
29%
70%
Some College
Four-year College Grad
45%
78%
College Grad
Graduate School
55%
Age
Age
18-24 years
22%
66%
18-29 years
25-34 years
25%
35-44 years
27%
62%
30-49 years
45-54 years
23%
55-64 years
18%
53%
50-64 years
65-74 years
16%
46%
65+ years
75+ years
10%
Gender
Gender
Female
23%
57%
Female
Male
21%
60%
Male
Race
50%
Race
Black
11%
Black
White
23%
59%
White
Other
25%
64%
Hispanic
12
The Audience for American Art Museums
Table 2 (Continued)
Urbanization
Size of Place
SMSA Central City
SMSA not Central City
Outside SMSA
25%
26%
14%
66%
58%
49%
Cities
Suburbs
Town/Rural
Sources: "Survey of Public Participation in the Arts," 1985. National Research Center of
the Arts, Americans and the Arts, 1984.
from the 1985 SPPA.11 This comparison shows very similar overall participa-
tion rates among these countries, with the exception of Sweden where
participation is slightly higher: in the United States, 22 percent for art
museums and art galleries; in Great Britain, 29 percent for all museums and
19 percent for art exhibitions; in France, 30 percent for all museums (net of
historic monuments) and 21 percent for temporary art exhibitions; and in
Quebec, 23 percent for art museums and 17 percent for other museums. In
Sweden, on the other hand, the participation rate is 31 percent. All of these
participation rates were measured with respect to attendance in the preceed-
ing twelve months. Where the participation rate is somewhat higher, it
appears that the difference can be attributed to the broader range of museums
included in the surveys.
The similarities across these studies are not limited to overall participa-
tion rates. With the exception of some higher participation rates in the
Swedish study, when the participation rates are disaggregated over various
demographic variables they remain remarkably similar across the other
studies. This is particularly true when differences as to which museums are
being considered are taken into account.
In many respects the French study most resembles the SPPA surveys; it
was commissioned to document the participation of the French population
in a wide variety of leisure and artistic activities. The British survey is more
akin to the Harris surveys, concerning itself with attitudes towards public
funding of the arts and correlating those opinions with participation rates and
demographic factors. (Because of this emphasis, the British study includes
several variables that are not available in other studies indicating, perhaps,
the relative politicization of arts policy questions in Great Britain: trade union
membership, voting intention by political party, support for or opposition to
public funding of various art forms, and whether or not the respondent had
heard of the Arts Council of Great Britain.)12
13
Table 3-A
A Cross-National Comparison of Participation Rates:
The United States and Great Britain
United States -— 1985 SPPA
Question: During the last 12 months did
you visit an art gallery or an art
museum?
Great Britain — 1981 MORI
Question: On another subject, which of
these have you personally been to in the
past 12 months? museum?
Overall
Age
18-24 years
25-34 years
35-44 years
45-54 years
55-64 years
65-74 years
75+ years
Gender
Female
Male
Occupation
Museum
Art Exhibition
Participation
Participation
Participation
Rate
Rate
Rate
22%
29%
19%
Overall
Age
22%
21%
17%
18-24 years
25%
34%
17%
25-34 years
27%
35%
21%
35-49 years
23%
18%
29%
20%
50-64 years
16%
20%
18%
65+ years
10%
23%
21%
27%
31%
18%
20%
Gender
Female
Male
Class
Professional
49% '
44%
37%
Upper
Middle Managerial
37%
39%
27%
Lower Middle
Sales/Clerical
27%
27%
15%
Skilled Manual
Craftsman
14%
19%
9%
Semiskilled and
Operatives
9%
Unskilled Manual
Laborers
10%
Service Workers
16%
Subregion
Region
New England
25%
16%
12%
Scotland
Mid Atlantic
19%
27%
14%
North
East Northcentral
20%
30%
19%
Wales/Midlands
West Northcentral
22%
32%
20%
South
South Atlantic
19%
34%
28%
Southeast
East Southcentral
10%
West Southcentral
23%
Mountain
28%
Pacific
32%
Sources: "Survey of Public Participation in the Arts," 1985. Market and Opinion
Research International survey (quota sample of 973 adults age 18+ interviewed at 51
points throughout Great Britain. Class is of household head.) conducted for BBC
"Panorama," 26 November 1981.
14
Table 3-B
A Cross-National Comparison of
Participation Rates: France
France Pratiques Culturelles des Frangais '81
Questions:
Overall
Education
No Diploma
Elementary School Grad
Certificate
Bachelor's Degree or more
Age
15-19 years
20-24 years
25-39 years
40-59 years
60-69 years
70+ years
Gender
Female
Male
Socio-Professional Category
Agriculture
Small Merchant/ Artisan
Wholesale and Industrial
Professional and Managerial
Middle Class
Clerical
Foreman
Laborer or Service Worker
Urbanization
Rural
Less than 20,000 residents
20,000-100,000 residents
More than 100,000 residents
Paris
Paris Region
1) Since December '80, have
2) Since December '80
you visited a museum?
have you visited a
temporary exhibition of
painting or sculpture
Museum
Exhibition
Participation
Participation
Rate
Rate
30%
21%
14%
7%
21%
10%
34%
25%
57%
49%
40%
26%
38%
27%
34%
29%
28%
18%
27%
15%
14%
9%
30%
22%
30%
21%
17%
8%
32%
26%
49%
33%
61%
53%
53%
40%
32%
28%
24%
18%
20%
13%
20%
13%
26%
19%
28%
21%
33%
23%
56%
50%
47%
36%
Source: Pratiques Culturelles des Frangais, survey (stratified quota sample of 3,984
individuals age 15 or over) conducted by ARCmc for the French Ministry of Culture
(Paris: Dalloz, 1982).
15
Table 3-C
A Cross-National Comparison of Participation Rates:
Sweden
Sweden Kulturstatistik
Percentage of the population age 16-74 years that visited a museum in the previous 12
months (1982/83)
Overall
Education
Pre-Secondary
Secondary
Post-Secondary
Age
16-24 years
25—44 years
45-64 years
65-74 years
Gender
Female
Male
Socio-Economic Group
All Workers
Unskilled and Semi-Skilled Workers
Skilled Workers
All Salaried Employees
Junior Salaried Employees
Intermediate Level Salaried Employees
Senior Salaried Employees
All Entrepreneurs
Entrepreneurs Without Employees
Entrepreneurs With Employees
Farmers
Regions
Stockholm
Goteborg and Malmo
Other Large Cities and Towns
Other Southern and Central Sweden
Northern Densely Populated Areas
Northern Sparsely Populated Areas
Source: Statistics Sweden, "Level of Living Survey 1982/83," as reported in Official
Statistics of Sweden, Cultural Statistics: Activities, Economy and Cultural Habits 1980-
1984 (Stockholm: Statistics Sweden, 1987), p. 340.
16
Museums and
Art Exhibitions
Exhibitions
and Art Galleries
(Other than Art)
Participation
Participation
Rate
Rate
31%
45%
20%
32%
31%
46%
61%
74%
25%
51%
32%
51%
35%
41%
29%
29%
34%
45%
28%
45%
19%
34%
19%
33%
20%
38%
48%
59%
38%
47%
49%
62%
65%
75%
29%
37%
32%
41%
40%
47%
17%
27%
41%
56%
35%
53%
30%
44%
26%
37%
31%
45%
21%
30%
Table 3-D
A Cross-National Comparison of
Participation Rates: Quebec
Quebec CROP '83
Percentage of the population having visited a museum at least once in 1983
Art Museum Other Museums
Participation Participation
Rate Rate
Overall 23% 17%
Education
0-7 years
8-11 years
12-15 years
16+ years
Income (Canadian $)
$10,000 or less
$10,000-$19,999
$20,000-$29,999
$30,000 or more
Age
15-17 years
18-24 years
25-34 years
35-44 years
45-54 years
55+ years
Gender
Female
Male
Source: Ministere des Affaires Culturelles du Quebec, ChiffresaL'Appui, Bulletin du
Service de la Recherche et de la Planification, Vol. 2, No. 2, May 1984, summary of a
public opinion poll (sample of 2,316 individuals age 15 or over) conducted by the Centre
de Recherche sur l'Opinion Publique (CROP) in 1983.
10%
7%
17%
14%
28%
20%
48%
29%
13%
6%
24%
16%
25%
17%
34%
25%
26%
36%
23%
18%
27%
16%
29%
22%
22%
15%
18%
10%
24%
16%
23%
18%
17
J. Mark Davidson Schuster
Both the British and the French surveys separated attendance at "art
exhibitions" or "temporary exhibitions of painting or sculpture" from more
general attendance at museums. In order to understand the stable, core
audience for art museums, it would be necessary to identify and separate out
those individuals who only attended because of a particular exhibition,
perhaps a well- advertised "blockbuster" show, and do not normally consider
themselves part of the museum's audience. However, the 1985 SPPA data
do not allow this distinction to be made.
While far from conclusive, all of these reports taken together suggest that,
at least in Western countries, museums may well be serving similar segments
of their national populations. Art galleries, art exhibitions, and art museums
relate more readily to certain individuals than to others and, indeed, are the
institutional creation of certain social groups. In large part this receptivity
seems to be a function of the same demographic factors. The extensive
Swedish social welfare state, greater citizen involvement in communal
activity, and a higher educational level may well explain the higher participa-
tion rates in Sweden.
This comparison does not speak, however, to the relative frequency of
attendance. It is certainly possible that while the cross- section of the popula-
tion being served is quite similar across countries, the frequency of attend-
ance might be rather different in places where "museum going" has become
more a part of daily life. But the Limited data we have on this question suggest
that frequency of attendance is not higher in these other countries. Against
the SPPA mean of 3.42 visits per visitor to art museums, for example, the
French study reports a mean of 3. 1 13 and the Quebec study a mean of 2. 1 1 .14
Neither the British nor the Swedish studies report any data on frequency of
attendance.
Participation Rates:
Controlling for "Third" Variables
In order to find better answers to questions about the effect of certain
demographic variables (e.g., are the high participation rates for upper-income
groups a function of that income level or of the fact that upper-income
individuals also tend to be more highly educated?), it is necessary to control
for and separate out the effects of other variables that might confound the
results. This section discusses the findings of two different methods of
controlling for these other variables: multiple classification analysis and logit
analysis.
18
The Audience for American Art Museums
Multiple Classification Analysis
Multiple classification analysis (MCA) is a mathematical method for
calculating the net value of variables whose behavior one is trying to
explain — in this case attendance — for each value of possible key explanatory
variables — such as various levels of income or education. MCA controls for
the contribution of other explanatory variables — such as race or marital
status — by averaging out their effects.15 Conventionally, the variable whose
behavior one is trying to explain is called the "dependent" variable and the
various explanatory variables are called the "independent" variables.
In studying museum attendance patterns, MCA estimates the addtional
effect of each independent variable on the participation rate. MCA then adds
(or subtracts) this additional effect to (from) the participation rate to create
an "adjusted" participation rate. This adjusted rate reflects, as much as is
mathematically possible, the pure effect of each independent variable on the
participation rate. In this way, for example, differences in the participation
rate that result from differences in income alone can be isolated.
Table 4 summarizes the results of a multiple classification analysis of
participation rates that considered five primary independent variables — in-
come, education, age, gender, and region — and four other independent
variables — marital status, number of children, race, and number of hours
worked per week. The first column reports the participation rates for the main
independent variables when each is considered by itself (prior to MCA); the
second column reports the adjusted participation rate for each variable once
it has been controlled for the other variables through MCA.
Income: When viewed in isolation, income appeared to be a useful pre-
dictor of museum attendance: participation rates ranged from 1 1 percent to
45 percent. But when one controls for the influence of the other variables,
the adjusted participation rate is roughly constant — approximately 20 per-
cent— over the lowest five income groups, and finally jumps to 32 percent
in the highest income group. This important result indicates that it is too
simple to say that income is an important predictor of museum attendance;
it is highly correlated with other variables that are better predictors, par-
ticularly education.
Education: After adjustment, these participation rates are slightly closer
together than before, ranging from 7 percent for those with a grade school
education to 54 percent for those with a graduate school education. But the
remaining 47 percentage point spread indicates that controlling for other
variables hardly diminishes education's ability to predict attendance. This
result reinforces the importance of education as the key demographic predic-
tor of attendance.
19
Table 4
Comparison of Unadjusted and Adjusted
Participation Rates, 1985
Question: During the last 12 months did you visit an art gallery or an art museum?
Overall
Income
$4,999 or less
$5,000-$9,999
$10,000-$ 14,999
$15,000-$24,999
$25,000-$49,999
$50,000 or more
Education
Grade School
Some High School
High School Grad
Some College
Four-year College Grad
Graduate School
Age
18-24 years
25-34 years
35^44 years
45-54 years
55-64 years
65-74 years
75+ years
Gender
Female
Male
Region
Northeast
Midwest
South
West
Source: "Survey of Public Participation in the Arts," 1985.
Notes: * In this example, multiple classification analysis (MCA) is used to control for
five main independent variables — income, education, age, gender, and region — and four
other independent variables — marital status, number of children, race, and number of
hours worked per week. Each adjusted participation rate separates the effect of one vari-
able by controlling for the effect of the others.
** These rates differ slighdy from those in Table 1 because missing values necessitated
dropping more cases from the analysis.
20
Participation
MCA Adjusted*
Rate
Participation Rate
22%
22%
16%
22%
11%
19%
15%
19%
19%
20%
28%
24%
45%
32%
4%
7%
8%**
9%
14%
15%
29%
28%
45%
43%
55%
54%
22%
23%
26%**
25%
27%
24%
23%
21%
18%
20%
17%**
21%
10%
14%
23%
24%
21%
20%
21%**
21%
21%
21%
19%
21%
31%
28%
The Audience for American Art Museums
Age: After adjustment, the effect of age on attendance nearly disappears.
The adjusted participation rates are fairly constant, from 20 to 25 percent,
and they do not fall off until more than 75 years of age (14 percent).
Gender: The adjusted ratio of female to male participation rates is 24:20,
whereas it was 23:21 before adjustment. Because women live longer and tend
to have lower educational levels than men, on average, when the effects of
those variables are removed, it becomes clear that women are even more
likely to attend, although the difference is not a dramatic one.
Region: Table 4 shows that before MCA, the West had the nation's
highest participation rate (31 percent). But is this high rate due to inherent
regional differences, or is it due to the fact that incomes are perhaps higher
in these states, or that educational levels are higher, or that people in the West,
on average, are younger? Controlling for the other independent variables
decreases the participation rate in the West by 3 percentage points (to 28
percent), and raises the participation rate in the South by 3 percentage points,
to the level of the Northeast and the Midwest (21 percent).
Of course, one cannot tell from these results alone whether the remaining
difference (7 percentage points between the West and other regions) is due
to some inherent "regionalness" or to some other variable that has not yet
been included in the analysis (such as the geographic distribution of mu-
seums). In this sense, the adjusted participation rates should not be thought
of as the "correct" rates, but as an attempt to isolate the effect of one
explanatory variable in the context of other, specified explanatory variables.
The distinction between the one-variable-at-a-time demographic analysis
in the previous section and multiple classification analysis is a reflection of
the way in which the analysis will be used. The former emphasizes predic-
tion— what is the probability that someone who lives in the West will be an
attender? — while the latter emphasizes explanation — How much does living
in the West contribute, by itself, to the participation rate? How well the MCA
explains variation in attendance levels when all independent variables are
used simultaneously can be measured with the R2 statistic,1" which is .147
here, indicating that 14.7 percent of the variation in participation rates is
explained by the independent variables.
Logit Analysis
While multiple classification analysis focuses on the average value of the
dependent variable for each value of each independent variable — explaining,
fR is a "goodness of fit" measure of how well a model predicts the variation in the dependent
variable. Measured on a scale of 0 to 1, the closer R2 is to 1, the better the fit (the better the
model predicts).
21
J. Mark Davidson Schuster
for example, what the participation rate is for high-income individuals while
controlling for other independent variables — it is often instructive to consider
instead the contribution that increases in each independent variable make to
the dependent variable. For example, to determine the relationship between
age and attendance, it would be useful to calculate how much the participa-
tion rate increases (or decreases) on average for every additional year of age.
In measuring museum attendance, the underlying dependent variable is
dichotomous: each person interviewed either attended an art museum in the
previous year or did not attend, and the individual's attendance can be
expressed mathematically with a one (if he or she did attend) or with a zero
(if he or she did not attend). To test the mathematical relationship between
this type of dependent variable and a series of independent variables, a
variation of regression analysis, called logit analysis, is often used. (Logit
analysis is described further in the Appendix, where the actual mathematical
results of the logit analyses used in this study are reported.) Logit analysis
uses the collected data on the attendance pattern of the surveyed individuals
to predict what the probability of attendance for another individual with a
particular set of characteristics would be.16
Without delving into the intricate mathematics of logit analysis, it is
possible to present the essential idea with a simple example. Consider two
variables: whether or not an individual attended an art museum in the
previous year and that individual ' s number of years of education. Given what
we already know about the relationship between these two variables, we
expect that individuals with higher levels of education are more likely to
attend. Suppose that a sample of 20 individuals revealed that 10 of them had
attended and that 10 had not. Graphing these two variables for these 20 cases
might lead to a graph like Figure la. Each square in Figure la represents one
surveyed individual and plots the number of years of education versus
whether or not that person attended an art museum in the previous year.
Figure 1a.
Sample Attendance Data Graphed by Years of Education
Yes = l
Attendance
No = 0
■ ■
6 8 10 12 14 16
Years of Education
22
The Audience for American Art Museums
Using these data as a starting point, logit analysis fits an "s- shaped" curve
to the data. The result would look like Figure lb. The curve is a simplified
mathematical summary of the relationship between the two variables, and its
shape reflects the fact that individuals with fewer years of education are much
less likely to have attended an art museum than are individuals with more
years of education. Note that the vertical axis of Figure lb is labelled
"Probability of Attendance." Thus, in this example, logit analysis is using
the actual attendance pattern in the survey data to predict the probability of
attendance for other individuals whose educational levels are known but
whose attendance patterns are unknown. The height of the curve can be
interpreted as either the relative percentage of individuals at each level of
education who are predicted to attend or the probability that an individual
with a particular level of education will attend.
Figure 1b
Logit Curve Fitted to Sample Attendance Data
Yes = l «
Probability
of 0.5
Attendance
No = 0 '
■ ■ ■■■■■■!
i
i
!■■■■■■ ■ ■ 1
0 2 4 6 8 10 12 14 16 18
Years of Education
Keeping this intuitive understanding in mind, we can now turn to an
analysis of the actual data. The results summarized here are from a model
that predicts the probability of museum attendance as a function of income
level, age, race, gender, educational level, whether the individual lives in a
Standard Metropolitan Statistical Area or its central city or outside a SMSA,
and whether or not the individual is a student. (The actual mathematical
results of running this logit analysis are reported in Table A of the Appendix.)
It is not necessary to understand the mathematics that lead to logit results
in order to be able to interpret the key findings. Logit results can be used to
help us answer three rather straightforward analytical questions concerning
museum attendance patterns:
Is an increase in each independent variable associated with an in-
crease or a decrease in the participation rate?
• How strong is the increase or decrease in each case? Is the increase
or decrease that is detected in the sample survey data strong enough
23
J. Mark Davidson Schuster
so that one can conclude that the same relationship holds for the entire
adult population of the United States?
• What do the results tell us about the probability of attendance for
particular individuals whose demographic characteristics are known?
Asking these questions of the logit analysis reported in Table A of the
Appendix leads to a number of interesting results. The signs of six of the
thirteen independent variables are negative, indicating that there is an inverse
relationship between these variables and the probability of attendance. The
negative coefficient of age indicates that as an adult gets older, all else being
equal, his or her probability of attendance goes down. The signs for Blacks
and other minority racial groups are also negative, indicating that the prob-
ability of attendance for these racial groups is lower than the probability of
attendance for whites. But the coefficient for "other" minority racial groups
is not statistically significant, so the evidence is not strong enough to be able
to conclude that in the overall population minority groups other than Blacks
have a probability of attendance that is actually different from that of whites.
The signs of the seven other variables are positive. The positive coeffi-
cients for the two highest income groups indicate that individuals in these
groups (incomes of $25,000 and above) have a higher probability of attend-
ance than individuals in lower income groups. The coefficient of education
is also positive, indicating that for every additional year of formal education,
the probability of attendance goes up. The positive coefficient of the gender
variable indicates that the probability of attendance for women is higher than
the probability for men. And the positive coefficients of the geography
variables indicate that people who live in Standard Metropolitan Statistical
Areas (i.e., relatively urbanized areas) are more likely to attend than people
who live outside of these areas. All of the positive coefficients are statistically
significant, indicating that these results would be expected to be replicated
in the population at large.
A summary measure of how well the logit curve actually fits the data is
R2. For this analysis R2 = .16; sixteen percent of the variation in the
dependent variable is explained by the independent variables in this model.
While the R2 statistics for both the multiple classification analysis and the
logit analysis seem low, it is important to keep them in mind as benchmarks
against which further analyses and other studies might be judged. As our
ability to explain museum attendance improves, the predictive capability of
our models will increase. The next section of this study, for example, will
report the results of a better logit model that includes variables that measure
the degree of an individual's socialization into the arts.
24
The Audience for American Art Museums
Finally, logit results can be used to predict the probability of attendance
for particular individuals whose characteristics are known. For example, the
probability of attendance for a white female who is 40 years old, has 16 years
of formal education, lives in the central city of an SMSA, has an income
between $15,000 and $24,999, and is not currently a student is estimated at
52 percent. The probability is calculated by inserting the actual values of the
variables for this sample individual into the equation that results from the
logit analysis.
These results can also be used to graph the relationship between the
probability of attendance and each of the independent variables. Figure 2a is
a graph of the relationship between probability of attendance and educational
level for the sample female attender; it is the result of letting years of
education vary while keeping all of the other variables the same as in the
example above. Overall, the graph shows the probability of attendance rising
dramatically across levels of education to a high of nearly 78 percent, a
striking depiction of the importance of education in explaining participation.
The specific example calculated above occurs on the right hand side of Figure
2a at the point where education is equal to 16 years and the probability of
attendance equals 52 percent.
Similarly, Figure 2b fixes all of the independent variables except age to
their values for the sample individual in order to graph the relationship
between probability of attendance and age. This graph shows that once the
other independent variables have been controlled for, the probability of
attendance tends to decline slightly as age increases.
In summary, using logit analysis affords a different view of the data; it
focuses on the contribution that each independent variable makes to the
probability of attendance and leads to a precise measurement of that con-
tribution. For most purposes, however, the most important contribution this
analysis makes to an understanding of these relationships is what it tells us
about the direction and the strength of these relationships. And the avail-
ability of the SPPA data allow researchers in the field to explore these
questions for the first time.
25
J. Mark Davidson Schuster
Figure 2a
Logit Analysis— Graph of the Probability of Attendance
by Education for Sample Individual
l
0.9
0.8
Attendance
p p
8M
o 0.5
S 0.4
£ 0.3
On
0.2
0.1
°0 2 4 6 8 10 12 14 16 18
Years of Education
0.9
0.8
V
<y
S
0.7
A
-o
e
4>
0.6
•t— '
<
o
0.5
>>
■**
15
0.4
«
A
o
1m
0.3
e-
0.2
0.1
0
Figure 2b
Logit Analysis— Graph of the Probability of Attendance
by Age for Sample Individual
10
• •-
30
50
70
90
Age
26
The Audience for American Art Museums
Part II: Socialization and Art Museum Attendance
The fact that the model presented in Part I had such a low ability to predict
the probability of attendance suggests that there must be factors in an
individual's background other than simple demographics that help explain
attendance at art museums. One place to look for additional explanatory
variables is to the possible role played by socialization activities such as art
lessons.
This section focuses on three SPPA socialization questions that are most
likely to be linked to attendance at art museums: whether or not, and at what
ages, the respondent had ever taken lessons in the visual arts; whether or not,
and at what ages, the respondent had taken art appreciation classes; and
whether or not, and the frequency with which, parents had taken the respon-
dent to art museums.
An analysis of these questions reveals that all three of these factors show
a strong relationship with increased attendance (Table 5). The overall
attendance rate of 22 percent rises to 45 percent for those who had taken
visual arts lessons. For those who had not taken these lessons, the participa-
tion rate is only 15 percent. The pool of visitors to art museums during the
previous year is divided approximately in half between those who have
taken art lessons and those who have not. Having taken a class in art
appreciation or art history raises the participation rate to 51 percent, but
among visitors the ratio of those who had not taken a class to those who had
is 5:4. Yet, actual museum visitors are split in half between those who had
taken lessons and those who had not.
Attendance is highest for those whose first lessons were either during the
elementary school years or during the college years (62 and 54 percent,
respectively), suggesting that both earliness of socialization and the indi-
vidual's explicit choice of a socialization experience — as opposed to an
educational or parental requirement — can be important factors in future
attendance.
Similarly, the influence of these classes is smaller during the high school
years (39 percent participation) than in either elementary school (66 percent)
or in the years after high school (57 and 6 1 percent) . The figure of 66 percent
attendance for adults who had taken an art appreciation course in elementary
school is one of the highest art museum participation rates found in the SPPA
data when considering the effects of a single independent variable.
The participation rate is 55 percent for those who remembered having
attended art museums frequently with their parents. The rates for those who
27
Table 5
Socialization and Attendance at Art Museums
and Art Galleries, 1985
Question: Have you ever taken lessons or a class in visual arts such as sculpture, painting,
print making, photography, film making, etc.?
Participation
rate
Per 1,000 Adults
Number
Number in
category
All adults
who had taken lessons
who had not taken lessons
22%
45%
15%
219
112
110
1,000
248
752
Adults who first took
lessons at less than 12 years
12-17 years
18-24 years
25+ years
62%
37%
54%
40%
21
43
30
18
33
115
56
44
Adults who attended an art museum
50% had taken lessons
50% had not taken lessons
110
109
219
219
Adults who had not
attended an art museum
18% had taken lessons
82% had not taken lessons
137
644
781
781
Question: Have you ever taken a
class
in art appreciation or art history?
All adults
who had taken a class
who had not taken a class
' 22% attended
51% attended
15% attended
219
99
122
1,000
194
806
Adults who first took a
class at less than 12 years
12-17 years
18-24 years
25+ years
66% attended
39% attended
57% attended
61% attended
4
26
60
8
6
68
105
14
Adults who attended an art museum
45% had taken a class
55% had not taken a class
98
121
219
219
Adults who had not
attended an art museum
12% had taken a class
88% had not taken a class
95
686
781
781
Question: Did your parents — or other adult members of the household — take you to art
museums or galleries often, occasionally , or never?
Adults who had attended
frequently with parents
55% attended
26
47
occasionally with parents
35% attended
105
297
never attended with parents
14% attended
92
656
Source: "Survey of Public Participation in the Arts," 1985.
28
The Audience for American Art Museums
visited occasionally with their parents and those who never visited with then-
parents are 35 percent and 14 percent, respectively.
A logit model was run to see what happens to the probability of atten-
dance participation rate when these three socialization factors are accounted
for simultaneously, along with the demographic variables considered earlier.
The results are reported in Table B in the Appendix. All of the socialization
variables, along with education, turn out to be highly significant statistically.
Age, the race variable for Blacks, and the highest income group variable also
have coefficients that are statistically significant.
All of the socialization variables add considerably to the probability of
attendance. This can be most clearly seen using a graph like the one presented
earlier. Figure 3 shows the relationship between the probability of attendance
and education level for the sample individual with none of the three socializa-
tion experiences (Example no. 1) and with all three (visual art lessons,
attendance with parents, and an art appreciation course) of the socialization
experiences (Example no. 2). While the probability of attendance still
increases with higher levels of education as before, the increase in the
probability of attendance due to socialization is very striking.
This model is a better one than the model formulated in Part I, which
used only demographic variables. The proportion of the variation explained
Figure 3
Logit Analysis— Graph of the Probability of Attendance by Education
for Sample Individual with Socialization Variables
14
c
«
■o
c
o
6 8 10
Years of Education
12
14
16
Example no. 1
No Socialization Experiences
• Example no. 2
All Three Socialization Experiences
29
J. Mark Davidson Schuster
improved from 16 percent to 22 percent. The improvement was primarily in
the model's ability to predict correctly those who actually attend (47 percent
in the socialization model as opposed to 27 percent in the raw demographic
model), but this model is still a long way from what one would like to have
in a predictive model.
Part III: Unsatisfied Demand and Barriers to
Attendance
If two adults out of nine attended an art museum or an art gallery in the
previous year, seven did not. Who are the individuals who do not attend
museums? Why? Who would like to attend more? Who are the potential
members of the museum audience? The answers to these questions are of
concern both to museums that would like to market their services more
effectively and to funding agencies that would like to expand the reach of
arts organizations into previously unserved or underserved segments of the
community.
Unsatisfied Demand
SPPA data on the responses of adults who said they would like to attend
art museums more often must be approached with a degree of skepticism for
two reasons. First, respondents' answers are based on hypothetical situations
rather than on actual behavior. It is easier to say you would like to go more
often than to actually exert the effort to go. Second, when attention is
restricted to demographic variables only, they become the only possible
explanations for unsatisfied demand or non-attendance that are readily
available. This again runs the risk of concluding that survey respondents are
prisoners to their demographics.
Table 6 shows that nearly a third of American adults would like to attend
art museums more often. Yet 58 percent of the individuals who are already
attenders would like to go more often, while only 23 percent of non-attenders
would like to attend more frequently. (Interestingly, this percentage remains
quite high across participation levels: of those individuals who indicated that
they had attended an art museum two or three times in the previous month,
62 percent indicated that they would like to go more during a year; of those
who attended six or more times in the previous month, 52 percent indicated
they would like to attend more often.) But because of the large number of
non-attenders in the adult population, nearly 60 percent of those who would
30
Table 6
Unsatisfied Demand for Attendance at Art Museums
and Art Galleries, 1985
Question: Few people can do everything they would like to do. But if you could do any of
the things listed on this card as often as you wanted, which ones would you do more often
than you have during the last 12 months?
Per 1,000 Adults
Percentage who
Number Who
Number in
checked museums
Checked Museums
Category
Overall
31%
307
1,000
Attendance
Attenders
58%
128
219
Non-Attenders
23%
179
718
Income
$5,000 or less
25%
21
82
$5,000-$9,999
25%
31
126
$10,00O-$14,999
27%
39
143
$15,00O-$24,999
29%
72
247
$25,00O-$49,999
36%
111
308
$50,000 or more
45%
42
94
Education
Grade School
12%
13
110
Some High School
22%
26
118
High School Grad
29%
108
376
Some College
38%
77
203
Four-year College Grad
44%
48
220
Graduate School
44%
36
82
Age
18-24 years
34%
55
161
25-34 years
35%
82
238
35-44 years
35%
63
182
45-54 years
27%
36
132
55-64 years
28%
36
130
65-74 years
26%
25
97
75+ years
17%
10
59
Gender
Females
33%
173
528
Males
28%
134
472
Race
Black
25%
27
108
White
32%
277
873
Other
18%
3
19
Source: "Survey of Public Participation in the Arts," 1985.
31
J. Mark Davidson Schuster
like to go more often are currently not attending. The problem for a museum
is that these individuals are considerably more difficult to identify than those
who are already attenders.
As both income and education levels increase unsatisfied demand rises
to a high of four adults out of nine. By income, more than a third of the
individuals with unsatisfied demand can be found in the $25,00O-$49,999
income group; by education, nearly a third can be found among those whose
highest level was graduation from high school. When we examine the effect
of age, unsatisfied demand remains roughly constant at 35 percent for
individuals age 16-44, but then begins to decline.
To determine which demographic variables predict best those individuals
who are most likely to have unsatisfied demand, a logit model was run. The
actual logit results are reported in Table C of the Appendix. Education
emerges from this model as the most important predictor of unsatisfied
demand, with a positive coefficient and the highest level of statistical
significance among the variables tested. The probability of unsatisfied de-
mand rises with the number of years of education and is generally higher at
higher levels of income (except for the $15,000-$24,999 category). The
probability of unsatisfied demand decreases gradually with age. Women are
more likely to have unsatisfied demand than men; whites are more likely to
have unsatisfied demand than Blacks or other racial groups; students are more
likely than non- students; and the probability of unsatisfied demand rises with
increased urbanization.
The results show that while the model does help in identifying the
variables that are most highly significant in a statistical sense, the overall
performance of the model is again very weak. The model only explains 5
percent of the variation in the dependent variable. Although there are
statistically detectable relationships between the demographic variables and
unsatisfied demand, there is a lot more variation in unsatisfied demand that
cannot be accounted for by these demographic variables. Together, these
findings suggest the beginning of an explanation, but they are far from being
determinant.
Barriers to Attendance
Do these results concerning unsatisfied demand reflect a general view
among the population that museums are worthy things to attend and that more
attendance would be preferable to less? Or is attendance actually constrained
by other factors, which, if they were removed, would result in increased
attendance? If the key binding constraints are ones that could be changed by
32
The Audience for American Art Museums
museums, some interesting possibilities could arise for museums that are
trying to decide how to attract new audiences and increase their old audiences.
During one of the six months of the 1985.SPPA survey, respondents were
asked about their reasons for not attending more often. The survey question-
naire offered the interviewers fifteen specific reasons according to which
they coded the oral responses; they could check more than one if several
factors seemed important. The results are summarized in Table 7.
Before examining these results, it is important to realize that few of the
barriers to attendance included in the SPPA are barriers that are within the
direct control of the museums themselves. This is not to say that there are
not important barriers to attendance that are the result of choices made by
museum officials, only that these cannot be documented within the confines
of the SPPA surveys.
Overall, few of these barriers seem to have a serious effect on attendance.
A very small percentage of the adult population cites each one (with the
exception of the vague reasons "not enough time" and "lack of motivation").
Yet, 3 1 percent of the adult population cited one or more of these reasons for
non-attendance. Although many people have reasons for not attending more,
those reasons are diffuse.
A second overall pattern of interest is that for every barrier except "prefer
to watch TV," the percentage of attenders who cite each barrier is greater
than or equal to the percentage of non-attenders. This further reinforces the
finding that unsatisfied demand is greater among those who are already
attenders.
Some of the individual findings deserve more attention. The most fre-
quently cited barrier is not having enough time (13.7 percent of the popula-
tion). One barrier that might have been expected to have been selected more
often is "feeling uncomfortable" — it is often suggested that arts institutions
make it very difficult for the uninitiated to feel that the institution is accessible
to them. Yet, only one-tenth of one percent of the population felt this to be
a problem. (Interestingly, low percentages like this are found across all of
the art forms included in SPPA.)
A moderate percentage of individuals cites "lack of availability" or "too
far to go" as reasons for lower attendance. While it seems that this could be
attributed to the geographic distribution pattern of museums, without further
comparative data on the distribution of respondents we cannot be sure; it is
also possible that these answers were used by respondents to express an
inaccessibility that was part psychological as well as geographical.
To better target potential museum audiences, it will help to take this
analysis one step further and ask, "Of those with unsatisfied demand, what
33
Table 7
Barriers to Attendance at Art Museums
and Art Galleries, 1 985
Question: What are the reasons you did not attend art galleries/art museums more often?
Any other reasons?
Per 1,000 Adults
Percent Citing
Number Citing
Number
Barrier
Barrier
in Category
Tickets sold out
All Adults
0.1%
1
1,000
Attenders
0.4%
1
219
Non-Attenders
0.0%
0
718
Cost
All Adults
4.0%
40
1,000
Attenders
5.7%
13
219
Non-Attenders
3.5%
27
718
Not available
All Adults
6.4%
64
1,000
Attenders
11.4%
25
219
Non-Attenders
4.9%
39
718
Too far to go
-
All Adults
_ 6.7%
67
1,000
Attenders
13.9%
30
219
Non-Attenders
4.6%
36
718
Transportation/Traffic/
Parking problems
All Adults
2.7%
27
1,000
Attenders
3.7%
8
219
Non-Attenders
2.5%
19
718
Crime or fear of crime
All Adults
0.6%
6
1,000
Attenders
0.6%
1
219
Non-Attenders
0.6%
5
718
Feel uncomfortable
All Adults
0.1%
1
1,000
Attenders
0.1%
*
219
Non-Attenders
0.1%
1
718
Poor Quality/Not very good,
etc.
All Adults
0.4%
4
1,000
Attenders
0.9%
2
219
Non-Attenders
0.3%
2
718
34
Table 7 (Continued)
Percent Citing
Barrier
Per 1,000 Adults
Number Citing
Barrier
Number
in Category
Don't have anyone to go with
All Adults
Attenders
Non-Attenders
1.6%
2.2%
1.4%
16
5
11
1,000
219
718
Problem related to a handicap
All Adults
Attenders
Non-Attenders
0.4%
0.5%
0.3%
4
1
3
1,000
219
718
Problem related to age/health
All Adults
Attenders
Non-Attenders
0.7%
1.2%
0.6%
7
3
5
1,000
219
718
Babysitter problems/
Must care for children
All Adults
Attenders
Non-Attenders
1.7%
3.5%
1.2%
17
8
9
1,000
219
718
Prefer to watch TV
All Adults
Attenders
Non-Attenders
0.9%
0.2%
1.1%
9
*
9
1,000
219
718
Don't have time
All Adults
Attenders
Non-Attenders
13.7%
27.9%
9.7%
137
61
75
1,000
219
718
Procrastination/
Lack of Motivation
All Adults
Attenders
Non-Attenders
4.0%
8.2%
2.7%
40
18
21
1,000
219
718
Source: "Survey of Public Participation in the Arts," 1985.
Notes: For each barrier to attendance, the number of attenders plus the number of non-
attenders who cited the barrier do not necessarily add up to the total number of adults
who cited it because of rounding errors.
*Less than one person per thousand.
35
Table 8
Barriers to Attendence and Unsatisfied Demand, 1985
Question: What are the reasons you did not attend art galleries/art museums more often?
*ercent Citing
Per 1,000 Adults
I
Number Citing
Number in
this Barrier
this Barrier
Category
Cost
All adults with unsatisfied demand
12.8%
39
307
Attenders
9.5%
12
129
Non-Attenders
15.2%
27
178
Not available
All adults with unsatisfied demand
20.3%
62
307
Attenders
18.6%
24
129
Non-Attenders
21.5%
38
178
Too far to go
All adults with unsatisfied demand
21.5%
66
307
Attenders
23.5%
30
129
Non-Attenders
20.1%
,36
178
Feel uncomfortable
All adults with unsatisfied demand
0.4%
1
307
Attenders
0.2%
*
129
Non-Attenders
0.5%
1
178
Poor quality/Not very good, etc.
All adults with unsatisfied demand
1.2%
*
307
Attenders
1.2%
*
129
Non-Attenders
1.2%
*
178
Don't have time
All adults with unsatisfied demand
44.3%
136
307
Attenders
47.6%
61
129
Non-Attenders
42.0%
75
178
Procrastination/Lack of motivation
All adults with unsatisfied demand
12.8%
39
307
Attenders
13.8%
18
129
Non-Attenders
12.0%
21
178
Source: "Survey of Public Participation in the Arts," 1985.
*Less than one person per thousand.
36
The Audience for American Art Museums
percentage cites each of these barriers?" (Table 8) In this case, both discom-
fort and lack of quality remain unimportant barriers to participation; but cost,
availability, distance, and lack of time are all of significantly higher impor-
tance among those who also said that they would like to be able to attend or
to attend more frequently. The responses to the last barrier, "lack of motiva-
tion," are more difficult to interpret; there is a paradox in the fact that even
12 to 13 percent of those who expressed a desire for more attendance cite
"lack of motivation" as a barrier to attendance.17
Part IV: Profiles of the Museum Audience(s)
Up to this point the analysis has focused on demographic groups one at
a time and asked what percentage of each group attends art museums, what
percentage of the group would like to attend more, and what percentage of
the group cites specific reasons for not attending more. This section takes a
different perspective, and examines how the demographic characteristics of
the SPPA respondents are distributed among the museum audience and how
this audience profile compares to the profile of the general population.
But proceeding with this analysis requires a much clearer definition of
the group of individuals we are actually referring to when we speak of the
"museum audience." The audience a museum sees coming through its doors
is not the same as the audience that is documented in a cross-sectional
sample of the population, such as the SPPA. This is true for two reasons.
First, the audience of a particular museum will differ from the overall
average audience profile resulting from a population survey, each museum
will be operating in the midst of a number of microf actors that are not typical
of the abstract, "average" museum. A museum's ability to attract certain
demographic groups is a function of both its own programming choices,
which make it more attractive to certain demographic groups than to others,
and of the demographic groups that actually live near enough to make access
easy of the museum.
Second, a cross-sectional survey of the adult population allows the
identification of visitors (and non- visitors), while a survey of admissions at
the door of the museum is a survey of visits. The fundamental difference lies
in differences in frequency of attendance. A visitor who is a frequent attender
is much more likely to be picked up in a survey within a museum than an
individual who attends, but infrequently. A museum that wishes to figure out
how many different individuals it is serving and who they are in demographic
terms must carefully account for the fact that frequent attenders are more
37
J. Mark Davidson Schuster
likely to appear in audience samples in proportion to their frequency of
attendance.18
While it is undoubtedly an oversimplification, it is not unreasonable to
suggest that the audience that is perceived by the museum is the audience of
visits, while the audience on which funding agencies focus is the audience
of visitors. But which focus is ultimately appropriate is a function of which
decisions are at stake. A museum that is interested in better targeting its
museum shop to its market, for example, will be concerned with the income
profile of visits. The museum that is trying to target its activities to new
population groups may be more concerned with the demographics of visitors.
The funding agency that is concerned about outreach and new constituencies
will stress visitors, while a funding agency that is trying to assess how reliant
a museum can become on paid admissions (in order to determine appropriate
levels of government or private funding) will stress visits.
Tables 9a and 9b summarize the distribution of visitors and visits
according to several of the demographic variables and compare those dis-
tributions to the corresponding distributions for the adult population. To
estimate visits from the 1985 SPPA data in order to construct these tables,
individuals who indicated they had visited an art gallery or art museum in
the previous year were weighted according to their stated frequency of
attendance.19
Figures 4a and 4b and 5a and 5b display some of the information in Tables
9a and 9b as bar charts, giving audience profiles according to income and
education — two of the key variables whose distribution museums attempt to
manage through reaching out to new and underrepresented constituencies —
and comparing them to the overall adult population.
Looking first at the distribution of visitors, the audience is composed
disproportionately of individuals with incomes over $25,000 (as compared
to their relative proportion in the overall population). Also overrepresented
are individuals with more than a high school education. Visitors are slightly
younger, more likely to be white, and less likely to come from outside of
urbanized areas than the overall adult population. Professionals are twice as
likely to be found among visitors to art museums as among the general
population.
Among visits, upper income and more highly educated individuals are
even more overrepresented, indicating that these individuals are not only
more likely to attend art museums but that they also attend more frequently.
From the individual museum's perspective, this means that an income or
education profile of visits will give a picture of an audience more weighted
toward the upper categories than a profile of the actual, identifiable visitors
38
Table 9a
Audience Profiles 1985: Percent Distribution
Adult Population
Visitors
Visits
Incomet
$4,999 or less
8%
6%
9%
$5,000-$9,999
13%
7%
7%
$10,000-$14,999
14%
9%
10%
$15,000-$24,999
25%
21%
20%
$25,000-$49,999
31%
38%
27%
$50,000 or more
9%
100%
19%
100%
27%
100%
Education
Grade School
11%
2%
1%
Some High School
12%
4%
4%
High School Grad
38%
24%
13%
Some College
20%
27%
29%
Four-year College Grad
11%
23%
25%
Graduate School
8%
21%
28%
100%
100%
100%
Age
18-24 years
16%
16%
21%
25-34 years
24%
28%
23%
35-44 years
18%
22%
23%
45-54 years
13%
14%
14%
55-64 years
13%
11%
8%
65-74 years
10%
7%
7%
75+ years
6%
3%
3%
100%
100%
100%
Gender
Female
53%
55%
52%
Male
47%
45%
48%
100%
100%
100%
Race
Black
11%
5%
3%
White
87%
93%
92%
Other
2%
2%
5%
100%
100%
100%
Urbanization
Central City of SMS A
27%
31%
45%
SMS A but not Central City
41%
49%
40%
Outside an SMS A
32%
20%
15%
100%
100%
100%
Source: "Survey of Public Participation in the Arts," 1985.
*The income distribution of the population reported here differs from the data in the 1985 Current Popula-
tion Survey, which shows a higher proportion of the population in the upper income groups. Nevertheless,
the figures reported here are internally consistent with the SPPA data and relative comparisons of the
population to visitors and visits are the best possible with the available data.
39
Table 9b
Audience Profiles 1 985: Number per 1 ,000 Adults
Adult Population
Visitors
Visits
Income'
$4,999 or less
Of 82 adults, there
were 13 visitors,
making 66 visits
$5,000- $9,999
126
14
53
$10,000-$ 14,999
143
21
77
S15,000-$24,999
247
47
149
$25,000-S49,999
308
85
200
$50,000 or more
94
42
206
Education
Grade School
110
4
7
Some High School
118
8
29
High School Grad
376
53
97
Some College
203
60
218
Four-year College Grad
110
50
188
Graduate School
82
45
212
Age
18-24 years
161
35
161
25-34 years
238
61
176
35^44 years
182
48
170
45-54 years
132
30
105
55-64 years
130
24
62
65-74 years
97
16
54
75+ years
59
6
24
Gender
-
Female
528
121
392
Male
472
99
359
Race
Black
108
12
23
White
873
203
693
Other
19
5
35
Urbanization
Central City of SMS A
271
69
339
SMS A but not Central City
413
107
302
Outside an SMSA
316
44
110
Source: "Survey of Public Participation in the Arts," 1985.
The income distribution of the population reported here differs from the data in the 1985 Current Popula-
tion Survey, which shows a higher proportion of the population in the upper income groups. Nevertheless,
the figures reported here are internally consistent with the SPPA data and relative comparisons of the
population to visitors and visits are the best possible with the available data.
40
Figure 4a
Education Profile of the Audience for Art Museums and Art Galleries, 1985:
Distribution of Visitors, Visits, and the Adult Population
50%
40%
30%
20%
10%
Grade
School
Some High
School
High
School
[ | Visitors
Some
College
Population
College
Grad
Graduate
School
SNN Visits
Figure 4b
Education Profile of the Audience for Art Museums and Art Galleries, 1985:
Total Number of Visitors, Visits, and the Adult Population
.a
E
3
z
Grade Some High High
School School School
Visitors
Some
College
Population
College Graduate
Grad School
S£3 Visits
41
Figure 5a
Income Profile of the Audience for Art Museums and Art Galleries, 1985:
Distribution of Visitors, Visits, and the Adult Population
50%
40%
30%
20%
10%
S0-$5K
$5-$10K $10-$15K $15-$25K $25-$50K $50K +
[ 1 Visitors
Population
777\ Visits
Figure 5b
Income Profile of the Audience for Art Museums and Art Galleries, 1985:
Total Number of Visitors, Visits and the Adult Population
60
50
40
S 30
4>
JO
$0-$5K $5-$10K
[ -J Visitors
$10-$15K $15-$25K $25-$50K
I Population
$50K +
Visits
42
The Audience for American Art Museums
who are being served. This is not a new phenomenon; earlier studies have
noticed much the same pattern, which has not changed substantially in the
25 years for which various data sources are available.20
While these distributions provide useful bases by which to compare both
aggregate changes in the museum audience over time and a particular
museum's audience to the aggregate audience, one should not be too hopeful
that interventions in the operation of art museums will succeed in dramati-
cally changing the audience profile. These aggregate profiles are very robust,
reflecting a variety of factors, not the least of which is the interaction of the
population's tastes with its demographic characteristics. Research into au-
dience demographics has repeatedly shown that while short-term changes in
the audience profile may be attained through very visible and popularly
attractive exhibitions or programs, it is much more difficult to sustain these
changes over a longer period.21
But note that a growth in attendance figures is not incompatible with an
overall stability in the profile of the audience. The size of the audience can
increase, either through new attenders or through increases in the frequency
of attendance of previous attenders, while the demographic profile of the
audience might change very little (except to reflect general societal changes
in the level of income or the level of education). Another way to state this is
that the raw numbers per 1,000 adults in Table 9b could increase while the
relative percentages in Table 9a remained more or less the same.
Table 10 shows the average number of visits per adult and visits per
visitor disaggregated by income and by education level. The average number
of visits per adult per year is 0.75; this means that the average American adult
attends an art museum or art gallery once every 16 months. Visits per adult
remain more or less at this level across income groups, with the exception of
individuals with incomes over $50,000. These adults attend art museums an
average of 2.26 times per year. Looking at only those visitors who actually
visited an art museum in the previous year, the average number of visits per
visitor is 3.42; individuals who go to art museums go slightly more than once
every four months. Only the lowest income group (5.33 visits per visitor)
and the highest income group (5.03 visits per visitor) have rates substantially
different from the overall rate. (Separating students from non-students does
not remove the apparent anomaly in the lowest income group.)
Across education levels, visits per person increase from 0.06 to 2.58.
Visits per visitor are lowest for individuals with only a grade school educa-
tion— 1.60 — and highest for those with at least some graduate school educa-
tion— 4.69. The dip to 1.84 visits per visitor for high school graduates is
another anomaly in the data.
43
J. Mark Davidson Schuster
Table 10
Frequency of Attendance by Income and Education,
1985
Visits Per
Visits Per
Adult
Visitor
Overall
0.75
3.42
Income
$4,999 or less
0.83
5.33
$5,000-$9,999
0.43
3.75
$10,000-$14,999
0.55
3.80
$15,000-524,999
0.62
3.28
$25,000-$49,999
0.67
2.41
$50,000 or more
2.26
5.03
Education
Grade School
0.06
1.60
Some High School
0.24
3.64
High School Grad
0.26
1.84
Some College
1.08
3.65
Four-year College Grad
1.71
3.80
Graduate School
2.58
4.69
Source: "Survey of Public Participation in the Arts," 1985.
Note: The number of visits per year for each respondent was estimated from the
respondent's answer to the question: "How many times did you do this [visit an art
museum or an art gallery] last month?" For a detailed discussion of the procedure used,
see Note 19.
Projecting the estimate of 0.75 visits per adult to the entire 1985 adult
population leads to a rough estimate of 128 million visits made by 37.5
million adult American visitors to art museums and art galleries in 1985.
However, because of the number of assumptions necessary to derive an
overall estimate from the SPPA data, one should not place too much
confidence in this overall estimate.
What do other sources say about the volume of attendance at American
art museums? Museums USA was the first major cross-sectional study of
American museums. It estimated that in 1971-1972 there were 1,821 mu-
seums that met the accreditation criteria of the American Association of
Museums, 340 of which were primarily art museums.22 According to the
survey results, art museums had an average attendance of 127,000 in that
year, for a total of 43 million visits. The 186 art/history museums had an
44
The Audience for American Art Museums
average attendance of 94,000 visits, or an additional 17.5 million visits, for
a total of 60.5 million visits to art and art/history museums. This study
employed a broad definition of attendance, including general attendance by
adults, children, and foreign tourists, and attendance at special exhibitions,
by school class groups, at workshops and classes, and performing arts
presentations, films, etc. Taken together these lead to a more inclusive total
attendance figure than the one that can be derived from SPPA.
More recently, the Institute of Museum Services commissioned the
National Center for Education Statistics to undertake a more comprehensive
study of the museum universe.23 This 1979 study used a slightly broader
definition of a museum that included nonprofit museums without profes-
sional staff. This study identified a universe of 4,408 museums, 609 of which
were defined as primarily art museums. These museums had an average
annual attendance of 81,817. This figure is lower than the Museums USA
figure from seven years earlier because of the broader definition of museums,
which brought many smaller museums into the overall calculations, rather
than because of any substantial fall in museum attendance. This figure
projects a total of 49.8 million visits in 1979. Yet, these figures are not
particularly reliable because the survey also uncovered the fact that only 247
of the art museums were using what could be termed "accurate attendance
measurement methods"; the others were forced to estimate. Once again, this
total includes many individuals beyond the American adults on whom SPPA
focused.
Despite their drawbacks, these benchmarks suggest that the aggregate
figures derived from SPPA reflect overestimation on the part of the respon-
dents. It would not be surprising if the SPPA-derived estimate is high by a
factor of two or more.
But the estimate derived from the SPPA data is not as high as the estimate
of total attendance that one would infer from the Harris Americans and the
Arts data.24 Though the documentation is not explicit as to how the calcula-
tions were made, Harris reports a mean of 2.7 visits per visitor for his data
(lower than the comparable estimate from SPPA data). Adjusting this figure
by the participation rate calculated by Harris leads to a mean of 1.57 visits
per adult in the U.S. population. Multiplying this figure by the size of the
adult population leads to the highest attendance estimate of all, 267 million
visits to art museums by American adults in 1984.
Although the overall estimates derived from the SPPA frequency of
attendance data seem high, that does not necessarily imply that the distribu-
tions of visits are incorrect. Unless one wishes to argue that individuals in
certain income groups or educational levels are more likely to overestimate
45
— I — __
J. Mark Davidson Schuster
their attendance patterns than individuals in other demographic groups, using
the relative frequency of attendance to generate the distributions of visits
presented in Tables 9a and 9b is a reasonable procedure and provides the best
currently available profiles of the American audience for art museums.
Conclusion
An understanding of audiences for museums begins with attendance
figures and is enhanced by demographic information, but it will not be
complete without a better understanding of why people visit museums and
how those visits are integrated into their value system. That work is just
beginning.
One of the next steps is to turn to measures of museum effectiveness:
What is the quality of a visit to a museum? In studying their audiences,
museums will do well to heed the reminder of Alma Wittlin:
Neither visitors' books in which the attendance is supposedly
registered nor the stricter control of the turnstile at the gate of the
museum which mechanically records the number of visitors is a
true indicator of performance. At their best they record the number
of warm bodies entering the premises.25
A museum can change itself or it can work to change its audience. Either
kind of change will be difficult, but it will be impossible to measure one
important aspect of that change — changes in the makeup of its audience — if
the museum does not document and understand its current audience first.26
It is my hope that in this monograph I have provided a solid base on which
museums can begin, or expand, the study of their own audiences in a
systematic fashion. It is increasingly important for a museum to understand
the population it serves as well as the population it does not yet serve.27
46
The Audience for American Art Museums
NOTES
1. Alma S. Wittlin, Museums: In Search of a Usable Future (Cambridge,
Mass.: M.LT. Press, 1970), p. 76.
2. Ibid., pp. 102-103.
3. Nathaniel Burt, Palaces for the People: A Social History of the American
Art Museum (Boston: Little, Brown and Company, 1977), pp. 282-283;
and Karl E. Meyer, The Art Museum: Power, Money, Ethics (New York:
William Morrow and Company, 1979), pp. 64, 121.
4. Paul DiMaggio, Michael Useem, and Paula Brown, Audience Studies of
the Performing Arts and Museums: A Critical Review, Research Division
Report #9 (Washington, D.C.: National Endowment for the Arts, No-
vember 1978).
5. Ibid, p. 33. An example of the latter approach is contained in Alan L.
Feld, Michael O'Hare, and J. Mark Davidson Schuster, Patrons Despite
Themselves: Taxpayers and Arts Policy (New York: New York Univer-
sity Press, 1983), pp. 74-75.
6. The reader who wishes to extrapolate these findings to estimates for the
entire American population can multiply any of the figures reported in
the tables that are expressed in terms of number per 1,000 adults by
170,520. This multiplication will weight these figures to the size of the
adult American population in 1985, which the U.S. Bureau of the Census
estimated at 170,520,000 in constructing its own weighting for SPPA '85.
7. Unfortunately, because the Bureau of the Census has masked the regional
variables on the data tape that is publicly available to protect the
confidentiality of the respondents, it is not possible to explore regional
differences any further than through the simple analyses that are pre-
sented at the end of Table 1. These analyses were prepared separately by
the Bureau of the Census from the complete data tape and provided to
the National Endowment for the Arts.
8. National Research Center of the Arts, Inc., Americans and the Arts: A
Survey of Public Opinion (New York: Associated Councils of the Arts,
1975), [1973 study]; National Research Center of the Arts, Inc., Ameri-
cans and the Arts: A Survey of the Attitudes Toward and Participation
in the Arts and Culture of the United States Public (New York: As-
sociated Councils of the Arts, August 1975), [1975 study]; National
Research Center of the Arts, Inc., Americans and the Arts (New York:
American Council for the Arts, 1981), [1980 study]; National Research
Center of the Arts, Inc., Americans and the Arts (New York: Louis Harris
and Associates, October 1984), [1984 study]; National Research Center
47
J. Mark Davidson Schuster
of the Arts, Americans and the Arts V: A Nationwide Survey of Public
Opinion (New York: American Council for the Arts, March 1988), [1987
study].
9. John P. Robinson, Carol A. Keegan, Terry Hanford, and Timothy A.
Triplett, Public Participation in the Arts: Final Report on the 1982
Survey, Appendix B, unpublished report available from the Research
Division, National Endowment for the Arts.
10. J. Mark Davidson Schuster, "Making Compromises to Make Com-
parisons in Cross-National Arts Policy Research," Journal of Cultural
Economics, Vol. 11, No. 2, December 1987.
11. Market & Opinion Research International Limited, unpublished report
on a survey conducted for BBC "Panorama," 26 November 1981 (some
results from this survey have been published in John Myerscough, Facts
About the Arts 2: 1986 Edition (London: Policy Studies Institute, Sep-
tember 1986), pp. 294-301); Ministere de la Culture, Service des Etudes
et Recherches, Pratiques Culturelles des Frangais: Description Socio-
Demographique— Evolution 1973-1981 (Paris: Dalloz, 1982), report of
a survey conducted by ARCmc; Official Statistics of Sweden, Cultural
Statistics: Activities, Economy and Cultural Habits 1980-1984 [Kul-
turstatistik] (Stockholm: Statistics Sweden, 1987); Ministere des Af-
faires Culturelles du Quebec, Chiffres d L App ui, Bulletin du Service de
la Recherche et de la Planification, Vol. 2, no. 2, May 1984, pp. 9-14,
report of a public opinion poll conducted by the Centre de Recherche sur
T Opinion Publique in 1983.
12. Market & Opinion Research International Limited, unpublished report
on a survey conducted for BBC "Panorama," 26 November 1981. This
study also found that trade union members were more likely to go to
museums than non-members (31%:28%), but non-members were more
likely to go to art exhibitions (15%:21%). Participation rates were
highest among persons intending to vote Conservative, somewhat lower
for those intending to vote Social Democrat/Liberal Alliance, and
lowest for Labour (35%:29%:25% for museums and 27%:21%: 13% for
art exhibitions). Not surprisingly, museum participation rates were the
highest among those who supported public funding for ballet, opera, or
theatre: 36-38 percent of supporters attended museums, 26 to 28 percent
attended art exhibitions. Participation rates were roughly three times
higher for individuals who had heard of the Arts Council of Great
Britain.
13. Ministere de la Culture, Service des Etudes et Recherches, Pratiques
Culturelles des Frangais, p. 151.
48
The Audience for American Art Museums
14. Ministere des Affaires Culturelles du Quebec, Chiffres d L'Appui, May
1984, pp. 9-14.
15. For a useful discussion of multiple classification analysis using an arts
example with SPPA data, see John P. Robinson, Carol A. Keegan,
Marcia Karth, and Timothy A. Triplett, Public Participation in the Arts:
Final Report on the 1985 Survey, "Volume I: Overall Project Report,"
1987, pp. 62-76, unpublished report available from the Research Divi-
sion, National Endowment for the Arts.
1 6. A relatively readable presentation of logit analysis is contained in Robert
S. Pindyck and Daniel L. Rubinfeld, Econometric Models and Economic
Forecasts (New York: McGraw-Hill, 1981), pp. 275-301.
17. When I wrote about this paradox in the first draft of this monograph, I
suggested that it might indicate a separation between societal expecta-
tions— "I ought to go to museums because it is considered a worthy thing
to do" — and personal desires — "I am not really motivated to go."
Since then readers have suggested two other possible explanations.
Pam Brusic has suggested that, ". . . today people are more burdened
with personal than societal expectations — Tf I want to be a well-
informed and cultured person, I ought to go to museums.' — and are more
likely to mean lack of ability to organize their personal time sufficiently
to attend when they cite 'lack of motivation.' . . . (T)here is an undertone
of self-disapproval in a 'lack of motivation' response and ... to lack
motivation is generally thought of as a personal character flaw . . . (T)his
alternate explanation . . reflects more on the respondent's attitude toward
himself than toward the museum he is not motivated to attend."
Harold Horowitz has offered a more prosaic explanation. When re-
spondents got to this point in the survey they were asked if they would
have liked to have gone to various artistic activities more often. Thinking
that the long survey was almost over, they answered "Yes," but they were
then asked a series of questions about barriers to attendance for each of
the art forms for which they had indicated a desire to attend more often.
Not having well-thought-out reasons in mind, they gave a vague reponse
that was invariably coded "lack of motivation."
These three rival explanations illustrate well the difficulty of extract-
ing definitive theories and explanations even from a dataset as complete
and as carefully collected as SPPA '85.
18. For a further discussion of these concepts see Feld, O'Hare, and Schuster,
Patrons Despite Themselves, p. 74; and Michael O'Hare, "The Audience
of the Museum of Fine Arts," Curator, Vol. 17, no. 2, June 1974, p. 129.
Unfortunately, this important distinction is often overlooked; for
49
J. Mark Davidson Schuster
example, the otherwise excellent manual, Surveying Your Arts Audience,
published by the Research Division of the National Endowment for the
Arts (Washington, D.C.: National Endowment for the Arts, 1985), is
silent on this subject.
19. SPPA '85 used two-part questions to ascertain levels of participation in
various artistic activities. The first part asked whether or not the respon-
dent had participated in the activity in the previous year, and the second
how often the respondent had actually attended in the previous month.
Robinson et al. have studied apparent inconsistencies between the
answers to these two parts and have concluded that it is most likely that
the monthly frequency question overestimates frequency of attendance
because respondents "telescope" their previous year's experience into
the previous month. (It is still logically possible, however, that respon-
dents underestimate their annual participation.) Robinson et al., Public
Participation in the Arts: Final Report on the 1982 Survey, pp. 227-233.
In the analysis of the relative profiles of the museum audience I have
used the frequency data to weight respondents' attendance to calculate
the distribution of visits. This procedure is valid as long as there is no
reason to believe that individuals in one demographic grouping are more
likely to overestimate their attendance than individuals in another
demographic grouping.
SPPA '85 was conducted over six months. I weighted each month's
respondents by a weight that was the product of how many individuals in
the total population each respondent represented (a function of his or her
demographic characteristics) times the frequency of attendance by that
respondent in the previous month. Adding the six estimates together gave
an estimate of the total number of visits to art museums made by the total
adult population over those six months. I then multiplied these factors by
two to represent one entire year of attendance. In cases where the
frequencies were reported in categories — e.g. two to three times in the
month — I used the lower bound of the interval to represent the frequency
of attendance, using the most conservative assumption in a situation
where there is reason to believe that overestimation is common.
I tested the reasonableness of this procedure by doing a sensitivity
analysis, performing a second analysis using the midpoints of the
categories (and 8 visits for the 6+ category). The distributions of visits
across the various demographic variables changed by only one percent-
age point in one or two cases. Thus, the percentage distributions are not
sensitive to the choice of frequency to represent the categories.
20. Feld, O'Hare, and Schuster, Patrons Despite Themselves, pp. 80-83;
50
The Audience for American Art Museums
also, J. Mark Davidson Schuster, unpublished comparison of results
from the Baumol and Bowen audience surveys in the 1960s (William J.
Baumol and William G. Bowen, Performing Arts: The Economic Dilem-
ma, Cambridge, Mass.: M.LT. Press 1967, pp. 71-98) with results from
the Americans and the Arts surveys of the 1970s (see Note 9).
21. A variety of studies done in Great Britain and France, particularly under
the auspices of the Research Division of the French Ministry of Culture,
suggest that audience demographics are surprisingly stable across fine
art forms, across regions, and over time. Unfortunately, there has been
no attempt to bring them together in one place to further explore the
resilience of this stability.
22. National Research Center of the Arts, Museums USA: A Survey Report
(Washington, D.C.: U.S. Government Printing Office, January 1975),
pp. xi and 130.
23. Lewis C. Price, Lisa DiRocco, and Janice D. Lewis, Contractor Report:
Museum Program Survey, 1979 (Washington, D.C.: National Center for
Education Statistics, March 1981), pp. 52-64. This report is also referred
to as the "Museum Universe Survey."
24. National Research Center of the Arts, Inc., Americans and the Arts [1984
study], pp. 62 and 65.
25. Wittlin, Museums, p. 161.
26. As a starting point see, for example, Marilyn G. Hood, "Getting Started
in Audience Research," Museum News, Vol. 64, no. 2, February 1986,
pp. 25-31; and Research Division, National Endowment for the Arts,
Surveying Your Arts Audience.
27. This is one of the points stressed in Wittlin 's "Twelve-Point Program for
Museum Renewal." Wittlin, Museums, pp. 212-213.
51
Appendix
Results from Three Logit Analyses
The text discusses the results of three different logit analyses that were
conducted with the SPPA museum attendance data. The actual mathematical
results of these three analyses are reported in this appendix.
It may be helpful for the more mathematically inclinded reader to
understand that logit analysis is a form of regression analysis in which the
"natural logarithm" (logarithm to the base "e" — a mathematical constant
equal to 2.7 1 83) of the odds ratio (the probability of attending divided by the
probability of not attending) is predicted as a linear combination of the
independent variables. In this way, the separate marginal contribution of each
of the independent variables to the logarithm of the odds ratio can be
calculated as the "coefficient" of each variable. (By comparison, ordinary
regression analysis calculates the separate marginal contribution of each
independent variable directly to a dependent variable.) The "intercept" is the
value of the logarithm of the odds ratio when all of the independent variables
are equal to zero. This value is necessary to position the logit curve in the
proper place. The probability of attendance at any point on the logit curve
can be calculated algebraically from the logit equation.
The results of using logit analysis to predict the probability of attendance
from seven independent demographic variables — income, age, race, gender,
education level, urbanization, and student status — is reported in Table A. The
logit results in Table B predict the probability of attendance from eight
independent variables, five of the most important demographic variables plus
three variables that measure whether or not the individual had different
socialization experiences. And Table C reports the results of a logit analysis
that predicts the probability of an individual having unsatisfied demand using
the seven original demographic variables.
52
The Audience for American Art Museums
Table A
Logit Results Predicting the Probability of Attendance
Variable Name
Definition
Coefficient
Significant at
.05 Level?
Intercept
-6.075
Income 2
= 1 if $4,999 < income < $10,000
= 0 otherwise
-O.l 50
No
Income 3
= 1 if $9,999 < income < $15,000
= 0 otherwise
-0.123
No
Income 4
= 1 if $14,999 < income < $25,000
= 0 otherwise
-0.004
No
Income 5
= 1 if $24,999 < income < $50,000
= 0 otherwise
+0.201
Yes
Income 6
= 1 if $49,999 < income
= 0 otherwise
+0.563
Yes
Age
= age in years
-0.004
Yes
Race 2
= 1 if individual is Black
= 0 otherwise
-0.815
Yes
Race 3
= 1 if individual is "other" race
= 0 otherwise
-0.064
No
Gender
= 1 if female
= 0 if male
+0.355
Yes
Educational Level
= number of years of formal education
+0.328
Yes
SMSA1
= 1 if live in central city of an SMS A
= 0 otherwise
+0.689
Yes
SMS A 2
= 1 if live in SMS A but not in central city +0.450
= 0 otherwise
Yes
Student
= 1 if currently a student
= 0 otherwise
+0.393
Yes
R =.16
Logit Equation:
If, P = Probability of attendance for a particular individual.
Then, Natural logarithm (P/l-P) = - 6.075 - 0. 150(Income 2) - 0. 123(Income 3) - 0.004(Income 4)
+ 0.201 (Income 5) + 0.563 (Income 6) -0.004(Age) -0.815(Race2)
- 0.064(Race 3) + 0.355(Gender) + 0.328(Educational Level)
+ 0.689(SMSA 1) + 0.450(SMSA 2) + 0.3 93 (Student)
53
J. Mark Davidson Schuster
Table B
Logit Results Predicting the Probability of
Attendance with Socialization Variables
Parents 2
Parents 3
R2 = .22
in art history or appreciation
= 0 otherwise
= 1 if parents took individual to arts
museum occasionally
= 0 otherwise
= 1 if parents took individual to arts
museum frequently
= 0 otherwise
Significant at
Variable Name
Definition
Coefficient
.05 Level?
Intercept
-4.137
Income 2
= 1 if $4,999 < income < $10,000
= 0 otherwise
-0.316
No
Income 3
= 1 if $9,999 < income < $15,000
= 0 otherwise
-0.091
No
Income 4
= 1 if $14,999 < income < $25,000
= 0 otherwise
-0.196
No
Income 5
= 1 if $24,999 < income < $50,000
= 0 otherwise
+0.144
No
Income 6
= 1 if $49,999 < income
= 0 otherwise
+0.540
Yes
Age
= age in years
-0.007
Yes
Race 2
= 1 if individual is Black
= 0 otherwise
-0.852
Yes
Race 3
= 1 if individual is "other" race
= 0 otherwise
-0.177
No
Gender
= 1 if female
= 0 if male
-0.008
No
Educational Level
= number of years of formal education
+0.186
Yes
Lessons
= 1 if individual has ever taken
visual arts lessons
+0.758
No
= 0 otherwise
Appreciation
= 1 if individual has ever taken course
+0.783
+0.625
+ 1.359
No
Yes
Yes
Logit Equation:
If, P = Probability of attendance for a particular individual.
Then, Natural logarithm (P/l-P) = - 4.137 - 0.316(Income 2) - 0.091(Income 3) - 0.196(Income 4)
+ 0.144(Income 5) + 0.540(Income 6) - 0.007(Age) - 0.852(Race 2)
- 0.177(Race 3) - 0.008(Gender) + 0. 186(Educational Level)
+ 0.758(Lessons) + 0.784(Appreciation) + 0.625(Parents 2)
+ 1.359(Parents3)
54
The Audience for American Art Museums
Table C
Logit Results Predicting the Probability
of Having Unsatisfied Demand
Variable Name
Definition
Coefficient
Significant at
.05 Level?
Intercept
-2.834
Income 2
= 1 if $4,999 < income < $10,000
= 0 otherwise
+0.107
No
Income 3
= 1 if $9,999 < income < $15,000
= 0 otherwise
+0.135
No
Income 4
= 1 if $14,999 < income < $25,000
= 0 otherwise
+0.105
No
Income 5
= 1 if $24,999 < income < $50,000
= 0 otherwise
+0.289
Yes
Income 6
= 1 if $49,999 < income
= 0 otherwise
+0.312
No
Age
= age in years
-0.007
Yes
Race 2
= 1 if individual is Black
= 0 otherwise
-0.339
Yes
Race 3
= 1 if individual is "other" race
= 0 otherwise
-0.749
Yes
Gender
= 1 if female
= 0 if male
+0.315
Yes
Educational Level
= number of years of formal education
+0.147
Yes
SMSA1
= 1 if live in central citv of an SMS A
+0.314
Yes
= 0 otherwise
= 1 if live in SMS A but not in central city +0.218
= 0 otherwise
= 1 if currently a student
= 0 otherwise
+0.290
No
No
SMS A 2
Student
R2 = .05
Logit Equation:
If, P = Probability of attendance for a particular individual.
Then, Natural logarithm (P/l-P) = - 2.834 + 0.107(Income 2) + 0.135(Income 3) + 0.105(Income 4)
+ 0.298(Income 5) + 0.312(Income 6) - 0.007(Age) - 0.339(Race 2)
- 0.749(Race 3) + 0.3 15(Gender) + 0. 147 (Educational Level)
+ 0.314(SMSA 1) + 0.218(SMSA 2) + 0.290(Student)
55
Acknowledgments
I would like to thank those individuals who served as my surrogate
audience by reading and commenting upon drafts of this monograph: Harold
Horowitz and Tom Bradshaw of the Research Division at the National
Endowment of the Arts, as well as the staff of the NEA Museum Program;
Pam Brusic, former Executive Director of the New England Museum
Association; Gary Burger, former Director of the Berkshire Museum; Janet
Saleh Dickson, Curator of Education, Yale University Art Gallery; and
Professor Joe Ferreira, Department of Urban Studies and Planning, Mas-
sachusetts Institute of Technology. They have forced me to clarify both my
thinking and my presentation. Thanks also to my colleagues Augustin Girard,
Leif Gouiedo, and Marie-Charlotte de Koninck who provided me with
comparable audience statistics for France, Sweden, and the province of
Quebec. Jun Han served as my Research Assistant and handled the intracies
of the computer programming with ease, competence, and good humor.
56
About the Author
Dr. J. Mark Davidson Schuster is the Ida and Cecil Green Career
Development Associate Professor of Urban Studies and Planning at the
Massachusetts Institute of Technology where he teaches courses on quan-
titative reasoning, nonprofit institutions, and environmental design policy.
His research focuses on government policy vis-a-vis the arts, culture, and
environmental design. He has written widely on issues of cultural policy and
is author of Supporting the Arts: An International Comparative Study, an
analysis of arts funding patterns in eight countries, and coauthor with Alan
Feld and Michael O'Hare of Patrons Despite Themselves: Taxpayers and
Arts Policy, a Twentieth Century Fund Report on tax incentives for the arts.
He and Milton Cummings are editors of Who's to Pay for the Arts? The
International Search for Models of Arts Support, a volume in the American
Council for the Arts Research Seminar Series. He is a coauthor with Judith
Blau of The Geography of Participation in the Arts and Government Funding
also published by Seven Locks Press. He was a postdoctoral fellow in the
Research Division of the French Ministry of Culture under the auspices of
the United States-France Exchange of Scientists. More recently, he was
named Fulbright Scholar and Distinguished Visitor to New Zealand under
the auspices of New Zealand-United States Educational Foundation and the
Queen Elizabeth II Arts Council. He has served as a consultant to the Arts
Council of Great Britain, the British American Arts Association, the British
Museum, the National Endowment for the Arts, National Public Radio, the
American Council for the Arts, the Canada Council, the Massachusetts
Council on the Arts and Humanities, and many other arts and cultural
organizations.
57
Other Publications of Interest
Readers of this report may wish to obtain more information about the details
of the study and about related research projects conducted for the Research
Division of the National Endowment for the Arts. The following reports are
available at libraries, bookstores or from their publishers:
Socialization and Participation in the Arts
Richard J. Orend
Research Division Report #21
54 pages
National Endowment for the Arts (1989)
Available from the American Council on the Arts, 1285 Avenue of the
Americas, New York, NY 10019
Who Reads Literature?
Nicholas Zill & Marianne Winglee
Research Division Report #22
104 pages, 0-932020-86-0
Seven Locks Press (1990) $9.95
Expanding the Audience for the Performing Arts
Alan R. Andreason
Research Division Report #24
64 pages, 0-929765-01-X
Seven Locks Press (1991) $10.95
In addition the following reports are available through the Education
Research Information Center (ERIC) system:
Dan Abreu, ''Survey of Public Participation in the Arts, Musical Theater,
Operetta, and Opera Attendees." April 1, 1987, ERIC Identification Number:
ED 289 760.
Carol Keegan, "Public Participation in Classical Ballet: A Special Analysis of
the Ballet Data Collected in the 1982 and 1985 Survey of Public Participation in
the Arts." April 30, 1987. ERIC Identification Number: ED 288 756.
59
David Waterman, "Public Participation in the Arts Via the Media." September
1987, ERIC Identification Number: ED 290 674.
Jerry West, "Public Participation in the Arts: Demands and Barriers." ERIC
Identification Number: ED 287 764.
Harold Horowitz, "The American Jazz Audience." ERIC Identification
Number: ED 280 757.
John Robinson, et a!., "Public Participation in the Arts: Final Report of the 1982
Survey." Survey Research Center, University of Maryland, January 1986. ERIC
Identification Number: ED 264 168.
John Robinson, et al., "Survey of Public Participation in the Arts: 1985 Volume I,
Project Report." Survey Research Center, University of Maryland, March 1987.
ERIC Identification Number: ED 289 763.
Judith R. Blau, "The Geography of Arts Participation: Report on the 1982 and
1985 Surveys of Public Participation in the Arts.' ' March 1987. ERIC Identifica-
tion Number: ED 289 762.
Paul DiMaggio, "Race, Ethnicity and Participation in the Arts: Patterns of Par-
ticipation by Black, Hispanic and White Americans in Selected Activities from
the 1982 and 1985 Surveys of Public Participation in the Arts." June 1987. ERIC
Identification Number: ED 293 759.
J. Mark Davidson Schuster, "An Inquiry into the Geographic Correlates of
Government Arts Funding." ERIC Identification Number: ED 298 023.
The documents are the original research reports as prepared by the investi-
gators. They contain extensive information about methods, and numerous tables
and figures. The ERIC collection is available at hundreds of libraries in the United
States and abroad, as well as "on-line" from computerized information services.
Requests for information about the purchase of microfiche or photocopies
of these reports should be sent to: ERIC Document Reproduction Services,
Consumer Service, P.O. Box 190, Arlington, VA 22210.
60
The Audience for
American Art Museums
In the last decade, public and private contributors to
the arts have taken as part of their mandate to increase both
the number and the diversity of people who are exposed to
the visual arts. Overall attendance figures and audience
demographics have thus become increasingly important to
museums.
This study offers a series of profiles of the audience
for American art museums and galleries based on an analysis
of data from the 1985 Survey of Public Participation in the
Arts and comparisons with several other sources.
A survey report by
J. Mark Davidson Schuster
Massachusetts Institute of Technology
NATIONAL
endowment
forMthe
S^r?
ARTS
Research Division Report #23
Seven Locks Press
Washington, D.C.
78
929"765
06
5 10
95
ISBND-'KTPhS-DD-l
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