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https://doi.org/10.1177/0305735619828810
Psychology of Music
2020, Vol. 48(6) 777 –794
© The Author(s) 2019
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DOI: 10.1177/0305735619828810
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On the relevance of music
genre-based analysis in
research on musical tastes
Romain Brisson1 and Renzo Bianchi2
Abstract
The investigation of the link between personality and musical tastes has led certain psychology
researchers to examine the latent dimensions of musical tastes. In this area of research, investigators
have largely relied on genre-based analysis, the relevance of which remains unclear. In this study,
we examined the impact of changes in the selection of musical items on the identification of musical
taste dimensions. Indeed, investigators have employed heterogeneous sets of music genres in prior
research. Such a heterogeneity may partly explain why no clearly reproducible structure of musical
tastes has emerged in the literature. Based on principal component analysis, our results indicate
that the apparent structure of musical tastes is highly affected by even subtle variations in the items
selected. Our findings also suggest that the identified structure of musical tastes strongly depends on
the social background and cultural capital of respondents. Finally, our results highlight the limitations
of the models that interpret the dimensions of musical tastes in terms of intrinsic musical properties.
Keywords
cultural capital, musical tastes, music genres, music dimensions, principal component analysis
To date, research on the link between musical tastes and personality has mainly relied on genre-
based analysis. Stimulated by the creation of the Musical Preference Scale (Litle & Zuckerman,
1986) and the Short Test of Music Preferences (STOMP; Rentfrow & Gosling, 2003), the use of
music genres in this research area has developed greatly since the early 2000s and has led psy-
chology researchers to investigate the underlying dimensions of musical tastes (Brown, 2012;
Colley, 2008; Delsing, ter Bogt, Engels, & Meeus, 2008; Gardikiotis & Alexandros Baltzis, 2012;
George, Stickle, Rachid, & Wopnford, 2007; Gouveia, Pimentel, Santana, Chaves, & Rodrigues,
2008; Langmeyer, Guglhör-Rudan, & Tarnai, 2012; Schäfer & Sedlmeier, 2009; Zweigenhaft,
1Université de Neuchâtel, Institut des Sciences du Langage et de la Communication, Neuchâtel, Switzerland
2Université de Neuchâtel, Institut de Psychologie du Travail et des Organisations, Neuchâtel, Switzerland
Corresponding author:
Romain Brisson, Université de Neuchâtel, Institut des Sciences du Langage et de la Communication, Pierre-à-Mazel 7,
Neuchâtel, 2000, Switzerland.
Email: romain.brisson@unine.ch
828810POM0010.1177/0305735619828810Psychology of MusicBrisson and Bianchi
research-article2019
Article
778 Psychology of Music 48(6)
2008). Researchers have used dimension-reduction techniques such as principal component
analysis (PCA) in order to compute correlations between the dimensions underlying musical
tastes and personality scale scores. While some authors have suggested that the dimensions of
musical preferences are rather inconsistent from one to study to another (e.g., Dunn, de Ruyter,
& Bouwhuis, 2012), others perceived a “considerable degree of convergence between these
studies” (Rentfrow, Goldberg, & Levitin 2011, p. 1141).
However, some investigators—including the creators of the STOMP themselves—have pro-
gressively pointed out methodological flaws in genre-based analysis. As a result, these investi-
gators have recommended, or opted for, the use of other musical taste indicators, such as
excerpts or artists (Rentfrow et al., 2011; Rentfrow et al., 2012; Ferrer, Eerola, & Vuoskoski,
2013; Greenberg, Baron-Cohen, Stillwell, Kosinski, & Rentfrow, 2015; Greenberg et al., 2016).
It has been suggested, indeed, that (a) music genres constitute ill-defined categories; (b) the
quantity and the quality of relevant music (sub)genres are difficult to specify; (c) music genres’
“ecological” validity is questionable, since artists and songs do not always fall within a unique
genre; and (d) the artists and musical pieces ascribed to a given genre are likely to vary as a
function of respondents. For instance, the STOMP and its revised form, the STOMP-r, include
problematic categories that contravene Rentfrow and Gosling’s intention (2003, p. 1241) to
circumscribe the analysis to music genres (i.e., to exclude both sub- and super-genres). As an
illustration, the “alternative” category may be viewed by participants as a subtype of rock, as
non-mainstream music, or even as a kind of music that transcends the notion of genre. In a
similar vein, so-called “religious music” involves a large range of styles, including traditional
and contemporary Christian, Hindu, or Islamic musical forms. The “oldies” category includes
several music genres (e.g., folk, jazz, rockabilly). “Soundtracks and theme songs” gather pieces
that greatly differ with each other: Nino Rota’s “Godfather Waltz” has little in common with
Cliff Martinez’s compositions for Soderbergh’s adaptation of Solaris. The “electronica/dance
music” category is also questionable, since it associates a super-genre that encompasses a large
array of music styles that are not necessarily geared for dancing (e.g., ambient, new wave, trip
hop) with a music genre specifically made for dancing. Because of these methodological prob-
lems, certain researchers investigated the connections between personality and musical tastes
by mobilizing music excerpts, artists, and musical or emotional attributes (e.g., “low tempo,”
“sad”; see Finnas, 1987; Schwartz & Fouts, 2003). For example, Rentfrow et al. (2011) chose
to rely on excerpts, thereby renewing with the method of investigation employed in the seminal
studies of the field (Cattell & Anderson, 1953; Cattell & Saunders, 1954). In these authors’
view, excerpts have higher “ecological” validity than genres and do not require participants to
possess label knowledge.
Despite the criticisms addressed to genre-based analysis and the availability of alternative
musical taste indicators, the use of music genres has persisted in recent years (see, e.g., Franken,
Keijsers, Dijkstra, & ter Bogt, 2017; Fricke & Herzberg, 2017; Vella & Mills, 2017). Such a meth-
odological option is rarely justified, though. In fact, very few articles have empirically tackled
the issue of the reliability of genre-based measures (Ferrer et al., 2013). Moreover, investigators
have relied on heterogeneous sets of music (sub)genres (Schäfer & Mehlhorn, 2017), the vari-
ety of which impedes between-study comparisons. For example, mobilizing several religious
music categories enables the identification of religious music dimension(s). By definition, mobi-
lizing a unique religious music category does not. The studies by George and colleagues (2007)
and Rentfrow and Gosling (2003) illustrate this point. Whereas the former used 30 (sub)gen-
res, including four religious music categories, and identified two religious music dimensions,
the latter employed 14 genres including one religious music category and found religious music
to be associated with country, soundtracks, and pop. Furthermore, some researchers have
Brisson and Bianchi 779
modified the STOMP or the STOMP-r in order to adapt those tests to the specificities of their
national context. For instance, Fricke and Herzberg (2017) excluded from the STOMP-r the
bluegrass, new age, and reggae categories and added Musical and Volksmusic, two categories
that cannot be considered equivalents to the excluded ones. Coupled with the use of different
dimension-extraction techniques (e.g., Kaiser rule, parallel analysis), such differences in item
selection may explain why no consistent structure of musical tastes emerged in past research.
In the present study, we examined the extent to which subtle changes in the selection of
musical items affect the identification of musical taste dimensions. To this end, we carried out
a series of PCAs involving various sets of musical (sub)genres from an original list of 40 items.
We focused on PCA because it is the statistical test that has been the most frequently used by
psychology researchers in their attempt to identify the dimensions underlying individuals’
musical tastes. We relied on two different samples in order to increase the external validity of
our study. Our first sample consisted of university students and our second sample of voca-
tional secondary school students. Both samples involved teenagers and young adults to allow
us to make comparisons with the existing literature, which has mainly focused on students
(Schäfer and Mehlhorn, 2017).
Method
Study sample and recruitment procedure
The present study involved two samples. The first sample comprised 522 students from a Swiss
university (MAGE = 22.72, SDAGE = 4.05; 68% female). We sent an email that contained a URL
to an online survey to all students. Students participated on a voluntary basis. The response
rate was 14%. The second sample involved 185 high schoolers from a vocational secondary
school located in France (MAGE = 17.20, SDAGE = 1.01; 59% female). We surveyed all eleventh-
and twelfth-grade classes. We administrated our questionnaire in the classrooms. Both samples
included French-speaking participants only.
Musical taste inventory
Respondents reported their degree of appreciation of 40 music genres and subgenres (e.g.,
rock, alternative rock) using a five-point rating scale (from 1 for I dislike very much to 5 for I like
very much). A supplementary response option allowed participants to indicate that they did not
know the music (sub)genre in question. We selected those 40 (sub)genres based on a prelimi-
nary survey in which 15 French and 15 Swiss undergraduate students were asked to specify
which music genres and subgenres they regularly listened and never listened to. We found that
47 categories were mentioned at least three times (i.e. by at least 10% of the pilot sample). Since
some participants explicitly discriminated American from French rap, we used such a distinc-
tion and included those two categories in our music inventory. We amalgamated “black metal,”
“death metal,” and “trash metal” into the category “extreme metal.” Because of its generality
and its overlap with “dance,” “electropop,” “house,” and “techno,” we excluded the category
“electro.” We neglected the categories “chill” and “minimal” (three occurrences for each) in
order to not over-represent electronic music in our inventory. We also excluded the categories
“indie” and “commercial” because they cover several music genres. The Appendix displays the
list of items included in our inventory and the corresponding means and standard deviations in
both samples. It also reports, for each (sub)genre, the rate of participants that indicated that
they were not familiar with the corresponding label. The same inventory was administered to
780 Psychology of Music 48(6)
both samples. We note that our pilot study did not involve high-schoolers. Because there is evi-
dence that musical tastes remain consistent from adolescence to early adulthood (Delsing et al.,
2008; Mulder, ter Bogt, Raaijmakers, Gabhainn, & Sikkema, 2010), we assumed that involving
high-schoolers in our pilot study was not needed. Nevertheless, we asked high-schoolers to
indicate which music genres and subgenres they regularly listened to and never listened to. A
vast majority of the responses referred to music categories that were already included in our
inventory. Although other categories appeared, they were mentioned by a very small propor-
tion of respondents. For instance, “flamenco,” “Guggenmusik,” “traditional Turkish music,”
and “tribe” were cited once, “dancehall” and “slam/spoken words” were cited twice, and “Afro-
trap” was cited thrice.
Music-genre sets
In order to examine the extent to which applying subtle modifications in the item selection
influences PCA results, we relied on six different music-genre sets.
The first set involved the genres included in the STOMP (Rentfrow & Gosling, 2003), with
one exception: “soundtracks.” This category was not mentioned by the respondents to our pilot
survey. Moreover, its vagueness was potentially problematic and led us to neglect it. In addition,
we did not employ the “electronica/dance” category, which problematically combines non-
dance-oriented music (e.g., ambient, post-punk) with dance. Instead, we created a score for
electronic dance music (EDM) appreciation by calculating the mean level of appreciation of
dance, house, and techno. We focused on EDM because our data indicate that non-dance-ori-
ented electronic genres (e.g., new wave) were unknown to a large part of our two samples (see
the Appendix). Finally, since our inventory mobilized the categories “American rap” and
“French rap,” we created a global score for rap appreciation by computing the mean level of
appreciation of these two subtypes. We used the STOMP as point of comparison because this
test has been abundantly mobilized in the literature.
The second set was similar to the first one but involved taste for dance instead of taste for
EDM. This set is more consistent with Rentfrow and Gosling’s goal to focus on musical genres
rather than on musical sub-genres or musical super-genres. It allowed us to assess how a very
subtle modification in the selection of music genres influenced the emerging structure of musi-
cal tastes.
In the third set, we excluded the “alternative rock” category, which is redundant with the
“rock” category. We instead added the “R&B” category, which refers to a fashionable genre
among teenagers and young adults.
The fourth set adapted the STOMP to local specificities and systematized the inclusion of
pairs of (sub)genres involved in the STOMP with the “alternative” and “rock” categories. We
thus replaced country and US folk by French variété and international variété, two popular gen-
res that can be considered equivalent to soft adult contemporary music. The retained pairs of
(sub)genres were alternative rock and rock, blues and jazz, classical and opera, conscious rap
and rap, dance and house, extreme metal and metal, and the two subtypes of variété.
The fifth set gathered the (sub)genres (n = 30) that were known by at least two-thirds of the
participants of both samples. We created this set in order to observe whether (potentially) con-
sistent between-item associations previously identified were retrieved when enlarging the item
selection.
The sixth set of (sub)genres (n = 20) was used only for the high-schooler sample. To reduce
the number of items (compared to the previous set) and provide sufficient musical variety, this
set included pairs of electronic, so-called “highbrow,” Latin, metal, rap, rock, and variété (sub)
Brisson and Bianchi 781
genres. The corresponding items were selected on a twofold basis: the degree of label knowledge
and the degree of appreciation. For instance, we neglected opera because it was the most
unknown “highbrow” genre. We included electropop and house because they were the most
and the least appreciated electronic subgenres. Moreover, because a non-negligible part of the
members of the high-schooler sample were French Arabs and Blacks, we also included African
music, raï, and zouk in the count. The sociological literature indeed highlighted that ethnic
features modulate musical tastes (Robinson et al., 1985). Finally, we added pop, reggae, and
R&B, because these genres were among the most appreciated in our high-schooler sample. The
reasons that led us to perform this supplementary PCA are described below, in the correspond-
ing section.
Data analyses
We carried out PCAs with promax rotation—a type of rotation used when between-component
independence is not assumed. In order to estimate the number of components to be extracted,
we performed parallel analyses (Horn, 1965). Parallel analysis enables investigators to avoid
both under- and over-extraction and to optimize the reliability of the components (O’Connor,
2000; Zwick & Velicer, 1986). This technique is considered more reliable than the Kaiser crite-
rion (Kaiser, 1960), which selects components based on eigenvalues higher than 1, and than
the scree test (Cattell, 1966), which consists in graphing the eigenvalues and retaining those
that appear to precede the point of inflexion (Costello & Osbourne, 2005; O’Connor, 2000;
Thompson & Daniel 1996). Because our survey allowed participants to respond “I do not know
this (sub)genre,” we treated such cases with the pairwise-deletion technique (Van Ginkel,
Kroonenberg, & Kiers, 2014). We used the Bartlett’s test of sphericity and the Kaiser–Meyer–
Olkin (KMO) measure of sampling adequacy as indicators of suitability. All Bartlett’s test results
were significant. All KMO measures indicated that our data were suitable for PCA (Hutcheson
& Sofroniou, 1999; Kaiser, 1974). Those values are reported in the following section.
Results
Given the high number of analyses involved in our study, we limited ourselves to concisely
reporting hereafter the main findings related to each performed PCA.
Sample 1: University students
Table 1 displays the results pertaining to the PCA involving the genres included in the STOMP,
soundtracks excluded and with electronic dance music (EDM) replacing electronica/dance. We
found a five-component solution accounting for 69.73% of the variance. The first component
(C1) accounted for 23.78% of the variance. One between-component correlation higher than
.25 was found, between C2 and C4 (r = .264). While all main loadings were positive and higher
than .6, metal presented another high (negative) loading on C3 and rap showed problematic
cross-loadings on C1 and C5. C1 clustered “classic” Afro-American genres, and C2, rock and
metal styles. C3 is hardly interpretable, especially for a Swiss sample, since it gathers pop, coun-
try, and US folk music. C4 combined classical and religious music, which might suggest that
respondents considered as “religious music” the sacred forms of European Christian music, not
styles such as Gospel or Hindu music. Finally, C5 involved EDM and rap, two relatively recent
and so-called “urban” genres.
782 Psychology of Music 48(6)
Table 2 repor ts the results pertaining to the PCA involving the genres included in the STOMP,
soundtracks excluded and with dance replacing electronica/dance. We found a four-
component solution accounting for 61.69% of the variance. C1 accounted for 23.8% of the
variance. Two between-component correlations higher than .25 were found, between C1 and
C4 (r = .258) and between C2 and C4 (r = .332). Thus, using dance instead of EDM resulted in
finding a more synthetic component solution. Compared with Table 1, Table 2 involved two
other main differences: contrary to EDM, dance clustered with pop and not with rap, which in
turn appeared to be (weakly) associated with blues, soul, and jazz. In addition, two genres
showed problematic cross-loadings. US folk did not load well, since all the corresponding
Table 1. Musical taste component scores: First set, university student sample.
C1 C2 C3 C4 C5
Blues .867 .050 −.022 .019 −.064
Jazz .825 −.014 −.061 .139 .071
Soul .817 −.027 .107 −.089 .150
Rock .016 .897 .150 −.121 .076
Alt rock .059 .868 .115 −.175 .043
Metal −.113 .728 –.403 .210 −.002
Pop −.212 −.057 .785 −.134 .291
Country .151 −.002 .612 .255 −.240
Folk .210 .206 .601 .080 −.143
Religious −.062 −.167 .034 .880 .155
Classical .151 −.003 .025 .724 .054
EDM −.129 .160 .156 .314 .821
Rap .386 −.058 −.183 −.119 .679
Notes: Loadings > .5 are in bold; loadings between .4 and .5 are in italics. Bartlett’s test of sphericity: χ²(78) = 1235.66,
p < .001; KMO = .663. EDM = Electronic Dance Music.
Table 2. Musical taste component scores; Second set, university student sample.
C1 C2 C3 C4
Blues .862 .092 −.169 .045
Soul .844 −.015 .056 −.093
Jazz .822 −.026 −.141 .137
Rap .465 −.264 .029 −.269
Rock −.005 .910 .076 −.142
Alt rock .045 .886 .015 −.191
Metal −.163 .632 −.461 .146
Pop −.154 .020 .827 −.104
Dance −.090 −.061 .704 .052
Country .123 .178 .405 .386
US folk .201 .367 .390 .188
Religious music −.103 −.252 −.003 .883
Classical .101 −.048 −.060 .745
Notes: Loadings > .5 are in bold; loadings between .4 and .5 are in italics. Bartlett’s test of sphericity: χ²(78) = 1255.15,
p < .001. KMO measure of sampling adequacy = .671.
Brisson and Bianchi 783
loadings were lower than .4 and the loadings on C2 and C3 were very close. Country exhibited
one loading slightly higher than .4 (C3) and one loading slightly lower than 0.4 (C4). In brief,
C1 clustered the Afro-American trio and rap; C2, rock and metal styles; C3, pop and dance; C4,
classical and religious music.
Table 3 repor ts the results pertaining to the PCA involving the genres included in the STOMP,
soundtracks and alternative excluded, R&B added. We found a four-component solution
accounting for 61.25% of the variance. C1 accounted for 22.65% of the variance. One between-
component correlation higher than .25 was found, between C3 and C4 (r = .285). Table 3
reveals that removing the “alternative” category severed the rock-metal component that we
found thus far. Here, rock was associated with folk and country, but not with metal, which
negatively loaded on a component combining pop, R&B, and dance. In addition, rap (see C1 and
C3) and pop (see C2 and C3) exhibited problematic cross-loadings. C1 associated, again, blues,
jazz, and soul with rap, which did not load very well. C2 reflected taste for pop, R&B, dance, and
distaste for metal. C3 clustered rock, US folk, and country; C4, classical and religious music.
Table 4 reports the results pertaining to the PCA adapting the STOMP to local specificities
and including seven pairs of allegedly close (sub)genres. We found a five-component solution
accounting for 70.15% of the variance. C1 accounted for 19.92% of the variance. One between-
component correlation higher than .25 was found, between C1 and C3 (r = −.291).
Interestingly, the electronic genres were the only ones to load on different components. Dance
loaded on the variété component and house on the metal component. Importantly, metal and
rock loaded separately, here. It should also be noted that blues, classical, jazz, and opera clus-
tered into a single component, that could be considered to involve “highbrow” genres. Finally,
metal (see C3 and C5) and house (see C4 and C5) exhibited problematic cross-loadings. In sum,
we found a variété component including dance (C1), a “highbrow” component (C2), a rock
component (C3), a rap component (C4), and a metal component including house (C5).
Table 5 reports the results pertaining to the PCA including 30 (sub)genres. We found a six-
component solution accounting for 63.44% of the variance. C1 accounted for 19.17% of the
variance. We found three between-component correlations higher than .25, between C1 and
Table 3. Musical taste component scores; Third set, university student sample.
C1 C2 C3 C4
Blues .848 −.177 .222 −.026
Soul .805 .133 .120 −.074
Jazz .796 −.104 .062 .151
Rap .420 .269 −.355 −.070
Pop −.210 .753 .376 −.088
R&B .218 .699 −.224 −.056
Dance −.143 .666 .231 .131
Metal −.036 –.615 .300 .051
Rock .084 −.206 .754 −.225
US folk .204 .167 .693 .026
Country .129 .244 .606 .180
Religious music −.073 .043 −.116 .881
Classical .125 −.105 .036 .752
Notes: Loadings > .5 are in bold; loadings between .4 and .5 are in italics. Bartlett’s test of sphericity: χ²(78) = 1391.97,
p < .001. KMO measure of sampling adequacy = .665.
784 Psychology of Music 48(6)
C3 (r = −.258), C3 and C4 (r = .297), and C3 and C5 (r = .254). C1 exclusively clustered rock
and metal (sub)genres. C2 underlined taste affinities between rap styles, African music, and
reggae, which all constitute genres that have ethnic and social connotations. C3 gathered var-
iété, pop, and Latin music, which can be considered soft, light forms of music. C4 involved the
already encountered trio of blues, jazz, and soul. C5 comprised all the electronic genres included
in the count, with dance showing the lowest loading. Finally, C6 gathered classical, opera, and
religious music. Importantly, enlarging the music-genre selection did not alter the associations
between blues, jazz, and soul and between classical and religious music that we consistently
found thus far. However, Table 5 involved some problematic cases. Country presented two load-
ings slightly lower than .4 on C3 and C4 and was not assignable to a specific component. R&B
exhibited cross-loadings slightly higher and slightly lower than .4 (see C2 and C3). Latin music
and reggaetón loaded on C3, but also presented a loading higher than .3 on C2. The same
applied to African music (see C2 and C4) and dance (see C5 and C3).
Sample 2: Vocational high school students
Table 6 displays the results pertaining to the three PCAs involving our first three sets of music
categories. Those sets comprised the genres included in the STOMP: (a) soundtracks excluded
and with EDM replacing electronica/dance; (b) soundtracks excluded, and with dance replac-
ing electronica/dance; and (c) soundtracks and alternative excluded, R&B added. In each case,
parallel analysis indicated that a one-component solution represented the optimal way to sum
up the data. Table 6 reports the component matrix for each unrotated PCA. The only point of
note, here, regards the negative, albeit low, loadings related to rap.
Table 7 reports the results pertaining to two PCAs. The first PCA (see the three columns on
the left) involved the set adapting the STOMP to local specificities and including seven pairs of
allegedly close (sub)genres. We found a two-component solution accounting for 50.68% of the
variance. C1 accounted for 34.77% of the variance. The between-component correlation was
Table 4. Musical taste component scores: Fourth set, university student sample.
C1 C2 C3 C4 C5
Inter. variété .908 −.036 .176 .001 −.205
French variété .894 .015 .243 .006 −.156
Dance .565 .035 −.056 .184 .112
Jazz −.190 .767 .079 .180 −.225
Classical .174 .761 −.076 −.194 .189
Opera .193 .731 −.161 −.168 .297
Blues −.190 .650 .223 .180 −.243
Rock .230 .008 .891 −.007 .159
Alt rock .146 .004 .852 −.002 .091
Rap .108 −.026 −.107 .884 .057
Conscious rap .029 −.014 .110 .859 .196
Extreme metal −.247 −.010 .278 .001 .758
Metal −.208 −.017 .425 .004 .749
House .149 .036 −.229 .393 .577
Notes: Loadings > .5 are in bold; loadings between .4 and .5 are in italics. Bartlett’s test of sphericity: χ²(91) = 1916.43,
p < .001; KMO = .600.
Brisson and Bianchi 785
equal to .337. Results are hardly interpretable but highlight an opposition between rock and
metal (sub)genres and all the other categories, house excepted. Numerous problematic cross-
loadings were found (e.g., see rap, blues, jazz, and classical). Compared with the previous sam-
ple, it is worth noting that involving pairs of allegedly close (sub)genres did not result in finding
corresponding, specific components.
The second PCA (see the three columns on the right) involved the set including 30 (sub)
genres. We found a two-component solution accounting for 39.56% of the variance. C1′
accounted for 26.65% of the variance. The between-component correlation was equal to .226.
Interpreting this two-component solution is arduous. Opera, country, and religious music
appeared to be component-free. Jazz exhibited loadings oscillating around 0.4. While French
rap negatively loaded on C1′ and presented a .39 loading on C2′, American rap and conscious
rap positively loaded on C2′, albeit poorly. Several items loaded only weakly (e.g., blues,
Table 5. Musical taste component scores: Fifth set, university student sample.
C1 C2 C3 C4 C5 C6
Hard rock .864 .033 −.026 −.079 −.007 .029
Metal .830 .117 −.119 −.209 −.026 .119
Rock .794 −.081 .161 .209 .057 −.177
Symphonic metal .784 .074 .069 −.269 −.070 .206
Punk .757 .154 −.002 .014 .103 −.034
Alt rock .718 −.065 .047 .272 .074 −.240
1960–70s rock .701 −.036 .026 .370 .051 −.148
Extreme metal .694 .147 −.108 −.231 −.060 .183
Conscious rap .229 .805 −.062 .108 .025 −.145
French rap −.058 .789 .039 .024 .144 −.059
American rap −.056 .733 −.138 .154 .217 −.218
Reggae/ska .247 .664 .200 .232 −.082 −.057
African music .015 .515 .203 .377 −.133 .207
R&B −.237 .405 .392 .042 .135 −.109
International variété .055 −.014 .843 −.195 .020 .180
French variété .129 −.018 .759 −.170 −.033 .242
Latin music −.174 .318 .665 −.066 −.111 .043
Reggaetón −.174 .345 .631 −.161 −.080 −.049
Pop −.033 −.227 .610 −.027 .272 −.108
Country .148 −.159 .398 .349 −.155 .236
Jazz −.150 .109 −.291 .886 .019 .303
Blues .023 .158 −.204 .863 −.129 .174
Soul −.075 .287 −.047 .794 −.070 .050
Techno .042 .227 −.169 −.045 .805 .180
House .069 .166 −.125 −.140 .785 .231
Electropop .029 −.057 .175 −.086 .770 −.059
Dance −.023 −.034 .366 −.020 .566 .112
Opera −.015 −.149 .116 .275 .134 .813
Religious music .004 −.052 .236 .055 .076 .794
Classical −.011 −.195 .064 .368 .132 .738
Notes: Loadings > .5 are in bold; loadings between .4 and .5 are in italics. Bartlett’s test of sphericity: χ²(435) = 5529.80,
p < .001. KMO = .813.
786 Psychology of Music 48(6)
classical, dance, electropop). The only clear pattern here refers to the high loadings of rock and
metal items on C1′. However, the loadings related to blues, classical, and electropop prevent C1′
from being considered a proxy for a hardcore, rock, and metal dimension. Finally, contrary to
the corresponding PCA among university students, electronic music genres did not load on the
same component.
In order to assess whether the structure of high-schoolers’ musical tastes was at best two-
fold, we performed a supplementary PCA involving a set of 20 (sub)genres, the selection princi-
ples of which are reported in the previous section. Table 8 displays a four-component solution
accounting for 60.95% of the variance. C1 accounted for 25.50% of the variance. Two
between-component correlations higher than .25 were found, between C2 and C3 (r = .314)
and between C2 and C4 (r = .325). Table 8 reveals that a greater number of genres and subgen-
res does not automatically entail a better understanding of the structure of musical tastes.
Contrary to Table 7, Table 8 highlights some clear patterns indeed. C2 gathers (sub)genres that
have ethnic connotations and may be perceived by the respondents as more danceable than the
others. C3 clustered “pop,” “soft,” and “unsophisticated” (sub)genres. C4 exclusively related to
rap, which has ethnic connotations as well but may be considered as less danceable than the
(sub)genres forming C2. C1, however, is hardly interpretable, since it associated rock and metal
(sub)genres with house, jazz, and classical. In addition, R&B presented very poor loadings, and
reggae and electropop exhibited problematic cross-loadings.
Discussion
Our research goal was to assess the extent to which the structure of musical tastes in genre-
based surveys is affected by modifications in the set of (sub)genres under study.
Our results indicate that applying even minor modifications in the selection of musical items
produces important differences in the emerging latent structure of musical tastes. At least, this
conclusion can be drawn from the analysis of the university student sample. As an illustration,
Table 6. Musical taste component scores: First, second, and third sets, high-schooler sample.
First set Second set Third set
Alt rock .815 Alt rock .817 Blues .772
Rock .772 Rock .776 Jazz .712
Blues .750 Blues .749 US folk .708
EDM .722 Metal .699 Rock .691
Metal .706 US folk .694 Classical .660
US folk .706 Jazz .676 Soul .659
Jazz .663 Classical .643 Metal .602
Classical .625 Soul .611 Country .594
Soul .615 Country .572 Pop .444
Country .554 Pop .380 Dance .430
Pop .377 Dance .353 R&B .241
Rap −.173 Rap −.154 Religious music .085
Religious music .026 Religious music .036 Rap −.059
% explained variance 38.70 35.90 31.68
KMO .788 .764 .764
Notes: All Bartlett’s tests were significant at p < .001. EDM = Electronic Dance Music.
Brisson and Bianchi 787
the data pertaining to that sample showed that mobilizing the “EDM” or the “dance” category
resulted in finding a different number of components (five vs. four) and different between-item
associations. Given the overlap between EDM and dance, this result prompts us to interpret the
obtained component solutions with great caution. In a similar vein, our data indicated that
removing the “alternative rock” category—which can be considered redundant with the “rock”
category—severed the association between rock and metal and revealed unseen links between
rock, folk, and country. Overall, while the number of components did not massively fluctuate
from one PCA to the other, numerous between-item associations substantially varied. For
example, depending on the cases, rap and/or its subtypes appeared to be associated with (a)
Table 7. Musical taste component scores: Fourth and fifth sets, high-schooler sample.
Fourth set Fifth set
C1 C2 C1′C2′
Metal .941 −.128 Metal .890 −.196
Alt rock .926 −.019 Hard rock .889 −.194
Extreme metal .888 −.166 Alt rock .864 −.089
Rock .814 .043 Extreme metal .825 −.181
Rap −.485 .403 Symphonic metal .813 −.175
House .425 .300 Rock .813 −.003
French variété −.203 .770 1960–70s rock .790 .045
Inter. variété −.180 .750 Punk .604 .083
Conscious rap −.036 .523 French rap −.520 .389
Blues .414 .521 House .483 .218
Dance −.092 .518 Blues .462 .348
Jazz .335 .489 Classical .452 .206
Classical .353 .470 Techno .424 .283
Opera .259 .456 Electropop .418 .323
– – – Opera .348 .214
– – – Country .346 .244
– – – Latin music −.166 .690
– – – Inter. variété .041 .626
– – – French variété .034 .572
– – – Reggaetón .162 .567
– – – African music −.226 .519
– – – Soul .272 .518
– – – R&B −.090 .516
– – – Reggae/ska .228 .509
– – – Pop .126 .508
– – – American rap −.218 .450
– – – Conscious rap .059 .436
– – – Dance .082 .409
– – – Jazz .382 .402
– – – Religious music −.115 .315
Bartlett’s test χ²(91) = 902.13; p < .001 Bartlett’s test χ²(435) = 1752.72, p < .001
KMO .697 KMO 810
Notes: Loadings > .5 are bolded; loadings between .4 and .5 are italicized.
788 Psychology of Music 48(6)
EDM; (b) blues, jazz, and soul but not with dance; (c) reggae and African music but not with
blues, jazz, and soul or with any electronic music genre; or (d) to form a single component. Such
variations impede our ability to interpret the obtained components in terms of underlying
music dimensions. Even apparently strong associations dislocated when applying subtle modi-
fications in the item selection. The cases of the rock/metal and dance/pop connections are
emblematic in that respect. Interestingly, our data also point out that using categories such as
“electronic dance music” may be too vague and may not account for nodal distinctions within
that taxon. We indeed observed that dance and house did not systematically load on the same
component. Importantly, we found only two consistent associations throughout our analyses
related to the university student sample. They refer to the connections between classical and
religious music and between blues, jazz, and soul. Notably, none of these associations was found
by Rentfrow and Gosling (2003), and we did not retrieve them within the high-schoolers’ dis-
tribution of musical tastes. Thus, our findings suggest that interpreting component solutions in
terms of general, underlying music dimensions may be hazardous and speculative. This being
said, the results related to high-schoolers may be perceived as providing counter-evidence to
that conclusion. Data pertaining to that sample appeared to be impermeable to minor changes
in the item selection, since we mainly found one- or two-component solutions when examining
the structure of high-schoolers’ musical tastes. In our view, however, those component solu-
tions may reflect an inadequacy between the selected items and the participants’ tastes rather
than an immunity to modifications in the item selection. The fact that we found a four-compo-
nent solution when mobilizing a specific set of music (sub)genres that combined selection crite-
ria such as main tastes and distastes, label knowledge, and ethnic features, advocates this view.
Table 8. Musical taste component scores: sixth set, high-schooler sample.
C1 C2 C3 C4
Hard rock .898 −.098 −.063 −.124
Metal .879 −.054 −.104 −.122
Extreme metal .840 −.022 −.172 −.048
Rock .783 −.091 .111 .013
House .574 .183 .142 −.174
Jazz .571 .155 −.010 .370
Classical .487 −.164 .156 .201
African music −.165 .880 −.147 −.068
Zouk −.028 .723 .183 −.112
Reggaetón .380 .704 −.072 .098
Raï −.269 .689 −.183 .069
Latin music −.146 .659 .292 −.041
Reggae/ska .426 .555 .015 .025
French variété −.098 −.045 .908 −.075
International variété −.068 .099 .895 −.126
Pop .134 −.016 .576 .223
Electropop .413 −.141 .521 −.005
R&B −.047 .274 .302 .266
American rap .072 .011 −.077 .854
French/francophone rap −.304 −.093 .026 .782
Notes: Loadings > .5 are in bold; loadings between .4 and .5 are in italics. Bartlett’s test of sphericity: χ²(190) = 1208.55,
p < .001. KMO = .723.
Brisson and Bianchi 789
In sum, data related to both samples suggest, though in different ways, that applying even sub-
tle modifications in the music-genre selection is likely to substantially alter PCA results. As a
consequence, it may be counter-productive to consider the component-solutions found in the
literature general, robust indicators of underlying music dimensions. The study of the links
between musical tastes and personality should therefore involve more reliable taste indicators
and/or methods of analysis than those employed thus far.
Interestingly, our analyses related to the high-schooler sample revealed unusual patterns of
results. The various one- and two-component solutions pertaining to that sample strongly con-
trast with the four-, five- or six-component solutions obtained when examining the tastes of
university students and with the component solutions generally found in the literature (Brown,
2012; Colley, 2008; Delsing et al., 2008; Franken et al., 2017; Gardikiotis & Alexandros Baltzis,
2012; George et al., 2007; Gouveia et al. 2008; Langmeyer et al., 2012; Rentfrow & Gosling,
2003; Schäfer & Sedlmeier, 2009; ter Bogt, Raaijmakers, Vollebergh, van Wel, & Sikkema,
2003; Vella & Mills, 2017; Zweigenhaft, 2008). Accounting for such differences is difficult
because several factors, either sociodemographic or methodological, probably contribute to
these atypical structures of musical tastes. In particular, one might assume that the mean age
of the surveyed high-schoolers and the administration of our questionnaire in the classrooms
partly explained our unusual results. However, because psychological researchers have pointed
out that (a) the structure of musical tastes is relatively age-invariant (Rentfrow et al., 2011;
Bonneville-Roussy, Rentfrow, Xu, & Potter, 2013), and (b) musical tastes are consistent during
adolescence and from adolescence to early adulthood (Delsing et al., 2008; Mulder et al., 2010),
effects of age are rather unlikely here. Similarly, it is unlikely that administrating our question-
naire in the classrooms played a determinant role. Delsing and colleagues (2008) did the same
with Dutch teenagers and found a four-component solution in their study involving “only” 11
music genres. In our estimation, the singularity of our findings may be mainly due to two fac-
tors. First, the common use of the Kaiser criterion, coupled or not with other techniques, in the
definition of the number of component(s) to be extracted. Although the Kaiser criterion has
long been showed to involve over-extraction (O’Connor, 2000; Zwick & Velicer, 1986), a num-
ber of psychology researchers have continued to rely on it (e.g., Gouveia et al. 2008; Vella &
Mills, 2017). This state of affairs may partly explain why one- or two-component solutions
have not been found in the psychological literature dedicated to the structure of musical tastes.
Second, the social background and the cultural capital of the respondents. Bourdieu (1984)
distinguished between the acquired dimension of cultural capital (indexed by individuals’ own
education) and its inherited dimension (indexed by parents’ occupation and/or education).
Within this framework, vocational high-schoolers possess a smaller volume of acquired cul-
tural capital than general high-schoolers and university students. Furthermore, sociologists
have shown that differences in acquired cultural capital were associated with differences in
cultural tastes and practices (Bourdieu, 1984), especially in terms of taste scope (Peterson,
1992). Our findings related to high-schoolers echo such dynamics. Indeed, our raw data indi-
cates that vocational high-schoolers reported to like, on average, only nine of the 40 (sub)gen-
res included in our music inventory. Five of these nine categories were associated with a mean
degree of appreciation very close to the neutral score (i.e. 3 on a five-point rating scale). Those
nine categories, moreover, do not cover a large array of music styles, since they refer, inter alia,
to three rap subtypes and two variété subtypes. Comparatively, university students reported to
like, on average, 27 of the same 40 (sub)genres. Thus, the singularity of our results may be the
consequence of the sociological singularity of our high-schooler sample. The great number of
genres disliked by vocational high-schoolers and the corresponding levels of distaste may
explain why (a) we mainly found one- and two-component solutions, and (b) subtle
790 Psychology of Music 48(6)
modifications in the item selection did not alter PCA results related to that sample. Because
most studies in psychology of music have surveyed university students (Schäfer and Mehlhorn,
2017) or volunteers (i.e., mostly people interested in music), and because university students
from the lower classes are likely to compensate their low inherited cultural capital with a rela-
tively high acquired cultural capital (Bourdieu, 1984), people endowed with low acquired cul-
tural capital may have been under-represented in psychological research on music. This may
explain why one- or two-component solutions have not been found in the literature. Although
only complementary studies would enable us to comprehensively address these issues, our
study suggests that variables such as cultural capital should not be neglected in the examina-
tion of the determinants of the structure of musical tastes—and in the creation of musical-
taste inventories. Given the links between openness and academic performance (Poropat, 2009,
2014), integrating psychological (e.g., personality) and sociological (e.g., cultural capital) vari-
ables may improve our understanding of musical tastes.
Importantly, results related to the university student sample (see the first two sets of music
genres) do not corroborate the structure of musical preferences that Rentfrow and Gosling
(2003) delineated when administrating the STOMP. While we identified, as these authors did, a
component including rock, alternative rock, and metal, this is the sole finding that both studies
have in common. Rentfrow and Gosling (2003) found that blues, jazz, classical, and folk formed
a component that they named “reflective and complex,” because those are “genres that seem to
facilitate introspection and are structurally complex” (p. 1241). We found that blues and jazz
consistently constituted a component with soul, but not with classical and folk, which loaded on
different components. This finding suggests that the question of intrinsic “structural complex-
ity” may be ancillary. Moreover, we do not see reasons to consider those genres as stronger facili-
tators to introspection than, for instance, religious music. Similarly, we did not retrieve the
“upbeat and conventional” component found by Rentfrow and Gosling (2003). This component
comprised four genres that “emphasize positive emotions and are structurally simple” (p. 1241),
namely country, soundtracks, religious music, and pop. Such mismatch cannot be solely imputed
to the exclusion from our study of the “soundtracks” category—that we considered far too inac-
curate and that refers to pieces that do not “emphasize positive emotions,” like in horror movies,
and pieces that are not “structurally simple” (e.g., listen to Danny Elfman’s orchestral composi-
tions). Indeed, we found religious music to form a two-item component with classical music. This
result further questions the consistency of the authors’ rationale based on structural complexity,
since this component gathers a “structurally complex” (i.e. classical) and a “structurally simple”
genre (i.e. religious music). Furthermore, although our data accounted for connections between
country and pop, the links in question were very weak. Notably, we found a stronger association
between country and folk than between country and pop. Again, our findings cast doubts upon
the validity of a rationale based on degrees of “structural complexity,” a concept that Rentfrow
and Gosling (2003) did not define. Finally, our results showed that rap was associated with EDM
but not with dance. Thus, we did not systematically retrieve the “energetic and rhythmic” com-
ponent that Rentfrow and Gosling (2003, p. 1242) identified and that involves “genres that are
lively and often emphasize the rhythm.” It should be noted, in passing, that genres such as hard
rock or metal also emphasize the rhythm and that genres assignable to the “electronica” cate-
gory (e.g., ambient) do not. Although the discrepancies between Rentfrow and Gosling’s results
and ours are likely national-dependent, these discrepancies question the reliability of the
authors’ line of interpretation. Again, our findings suggest that the component solutions
obtained via PCAs should be interpreted with the utmost caution.
Analogous conclusions can be drawn from the comparison between our results and the
results derived from the MUSIC model developed by Rentfrow and colleagues (Bonneville-Roussy
Brisson and Bianchi 791
et al., 2013; Rentfrow et al., 2011; Rentfrow et al., 2012). Used by its creators in both excerpt-
and genre-based framework, the MUSIC model discriminates between five music dimensions:
“mellow” (i.e. smooth, quiet, and slow; e.g., R&B and soft rock), “unpretentious” (i.e. not loud,
distorted, nor fast; e.g., country and folk), “sophisticated” (instrumental, not electric, distorted,
nor loud; e.g., classical and traditional jazz), “intense” (i.e. electric, distorted, loud, percussive;
e.g., metal and rock), and “contemporary” (i.e. percussive, electric, not sad; e.g., electronica,
Latin, and rap). Our results did not reflect the MUSIC model. In particular, we did not retrieve the
“contemporary” dimension, since rap, Latin, and electronic (sub)genres loaded on distinct com-
ponents. Because these (sub)genres were well known by the participants and may be more easily
recognizable than soul, R&B, or alt rock, those are solid findings that question the general valid-
ity of the MUSIC model. Indeed, the sole loadings of rap, Latin, and electronic (sub)genres on
different components almost totally reshape the structure that the MUSIC model delineates. In
addition, depending on the cases, we found rock to be associated with country, R&B with dance,
house with metal, or classical and jazz with rock. In other words, we did not retrieve the “mel-
low,” “unpretentious,” and “sophisticated” dimensions. Our results therefore suggest that the
dimensions involved in the MUSIC model are not robust enough to be generalized.
Conclusion
The present study examined the relevance of the genre-based analyses commonly used in psy-
chological research on musical tastes. We found that even subtle modifications in the item
selection sufficed to substantially alter PCA results and identify antithetic patterns in the struc-
ture of musical tastes. Given the inconsistency of the obtained components, interpreting them
as reflecting general music dimensions is problematic. Moreover, our study suggests that social
background in general, and cultural capital in particular, may markedly affect the structure of
musical tastes. Such variables should not be neglected in research on musical tastes. Finally,
our findings indicate that interpreting the dimensions of music genres in terms of their intrin-
sic properties is probably misleading. All in all, the present study questions the relevance and
the validity of the genre-based analyses ordinarily employed in psychological research on musi-
cal tastes. It also suggests that research on musical tastes may benefit from a concomitant
examination of sociological and psychological variables.
Declaration of conflicting interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or
publication of this article.
Funding
The authors received no financial support for the research, authorship, and/or publication of this article.
ORCID iD
Romain Brisson https://orcid.org/0000-0002-4063-8186
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794 Psychology of Music 48(6)
Appendix. Music inventory statistics: Label knowledge and mean scores of appreciation
(five-point rating scale).
Sample 1 (n = 522) Sample 2 (n = 185)
DK (%) M SD DK (%) M SD
1960–1970s progressive rock 17.05 3.89 1.13 22.70 1.77 1.31
African music 6.70 3.30 1.14 5.95 2.79 1.53
Alt rock 21.07 4.01 1.05 24.86 1.69 1.21
American rap/hip-hop 1.34 3.48 1.28 1.62 4.27 1.14
Blues 1.15 3.65 1.02 25.41 1.97 1.07
Classical 0.00 3.77 1.02 4.32 1.80 1.13
Conscious rap 10.54 3.44 1.36 15.68 3.03 1.55
Country 0.38 3.07 1.19 10.81 1.81 1.06
Dance 0.19 3.33 1.14 4.32 2.84 1.35
Electropop 2.30 3.34 1.21 11.89 2.87 1.43
Experimental rock/art rock 39.08 3.46 1.19 24.32 1.66 1.20
Extreme metal 5.36 1.93 1.27 15.68 1.46 0.98
French/francophone variété 2.68 3.37 1.23 5.95 3.07 1.42
French/francophone rap/hip-hop 1.72 3.07 1.36 1.08 4.44 1.09
Gypsy jazz 44.83 3.48 1.19 34.59 1.64 1.10
Hard rock 1.53 2.87 1.41 13.51 1.65 1.09
House 8.05 2.66 1.36 30.81 2.09 1.39
International variété 10.73 3.34 1.06 8.65 3.08 1.43
Jazz 0.38 3.55 1.16 7.57 1.89 1.15
Latin music 1.92 3.35 1.29 6.49 3.13 1.49
Metal 1.53 2.60 1.47 8.11 1.59 1.11
New age/atmospheric 46.93 3.12 1.20 56.76 1.74 1.17
New wave/goth/post-punk 43.68 2.87 1.31 54.59 1.60 1.04
Opera 0.96 2.89 1.21 8.11 1.48 1.01
Pop 0.00 3.95 1.00 3.78 3.44 1.37
Punk rock 8.81 3.04 1.23 17.30 1.80 1.13
R&B 3.07 3.05 1.25 9.73 3.62 1.46
Raï 61.88 2.58 1.10 18.92 3.04 1.56
Rap-metal 33.33 2.64 1.35 26.49 2.10 1.38
Reggae/ska 3.26 3.15 1.26 13.51 2.84 1.57
Reggaetón 7.09 2.76 1.32 24.86 2.76 1.56
Religious music 4.60 2.38 1.22 9.19 2.14 1.41
Rock 0.57 4.02 1.00 3.78 1.99 1.36
Soul 5.17 3.62 1.04 24.32 1.99 1.24
Symphonic metal 21.07 2.57 1.47 21.08 1.46 0.98
Techno 1.15 2.72 1.35 10.81 2.42 1.44
Text songs 7.09 3.90 0.98 42.16 2.75 1.40
Trip hop 60.34 3.10 1.40 47.03 2.01 1.42
US folk 18.77 3.30 1.10 41.62 1.93 1.20
Zouk 36.59 2.53 1.14 17.84 2.77 1.54
Note: “DK” refers to the percentage of participants who reported not to know the corresponding (sub)genre.
