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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-
itivelycorrelatedwithlikinghardrockmusicand
negatively with liking electronic, religious, and soundtrack
music.
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
Stimuli
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?
Methods
Sample
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
Instruments
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 one’s
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”
(Nirvana)
– 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).
Analyses
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
normed
; Jöreskog, 1969)
was considered. Recommendations of what can be consid-
ered sufficient model fit vary between χ²
normed
<2and
χ²
normed
< 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.
Results
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
[59]
= 262.80, p < .000, p
BS
= .005). The
normed χ² value of χ²
normed
= 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
men
=4.76,z
women
=2.07,p <.05;χ²
[129]
=
320.59, p <.000,p
BS
= .005, χ²
normed
= 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
[7]
= 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;
χ²
[113]
=406.76,p < .000, p
BS
=.005,χ²
normed
= 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:
z
men
=7.37,z
women
=2.51,p < .05; χ²
[239]
=498.04,p <.000,
p
BS
=.005,χ²
normed
=2.08;Δχ²
13
= 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
[160]
= 528.57, p <.000,p
BS
=.005,χ²
normed
=
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
men
= 7.18, z
women
= 4.63, p < 0.5;
χ²
[335]
= 704.88, p < .000, p
BS
= .005, χ ²
normed
= 2.10; Δχ²
[15]
=
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
sample.
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
S
: STOMP dimensions
mean scores; L
A
: 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)
S
–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,
S
= 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,
L
A
).
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
O_05
1
likes to reflect, plays with ideas .48*
O_03 values artistic, esthetic experiences .77*
O_04 has an active imagination .49*
O_10
–
has only few artistic interests –.72*
C_04 is a reliable worker .81*
C_01 does a thorough job .79*
C_14
–
is easygoing –.48*
C_09
–
tends to be disorganized –.42*
E_01 is outgoing, sociable .76*
E_11 generates a lot of enthusiasm .56*
E_06
–
is reserved –.86*
E_08
–
tends to be quiet –.75*
A_01 is considerate and kind to almost everyone .44*
A_00
m
rather likes to cooperate than to compete .45*
A_06
m–
often has a tiff with others –.43*
A_08
–
is sometimes rude to others –.61*
N_03 gets nervous easily .74*
N_01 worries a lot .62*
N_05
m–
remains calm, even in tense situations –.47*
N_06
m–
is emotionally stable, not easy to upset –.43*
Notes. n = 422, *p < .05; O = Openness, C = Conscientiousness, E = Extraversion, A = Agreeableness, N = Neuroticism.
1
numbered according
to Table 1 in Lang et al. (2001);
m
modified from Lang et al. (2001);
–
items are negatively aligned.
Table 4. SEM correlations of the five latent personality fac-
tors in the CFA
OCEA
C .12
(.03/.20)
E .13*
(–.07/.23*)
–.01
(–.03/–.11)
A .20*
(.31*/–.13)
.13
(.10/.24)
–.02
(–.03/–.09)
N .08
(.08/–.21)
–.07
(–.16*/–.05)
–.40*
(–.42*/–.65*)
–.02
(.07/–.12)
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
OCEAN
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)
S
2.50 (2.17/3.37)
S
2.33 (2.00/3.21)
S
2.37 (2.30/2.55) –1.44 (–1.91/–.22)
S
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),
S
significant gender difference (T
O
= –3.42, T
C
= –3.93, T
E
= –3.36, T
N
= –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
[607]
= 1164.36, p < .000, p
BS
= .005, χ²
normed
=
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
men
= 13.24, z
women
=
3.37, p <.05;χ²
[1242]
= 1672.79, p <.000,p
BS
= .005;
χ²
normed
=1.35;Δχ²
[28]
= 58.41, p = .001; CFI = .588,
RMSEA = .029, SRMR = .082). Regarding the nested χ²
value, even the structural weights can be fixed (Δχ²
[20]
=
23.99, p = .243, CFI = .584, RMSEA = .029, SRMR =
.083).
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
L
S
.29* (.30*/.25*) .02 (.01/.03) –.01 (–.06/.15) .00 (–.02/.04) –.06 (–.07/–.09)
L
A
.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
L
S
.10* (.10/.16) –.14* (–.15*/–.07) –.07 (–.06/–.05) .04 (.00/.16) –.01 (–.01/.05)
L
A
.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
L
S
–.12* (–.16*/–.14) .11* (.06/.14) .07 (.10/–.17) .04 (.02/.06) .10* (.05/.05)
L
A
–.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
L
S
.01 (.02/–.07) .05 (.08/–.11) .14* (.14*/.10) .04 (.06/–.06) .01 (–.00/–.03)
L
A
.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:
L
S
= correlations between STOMP mean scores and personality mean scores, L
A
= 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.
Discussion
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,
2003).
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
congruent.
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 Gosling’s (2003) and Zweigenhaft’s (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
sample.
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
Germany
E-mail alexandra.langmeyer@unibw.de,
alexandra.langmeyer@edu.lmu.de
130 A. Langmeyer et al.: Music Preference and Personality
Journal of Individual Differences 2012; Vol. 33(2):119–130 © 2012 Hogrefe Publishing
