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What Do Music Preferences Reveal About Personality?: A Cross-Cultural Replication Using Self-Ratings and Ratings of Music Samples


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The present study is the first to examine the relationship between music preferences and personality among a sample of young Germans (N = 422, age range 21–26 years). We replicated the factor structure of the Short Test of Music Preferences (STOMP, Rentfrow & Gosling, 2003) by means of confirmatory factor analysis (CFA). The validity of the STOMP was also confirmed for the first time by rating soundclips. The relationship between the dimensions of personality (Big Five Inventory) and music preferences (STOMP and soundclips) was analyzed with a structural equation model (SEM). Gender differences were examined with multigroup analyses (MGA). Our findings corroborate earlier findings on the relationship between music preferences and personality: Individuals open to experience prefer reflective and complex music (e.g., classical) and intense and rebellious music (e.g., rock), whereas they dislike upbeat and conventional types of music (e.g., pop music). Extraverts, on the other hand, prefer upbeat and conventional and energetic and rhythmic types of music (e.g., rap/hip-hop). The results reveal some gender differences.
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Original Article
A. Langmeyer et al.: Music Preference and PersonalityJournal of Individual Differences201 2; Vol. 33(2):119–130© 2012 Hogrefe Publishing
What Do Music Preferences
Reveal About Personality?
A Cross-Cultural Replication Using
Self-Ratings and Ratings of Music Samples
Alexandra Langmeyer, Angelika Guglhör-Rudan, and Christian Tarnai
Universität der Bundeswehr München, Neubiberg, Germany
Abstract. The present study is the first to examine the relationship between music preferences and personality among a sample of
young Germans (N = 422, age range 21–26 years). We replicated the factor structure of the Short Test of Music Preferences (STOMP,
Rentfrow & Gosling, 2003) by means of confirmatory factor analysis (CFA). The validity of the STOMP was also confirmed for the
first time by rating soundclips. The relationship between the dimensions of personality (Big Five Inventory) and music preferences
(STOMP and soundclips) was analyzed with a structural equation model (SEM). Gender differences were examined with multigroup
analyses (MGA). Our findings corroborate earlier findings on the relationship between music preferences and personality: Individuals
open to experience prefer reflective and complex music (e.g., classical) and intense and rebellious music (e.g., rock), whereas they
dislike upbeat and conventional types of music (e.g., pop music). Extraverts, on the other hand, prefer upbeat and conventional and
energetic and rhythmic types of music (e.g., rap/hip-hop). The results reveal some gender differences.
Keywords: music preference, audio stimuli, music and personality, Short Test of Music Preference, STOMP
For years researchers have studied the psychological ef-
fects of music and its impact on people (Cattell & Saunders,
1954; Hilliard, 2001), some stating that music fans vary in
their characteristics (e.g., Adorno, 1962). One aspect that
gained importance during recent years is the interaction be-
tween music preferences and personality (e.g., Cattell &
Anderson, 1953; Delsing, Ter Bogt, Engels, & Meeus,
2008; Litle & Zuckerman, 1986; Rentfrow & Gosling,
2003, 2006, 2007; Zweigenhaft, 2008); that aspect is ex-
plored in the present study.
Approaches to Studying Music Preferences
and Personality
Individual differences in music preferences and personality
have been examined over the years with a variety of meth-
ods and instruments (Dunn, 2009). Early investigations
measured music preferences with the IPAT Music Prefer-
ence Test (Cattell & Anderson, 1953; Cattell & Saunders,
1954), where participants had to rate pieces of music they
had heard before. The ratings were then interpreted as un-
conscious personality traits. In their analysis of different
groups of individuals, researchers found 12 factors. Yet the
results were contradictory, and subsequent validity and re-
liability analyses turned up inconsistent (Healey, 1973).
A second line of research considered ratings of music
genres for measuring music preferences. Litle and Zucker-
man (1986) developed the Music Preference Scale (MPS)
and related it to the Sensation Seeking Scale Form V
(Zuckerman, Eysenck, & Eysenck, 1978). The MPS re-
quires subjects to rate how they like particular styles of
music such as classical music. Sensation seeking correlated
positively with all types of rock music and negatively with
bland film and television soundtrack music (Litle &
Zuckerman, 1986; McNamara & Ballard, 1999). Several
research groups used a short form of the MPS and the NEO
Personality Inventory (NEO-PI, Costa & McCrae, 1985) or
alternatively the revised version NEO-PI-R (Costa & Mc-
Crae, 1992) in their studies (Dollinger, 1993; Rawlings &
Ciancarelli, 1997). Results revealed three patterns of pref-
erences: rock music, popular music, and general breadth of
musical preferences (Rawlings & Ciancarelli, 1997). Major
predictors for music preferences were found to be Extra-
version and Openness (Dollinger, 1993; Rawlings & Cian-
carelli, 1997), with extraverts scoring high on the popular
music factor (Rawlings & Ciancarelli, 1997) and on music
with higher arousal potential, such as jazz and hard rock
music (Dollinger, 1993), respectively. Openness was found
DOI: 10.1027/1614-0001/a000082
© 2012 Hogrefe Publishing Journal of Individual Differences 2012; Vol. 33(2):119–130
to be positively correlated with the factors breadth of pref-
erences and rock music (Rawlings & Ciancarelli, 1997),
and with new age, classic, jazz, reggae, folk-ethnic, and
soul music (Dollinger, 1993). As in previous studies
(Daoussis & McKelvie, 1986; Litle & Zuckerman, 1986),
the Extraversion facet “excitement seeking” was found to
be connected with preferring hard rock (Dollinger, 1993;
Rawlings & Ciancarelli, 1997).
In other studies the design was extended by adding rat-
ings of soundclips to the MPS (Rawlings, Hodge, Sherr, &
Dempsey, 1995; Rawlings, Twomey, Burns, & Morris,
1998). Personality was measured by the Eysenck Person-
ality Inventory (EPI; Eysenck & Eysenck, 1976). The mu-
sic excerpts turned up similar results as with the MPS. Psy-
choticism (tough-mindedness) and Extraversion were pos-
negatively with liking electronic, religious, and soundtrack
In sum, the personality dimensions Psychoticism, Open-
ness, and Extraversion, particularly Sensation Seeking,
were deemed the strongest predictors of music preferences.
Studies About Personality and Music
Preferences Using STOMP
The first comprehensive measure of music preferences, the
Short Test of Music Preferences (STOMP; Rentfrow &
Gosling, 2003), has been applied in research since 2003.
Out of 14 overall rated music genres, four general dimen-
sions were identified using exploratory and confirmatory
factor analyses:
Reflective & Complex (R&C; covering blues, jazz, clas-
sical, and folk music)
Intense & Rebellious (I&R; rock, alternative, heavy met-
al music)
Upbeat & Conventional (U&C; country, sound tracks,
religious, and pop music)
Energetic & Rhythmic (E&R; rap/hip-hop, soul/funk,
electronic/dance music).
Only one study measuring music preferences with the
STOMP (Dunn, de Ruther, & Bouwhuis, 2011) did not con-
firm the factor structure. Consequently, a six-factor solu-
tion or, alternatively, a different four-factor solution was
suggested. However, the composition of the I&R dimen-
sion has remained consistent.
Several researchers (Delsing et al., 2008; George, Stick-
le, Rachid, & Wopnford, 2007; Rentfrow & Gosling, 2003;
Zweigenhaft, 2008) investigated the correlation between
these main dimensions of music preferences and the Big
Five personality factors. Delsing and colleagues (2008)
presented two substudies, one of them being a longitudinal
study, and the research by Rentfrow and Gosling (2003)
contained six different substudies.
Rentfrow and Gosling (2003) as well as George and col-
leagues (2007) measured personality using the Big Five In-
ventory (BFI, John & Srivastava, 1999). Zweigenhaft (2008)
applied the NEO-PI, Delsing and colleagues (2008) assessed
personality with a Dutch adaptation of 30 adjective Big Five
factor markers selected from Goldberg (1992). To measure
music preferences, Rentfrow and Gosling (2003) and Zwei-
genhaft (2008) used the STOMP. Delsing and colleagues
(2008) preferred the Musical Preference Questionnaire
(MPQ, Sikkema, 1999), which is similar to the STOMP, ex-
cept that styles of folk, country, blues, and soundtracks were
not included. Contrary to the STOMP, the music preference
categories were Elite (R&C), Rock (I&R), Pop/Dance
(U&C), and Urban (E&R). George and colleagues (2007)
used a list of 30 different types of music, which contained the
genres of the four STOMP dimensions, among others. A
comparison of correlations found in these four studies is
shown in Table 5 (results). In sum, all studies found positive
correlations between R&C and Openness, ranging from r =
.17(Delsingetal.,2008)tor = .44 (Rentfrow & Gosling,
2003). Openness was also found to be related, albeit weaker,
to I&R, ranging from r = .13 (George et al., 2007) up to r =
.22 (Delsing et al., 2008). For U&C, divergent findings were
reported concerning its relationship to Extraversionand Con-
scientiousness. Whereas all other studies report weak corre-
lations with Extraversion, Delsing and colleagues failed to
find such relationships. On the contrary, similar to Rentfrow
and Gosling (2003), they found connections to Agreeable-
ness, which in turn are not reported by Zweigenhaft (2008)
or by George and colleagues (2007). The factor E&R was
positively correlated with Extraversion in all studies, except
by George and colleagues (2007).
All things considered, Openness is by far the best pre-
dictor for music preferences. The second strong predictor
is Extraversion. However, the varying results might be due
to the different methods applied.
Measuring Music Preferences by Audio
As previously stated, early research measured music pref-
erences using audio stimuli (e.g., IPAT Music Preference
Test; Cattell & Anderson, 1953). In recent research, how-
ever, measuring music preferences by means of rated music
genres has become a common and widespread method.
Measuring music by audio stimuli has become highly top-
ical again because of genre labels being somewhat subjec-
tive: Every participant might have a different understand-
ing of these genres. Given that genres activate stereotypes
associated with traits, individuals’ ratings of genres may be
biased (Rentfrow, Goldberg, & Levitin, 2011). Moreover,
genre labels might not be able to fully describe someone’s
music preference. If someone likes one kind of music from
a special genre this does not mean that he or she likes all
other kinds of music from this genre (Dunn, 2009). In the
latest studies different methods have been established to
120 A. Langmeyer et al.: Music Preference and Personality
Journal of Individual Differences 2012; Vol. 33(2):119–130 © 2012 Hogrefe Publishing
measure music preferences without genres (Rentfrow et al.,
2011). In one of the methods, participants are asked to rate
selected music excerpts (Dunn, 2009; Rentfrow et al.,
2011). Using this approach in three independent studies,
Rentfrow and colleagues (2011) found five latent underly-
ing factors in music preferences:
Mellow (e.g., pop or soft rock),
Unpretentious (e.g., country or rock ’n’ roll),
Sophisticated (e.g., classical or jazz),
Intense (e.g., heavy metal or rock), and
Contemporary (e.g., rap and electronica).
These are by and large comparable to those found in stud-
ies with the STOMP. In his study, Dunn (2009) extracted
nine music preferences components and found them to be
related to personality. The strongest connection was that
reported in the previous studies between the Extraversion
facet “excitement-seeking” and rap music. This study
also showed that music clips familiar to participants were
rated higher than unfamiliar music clips. Another ap-
proach assesses music preferences by measuring listen-
ing behavior. Dunn and colleagues (2011) used this meth-
od as well as the STOMP and the NEO-PI-R to analyze
participants’ listening behavior in the course of a mini-
mum 3-month period. It was found that the duration of
listening to a certain music type was positively correlated
to the reported music preferences for the same genre.
Correlations varied between r = .11 (alternative) and r =
.43 (dance). It is noteworthy that overlapping correlations
between different genres occurred as well. Moreover,
with respect to personality there are different results for
music preferences (STOMP) and listening behavior (du-
ration). Only two comparable correlations were found:
Neuroticism related to classical music and Openness to
jazz music. Considering music preferences there are ad-
ditional correlations between Extraversion and pop,
dance, and rap. However, regarding listening behavior
there are also correlations between Extraversion and re-
ligious music as well as between Agreeableness and
soundtracks (Dunn et al., 2011).
Gender Differences in Music Preferences
and Personality
No studies have yet considered gender differences in the
relationship between music preferences and personality, al-
though a few report gender differences in music preferenc-
es. For example, George and colleagues (2007) found men
prefer rebellious music, i.e., heavy metal or punk music,
and women like easy listening music, i.e., pop or country
music. Colley (2008) and Zweigenhaft (2008) showed that
women rated pop music more favorably; Zweigenhaft
(2008) revealed that they also like punk music more than
men. Similarly, Dunn (2009) found gender differences in
all nine music preferences components. The underlying
factor structure of music preferences also does not have to
be necessarily the same (Colley, 2008). Dunn and col-
leagues (2011) report no gender effects concerning the
amount of music listened to. However, because gender dif-
ferences must be assumed in personality traits (Feingold,
1994) and gender differences may occur regarding the kind
of music listened to, gender effects should be investigated
in further research.
Research Questions
Most of the reported studies have been performed in the
United States and in The Netherlands (Delsing et al., 2008;
Dunn et al., 2011). Although Rawlings, Vidal, and Furn-
ham (2000) provided evidence that there are few crosscul-
tural differences in the association between music prefer-
ences and personality, one cannot generalize these findings
across countries (see Delsing et al., 2008). Therefore, the
current study investigates whether music preferences can
be validly measured with the STOMP in Germany, and
whether personality is a predictor for music preferences in
Germany as well.
Contrary to the majority of studies, our investigation ad-
ditionally analyzes whether ratings of soundfiles differ
from STOMP ratings. Taking into account the difficulties
of rating music genres, a similar pattern would be a good
validation of the STOMP in Germany. Gender differences
in music preferences can be assumed but have not been
tested in relation to personality before.
Altogether, the following four research questions have
been considered:
Is it possible to replicate the factor structure of the
STOMP with German data?
Is it possible to validate the STOMP by soundclips? Or
is there a difference between rating music genres vs. lis-
tening to soundclips?
Is there a relationship between the dimensions of per-
sonality and the music preferences in the German sam-
ple? If so, is it the anticipated relationship?
Are there gender differences regarding the factor struc-
ture of the STOMP and regarding the relationship be-
tween personality and music preferences?
In 2007 and 2008, 422 students at the Universität der Bun-
deswehr München (72.3% male) completed an online ques-
tionnaire. The age of the participants ranged from 21 to 26
years. In order to avoid age effects as reported in previous
studies (e.g., Delsing et al., 2008; George et al., 2007;
Zweigenhaft, 2008), we intentionally sampled a narrow
age range for this study.
A. Langmeyer et al.: Music Preference and Personality 121
© 2012 Hogrefe Publishing Journal of Individual Differences 2012; Vol. 33(2):119–130
The present study was part of a larger study examining the
relationships between personality, vocational interests, lei-
sure, and study preferences. In that context, a slightly modi-
fied version of the 14-item STOMP (Rentfrow & Gosling,
2003) was used. As mentioned above, the STOMP includes
four dimensions: Reflective & Complex (R&C), Intense &
Rebellious (I&R), Upbeat & Conventional (U&C), and En-
ergetic & Rhythmic (E&R). For item wording and scale com-
position see Table1. Due to low popularity of religious music
and differences in the context and meaning of folk and coun-
trymusic in Germany, these originalcategories were replaced
by the more common German genres “Popular German Mu-
sic” (PGM, “Populäre Volksmusik”) and “New German
Wave” (NGW, “Neue Deutsche Welle”). After rating ones
preference for each genre on a 7-point Likert-type scale (–3 =
I dislike very much; +3 = Ilikeverymuch), the participants
had to rate the following four soundclips including several
short excerpts of pieces of music using the same type of re-
sponse scale. The four soundfiles are composed as suggested
by Rentfrow (2004) based on a former version of his home-
page andtherefore reflect the fourdimensionsoftheSTOMP:
Reflective & Complex: “Ride” (Nick Drake), “Fantasy
and Fugue in C minor, BWV 906” (composed by Johann
Sebastian Bach, performed by Glenn Gould), “Stella by
Starlight” (Herbie Hancock), “40 Days and 40 Nights”
(Muddy Waters), “Time Out” (Dave Brubeck Quartet)
Intense & Rebellious: “Bullet with Butterfly Wings”
(Smashing Pumpkins) “Voodoo Child” (Jimi Hendrix),
“Fight Song” (Marilyn Manson), “Angel of Death
(Slayer), “Money” (Pink Floyd), “Verse Chorus Verse”
Upbeat & Conventional: “Tell me that I’m Dreaming”
(Backstreet Boys), “Come, Now is the Time to Worship”
(WOW Worship), “Ready to Run” (Dixie Chicks), “I’m
a Slave (4 U)” (Britney Spears)
Energetic & Rhythmic: “It Takes Two” (Rob Base and DJ
EZ Rock), “In-Flux” (DJ Shadow), “The Next Episode”
(Dr. Dre featuring Nate Dogg and Snoop Dogg), “Pick Up
the Pieces” (Average White Band), “Roll it Up” (Crystal
Method), “Everything is Everything” (Lauryn Hill)
To assess personality, we used a shortened 20-item version
of the German 42-item version (Lang, Lüdtke, & Asen-
dorpf, 2001) of the Big Five Inventory (BFI, John & Sris-
tava, 1999). Each personality dimension consists of four
items, which were rated (“I see myself as someone who
. . .”) on a 5-point Likert-type scale (–2 = very inapplicable;
+2 = very applicable). 16 items from the original German
BFI (Lang et al., 2001) were selected based on analyses of
our working group (Schmolck, 2004). Two items were
modified for each of the dimensions Agreeableness and
Neuroticism. For the wording see Table 3 in which the orig-
inally selected items are numbered according to Table 1 in
Lang and colleagues (2001).
A structural equation model (SEM) was estimated (AMOS
19.0) for the first research question. We used the generalized
least square (GLS) fit function to estimate the model param-
eters. Goodness of fit was assessed with the χ² test and select-
ed global fit indices (see Hu & Bentler, 1999). The assump-
tion of multivariate normal distributed data was tested. In
case of nonnormal data, the χ² test for the assessment of mod-
el fit is known to overly reject models of acceptable fit.
Hence, bootstrap analyses were performed (Bollen & Stine,
1993), and the normed χ²measure(χ²
; Jöreskog, 1969)
was considered. Recommendations of what can be consid-
ered sufficient model fit vary between χ²
< 5 (Bollen & Long, 1993). Three types of global fit
indices were applied: the comparative fit index (CFI; Bentler,
1990; cutoff .95), the root mean square error of approxima-
tion (RMSEA; Steiger, 1989; cutoff = .06), which is particu-
larly recommended for personality studies (Raykov, 1998),
and the standardized root mean square residual (SRMR;
Jöreskog & Sörbom, 1981; cutoff = .11). A critical α of .05
was assumed in all of our analyses.
We estimated a confirmatory factor analysis (CFA) in-
cluding the four latent music variables and the related three
or alternatively four music items per dimension to validate
the original structure of the STOMP. A multigroup analysis
(MGA) was performed with regard to research question
four. In the first step, the measurement weights were con-
strained, in following steps each of the remaining free pa-
rameters. Goodness of fit was assessed with the overall χ².
For each step of equality constraints, the nested χ² differ-
ence test and the global fit indices were considered. Be-
cause a model does not imperatively have to be refused
considering a significant χ² difference (Cheung & Rens-
vold, 2002; Little, 1997), the same cutoff criteria as cited
above held good for the global fit indices.
In the next step, descriptive statistics are reported in-
cluding reliabilities of the STOMP scales for better com-
parability to the results of previous studies. For this reason,
we calculated averages of scales (SPSS 19.0).
For the second research question, we calculated bivari-
ate correlations of averages of STOMP dimensions with
the soundfiles. Furthermore, the STOMP CFA model was
extended with the ratings of the soundfiles as additional
manifest variables for the latent music dimensions. To con-
trol for gender effects, an MGA was performed.
To address the third research question, we first tested the
Big Five personality model with a CFA. Next, we calculat-
ed descriptive statistics and computed mean scores for the
personality dimensions. These five mean scores of person-
ality were correlated with the STOMP mean scores and
with the four soundfiles. Then, a comprehensive SEM was
defined, including the Big Five personality dimensions,
modeled in the exogenous measurement model, and the
four STOMP dimensions, modeled in the endogenous mea-
surement model (see Figure 1). The STOMP dimensions
122 A. Langmeyer et al.: Music Preference and Personality
Journal of Individual Differences 2012; Vol. 33(2):119–130 © 2012 Hogrefe Publishing
were estimated using both the particular STOMP items and
the corresponding soundfiles, according to the CFA model
for research question two. In the structural model, all paths
were estimated. No error correlations were allowed. How-
ever, correlations between Extraversion and Neuroticism
and between I&R and U&C were allowed. To address re-
search question four, again, a MGA model was estimated.
Factor Structure of the STOMP
The structure of the four-dimensional CFA including the
STOMP items is shown in Table 1. The results indicate that
our recursive model provides an adequate fit (Mardia test:
z = 5.68, p < .001; χ²
= 262.80, p < .000, p
= .005). The
normed χ² value of χ²
= 4.45 can be considered accept-
able. Analysis of the global model fit indicates a rather poor
model (CFI = .638, RMSEA = .091). However, the stan-
dardized root mean square residual (SRMR = .096) indi-
cated a rather good model fit. In a simulation study, Fan,
Thompson, and Wang (1999) found that the incremental fit
indices underestimate model fit when applied in context
with the GLS method. Hence, the global fit indices for our
model can be considered satisfying.
The two new items “Popular German Music (PGM)”
and “New German Wave (NGW)” were assigned to the
U&C dimension. Negative medium to strong correlations
were found between Reflective & Complex (R&C) and
Upbeat & Conventional (U&C), between Intense & Rebel-
lious (I&R) and Energetic & Rhythmic (E&R), and a pos-
itive weak correlation between I&R and U&C. One pecu-
liarity has to be mentioned: The item “electronica” had on-
ly a weak loading on E&R.
As previously stated, gender differences in the CFA model
were tested applying multigroup analysis (MGA). The model
fit (Mardia test: z
=2.07,p <.05;χ²
320.59, p <.000,p
= .005, χ²
= 2.49) showed similar
results, but looking at the global fit indices, there was evi-
dence that the model could be enhanced to account for gen-
der. Gender differences in the measurement weights could be
refused (Δχ²
= 20.11, p = .029; CFI = .637, RMSEA = .059,
SRMR = .103). Hence, the measurement model was con-
strained equally, and only the correlations were freely esti-
mated (see Table 1, lower part). All relationships showed
gender differences: For men, U&C and R&C as well as I&R
and E&R were strongly negatively correlated. There were no
other significant correlations. The relationships in our female
sample showed a similar pattern as in the male sample. Un-
expectedly, the relationships for women were more defined
by the positive correlations (U&C and E&R, also R&C and
I&R, R&C and E&R) and less by the negative correlations
(U&C and R&C, I&R and E&R). However, for women, none
of the correlations was significant at all which corresponds to
the sample size.
The reliabilities (Table 2) of the four STOMP dimen-
sions were moderate. The means and standard deviations
differed between the four STOMP dimensions. While par-
ticipants in this sample tended to like I&R, they rejected
Table 1. Standardized regression weights of the 15 music genres and correlations of the four latent factors in the CFA of
music preferences
Music preference dimensions
Genre Reflective & Complex Intense & Rebellious Upbeat & Conventional Energetic & Rhythmic
Blues .93*
Jazz .73*
Classic .51*
Rock .83*
Heavy metal .74*
Alternative .58*
Pop .63*
NGW .56*
Film music .40*
PGM .32*
Soul/R&B .95*
Rap/Hip hop .71*
Electronica .18*
SEM correlations
I&R .07 (.08/.23)
U&C –.21* (–.25*/–.11) .08 (.17/.10)
E&R .10 (–.02/.22) –.38* (–.49*/–.05) .18* (.13/.34)
Notes. n = 422, * p < .05; SEM correlations: I&R = Intense and Rebellious, U&C = Upbeat and Conventional, E&R = Energetic and Rhythmic.
Total coefficients are results of the CFA, (men/women) are results of the MGA with constrained measurement weights.
A. Langmeyer et al.: Music Preference and Personality 123
© 2012 Hogrefe Publishing Journal of Individual Differences 2012; Vol. 33(2):119–130
the dimensions E&R and R&C. Only for the dimension
were U&C gender differences significant. Women like this
kind of music more than men.
Validation of the STOMP
There were reasonable correlations between each soundfile
and the corresponding STOMP dimension (Table 2, lower
part). Nevertheless, some lower correlations across dimen-
sions occurred as well. No gender differences were found
in the relationship between corresponding STOMP dimen-
sions and soundfiles.
Moreover, we performed a CFA model, including the
STOMP items, and added each soundfile as an additional
manifestvariable to the corresponding dimension.Themodel
showed rather poor fit (Mardia test: z = 8.21, p < .001;
=406.76,p < .000, p
= 3.60; CFI =
.598, RMSEA = .079, SRMR = .096). Overall, the relation-
ships between soundfiles and corresponding STOMP dimen-
sions were high (R&C: λ = .78, I&R: λ = .81, U&C: λ = .77,
E&R: λ = .76). The computation of a SEM accounting for
gender in a MGA model showed no differences in the mea-
surement weights, and the fit indices improved (Mardia test:
=2.51,p < .05; χ²
=498.04,p <.000,
= 16.14, p = .242; CFI =
.609, RMSEA = .051, SRMR = .100). This means gender
differences were only found in the correlations between the
music dimensions as reported in the STOMP CFA without
soundfiles. However, path loadings of the soundclips on the
corresponding dimension had no gender differences. There-
fore, the validation was successful.
Music Preferences and Personality
Based on the replication of the factor structure and the val-
idation of the STOMP, we looked at whether the relation-
ship between the music preferences and the personality di-
mensions could also be replicated in our German sample.
First, the Big Five personality dimensions were tested in
a CFA as well (see Tables 3 and 4).
The model included the five personality dimensions
Openness (O), Conscientiousness (C), Extraversion (E),
Agreeableness (A), and Neuroticism (N) as latent vari-
ables. The model fit was acceptable (Mardia test: z = 9.71,
p <.001;χ²
= 528.57, p <.000,p
3.30; CFI = .823, RMSEA = .074, SRMR = .076).
As to gender in an MGA model, no differences in the
measurement weights were found, and fit indices were
quite good (Mardia test: z
= 7.18, z
= 4.63, p < 0.5;
= 704.88, p < .000, p
= .005, χ ²
= 2.10; Δχ²
32.26, p = .006; CFI = .820, RMSEA = .051, SRMR =
.074). The strongest correlations were found between E and
N, and between A and O. All other dimensions were un-
correlated or had rather weak correlations, which were
mostly stronger for the female sample than for the male
Table 5 shows the reliabilities, means, and standard de-
viations of the five personality dimensions. The reliabilities
were satisfying. Women scored significant higher on all
dimensions except Agreeableness.
In Table 6, Pearson correlations between the five per-
sonality and music preferences (L
: STOMP dimensions
mean scores; L
: soundfiles) are compared to previous
findings by Delsing and colleagues (2008), George and col-
leagues (2007), Rentfrow and Gosling (2003), and Zwei-
genhaft (2008).
The strongest correlation in our study was found be-
tween Openness and R&C, although Openness was also
positively correlated with I&R and negatively with U&C.
Conscientiousness was negatively connected to I&R and
positively to U&C. Extraversion was correlated with E&R,
Neuroticism was weakly correlated with U&C, and Agree-
ableness was not associated with any of the music prefer-
ences. Compared to previous findings, the present correla-
Table 2. Reliabilities (Cronbach’s α), mean scores, and standard deviations of the STOMP dimensions; correlations of
STOMP mean scores with sound files
STOMP dimension
Reflective & Complex Intense & Rebellious Upbeat & Conventional Energetic & Rhythmic
Numberof items3343
Reliability .71 (.76/.53) .67 (.71/.57) .54 (.53/.52) .51 (.49/.61)
Mean –.08 (–.11/–.02) .55 (.61/.40) .18 (.06/.49)
–0.14 (–.21/.03)
Standard deviation 1.35 (1.42/1.15) 1.41 (1.46/1.27) 0.96 (.97/.86) 1.47 (1.49/.1.41)
Correlations to sound files
R&C .72* (.75*/.60*) .08 (.08/.10) –.00 (.00/–.05) .08 (.03/.24*)
I&R .05 (07/.01) .70* (.70*/.67*) –.15* (–.14*/–.14) –.12* (–.16*/.03)
U&C .01 (–.02/.00) –.13 (–.10/–.14) .49* (.48*/.44*) .18* (.19*/.04)
E&R .01 (–.00/.05) –.16* (–.19*/–.04) .27* (.29*/.16) .66* (.69*/.57*)
Notes. n = 422, * p < .05,
= significant gender difference (T = –4.35, df = 235,01, p < .05); correlations to sound clips: R&C = Reflective &
Complex, I&R = Intense and Rebellious, U&C = Upbeat and Conventional, E&R = Energetic and Rhythmic; total coefficients (men/women).
Bold = corresponding dimensions of sound clips and STOMP
124 A. Langmeyer et al.: Music Preference and Personality
Journal of Individual Differences 2012; Vol. 33(2):119–130 © 2012 Hogrefe Publishing
tions are equally strong and point in the same direction. The
direction and power of the correlations do not differ much
between men and women.
Regarding the soundfiles instead of the STOMP dimen-
sions, there was a similar pattern between the five person-
ality dimension mean scores and the soundfiles (Table 6,
The final analysis uses a SEM that combines the exam-
ination of the factor structure of the STOMP, the affiliation
of the soundclips, and the relationship between music pref-
erences and personality (Figure 1). Music preferences were
estimated by both the particular STOMP genres and the
corresponding soundfiles. Previous results showed remark-
able differences; hence, no relationships between person-
ality and music preferences could be excluded from the
Table 3. Standardized regression weights of the 20 items in the CFA of the Big Five personality dimensions
Big Five personality dimensions
I see myself as someone who . . . O C E A N
likes to reflect, plays with ideas .48*
O_03 values artistic, esthetic experiences .77*
O_04 has an active imagination .49*
has only few artistic interests –.72*
C_04 is a reliable worker .81*
C_01 does a thorough job .79*
is easygoing –.48*
tends to be disorganized –.42*
E_01 is outgoing, sociable .76*
E_11 generates a lot of enthusiasm .56*
is reserved –.86*
tends to be quiet –.75*
A_01 is considerate and kind to almost everyone .44*
rather likes to cooperate than to compete .45*
often has a tiff with others –.43*
is sometimes rude to others –.61*
N_03 gets nervous easily .74*
N_01 worries a lot .62*
remains calm, even in tense situations –.47*
is emotionally stable, not easy to upset –.43*
Notes. n = 422, *p < .05; O = Openness, C = Conscientiousness, E = Extraversion, A = Agreeableness, N = Neuroticism.
numbered according
to Table 1 in Lang et al. (2001);
modified from Lang et al. (2001);
items are negatively aligned.
Table 4. SEM correlations of the five latent personality fac-
tors in the CFA
C .12
E .13*
A .20*
N .08
Notes. n = 422, *p < .05; O = Openness, C = Conscientiousness, E =
Extraversion, A = Agreeableness, N = Neuroticism. Total coefficients
are results of the CFA; (men/women) are results of the MGA with
constrained measurement weights.
Table 5. Reliabilities (Cronbach’s α), mean scores, and standard deviations of the personality dimensions
Big Five dimensions
Number of items 4 4444
Reliability (α) .73 (.74/.73) .69 (.70/.60) .82 (.82/.83) .54 (.55/.51) .67 (.64/.65)
Mean 2.21 (1.89/3.04)
2.50 (2.17/3.37)
2.33 (2.00/3.21)
2.37 (2.30/2.55) –1.44 (–1.91/–.22)
Standard deviation 3.12 (3.21/2.82) 2.86 (2.88/2.58) 3.37 (3.38/3.20) 2.49 (2.53/2.35) 2.87 (2.81/2.65)
Notes. n = 422; O = Openness, C = Conscientiousness, E = Extraversion, A = Agreeableness, N = Neuroticism. total coefficients (men/women),
significant gender difference (T
= –3.42, T
= –3.93, T
= –3.36, T
= –5.61; df = 420, p < .05).
A. Langmeyer et al.: Music Preference and Personality 125
© 2012 Hogrefe Publishing Journal of Individual Differences 2012; Vol. 33(2):119–130
theoretical consideration. For the sake of clarity, only the
standardized significant paths are drawn in Figure 1.
Because the latent factors in both the personality dimen-
sions and the STOMP were assumed to be independent, no
correlations between these factors were estimated, except
the correlation between Extraversion and Neuroticism and
between I&R and E&R, which showed very strong signif-
icant correlations in the previous CFA. This decision was
also made for the parsimony of the model.
The model fit was acceptable (Mardia test: z = 15.69,
p < .001; χ²
= 1164.36, p < .000, p
= .005, χ²
1.92; CFI = .563, RMSEA = .047, SRMR = .081). Allow-
ing for gender differences in an MGA model, there were
no differences in the measurement weights, and the fit in-
dices were acceptable (Mardia test: z
= 13.24, z
3.37, p <.05;χ²
= 1672.79, p <.000,p
= .005;
= 58.41, p = .001; CFI = .588,
RMSEA = .029, SRMR = .082). Regarding the nested χ²
value, even the structural weights can be fixed (Δχ²
23.99, p = .243, CFI = .584, RMSEA = .029, SRMR =
The SEM analysis revealed the following: First, the two
measurement models were well represented by the appro-
priate items, as reported in the primary CFA models with
high path loadings stronger than λ = .36. Second, the SEM
analysis showed that all soundclips were good proxies for
the respective music dimensions, with high path loadings
stronger than λ = .70. The four music dimensions were also
well represented by all corresponding items with loadings
higher than λ = .36. Only the STOMP genre “electronica”
had a weak loading.
As to standardized paths in the structural model, unam-
biguous relationships between personality and music pref-
erences were found: R&C was strongly affected by Open-
ness; I&R was affected by Openness and Extraversion, as
well as being slightly negatively influenced by Neuroti-
cism; U&C was highly positively affected by Extraversion,
but also by Neuroticism, Agreeableness, and negatively by
Table 6. Review of correlations found between dimensions of music preferences and personality in literature, and corre-
lations of STOMP mean scores and sound files with personality mean scores in the present study
Openness Conscientiousness Extraversion Agreeableness Neuroticism
Min Max Min Max Min Max Min Max Min Max
R&C D .17* .28* .05 .08* .01 –.05 .13* .18* .07* .12*
R .41* .44* –.02 –.06 .01 –.02 .01 .03 –.04 –.08
G .24* .03 .08 .08 –.08
Z .35* –.01 .00 .09 –.18
.29* (.30*/.25*) .02 (.01/.03) –.01 (–.06/.15) .00 (–.02/.04) –.06 (–.07/–.09)
.24* (.24*/.22*) .04 (.03/.02) –.02 (–.09/.16) –.02 (–.04/.05) –.10 (–.06/–.32*)
I&R D .15* .22* –.09* –.17* .00 –.17* –.01 .03 .00 .05
R .15* .18* –.03 –.04 .00 .08 .01 –.04 .01 .01
G .13* –.24* .06 –.21* .08
Z .15 –.10 .04 –.03 –.11
.10* (.10/.16) –.14* (–.15*/–.07) –.07 (–.06/–.05) .04 (.00/.16) –.01 (–.01/.05)
.07 (.08/.12) –.20* (–.18*/–.21*) –.07 (–.07/–.03) –.02 (–.02/.01) –.02 (.01/–.00)
U&C D –.00 .04 –.01 .09* .12* .15* .08* .11* .00 –.05
R –.08 –.14 .15* .18* .15* .24* .23* .24* .04 .07
G .03 .12* .09* .05 –.00
Z –.36* .23* .09 .13 .09
–.12* (–.16*/–.14) .11* (.06/.14) .07 (.10/–.17) .04 (.02/.06) .10* (.05/.05)
–.13* (–.20*/–.12) .09 (.06/.03) .12* (.10/.04) .04 (.02/.05) .09 (–.00/.11)
E&R D .00 .03 –.02 .07* .10* .16* .06 .10* .00 –.03
R .03 .04 .00 –.03 .19* .22* .08* .09* –.01 .01
G .12* –.10* .06 –.12* .07
Z .32* –.12 .22 –.07 .02
.01 (.02/–.07) .05 (.08/–.11) .14* (.14*/.10) .04 (.06/–.06) .01 (–.00/–.03)
.03 (.02/–.03) .03 (.08/–.26*) .16* (.12/.21*) .00 (.01/–.06) .01 (.02/–.19*)
Notes. D = Delsing, Ter Bogt, Engels, & Meeus (2008); R = Rentfrow & Gosling (2003); G = George, Stickle, Rachid, & Wopnford (2007);
Z = Zweigenhaft (2008); Min = weakest correlation reported by the authors, Max = strongest correlation reported by the authors. Present study:
= correlations between STOMP mean scores and personality mean scores, L
= correlations between sound files and personality mean scores;
total coefficients (men/women); bold = strong similar results were found in nearly all studies reported here; bold and italics = strong (r .10)
results only in some of the studies, *p < .05.
126 A. Langmeyer et al.: Music Preference and Personality
Journal of Individual Differences 2012; Vol. 33(2):119–130 © 2012 Hogrefe Publishing
Figure 1. Standardized parameters of the SEM with music preferences and personality, only significant paths are drawn.
Line = p < .05, *items are negatively aligned. Furthermore, correlation between Extraversion and Neuroticism (φ = –.47),
and between I&R and E&R (ψ = –.30) allowed.
A. Langmeyer et al.: Music Preference and Personality 127
© 2012 Hogrefe Publishing Journal of Individual Differences 2012; Vol. 33(2):119–130
Openness; E&R was positively influenced by Extraversion.
In other words, Openness, Extraversion, and Neuroticism
together were connected to all music styles, Agreeableness
only to U&C. Specifically, Openness had a strong positive
effect on liking R&C, disliking U&C, and a medium effect
on liking I&R. Conscientiousness had no effect on liking
or disliking of any kind of music. Extraversion had a strong
positive effect on liking U&C and E&R, and also a medium
positive effect on liking I&R. Agreeableness affected only
the liking of U&C. Neuroticism had a strong effect on lik-
ing U&C and a weak effect on disliking I&R.
Furthermore, a strong, negative correlation was found as
assumed between Extraversion and Neuroticism (φ = –.47),
and between I&R and E&R (ψ = –.30).
The patterns of these results are quite similar to what
resulted from the bivariate analyses, although they are
stronger and the weak paths from Conscientiousness to
U&C vanished; Extraversion had a new medium connec-
tion to I&R, a now stronger path to U&C and to E&R;
Neuroticism had a new negative path to I&R and a stronger
positive path to U&C.
We were able to replicate the factor structure of the Short
Test of Music Preferences (STOMP, Rentfrow & Gosling,
2003) with a German sample in a CFA model. We found
gender differences in the structural model: Men are char-
acterized by mutually exclusive ratings of music genres,
whereas women emphasize similarities. Strong negative
correlations between Intense & Rebellious (I&R) and En-
ergetic & Rhythmic (E&R) were also found by Delsing and
colleagues (2008), and strong positive correlations between
Upbeat & Conventional (U&C) and E&R as also oc-
curred especially in our female sample were found by
other studies (Delsing et al., 2008; Rentfrow & Gosling,
The present study was the first to validate the STOMP
by rating audio samples. Correlations of averages between
STOMP factors and corresponding soundfiles were strong
overall. This corresponds to the findings of Dunn and col-
leagues (2011), who found relations were weaker overall;
however, they measured duration of listening instead of
preferences for soundfiles. In our study, the weakest corre-
lation was between U&C and the corresponding soundfile.
The U&C dimensions “Popular German Music” and “New
German Wave” were added to the STOMP, although the
original soundfile did not include those two genres. More-
over, a further CFA model containing the soundfiles con-
firmed the validity as well.
We also addressed the relationship between personality
dimensions and music preferences. We estimated a SEM
including the music preferences, measured by correspond-
ing soundfiles and STOMP items, as well as the Big Five
personality dimensions, measured by the corresponding
personality items. A consistent pattern of associations be-
tween music preferences and personality emerged. The
most important findings were that the more open to expe-
riences individuals were, the more they preferred Reflec-
tive & Complex (R&C; e.g., classical music) and I&R (e.g.,
rock music), and the less they liked U&C music. The more
extraverted individuals were, the more they preferred U&C
types of music (e.g., pop-music), E&R music (e.g., rap/hip-
hop) as well as I&R (e.g., rock music). This study also
revealed that the more neurotic individuals were, the less
likely they were to enjoy I&R music, but rather preferred
U&C. It was further found that Agreeableness only affect-
ed ratings of U&C music, while Conscientiousness did not
have an influence on music preferences. How can these
differences be explained? The examination of complex
types of music might be rather special for young people. If
you are open to new experiences, you may also be open to
new and complex music experiences. This means you do
not necessarily listen to complex music regularly, but that
you might report liking it because it is new and unknown
to you. If you like I&R music, you might be an energetic
extravert and open to fervid music experiences, yet not neu-
rotic. If you like U&C music, you might also be an ener-
getic extravert, yet also agreeable (in contrast to somebody
who likes I&R) and neurotic. If you like E&R, you might
be extraverted, perhaps also sociable and therefore might
like music heard in clubs and at parties. All in all, the results
point out that personality traits and music preferences are
Comparable SEM models including personality dimen-
sions and music preferences have not been reported else-
where. Therefore, we compared the correlations computed
based on mean scores to previous findings (Table 5), which
showed by and large similar patterns and correlations as in
previous studies (Delsing et al., 2008; George et al., 2007;
Rentfrow & Gosling, 2003; Zweigenhaft, 2008). In accor-
dance with these studies, correlation analyses as well as the
SEM analysis showed that the personality dimensions
Openness and Extraversion are the best predictors of music
preferences. Further, on the basis of the STOMP dimen-
sions as well as on the soundfiles the present study also
replicated the finding that Openness is positively correlated
to R&C and I&R music, and also that Extraversion is pos-
itively related to E&R types of music. Moreover, Rentfrow
and Goslings (2003) and Zweigenhafts (2008) finding
that Openness is negatively associated with U&C was rep-
licated, as well as that Conscientiousness is negatively cor-
related with I&R (see Delsing et al., 2008; George et al.,
2007; Zweigenhaft, 2008) and weak correlated with U&C
(see Rentfrow & Gosling, 2003; George et al., 2007; Zwei-
genhaft, 2008) as most other studies report. Further, the
result of Rentfrow and Gosling (2003) and Delsing and col-
leagues (2008) that Extraversion is correlated with U&C
was confirmed in the male sample and by means of rating
soundfiles. Audio stimuli ratings revealed comparable cor-
relations to personality traits as the STOMP ratings. These
results differ from the findings of Dunn and colleagues
128 A. Langmeyer et al.: Music Preference and Personality
Journal of Individual Differences 2012; Vol. 33(2):119–130 © 2012 Hogrefe Publishing
(2011), which compared the relationship of music prefer-
ences and personality to the relationship of music behavior
and personality.
All in all, the results of the studies from the United States
and The Netherlands regarding the factor structure of the
STOMP dimensions as well as the correlations between
music preferences and personality also apply to the German
Although the results of the present study were generally
consistent with those of Rentfrow and Gosling (2003),
George and colleagues (2007), Delsing and Colleagues
(2008), and Zweigenhaft (2008), it still has some limita-
tions. The first is that the analyses are based on cross-sec-
tional data, so that no causal conclusions could be made. It
is therefore possible that personality influences music pref-
erences. Yet, according to self-expression theory (Rentfrow
& Gosling, 2007), it is also just as likely that music prefer-
ences can influence personality. Furthermore, similar to
most previous studies, nearly all reported correlations are
rather weak, as discussed by Dunn and colleagues (2011).
This lead us to speculate that personality is not the only
predictor of music preferences. Potential other predictors
of music preferences may be differences in knowledge of
music (e.g., knowing how to play an instrument), perceived
tempo and loudness as reported by Kantor-Martynuska
(2009), or why and how people listen to music in their ev-
eryday lives (use of music; Chamorro-Premuzic & Furn-
ham, 2007; Chamorro-Premuzic, Swami, Furnham, &
Maakip, 2009; Getz, Chamorro-Premuzic, Roy, & Dev-
roop, 2011).
A further limitation is, though intended, that the sample
of the present study was restricted to only young students
and disproportional in gender size. It is very likely that the
relationship between personality and music preferences is
determined by other personal indicators, such as age and
personal background. For example, someone who likes rap
music and is 20 years old might have a different personality
from somebody who is 60 years old and (still) a fan of rap
music. In future research an expanded and differentiated
sample would be preferable to help generalize findings.
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Accepted for publication: October 11, 2011
Alexandra Langmeyer
Universität der Bundeswehr München
Fakultät für Pädagogik
Werner-Heisenberg-Weg 39
85577 Neubiberg
130 A. Langmeyer et al.: Music Preference and Personality
Journal of Individual Differences 2012; Vol. 33(2):119–130 © 2012 Hogrefe Publishing
... Moreover, individuals will acquire tastes for listening to music styles (e.g., genres) based on various factors, including but not limited to their psychographics and upbringing (Schafer and Mehlhorn 2017). These acquired genre preferences have been noted to mirror one's personality traits (Langmeyer et al. 2012), which also influence spending and socialization behaviors (Huang and Labroo 2020;Juslin et al. 2022). In this regard, firms have utilized music to develop connections with potential customers while building stronger relationships among their established customers (Ausin et al. 2021). ...
... With music integrated into our lives, listeners' genre preferences have been shown to correlate with their personality/ cognitive traits (Langmeyer et al. 2012;Rentfrow and Gosling 2003;Schafer and Mehlhorn 2017), and elicit personal moods (Huang and Labroo 2020). Individuals will also establish genre preferences during their youth, which are molded by their cultural upbringing (Schafer and Mehlhorn 2017). ...
... STOMP has since been expanded to include up to 23 genre items, denoted as STOMP-R (Rentfrow et al. 2011). While new genres constantly evolve with distinct lines becoming more blurred (Rentfrow and Goosling 2003), numerous studies have shown that listeners' music preferences within western cultures fall into a few dimensions (Langmeyer et al. 2012;Nave et al. 2018;Rentfrow and Goosling 2003;Rentfrow et al. 2011;Schafer and Mehlhorn 2017). Studies that have adopted variations of STOMP-R have further shown that listening preferences can be reduced into a five-dimensional taxonomy termed MUSIC, which has been found to account for more than half of the variation in listening preferences (Langmeyer et al. 2012;Nave et al. 2018;Rentfrow et al. 2011;Schafer and Mehlhorn 2017). ...
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Casinos have heavily invested in providing live entertainment to generate visitation. However, literature on how patrons’ music preferences affect their perception of complimentary tickets to live shows, a common practice by casinos, is scant. A series of discrete choice surveys to multiple pools were utilized in this study and showed that listeners’ genre preferences significantly influence perceptions of ticketing offers. The results also point to a potential misalignment of fit between the genre preferences of patrons and live shows hosted by casinos, which may account for the poor ROI from casino showrooms. Additionally, the findings showed that live performances from select genres might make it more challenging for casinos to yield satisfactory returns since listeners of these genres place less value on complimentary tickets. Thus, it is recommended that casinos take a multi-pronged strategy approach to diversify their loyalty offerings.
... Employing personality questionnaires and surveys on musical tastes, a series of studies have previously examined individual differences in musical preference behaviors and the relationships between people's taste in music and sociopsychological factors [27,29,36,37]. Since personality and social behaviors tend to vary as a function of demographic factors, many recent studies have investigated age trends and the associated temporal stability in the development of musical preferences [28,38,39,40]. ...
... This finding suggests that economic development could thus contribute to the reduced gender gaps in China when it comes to development of musical tastes. Interestingly, this negative correlation between gender differences and income is stronger in the older age group (26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40) than in younger age groups ( Fig. 15(a)), even though the observed gender differences generally tend to decrease with an increasing listeners' age ( Figure 11). In addition, we note that the outcomes of the correlation analyses shown in Fig. 15 were almost identical when using the Jensen-Shannon divergence (JSD) instead of the KLD measure (not shown). ...
We investigate the formation of musical preferences of millions of users of the NetEase Cloud Music (NCM), one of the largest online music platforms in China. We combine the methods from complex networks theory and information sciences within the context of Big Data analysis to unveil statistical patterns and community structures underlying the formation and evolution of musical preference behaviors. Our analyses address the decay patterns of music influence, users' sensitivity to music, age and gender differences, and their relationship to regional economic indicators. Employing community detection in user-music bipartite networks, we identified eight major cultural communities in the population of NCM users. Female users exhibited higher within-group variability in preference behavior than males, with a major transition occurring around the age of 25. Moreveor, the musical tastes and the preference diversity measures of women were also more strongly associated with economic factors. However, in spite of the highly variable popularity of music tracks and the identified cultural and demographic differences, we observed that the evolution of musical preferences over time followed a power-law-like decaying function, and that NCM listeners showed the highest sensitivity to music released in their adolescence, peaking at the age of 13. Our findings suggest the existence of universal properties in the formation of musical tastes but also their culture-specific relationship to demographic factors, with wide-ranging implications for community detection and recommendation system design in online music platforms.
... Based on previous studies, musical preferences were found to be influenced by the personality traits they possess. Creative, open-minded, and unconventional (openness) individuals were having novel and sophisticated musical preferences; extraverted and agreeable people preferred traditional and upbeat music whereas conscientious and neurotic individuals were likely to hear rebellious and intense music (Chamorro-Premuzic, 2010;Dunn, 2012;Langmeyer, 2012). Neurotic individuals used music for managing emotions whereas conscientious people were not able to do so; openness to experience prevised cognitive music use, music is employed by extraverted individuals to avoid distraction or as background (Chamorro-Premuzic et al., 2009). ...
... Thus, user modelling is a key element. A line of research has tried to untangle the relationship between personality and the users' musical preferences [37,51,52]. Volokhin and Agichtein [60] introduced the concept of music listening intents and showed that intent is distinct from context (user's activity). ...
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Music recommender systems are an integral part of our daily life. Recent research has seen a significant effort around black-box recommender based approaches such as Deep Reinforcement Learning (DRL). These advances have led, together with the increasing concerns around users' data collection and privacy, to a strong interest in building responsible recommender systems. A key element of a successful music recommender system is modelling how users interact with streamed content. By first understanding these interactions, insights can be drawn to enable the construction of more transparent and responsible systems. An example of these interactions is skipping behaviour, a signal that can measure users' satisfaction, dissatisfaction, or lack of interest. In this paper, we study the utility of users' historical data for the task of sequentially predicting users' skipping behaviour. To this end, we adapt DRL for this classification task, followed by a post-hoc explainability (SHAP) and ablation analysis of the input state representation. Experimental results from a real-world music streaming dataset (Spotify) demonstrate the effectiveness of our approach in this task by outperforming state-of-the-art models. A comprehensive analysis of our approach and of users' historical data reveals a temporal data leakage problem in the dataset. Our findings indicate that, overall, users' behaviour features are the most discriminative in how our proposed DRL model predicts music skips. Content and contextual features have a lesser effect. This suggests that a limited amount of user data should be collected and leveraged to predict skipping behaviour.
... Carlson et al. (2017) reported similar results, with the difference that correlation strength for trait Extraversion was much lower, closer to Neuroticism and Conscientiousness. Other studies measuring correlation between Big Five personality traits and preference for music have found distinct stronger correlations for Openness, and the other traits having weaker correlations (Cleridou & Furnham, 2014;Langmeyer et al., 2012;. Additionally, these observations are consistent with previous research that has found evidence that the preference for music is related to the emotional content of music (Hunter et al., 2011;Ladinig & Schellenberg, 2012;Naser & Saha, 2021;Schäfer & Sedlmeier, 2011) or that has hypothesized it based on the relation between preference and bodily features of spontaneous dance . ...
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We explored the hypothesis that musical emotions are embodied differentially by people according to their personality. Nine hundred and fifty two individuals completed the Big Five personality inventory. A subset of 60 participants were asked to spontaneously move to 30 short musical stimuli while being recorded with a motion-capture system. The musical stimuli were separately rated for perceived emotions. Embodied musical emotions were evaluated as the correlation between features derived from the motion-capture data and the mean ratings of perceived emotions. Correlations between embodied musical emotions and personality traits provided tentative support for our hypothesis. A series of linear regression analyses revealed that scores on Openness and Agreeableness were most strongly, and Neuroticism and Conscientiousness most weakly, predicted by embodied musical emotions. Overall, our results offer tentative support for the existence of differential relationships between embodied musical emotions and personality, and describe statistical models that might be empirically tested in future studies.
... Music is an essential part of our lives, but it remains unclear why humans like to listen to music. To investigate why humans listen to music, studies have explored the influence of individual personality on music preference (Kopacz, 2005;Chamorro-Premuzic and Furnham, 2009;Langmeyer et al., 2012). In addition, recent studies have revealed that music enhances social communication, such as by sharing one's preferred types of music (Soley and Spelke, 2016;Soley, 2019) as well in terms of individual listening (Lonsdale and North, 2011). ...
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Music, and listening to music, has occurred throughout human history. However, it remains unclear why people prefer some types of music over others. To understand why we listen to a certain music, previous studies have focused on preferred tempo. These studies have reported that music components (external), as well as participants’ spontaneous motor tempo (SMT; internal), determine tempo preference. In addition, individual familiarity with a piece of music has been suggested to affect the impact of its components on tempo preference. However, the relationships among participants’ SMT, music components, and music familiarity as well as the influence of these variables on tempo preference have not been investigated. Moreover, the music components that contribute to tempo preference and their dependence on familiarity remain unclear. Here, we investigate how SMT, music components, and music familiarity simultaneously regulate tempo preference as well as which music components interact with familiarity to contribute to tempo preference. A total of 23 participants adjusted the tempo of music pieces according to their preferences and rated the familiarity of the music. In addition, they engaged in finger tapping at their preferred tempo. Music components, such as the original tempo and the number of notes, were also analyzed. Analysis of the collected data with a linear mixed model showed that the preferred tapping tempo of participants contributed to the preferred music tempo, regardless of music familiarity. In contrast, the contributions of music components differed depending on familiarity. These results suggested that tempo preference could be affected by both movement and memory.
The present study aimed to observe the relationships between music preference, pro-sociability, and personality, considering the mediating role of music preference. A total of 236 Brazilians participated in the study (59.7% female, Mage=20.11, SD=4.74). The results showed that the music preference factors (Energetic-Rhythmic, Reflexive-Complex, Popular Music, and Conventional Music) correlated positively with pro-sociability, especially the preference for popular music, which encompasses some of the most heard genres in the Brazilian context (r=.42, p<.01). Regarding personality, the relationships between openness and reflexive-complex music (r=.28, p<.01), and between extroversion (r=.22, p<.01) and agreeableness (r=.13, p<.05) and popular music stand out. Finally, a statistically significant explanatory model (goodness-of-fit index=0.99, comparative fit index=0.99, root-mean-square error of approximation=0.01, 90% CI=[0.00, 0.01], root-meansquare of residual=0.01) indicated that the preference for popular music mediated indirect effects of extroversion (λ=0.06, 95% CI=[0.02, 0.11], p<.01) and agreeableness (λ=0.05, 95% CI=[0.01,0.11], p<.05) in pro-sociability.
Previous research suggests there may be links between people’s self-esteem and their musical preferences, although this evidence is inconsistent and inconclusive. The present study aimed to reexamine these links using measures of collective self-esteem, while also taking into account factors that are likely to moderate these links (i.e., age, gender, and personality). One hundred thirty-nine young adults completed an online questionnaire assessing their musical preferences, collective self-esteem, and personality. Participants’ musical preferences were found to be linked to their self-reported collective self-esteem. When controlling for the effects of age, gender, and personality, scores on the private collective self-esteem subscale were found to positively predict preference for “intense and rebellious” music (i.e., hard rock, heavy metal, punk). Scores on the importance to identity subscale, however, were found to negatively predict participants’ preference for “reflective and complex” music (e.g., blues, classical music, folk). These findings suggest that collective self-esteem might play a role in how our musical preferences develop and offer further evidence for the idea that our music preferences are somehow linked to our sense of identity.
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There is a universal love of music among young people all over the world. In previous studies. In college students, the individual's personality traits and the type of music have been very stable. Individual music preference is influenced by many conditions, Such as age, gender, knowledge structure, and personality traits. This study aims to investigate how people’s personality influence their’s music preferences, A Short Test of Music Preference Questionnaire (STOMP) was succeed in testing foreigners’ musical preferences, excluding certain music genres, showing the same result that the questionnaire has cross-cultural consistency and validity. Overall, the aim of this study is to gain a deeper understanding of the relationship between music preference and personality. We found that the personality characteristics of Openness to Experience and Conscientiousness have an important influence on the choice of music preference. At the same time, there is a significant correlation between the Big Five personality traits and music preference choices.
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Previous research relating personality and music preferences has often measured such reported preferences according to genre labels. To support previous research, the current paper has expanded investigation of the relation between personality and music preferences to include direct measurement of music listening behavior. A study (N = 395) measured participants' personality, reported music preferences, and their listening behavior, which was tracked while using a music database for a minimum period of three months. Results indicated that reported music preferences were correlated to listening behavior, and indicated robust positive relations between Neuroticism and Classical music preference, and between Openness to Experience and Jazz music preference. Results also indicated issues when using genre labels to measure music preferences, which are discussed.
Four meta-analyses were conducted to examine gender differences in personality in the literature (1958-1992) and in normative data for well-known personality inventories (1940-1992). Males were found to be more assertive and had slightly higher self-esteem than females. Females were higher than males in extraversion, anxiety, trust, and, especially, tender-mindedness (e.g., nurturance). There were no noteworthy sex differences in social anxiety, impulsiveness, activity, ideas (e.g., reflectiveness), locus of control, and orderliness. Gender differences in personality traits were generally constant across ages, years of data collection, educational levels, and nations.
Four meta-analyses were conducted to examine gender differences in personality in the literature (1958-1992) and in normative data for well-known personality inventories (1940-1992). Males were found to be more assertive and had slightly higher self-esteem than females. Females were higher than males in extraversion, anxiety, trust, and, especially, tender-mindedness (e.g., nurturance). There were no noteworthy sex differences in social anxiety, impulsiveness, activity, ideas (e.g., reflectiveness), locus of control, and orderliness. Gender differences in personality traits were generally constant across ages, years of data collection, educational levels, and nations.
We describe a general procedure by which any number of parameters of the factor analytic model can be held fixed at any values and the remaining free parameters estimated by the maximum likelihood method. The generality of the approach makes it possible to deal with all kinds of solutions: orthogonal, oblique and various mixtures of these. By choosing the fixed parameters appropriately, factors can be defined to have desired properties and make subsequent rotation unnecessary. The goodness of fit of the maximum likelihood solution under the hypothesis represented by the fixed parameters is tested by a large sample χ2 test based on the likelihood ratio technique. A by-product of the procedure is an estimate of the variance-covariance matrix of the estimated parameters. From this, approximate confidence intervals for the parameters can be obtained. Several examples illustrating the usefulness of the procedure are given.