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Adolescents' Music preferences and personality characteristics

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The present paper examined the structure of Dutch adolescents' music preferences, the stability of music preferences and the relations between Big-Five personality characteristics and (changes in) music preferences. Exploratory and confirmatory factor analyses of music-preference data from 2334 adolescents aged 12–19 revealed four clearly interpretable music-preference dimensions: Rock, Elite, Urban and Pop/Dance. One thousand and forty-four randomly selected adolescents from the original sample filled out questionnaires on music preferences and personality at three follow-up measurements. In addition to being relatively stable over 1, 2 and 3-year intervals, music preferences were found to be consistently related to personality characteristics, generally confirming prior research in the United States. Personality characteristics were also found to predict changes in music preferences over a 3-year interval. Copyright © 2007 John Wiley & Sons, Ltd.
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Adolescents’ Music Preferences and Personality
Characteristics
MARC J. M. H. DELSING
1
*, TOM F. M. TER BOGT
2
,
RUTGER C. M. E. ENGELS
3
and WIM H. J. MEEUS
1
1
Research Centre Adolescent Development, Utrecht University, The Netherlands
2
Department of General Social Sciences, Utrecht University, Utrecht, The Netherlands
3
Radboud University Nijmegen, Nijmegen, The Netherlands
Abstract
The present paper examined the structure of Dutch adolescents’ music preferences, the
stability of music preferences and the relations between Big-Five personality character-
istics and (changes in) music preferences. Exploratory and confirmatory factor analyses of
music-preference data from 2334 adolescents aged 12–19 revealed four clearly interpret-
able music-preference dimensions: Rock, Elite, Urban and Pop/Dance. One thousand and
forty-four randomly selected adolescents from the original sample filled out questionnaires
on music preferences and personality at three follow-up measurements. In addition to
being relatively stable over 1, 2 and 3-year intervals, music preferences were found to be
consistently related to personality characteristics, generally confirming prior research in
the United States. Personality characteristics were also found to predict changes in music
preferences over a 3-year interval. Copyright #2007 John Wiley & Sons, Ltd.
Key words: music preferences; Big-Five personality characteristics; latent growth curve
modelling; Dutch adolesecents
INTRODUCTION
Over the last decades, researchers have shown interest in people’s musical preferences as
an individual difference variable that relates to personality traits (Cattell & Anderson,
1953; Dollinger, 1993; Little & Zuckerman, 1986; McCown, Keiser, Mulhearn, &
Williamson, 1997; Robinson, Weaver, & Zillmann, 1996). Some support has been found
for the notion that people prefer listening to music that reflects specific personality
characteristics (Rentfrow & Gosling, 2003; Schwartz & Fouts, 2003). However, the picture
emerging from this research is incomplete since most studies have collected data at only
one time-point. As a result, little is known about the stability of music preferences over
European Journal of Personality
Eur. J. Pers. 22: 109–130 (2008)
Published online 7 November 2007 in Wiley InterScience
(www.interscience.wiley.com) DOI: 10.1002/per.665
*Correspondence to: Marc J. M. H. Delsing, Research Centre Adolescent Development, Utrecht University,
P.O. Box 80140, 3508 TC Utrecht, The Netherlands. E-mail: m.j.m.h.delsing@fss.uu.nl
Copyright #2007 John Wiley & Sons, Ltd.
Received 21 September 2006
Revised 29 August 2007
Accepted 10 September 2007
time as well as about the way personality characteristics influence over-time changes in
music preferences. Additionally, most studies on the personality correlates of music
preferences have used samples of American university students. It is unclear to what extent
results from these studies generalise to other age groups (e.g. adolescents) living in other
cultures or countries. The aim of the present paper was to address these empirical gaps by
longitudinally examining personality characteristics and music preferences in a sample of
Dutch adolescents. The present study is intended to contribute to our understanding of the
associations between personality and behaviour that occurs in everyday life, an area
regarded to be overly neglected by personality psychologists (see e.g. Funder, 2001;
Rentfrow & Gosling, 2003; Rozin, 2001).
Music plays an important role in the social and personal lives of people young and old.
Estimates of annual sales in the United States, for example, put the popular music market at
$10 billion for 1993 and at over $12 billion for 1994 (Schwartz & Fouts, 2003). More
recent reports still show physical sales figures of over $12 billion for 2005, whereas, at the
same time, digital downloading of music has increased vastly over the last couple of years
(Recording Industry Association of America, 2006). Of all age groups, adolescents can be
considered to be the most fanatic music adepts (Christenson & Peterson, 1988; Schwartz &
Fouts, 2003). North, Hargreaves, and O’Neill (2000) reported British adolescents to listen
to music for an average of 2.45 hours per day. Earlier estimates indicate that, from 7th to
12th grade, American adolescents average 10500 hours of elected exposure to popular
music (Zillman & Gan, 1997). The times spent listening to music approximate those spent
in the classroom from kindergarten through high school. Although there is comparatively
little data from other countries, studies with Irish (Fitzgerald, Joseph, Hayes, & O’Regan,
1995), Swedish (Bjurstro
¨m & Wennhall, 1991) and Dutch (Ter Bogt, 2000) adolescents
confirm that music is of central importance in the lives of most young people.
Personality and music preferences
Although adolescents generally share a fascination for music, adolescents differ in their
preferences for musical styles. Social factors such as ethnicity, social class (e.g. Frith,
1981; Gans, 1974), youth cultures, as well as individual factors (e.g. personality,
physiological arousal, social identity) have been proposed to account for the heterogeneity
of adolescents’ music preferences (Rentfrow & Gosling, 2003; Zillman & Gan, 1997). One
line of research has focused on the role of personality traits in the determination of
adolescents’ musical taste (e.g. Dollinger, 1993; Little & Zuckerman, 1986; McCown
et al., 1997; Pearson & Dollinger, 2002; Robinson et al., 1996). One of the most
comprehensive studies to date in this respect is Rentfrow and Gosling’s (2003)
investigation, in which the authors first determined the major dimensions of music
preferences by means of exploratory and confirmatory factor analysis (CFA), and
subsequently examined the associations of these dimensions with the well-established
Big-Five personality factors. Four music-preference dimensions that were highly
consistent across samples and time emerged from their analyses: The Reflective and
Complex dimension, which was defined by the genres blues, jazz, classical and folk music;
The Intense and Rebellious dimension, which was defined by Rock, alternative and heavy
metal music; The Upbeat and Conventional dimension, which was defined by country,
sound track, religious and pop music; The Energetic and Rhythmic dimension, which was
defined by rap/hip-hop, soul/funk and electronica/dance music.
Copyright #2007 John Wiley & Sons, Ltd. Eur. J. Pers. 22: 109–130 (2008)
DOI: 10.1002/per
110 M. J. M. H. Delsing et al.
Rentfrow and Gosling (2003) found both the Reflective and Complex and the Intense and
Rebellious dimensions to be positively related to Openness to Experience. The Upbeat and
Conventional dimension was found to be positively related to Extraversion, Agreeableness
and Conscientiousness, and negatively to Openness to Experience. The Energetic and
Rhythmic dimension was positively related to Extraversion and Agreeableness. No
substantial correlations were found between the music-preference dimensions and
Emotional Stability.
Theories linking personality to music preferences
The uses and gratification approach (Rosengren, Wenner, & Palmgreen, 1985) may serve
as a general theoretical framework for explaining associations between personality factors
and music preferences. This approach has focused on the motives for individuals’ music
consumption and stresses individual choice and how ‘people intentionally participate and
select media messages from communication alternatives...what people do with the media,
instead of what the media do to people’ (Rubin, 1994, p 421). From this line of research, it
appears that people prefer particular kinds of music because they have particular
personality characteristics that the music satisfies (Arnett, 1995; Arnett, Larson, & Offer,
1995; Gantz, Gartenberg, Pearson, & Schiller, 1978; Larson, 1995). For example
extraverts, who generally enjoy socialising and like spending time with others, tend to
enjoy music that facilitates social interactions with peers (e.g. party music). Similarly,
individuals high on Openness to Experience, who have a desire for ‘variety, intellectual
stimulation and aesthetic experiences’ (Costa & McCrae, 1988, p 261), may prefer
relatively ‘difficult’ or obscure types of music.
The music people choose may also serve to gratify physiologically based needs.
According to the model of optimal stimulation (Eysenck, 1990; Zuckerman, 1979), people
tend to choose the type of music that moves them toward their optimal arousal level. For
example extraverts are considered to be on the low level of the cortical arousal scale and
tend to choose the types of music which have the property to raise that level. Introverts,
however, who are normally highly aroused, tend to avoid overstimulation by choosing less
stimulating music (Daoussis & McKelvie, 1986).
Replication and extension of Rentfrow and Gosling
The present study builds on Rentfrow and Gosling’s (2003) groundbreaking work and
extends it in several ways. First, Rentfrow and Gosling used a sample of undergraduate
college students. It is unclear to what extent their findings can be generalised to younger
adolescents. Theoretically, adolescence can be considered a particularly relevant period for
the study of music preferences. As already indicated, adolescence is the period when the
amount of time devoted to listening to music is at its peak (Larson, Kubey, & Colletti, 1989;
Zillman & Gan, 1997). Furthermore, one might expect more change in music preferences
during adolescence than, for example, in older adults due to the changes in relationships
with peers, who have been shown to be very influential in shaping adolescents’ music
preferences (Zillman & Gan, 1997). Lastly, adolescence is generally viewed as a formative
phase for the development of music preferences, and it has been argued that music
preferences crystallise during adolescence (Holbrook & Schindler, 1989). Therefore, it
would be very important to examine (changes in) music preferences and their personality
correlates during this critical period.
Copyright #2007 John Wiley & Sons, Ltd. Eur. J. Pers. 22: 109–130 (2008)
DOI: 10.1002/per
Music preferences and personality characteristics 111
Second, as in most studies on music preferences, Rentfrow and Gosling (2003) used an
American sample. It is unclear to what extent the structure of music preferences identified
in their research, as well as their pattern of associations between personality and music
preferences, generalises to other cultures or countries. Recently, inconsistent findings have
been reported for Spanish and English samples regarding the association between
Sensation seeking and Openness to Experience on the one hand and music preferences on
the other hand (Rawlings, Vidal, & Furnham, 2000), suggesting that findings from this type
of research cannot automatically be generalised across people from different regions. The
present study tested the generalisability of Rentfrow and Gosling’s findings to a sample of
adolescents growing up in the Netherlands.
Third, as argued by Rentfrow and Gosling’s (2003), a theory on music preferences
should inform us on how music preferences develop and what factors influence their
development. It should also give insight into the trajectory of music preferences and
provide answers to questions such as how, when and why music preferences change. To
date, however, most of what is known about changes in music preferences comes from
comparisons between individuals of different ages. Age-group differences cannot
automatically be interpreted as intra-individual (i.e. aging or within individual) effects
such that as people grow older they increasingly prefer a certain type of music. To enable
such interpretations, longitudinal studies of changes in music preferences are needed.
Although Rentfrow and Gosling did compute test-retest reliabilities for their factors on the
basis of two measurements with a 3-week interval, their data do not provide information on
the stability and trajectory of music preferences over much longer periods of time (e.g. 1,
2 and 3 years). Their data also do not reveal to what extent personality characteristics
predict over-time changes in music preferences. Such information would provide a more
specific account of the role of personality characteristics regarding the development of
music preferences. Therefore, in the present study, music-preference data were collected at
four annual measurements.
In sum, the present study sought to examine the relations between Big-Five personality
characteristics and (changes in) music preferences in a sample of Dutch adolescents. But
before doing so, we used a similar factor-analytic approach as the one used by Rentfrow
and Gosling (2003) to determine the major dimensions of adolescents’ music preferences
in the Netherlands. We specified the following research questions.
Research questions
1. What are the basic dimensions of adolescents’ music preferences?
2. How stable are adolescents’ music preferences over time?
3. How do adolescents’ music preferences relate to existing dimensions of personality?
4. To what extent do personality characteristics predict over-time changes in adolescents’
music preferences?
Given the limited empirical literature on these topics in the Netherlands, we had no
a priori theories or expectations about the number of music-preference dimensions or the
nature of the underlying structure. Consequently, we could not formulate any hypotheses
regarding the associations between the music-preference dimensions that would emerge
from our analyses and the Big-Five personality factors.
Copyright #2007 John Wiley & Sons, Ltd. Eur. J. Pers. 22: 109–130 (2008)
DOI: 10.1002/per
112 M. J. M. H. Delsing et al.
METHOD
Participants
The sample consisted of 2334 adolescent children in grades 7–12 who were between 12 and
19 years of age (M¼14.37, SD ¼2.33). Of those who indicated, 1097 were boys,
1234 were girls, 1755 (75.2%) were Dutch, 55 (2.4%) were Surinamese/Antillean,
209 (9%) were Moroccan, 92 (3.9%) were Turkish and 86 (3.7%) had other ethnic
backgrounds.
A subsample of 1243 adolescents was randomly selected from the original sample to
participate in three follow-up assessments which took place 1, 2 and 3 years, respectively,
after the initial assessment. At each of the four measurement waves, data on adolescents’
music preferences and personality were collected. Eventually, 1044 adolescents (515 boys,
529 girls) participated at all four measurements. The mean age of these adolescents was
13.82 (SD ¼2.10) at T1.
Procedure
Data of this study come from 12 schools participating in the first wave of the CONAMORE
2001–2006 longitudinal study (CONflict And Management Of RElationships; Meeus et al.,
2002). Parents and students received a letter in which the aims of the study were described
and information was given about the option of not participating. Less than 1% of the
students decided not to participate. Participants completed a series of questionnaires in
their classrooms, aided by research assistants who gave verbal instructions about the
questionnaires. Written instructions were also included. Students who were absent on the
days of testing were not assessed.
Measures
Adolescents’ music preferences were assessed by means of the Musical Preference
Questionnaire (MPQ: Sikkema, 1999). The MPQ consists of a list of 11 established
categories of music. The items of the scale were partly generated on the basis of interviews
with a large number of CD retailers in the Netherlands, as well as on a pilot-study
conducted at several secondary schools. At these schools, a large number of students were
interviewed and asked to name all the music genres they could think of. Genres that were
consistently reported by the CD retailers and adolescents were included in
the questionnaire. The eventual questionnaire consists of items representing the major
contemporary music styles that have some degree of familiarity to Dutch adolescents (see
Table 1). The items of the MPQ closely resemble the items of the Short Test Of Music
Preferences questionnaire (STOMP) used by Rentfrow and Gosling (2003). In comparison
with the STOMP, however, the MPQ does not contain the genres ‘folk’, ‘country’ and
‘blues’, because they were deemed to be too unfamiliar to Dutch adolescents. Also the
MPQ does not contain the genre ‘sound tracks’ because of its heterogeneity. Subjects were
asked to indicate on five-point Likert scales (1 ¼very bad,5¼very good) the extent to
which they liked each of the music genres listed.
Adolescents’ personality was assessed by means of Big-Five factors. A Dutch adaptation
(Gerris, Houtmans, Kwaaitaal-Roosen, Schipper, Vermulst, & Janssens, 1998) of
30 adjective Big-Five factors markers selected from Goldberg (1992) was used to have
Copyright #2007 John Wiley & Sons, Ltd. Eur. J. Pers. 22: 109–130 (2008)
DOI: 10.1002/per
Music preferences and personality characteristics 113
adolescents judge their personalities. The participants rated the 30 adjectives on 7-point
Likert scales ranging from 1 (very untrue for me) through 4 (sometimes untrue, sometimes
true for me)to7(very true for me). All the Big-Five factors were rated: Extraversion,
Agreeableness, Conscientiousness, Emotional Stability and Openness to Experience. The
internal consistencies (Cronbach’s alpha) for the different dimensions of Big-Five factors
ranged from .77 for Opennness to Experience to .87 for Agreeableness.
RESULTS
The structure of adolescents’ music preferences: Exploratory and CFA
To identify the major dimensions of adolescents’ music preferences, a two-step procedure
was applied. For this purpose, our original sample was randomly divided into two
independent subsamples of about equal size. First, exploratory factor analysis (EFA) was
performed on the data of Subsample 1 (N¼1183). For reasons of comparability, the same
factor analytic procedure (i.e. principal components factor analysis with Varimax rotation)
was used as the one employed by Rentfrow and Gosling (2003).
1
Second, the generalisability
and robustness of the factor solution obtained in Subsample 1 was evaluated by means of
CFA on the data of Subsample 2 (N¼1151). The structural equation modelling (SEM)
program LISREL 8 (Jo
¨reskog & So
¨rbom, 1996) was used to perform the CFA.
EFA
Initially, the EFA of the Subsample 1 data was done separately for boys and girls and for
younger (i.e. 12- to 15-year olds) and older (i.e. 16- to 19-year olds) adolescents. Because
the overall pattern of loadings was highly similar for boys and girls and for younger and
older adolescents, an EFA was performed for Subsample 1 as a whole.
Table 1. Factor loadings of the 11 music genres on four varimax-rotated principal components
Genre
Music-preference dimension
Rock Elite Urban Pop/Dance
Heavy metal/hardrock .88 .01 .05 .04
Punk/hardcore/grunge .87 .08 .02 .01
Gothic .72 .22 .03 .10
Rock .70 .22 .04 .05
Jazz .14 .75 .24 .01
Classical music .09 .74 .33 .10
Gospel .29 .67 .22 .05
Hip-hop/rap .15 .00 .86 .09
Soul/R&B .16 .20 .71 .35
Trance/techno .19 .17 .10 .78
Top 40/charts .07 .17 .18 .77
Note:N¼1183. The highest factor loadings for each dimension are listed in boldface type.
1
To investigate the robustness of our EFA solution, alternative factor analytic procedures (Principle Axis and
Maximum Likelihood) and rotations (Direct Oblimin) were used. The pattern of loadings was highly similar
across procedures and rotation methods, whereas all procedures suggested the same number of factors (i.e. four) to
be extracted.
Copyright #2007 John Wiley & Sons, Ltd. Eur. J. Pers. 22: 109–130 (2008)
DOI: 10.1002/per
114 M. J. M. H. Delsing et al.
Table 1 shows the varimax-rotated factor solution resulting from our EFA of the
Subsample 1 music-preference data. On the basis of the scree test (Catell, 1966), the Kaiser
rule (i.e. eigenvalues of 1 or greater) and the interpretability of the solution (see Zwick &
Velicer, 1986), a four-factor solution was retained, which accounted for 67% of the total
variance. As can be seen in Table 1, the factor structure was very clear and interpretable,
with very few cross-loading genres. The genres loading most strongly on Factor 1 were
heavy metal/hardrock, punk/hardcore/grunge, gothic and Rock, and this factor was named
Rock. Factor 2 was defined by jazz, classical and gospel music, and this factor was named
Elite. Factor 3 was defined by hip-hop/rap and soul/R&B, and was named Urban. Factor
4 was defined by trance/techno and top 40/charts, and was named Pop/Dance. The results
from this exploratory investigation suggest that there is a clear underlying structure to
adolescents’ music preferences. Four interpretable factors were identified that capture a
broad range of music genres.
CFA
To examine the generalisability and robustness of the four music-preference dimensions
obtained in Subsample 1, we performed a CFA on the music-preference data of Subsample
2. We specified a model with four latent factors representing the four music-preference
dimensions. All the genres that loaded highly (i.e. loadings of .40 or greater) on each of
the respective factors in the EFA were freely estimated. In addition, the correlations
between the latent factors were freely estimated. Evaluation of the fit of our model was
based on multiple criteria (Bentler, 1990; Browne & Cudeck, 1989, 1993; Hu & Bentler,
1999; Loehlin, 1998). The results indicated that our model provided an adequate fit, x
2
(38,
458) ¼136.99, p<.01 (GFI ¼.95, CFI ¼.93, NNFI ¼.90, SRMR ¼.06).
Figure 1 shows the standardised parameter estimates for our CFA model. As can be seen,
the factor loadings of all genres were significant and in the expected direction.
Furthermore, all but one (Elite with Pop/Dance) intercorrelations among the
music-preference dimensions were significant at the 1% level. The strongest correlations
were found between the Rock and Elite dimension (.48), and between the Urban and Pop/
Dance dimension (.40). In sum, the cross-sample congruence of the music-preference
dimensions identified in our EFA and the fit from our CFA provide compelling evidence for
the existence of four music-preference dimensions.
Stability of music preferences
To assess the stability of adolescents’ music preferences and personality characteristics
over time, unit-weighted scales were created to obtain scores for each of the
music-preference and personality factors at all four measurement points. Next, we
computed the correlations between scores of all measurement points for each of the music
and personality dimensions. To check for age differences in these stabilities, analyses were
done separately for a younger (i.e. 12- to 15-year olds) and an older (i.e. 16- to 19-year
olds) subsample. As can be seen in Table 2, preferences for all four music dimensions
remained fairly stable across 1, 2 and 3-year intervals. Although differences were not tested
for statistical significance, there was a general trend of increasing stabilities across the
three successive 1-year intervals (columns 1–6). Additionally, stabilities appear to be
consistently higher in the older group than in the younger group. Taken together these
findings suggest that music preferences are already fairly stable at early adolescence and
become increasingly stable as adolescents grow older.
Copyright #2007 John Wiley & Sons, Ltd. Eur. J. Pers. 22: 109–130 (2008)
DOI: 10.1002/per
Music preferences and personality characteristics 115
Contemporary associations between music-preference dimensions and
personality characteristics
Having established the music-preference dimensions and their stability over time, we could
address the question how music preferences are related to personality characteristics.
Contemporary associations between adolescents’ music preferences and personality
characteristics were examined in two ways. First, at each of the four measurement waves,
correlations were computed between the scale scores on the music-preference dimensions
on the one hand and the personality dimensions on the other hand. An interesting pattern of
associations was found that was highly similar across the four waves. As can be seen in
Table 3, the Rock dimension was found to be positively related to Openness to Experience
and negatively to Conscientiousness. Also, at two of the four measurement occasions (i.e.
Figure 1. Standardised parameter estimates for CFA model of the music-preference data from the EFA. x
2
(38,
458) ¼136.99, p<.01 (GFI ¼.95, CFI ¼.93, NNFI ¼.90, SRMR ¼.06). Note:
p.05;

p.01; e¼error
variance.
Table 2. Stability correlations music preferences
T1–T2 T2–T3 T3–T4 T1–T3 T2–T4 T1–T4
12–15 16–19 12–15 16–19 12–15 16–19 12–15 16–19 12–15 16–19 12–15 16–19
Rock .49 .68 .57 .76 .69 .79 .35 .64 .46 .62 .24 .57
Elite .48 .59 .49 .66 .57 .72 .39 .61 .46 .58 .43 .52
Urban .49 .71 .58 .76 .64 .79 .43 .68 .49 .71 .38 .65
Pop/Dance .48 .68 .62 .68 .65 .71 .33 .65 .49 .62 .37 .57
Note:Allps.01; T1–T2 ¼interval from 1st to 2nd measurement, T2–T3 ¼interval from 2nd to 3rd measure-
ment, etc.
Copyright #2007 John Wiley & Sons, Ltd. Eur. J. Pers. 22: 109–130 (2008)
DOI: 10.1002/per
116 M. J. M. H. Delsing et al.
Table 3. Correlations between personality and music-preference dimensions at T1, T2, T3 and T4
Rock Elite Urban Pop/Dance
T1 T2 T3 T4 T1 T2 T3 T4 T1 T2 T3 T4 T1 T2 T3 T4
Extraversion .00 .03 .11

.17

.01 .03 .05 .03 .11

.10

.10

.16

.14

.12

.15

.14

Agreeableness .03 .02 .01 .01 .13

.16

.13

.18

.06 .08
.08
.10

.09

.08
.09

.11

Conscientiousness .09

.10

.17

.15

.05 .08
.06 .07
.02 .04 .07
.07
.01 .02 .09

.05
Emotional stability .00 .03 .05 .04 .07
.09

.09

.12

.03 .01 .02 .00 .05 .01 .00 .04
Openness .15

.17

.18

.22

.17

.22

.20

.28

.03 .00 .01 .00 .04 .00 .03 .04
Note:N¼1044; T1¼data from wave 1; T2 ¼data from wave 2; T3¼data from wave 3; T4 ¼data from wave 4.
p.05;

p.01.
Copyright #2007 John Wiley & Sons, Ltd. Eur. J. Pers. 22: 109–130 (2008)
DOI: 10.1002/per
Music preferences and personality characteristics 117
T3 and T4), negative associations were found between the Rock dimension and
Extraversion. The Elite dimension was found to be positively related to Agreeableness and
Openness to Experience. At two of the four measurements (i.e. T2 and T4), the Elite
dimension was also found to be positively related to Conscientiousness. Negative
associations were found between the Elite dimension and Emotional Stability. The Urban
dimension and the Pop/Dance dimension were both found to be positively related to
Extraversion and Agreeableness. Finally, both dimensions were found to be positively
related to Conscientiousness at two (i.e. T3 and T4) and one (i.e. T3) of the four
measurements, respectively.
The second way to examine the association between adolescents’ music preferences and
personality characteristics was by specifying an SEM model in which information from the
four measurements was combined to estimate the associations between the four
music-preference dimensions and the five personality dimensions. In this procedure, each
wave was treated as an item of a four-item (i.e. one for each wave) scale. This approach was
partly motivated by our finding that all four music-preference dimensions and all five
personality factors were considerably stable across time. Prior to the SEM analysis, for
each of the music-preference and personality factors, a mean score was computed on the
basis of the scores across the four measurements. In the SEM model, disattenuated
correlations were estimated by controlling for measurement errors, which were computed
on the basis of the reliabilities of the four-item scales.
Again the program LISREL 8 (Jo
¨reskog & So
¨rbom, 1996) was used to perform the SEM
analysis. Since our SEM model is saturated, with zero degrees of freedom, the fit is perfect
(p¼1). Table 4 gives the standardised coefficients for the correlations between music
preferences and personality resulting from this analysis.
2
The results strongly corroborate
the findings of the previous analyses reported in Table 3. Multigroup analyses were
performed to check for age differences in the associations between music preferences and
personality factors. Only 3 (out of the possible 20) significant age differences were found.
First, preference for Elite music was negatively related to Extraversion for the younger age
Table 4. Standardised SEM coefficients for the correlations between four-wave averages of
personality and music-preference scores
Big Five Rock Elite Urban Pop/Dance
Extraversion .12

(.01) .02 (.00) .19

(.02) .22

(.05)
Agreeableness .03 (.00) .28

(.08) .10
(.01) .15

(.02)
Conscientiousness .21

(.04) .10

(.01) .05 (.00) .04 (.00)
Emotional stability .03 (.00) .18

(.03) .00 (.00) .00 (.00)
Openness .30

(.09) .38

(.14) .03 (.00) .04 (.00)
Note: Explained variances are between brackets.
p.05;

p.01.
2
Uncorrected correlations between Rock and Extraversion, Agreeableness, Conscientiousness, Emotional
Stability and Openness were .10, .03, .18, .03 and .24, respectively; Uncorrected correlations between
Elite and Extraversion, Agreeableness, Conscientiousness, Emotional Stability and Openness were .01, .22, .09,
.15 and .31, respectively; Uncorrected correlations between Urban and Extraversion, Agreeableness, Con-
scientiousness, Emotional Stability and Openness were .16, .08, .04, .00 and .02, respectively; Uncorrected
correlations between Pop/Dance and Extraversion, Agreeableness, Conscientiousness, Emotional Stability and
Openness were .18, .12, .04, .00 and .04, respectively.
Copyright #2007 John Wiley & Sons, Ltd. Eur. J. Pers. 22: 109–130 (2008)
DOI: 10.1002/per
118 M. J. M. H. Delsing et al.
group (i.e. 12- to 15-year olds), but positively related to Extraversion for the older age
group (i.e. 16- to 19-year olds) (.10 and .15, respectively; Dx
2
¼10.23, Ddf ¼1, p<.01).
Second, preference for Elite music was positively related to Conscientiousness for the
younger age group, but (nonsignificantly) negatively related to Conscientiousness for the
older age group (.15 and .04, respectively; Dx
2
¼4.37, Ddf ¼1, p<.05). Finally,
preference for Urban music was (nonsignificantly) positively related to Emotional Stability
for the younger age group, but (nonsignificantly) negatively related to Emotional Stability
for the older age group (.07 and .10, respectively; Dx
2
¼4.73, Ddf ¼1, p<.05).
Next, we tested to what extent the pattern of associations between personality and music
preferences reported by Rentfrow and Gosling (2003) fitted the present data. For this
purpose, the fit of a restrictive and a less-restrictive model was assessed. In these models,
Rentfrow and Gosling’s Intense and Rebellious dimension corresponded with our Rock
dimension (both dimensions are largely defined by the genres Rock and heavy metal), their
Energetic and Rhythmic dimension corresponded with our Urban dimension (both
dimensions are largely defined by the genres hip-hop/rap and soul), their Upbeat and
Conventional dimension corresponded with our Pop/Dance dimension (both dimensions
are largely defined by the genre pop) and their Reflective and Complex dimension
corresponded with our Elite dimension (both dimensions are largely defined by the genres
jazz and classical). In the restrictive model, the correlations between the personality factors
and the music-preference dimensions were fixed on the values reported by Rentfrow and
Gosling for their Study 2 sample. In the less-restrictive version of the model, the
correlations Rentfrow and Gosling reported to be statistically significant were freely
estimated, whereas the nonsignificant correlations were fixed to zero. The retrictive model
yielded a reasonable fit to the data (x
2
(20, 1044) ¼248.43, p<.01, GFI ¼.96, CFI ¼.86,
SRMR ¼.07). However, the less-restrictive model fitted the data significantly
(Dx
2
¼125.64, Ddf ¼9, p<.01) better (x
2
(11, 1044) ¼122.79, p<.01, GFI ¼.98,
CFI ¼.93, SRMR ¼.05). In Table 5, the correlations emerging from our less-restrictive
model, as well as those reported by Rentfrow and Gosling are given. Altogether, the
different types of analyses indicate that our pattern of associations between music
preferences and personality characteristics closely matches the one found by Rentfrow and
Gosling. However, two correlations did not seem to match across both studies. First, the
correlation between Emotional Stability and Rentfrow and Gosling’s Reflective and
Complex dimension was significantly positive, whereas the correlation between Emotional
Stability and the corresponding Elite dimension in the present study is negative. Second,
the correlation between Openness and Upbeat and Conventional was significantly negative
in Rentfrow and Gosling’s study, whereas the correlation between Openness and the
corresponding Pop/Dance dimension was nonsignificant in the present research.
Associations between personality characteristics and changes in music
preferences: Latent growth curve modelling (LGM)
LGM (Duncan, Duncan, & Strycker, 2001; Mehta & West, 2000; Muthe
´n & Curran, 1997)
was used to examine associations between the Big-Five factors and changes in music
preferences. These LGM analyses were performed in two steps. In the first step, growth
curve models were constructed separately for each music-preference dimension in order to
investigate the extent of individual variation in the initial level and the linear growth
component of each music-preference variable. The models included two latent factors. The
Copyright #2007 John Wiley & Sons, Ltd. Eur. J. Pers. 22: 109–130 (2008)
DOI: 10.1002/per
Music preferences and personality characteristics 119
Table 5. Correlations between Big-Five personality factors and music-preference dimensions in Rentfrow and Gosling’s (2003) Study 2 sample and in our
less-restrictive model
Rentfrow and Gosling (2003) Less-restrictive model
Intense and rebellious Reflective and complex Energetic and rhythmic Upbeat and conventional Rock Elite Urban Pop/Dance
Extraversion .00 .01 .22
.24
.18
.22
Agreeableness .04 .01 .08
.23
.11
.22
Conscientiousness .04 .02 .00 .15
.05
Emotional stability .01 .08
.01 .07 .16
Openness .18
.44
.03 .14
.33
.22
.02
Note:N¼1044; Blanks represent the parameters that were fixed to zero.
p.05.
Copyright #2007 John Wiley & Sons, Ltd. Eur. J. Pers. 22: 109–130 (2008)
DOI: 10.1002/per
120 M. J. M. H. Delsing et al.
first latent factor is labelled the intercept and corresponds to the initial status of the
dependent variable: for example the adolescents’ preference for Rock music at Time 1. The
intercept is a constant for any individual across time that represents information about
the mean and the variance of the collection of individual intercepts. The loadings of all four
measured variables on the intercept factor are constrained to 1. The second factor, labelled
slope, represents the rate of change (increase, decrease) in preferences for a music
dimension over the period of the study (i.e. from Time 1 to Time 4).
We specified a linear change trajectory by fitting a model with the slope factor loadings
for Time 1, Time 2, Time 3 and Time 4 being 0, 1, 2 and 3, respectively. To account for age
differences, adolescents’ age at the first measurement was used as a predictor of the
intercept and slope factors (see also Duncan, Duncan, Strycker, Li, & Alpert, 1999; Mehta
& West, 2000; Meredith & Tisak, 1990). No other predictors were included in these initial
models. In the second step, growth curve models were tested in which, in addition to
adolescents’ age at the first measurement, Big-Five personality scores at the first
measurement were included as predictors of the intercept and slope factors. To control for
possible gender effects, adolescents’ gender was included as an additional predictor
variable (Figure 2). In these models, personality at T1 was allowed to covary with both age
and gender, as is indicated by the curved arrows between these variables. Again the SEM
program LISREL 8 (Jo
¨reskog & So
¨rbom, 1996) was used to perform the LGM analyses.
3
Table 6 contains the parameter estimates of the first series of growth curve analyses. The
fit indices indicate that these models generally provided a good fit to the data. Chi-squares
Figure 2. General growth curve model that was estimated for each Big-Five factor and each music-preference
dimension. The double-headed curved arrows between the factors indicate that latent factors are allowedto covary.
T1, T2, T3 and T4 refer to the dependent variable measured annually for 4 years (T1 ¼Time 1; T2 ¼Time 2;
T3 ¼Time 3; T4 ¼Time 4).
3
Alternative LGM models were tested to examine possible associations between adolescents’ music preferences at
T1 and over-time changes in Big-Five personality characteristics. None of the effects of T1 music preferences on
the Big-Five slope factors turned out to be statistically significant, indicating that initial music preferences did not
predict subsequent changes in personality.
Copyright #2007 John Wiley & Sons, Ltd. Eur. J. Pers. 22: 109–130 (2008)
DOI: 10.1002/per
Music preferences and personality characteristics 121
ranged from 107.31 to 218.80, with a mean of 157.56 for models with 10 degrees of
freedom (Nranging from 908 to 1001), all ps<.01. The GFI ranged from .93 to .97 with a
mean of .95, the CFI ranged from .98 to .99 with a mean of .99, the NNFI ranged from .97 to
.99 with a mean of .98 and the SRMR ranged from .01 to .05 with a mean of .04.
The significant mean estimates for the intercepts in the first column of Table 6 show
adolescents’ initial mean scores on the music-preference factors; their significance only
indicates that the scores significantly differed from zero (which is trivial for ratings on 1–
5 scales). These mean scores indicate that Pop/Dance is rated most positively, followed by,
Urban, Rock and Elite, respectively. As can be seen in the second column, the variance for
the intercept factors was significantly different from zero for all music-preference scores,
which indicates that there were systematic individual differences in adolescents’ initial
(Time 1) music preferences.
The slope mean estimates (see Table 6, third column) indicate that for three of the four
music-preference dimensions (i.e. Rock, Elite, Pop/Dance), the slope mean was
significantly negative, indicating that adolescents’ mean levels showed a decreasing
trajectory from Time 1 to Time 4. In other words, adolescents on average show weaker
preferences for these music categories over time. For the dimension of Urban, the slope
mean was significantly positive, indicating that adolescents’ mean levels showed an
increasing trajectory over a 3-year period. In other words, adolescents on average show
stronger preferences for this music category over time. For all four music factors, the slope
factor variance was found to be significantly different from zero (p<.01) (see Table 6,
fourth column), indicating that systematic individual differences were found for
adolescents’ changes in their preferences for these music categories.
In the second step of our LGM analyses, growth curve models were specified to
investigate the associations between Big-Five personality factors and changes in
adolescents’ music preferences. For each of the four music-preference factors, five growth
curve models were tested in which, in addition to adolescents’ age at the first measurement,
the T1 scores on one of the five personality factors as well as adolescents’ gender were
included as predictors of the intercept and slope factors (see Figure 2), resulting in a total of
20 models in this second series of LGM analyses.
The fit of these 20 LGM models to the data was generally good, with chi-squares ranging
from 95.11 to 207.76, and a mean of 154.48 for models with 15 degrees of freedom (N
ranging from 785 to 876), p<.01, GFI ranging from .94 to .97 with a mean of .96, the CFI
ranged from .99 to 1.00 with a mean of .99, the NNFI ranged from .98 to .99 with a mean of
.99 and the SRMR ranged from .01 to .04 with a mean of .03.
Table 6. Univariate latent growth curve results for adolescents’ music preferences
Music preference
Intercept Slope
Ms
2
Ms
2
Rock 2.92

.60

0.22

.07

Elite 2.01

.38

0.25

.02

Urban 3.95

.65

0.19
.05

Pop/Dance 4.27

.48

0.35

.05

p.05;

p.01.
Copyright #2007 John Wiley & Sons, Ltd. Eur. J. Pers. 22: 109–130 (2008)
DOI: 10.1002/per
122 M. J. M. H. Delsing et al.
The coefficients for the effects of personality at the first measurement on the intercept
and slope factors of the music-preference dimensions (paths c and d, respectively, in
Figure 2) are given in Table 7. With regard to the correlations between wave-1 personality
and the intercept factors of music preferences (see columns 1, 3, 5 and 7), our findings
generally corroborate our previous findings (see Tables 3–5) regarding the associations
between music preferences and personality. Also several significant associations were
found between wave-1 personality and the slope factors of music preferences (see columns
2, 4, 6 and 8), indicating that individual differences in personality at Time 1 predicted
individual differences in the rate of change in music preference from Time 1 to Time 4.
Adolescents’ initial level of Openness to Experience predicted changes in preference for
Pop/Dance music (.21, p<.01) and Urban music (.16, p<.01). This means that
adolescents who had higher initial levels of Openness to Experience tended to report higher
rates of decrease in preference for Pop/Dance music over time and lower rates of increase
in preference for Urban music. Changes in preference for Pop/Dance music were also
significantly predicted by initial levels of Agreeableness (.14, p<.05). This means that
adolescents who had higher initial levels of Agreeableness tended to report higher rates of
decrease in preference for Pop/Dance music over time. Finally, adolescents’ initial level of
Extraversion was found to predict changes in preference for Rock music (.11, p<.05).
This means that adolescents who had higher initial levels of Extraversion tended to report
higher rates of decrease in preference for Rock music over time.
Our LGM analyses also revealed several interesting associations between age and the
music-preference intercepts and slopes (paths a and b, respectively, in Figure 2). Age was
negatively related to the intercepts of Rock (.12, p<.01) and Pop/Dance (.10, p<.05)
music, indicating that, at the first measurement, older adolescents show weaker preferences
for these music categories. Furthermore, age was found to be positively related to the
intercept of Elite (.09, p<.05) music, indicating that older adolescents show stronger
preferences for this music category.
In addition to these age-intercept correlations, significant associations were found
between adolescents’ age and the linear trajectory of all four music-preference dimensions.
Positive associations were found between age and the slopes of Rock (.10, p<.05), Elite
(.25, p<.01) and Pop/Dance (.15, p<.01) music. This means that older adolescents
tended to report lower rates of decrease over time in preference for Rock, Elite and Pop/
Dance music. A negative association was found between age and the slope factor of Urban
Table 7. Standardised beta coefficients for the effects of personality at wave 1 on intercept and
slope factors music preferences
Big Five
Rock Elite Urban Pop/Dance
ISISI SIS
Extraversion .01 .11
.01 .05 .13

.07 .15

.07
Agreeableness .06 .01 .13

.04 .03 .05 .10
.14
Conscientiousness .12

.03 .05 .04 .01 .05 .02 .02
Emotional stability .01 .08 .09
.07 .03 .10 .04 .05
Openness .19

.07 .27

.09 .02 .16

.04 .21

Note: I, intercept; S, slope.
p.05;

p.01.
Copyright #2007 John Wiley & Sons, Ltd. Eur. J. Pers. 22: 109–130 (2008)
DOI: 10.1002/per
Music preferences and personality characteristics 123
music (.15, p<.01), indicating that older adolescents tended to report lower rates of
increase in their liking for this type of music.
Finally, several significant associations between gender and the music-preference
intercepts and slopes were found (paths e and f, respectively, in Figure 2). Boys showed
stronger preferences for Rock, whereas girls showed stronger preferences for Elite and
Urban at the first measurement. In addition to these gender-intercept correlations,
significant associations were found between adolescents’ gender and the linear trajectories
of Rock and Urban. In comparison with boys, girls showed lower rates of decrease over
time in preference for Rock, and higher rates of increase over time in preference for Urban.
DISCUSSION
The purpose of this paper was to examine the structure of Dutch adolescents’ music
preferences, the stability of these preferences over time and the associations between
(changes in) these preferences and Big-Five personality characteristics.
Factor structure and stability of music preferences
Exploratory and confirmatory factor analyses revealed four clearly interpretable
music-preference dimensions which were labelled Rock, Elite, Urban and Pop/Dance.
The pattern of loadings strongly resembled the one reported by Rentfrow and Gosling
(2003), thus providing support for the generalisability of Rentfrow and Gosling’s
four-factor structure of music preferences across cultures and age groups. In spite of this
general cross-sample consistency, however, several differences could be noted between the
Dutch and American factor solutions. In the Dutch adolescent sample, for example, the
genre trance/techno loaded on the Pop/Dance factor, whereas in the United States, the
comparable genre electronica/dance loaded on the Energetic and Rhythmic factor (instead
of on the Upbeat and Conventional factor which corresponds to the Dutch Pop/Dance
factor). Furthermore, in the Netherlands, the genre gospel loaded on the Elite factor,
whereas in the United States, the comparable genre religious music loaded on the Upbeat
and Conventional factor (instead of on the Reflective and Complex factor which
corresponds to the Dutch Elite factor). An explanation for these differences may lie in the
relative popularity of these genres in the Netherlands and in the United States. The fact that
in the Netherlands, trance/techno and top 40/charts load on the same factor (i.e. Pop/
Dance) may be due to the fact that trance/techno music appears to be far more popular in
the Netherlands, and probably most of Europe, than in the United States (see e.g. Stevens,
2001; Stevens & Elchardus, 2001; Ter Bogt, Engels, Hibbel, Van Wel, & Verhagen, 2002).
Over the last decade, trance/techno music has become part of conventional mainstream
culture in the Netherlands, which may explain why adolescents who like top-40 music also
tend to like trance/techno music. Religious music, on the contrary, appears to be far more
popular in the United States than in the Netherlands, which may explain why in Rentfrow
and Gosling’s (2003) study this genre loads on the Upbeat and Conventional factor, as does,
for example, the genre pop. In Dutch society, which is highly secularised, religious music
plays a marginal role and appears to belong mainly to the domain of elite culture. Taken
together, these findings suggest that, although the overall factor structure was highly
similar in both the United States and the Netherlands, differences in popularity of genres in
different regions may impact the dimensional structure of music preferences. Future
Copyright #2007 John Wiley & Sons, Ltd. Eur. J. Pers. 22: 109–130 (2008)
DOI: 10.1002/per
124 M. J. M. H. Delsing et al.
research in other regions and cultures, and across other age groups, should provide further
information on the generalisability of the factor structures found in this and Rentfrow and
Gosling’s study.
The relatively high stability correlations that were found for the music-preference
dimensions indicate that music preferences remain fairly stable across time. Our findings
also suggest that music preferences are becoming more stable during the course of
adolescence. This increasing stabilty of adolescents’ music preferences with age may be
associated with the fact that adolescents’ self-views become more stable as a result of
adolescents’ identity formation (Erikson, 1968). This finding is consistent with the idea
that music preferences crystallise during adolescence (Holbrook & Schindler, 1989).
Associations between personality and (changes in) music preferences
Across different types of analyses, a consistent pattern of contemporary associations
between music preferences and personality characteristics emerged. Adolescents who
enjoy Rock tend to be relatively low on Conscientiousness and relatively high on Openness
to Experience. Adolescents who enjoy Elite tend to be relatively high on Agreeableness,
Conscientiousness and Openness to Experience and relatively low on Emotional Stability.
Adolescents who enjoy Urban tend to be relatively high on Extraversion and
Agreeableness, as are adolescents who enjoy Pop/Dance. Our SEM analyses indicate
that the pattern of correlations we found between music-preference dimensions and
Big-Five personality characteristics was highly similar across age groups and closely
resembles the pattern of associations reported by Rentfrow and Gosling (2003). Age
differences were found for Elite, which was was negatively related to Extraversion and
positively related to Conscientiousness for the younger age group, but positively related to
Extraversion and (nonsignificantly) negatively related to Conscientiousness for the older
age group. Preference for this type of music may point at a somewhat more introverted and
careful nature in younger adolescents, whereas during late adolescence, when preference
for Elite may have become somewhat more common, it may point at a somewhat more
outgoing personality. Also with regard to Urban, an age-group difference was found in
the relation with Emotional Stability. This difference, however, should be interpreted with
caution since effects in both the younger and older age group were nonsignificant.
Although our pattern of associations between music preferences and personality
characteristics closely resembles the one reported by Rentfrow and Gosling (2003), a
striking difference is that, in our study, preference for Elite music was negatively related to
Emotional Stability, whereas Rentfrow and Gosling did not find substantial consistent
associations between this trait and any of the four music dimensions. This difference may
be due to age differences. A strong preference for Elite music may be quite appropriate for
the college students in Rentfrow and Gosling’s sample, but it may be relatively odd and
associated with signs of neuroticism for the somewhat younger adolescents in our sample.
Note, however, that, consistent with Rentfrow and Gosling’s study, adolescents who prefer
Rock music do not appear to display signs of neuroticism or disagreeableness, despite
previous findings that the Rock dimension contains music that emphasises negative
emotions.
Another difference between Rentfrow and Gosling’s (2003) study and our study is that,
in the present study, correlations between music-preference dimensions and personality
factors generally appear to be somewhat lower. A possible explanation for this finding
could lie in the age difference between the two samples. Personality factors may have a
Copyright #2007 John Wiley & Sons, Ltd. Eur. J. Pers. 22: 109–130 (2008)
DOI: 10.1002/per
Music preferences and personality characteristics 125
larger effect on the musical preferences of the older, more autonomous, college students in
Rentfrow and Gosling’s sample than on those of the younger adolescents in our sample, for
which peer influences might be more salient. Note, however, that hardly any age
differences showed up in our multigroup analysis comparing the associations between
personality factors and music preferences for the older and younger adolescents.
Therefore, other differences between the two samples (e.g. cultural differences) may
account for the somewhat lower correlations in the present study. Future studies with
samples of adolescents in the US and college students in the Netherlands could further
clarify the role of age and culture with regard to the associations between personality and
music preferences.
Our LGM analyses revealed that, in addition to being cross-sectionally related to music
preferences, personality factors predicted changes in these preferences. Adolescents who
had higher initial levels of Openness to Experience tended to report higher rates of decrease
in preference for Pop/Dance music over time and lower rates of increase in preference for
Urban music. Furthermore, adolescents who had higher initial levels of Agreeableness
tended to report higher rates of decrease in preference for Pop/Dance music over time.
Finally, adolescents who had higher initial levels of Extraversion tended to report higher
rates of decrease in preference for Rock music over time.
Theoretical explanations for the associations between personality and (changes
in) music preferences
The uses and gratifications perspective (Arnett, 1995; Arnett et al., 1995; Gantz et al.,
1978; Larson, 1995; Rubin, 1994), according to which people like the kinds of music that
satisfy certain needs, may provide hints to explain some of the associations that were found
between personality characteristics and (changes in) music preferences. The positive
contemporary associations between Extraversion and both Urban and Pop/Dance are in
line with extraverts’ desire to socialise with peers and to have fun. Urban and Pop/Dance
music are the two most popular styles that are most often played at parties and social
gatherings of youngsters. Extraverts may show more rapid declines in preference for Rock
music, because this more alternative, and less popular, style is less suited to provide them
with the social contacts they desire.
Parties and social gatherings may also be the settings that satisfy the interpersonal needs
(e.g. an eagerness to help others) of agreeable adolescents, which may account for the fact
that positive associations with both Urban and Pop/Dance were also found for
Agreeableness. Compassion for others may also be reflected in the lyrics of religious
or gospel music, which may account for the positive association that was found between
Agreeableness and the Elite dimension. Maybe, as they grow older, these relatively
sociable adolescents do not need the most popular music genres anymore to facilitate social
interactions with peers, which may account for their more rapid decrease in their liking of
Pop/Dance.
The positive contemporary associations that were found between Openness to
Experience and the relatively complex Elite and nonmainstream Rock dimensions may
be explained by the fact that individuals high on Openness have a desire for variety,
intellectual stimulation and unconventionality (Costa & McCrae, 1988). Adolescents who
are relatively open minded and interested in new experiences may also develop a more
negative attitude toward the more popular and conventional musical genres as they grow
older as they may have a greater tendency to look for experiences outside of the mainstream
Copyright #2007 John Wiley & Sons, Ltd. Eur. J. Pers. 22: 109–130 (2008)
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126 M. J. M. H. Delsing et al.
culture. This may account for our finding that adolescents relatively high on Openness
showed a more rapid decrease in their liking of Pop/Dance and a less rapid increase in their
liking of Urban. Finally, the negative association that was found between Conscientious-
ness and Rock may be explained by the fact that the ‘will to achieve’, typical for individuals
high on Conscientiousness, may be relatively absent in Rock fans.
Some of the above-mentioned needs may be grounded in physiological characteristics.
Thus, for example, the positive associations we found between Extraversion and both
Urban and Pop/Dance may also be explained from the model of optimal stimulation
(Eysenck, 1990; Zuckerman, 1979), according to which individuals tend to prefer the
music that moves them toward their optimal arousal level. Extraverts may like these music
styles in particular because of their capacity to move them up toward their optimal arousal
level. Likewise, emotionally unstable adolescents may tend to avoid overstimulation by
choosing less stimulating music (Daoussis & McKelvie, 1986), which may account for the
negative association we found between Emotional Stability and Elite music.
Clearly, not all associations found between personality characteristics and music
preferences can equally easily be explained from a uses and gratifications perspective. To
bridge the remaining gaps between personality factors and music preferences, we need to
know more about the specific (physiologically grounded) needs that are associated with
these personality factors (see e.g. Costa & McCrae, 1988), as well as about the needs
expected to be gratified by certain types of music.
One should note that correlations between personality characteristics and (changes in)
music preferences were generally found to be small-to-moderate. Essentially, this means
that, when explaining (changes in) music preferences, factors other than personality
characteristics have to be taken into account. Likely candidates include factors such as
cognitive abilities, peer influences and social class. Future studies will need to examine
many other possible determinants in order to develop a more comprehensive theory of
music preferences.
Additional findings
Our first series of LGM analyses revealed several other findings that were not directly
related to our research questions. It was found that adolescents show weaker preferences
for Rock, Elite and Pop/Dance music, but stronger preferences for Urban music over time.
The declining trajectories for Rock and Pop/Dance music are in line with our finding that at
T1, older adolescents show weaker preferences for these music categories. These findings
suggest that, as adolescents get older, they become less defiant and more adventurous and
autonomous in their musical taste. The less rapid decline we found for older adolescents’
preferences for these genres may be due to the fact that older adolescents already showed
weaker preferences for these categories at the start of the study. The decreasing trajectory
for Elite may, at first glance, seem discordant with our finding that at T1, older adolescents
show stronger preferences for this genre. Note, however, that mean level trajectories result
from a complex mixture of age-related changes and, for example, changes related to the
overall popularity of musical genres at a given point in time. Maybe in this case, increasing
preferences for Elite music as one gets older have been compensated for by a general
decline in the popularity of this genre among adolescents over the 3 years of this study.
Closer inspection of our data indeed revealed that for most age groups, the popularity of
Elite music showed a decreasing trend over the 3-year period. In line with our earlier
suggestion that older adolescents may be more adventurous, less conventional, in their
Copyright #2007 John Wiley & Sons, Ltd. Eur. J. Pers. 22: 109–130 (2008)
DOI: 10.1002/per
Music preferences and personality characteristics 127
musical taste, older adolescents were found to show lower rates of decrease over time for
Elite music.
The fact that adolescents were found to show stronger preferences for Urban over time
may partly be explained by the increasing popularity of this music category over the last
couple of years. It does not seem to be an effect of increasing age, since no association was
found between age and preference for Urban at T1. Note, however that older adolescents
were found to report lower rates of increase in their liking for Urban music, which, again,
is consistent with their supposed more adventurous, less mainstream, music taste.
Limitations
The present study has several limitations. First, although personality characteristics at T1
were found to predict over-time changes in music preferences, causal inferences should be
made with caution. Second, adolescents in the present sample are nested within school
grades. This may lead to dependencies in the data that are not accounted for by our
analyses. Application of Multilevel analyses (Raudenbush & Bryk, 2002) that do account
for these dependencies could be a direction of future research. Third, only self-reports of
music preferences were used. By doing so, we assumed that adolescents are able to
accurately report on their music preferences. It may not be ruled out, however, that
impression-management motivations play a role in these reports. For example an
individual may enjoy listening to classical music but might report no preference for it if
listening to classical is considered ‘uncool’. The impact of this impression-management
bias may be relatively minor, however, since Rentfrow and Gosling (2003) have
demonstrated that a similar factor structure emerged using either self-report data or data
based on the music individuals had downloaded from the Internet.
Despite these limitations, the present investigation provides compelling evidence that
there is a clear structure underlying Dutch adolescents’ music preferences. This structure
shows close resemblance to the one reported by Rentfrow and Gosling (2003) for a
somewhat older group of college students in the United States. This suggests that the
structure identified in our and in Rentfrow and Gosling’s study may show considerable
generalisability across cultures and age groups. Future research in other age groups and
other, especially nonwestern, cultures should provide further evidence for the universality
of the structure of music preferences identified in this study. Furthermore, our findings
clearly demonstrate that music preferences are already fairly stable during early
adolescence and become increasingly stable toward late adolescence. Finally, our results
are consistent with the idea that personality has an impact on music preferences. The music
adolescents select partly reflects their personalities and associated needs and thus knowing
what music a person likes may serve as a clue to his or her personality.
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... De bevindingen zijn in diverse vervolgstudies verder onderzocht en bevestigd. De studie van Rentfrow & Gosling is herhaald in onder Duitsland (Langmeyer, Guglhör-Rudan & Tarnai, 2012) en Nederland (Delsing et al., 2008) met een bevestiging voor de bevindingen in de originele studie. Zo concluderen Delsing et al. (2008) in een groot Nederlands onderzoek onder adolescenten dat hun onderzoeksresultaten in sterke mate overeenkomen met de resultaten van Rentfrow & Gosling voor wat betreft de relatie tussen muziekvoorkeuren en de Big Five persoonlijkheidsdimensies. Door de verschillen tussen Amerika en Nederland in de populariteit van religieuze muziek (gospel) en dance scoorden deze genres wel iets anders op de dimensies. ...
... De studie van Rentfrow & Gosling is herhaald in onder Duitsland (Langmeyer, Guglhör-Rudan & Tarnai, 2012) en Nederland (Delsing et al., 2008) met een bevestiging voor de bevindingen in de originele studie. Zo concluderen Delsing et al. (2008) in een groot Nederlands onderzoek onder adolescenten dat hun onderzoeksresultaten in sterke mate overeenkomen met de resultaten van Rentfrow & Gosling voor wat betreft de relatie tussen muziekvoorkeuren en de Big Five persoonlijkheidsdimensies. Door de verschillen tussen Amerika en Nederland in de populariteit van religieuze muziek (gospel) en dance scoorden deze genres wel iets anders op de dimensies. Bovendien gebruikten Delsing et al. (2008) niet het STOMP-meetinstrument maar de Musical Preference Questionnaire (MPQ) die niet de genres folk, country, blues en soundtracks bevat. ...
... Zo concluderen Delsing et al. (2008) in een groot Nederlands onderzoek onder adolescenten dat hun onderzoeksresultaten in sterke mate overeenkomen met de resultaten van Rentfrow & Gosling voor wat betreft de relatie tussen muziekvoorkeuren en de Big Five persoonlijkheidsdimensies. Door de verschillen tussen Amerika en Nederland in de populariteit van religieuze muziek (gospel) en dance scoorden deze genres wel iets anders op de dimensies. Bovendien gebruikten Delsing et al. (2008) niet het STOMP-meetinstrument maar de Musical Preference Questionnaire (MPQ) die niet de genres folk, country, blues en soundtracks bevat. Daarnaast vond de meting plaats middels een vijfpuntsschaal in plaats van een zevenpuntsschaal zoals bij STOMP. ...
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... This extends earlier work on musical expertise and its relation to users' current preferences, i.e., how musical expertise influences users' current preferences with music recommendations (Jin et al., 2018a(Jin et al., , 2018bKamehkhosh et al., 2020;Millecamp et al., 2020) into how musical expertise might play a role in users' musical preference development and their exploration behavior toward new musical tastes. Users' musical preferences are not just related to expertise but also to other personal characteristics, such as personality traits (Delsing et al., 2008;Rentfrow & Gosling, 2003) and other social factors (Schedl et al., 2018), which we did not study in this article. Future work could explore how these factors are related to the consistency of users' preferences and their exploration behavior. ...
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... There is also evidence that people who enjoy styles of music like rock, heavy metal, and punk, score high on psychological measures of thrill-seeking, openness, and also value freedom and independence (Rentfrow & Gosling 2003, 2006Zweigenhaft, 2008). The referential work of Rentfrow & Gosling (2003) was duplicated by Delsing et al. (2008) but on adolescents analyzing the stability of music preferences over time and their relation to personality characteristics. Chamorro-Premuzic and Furnham (2007) studied the relationship between personality, individual ability, and uses of music (emotional, cognitive, and background). ...
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Data
Chapter Data, Program Inputs and Outputs for all LGM Examples in the textbook "An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Applications, Second Edition". Model specifications are included providing program syntax for Amos, EQS, LISREL, and Mplus software programs. The files are arranged by chapter and include syntax, data, and output files for all examples a particular software program is capable of estimating. The first three chapters (specification of the LGM, LGM and repeated measures ANOVA, and multivariate representations of growth and development) cover the development of the LGM. These are followed by three chapters involving multiple group issues and extensions (analyzing growth in multiple populations, accelerated designs, and multilevel longitudinal approaches), and followed by the chapter on growth mixture modeling, which addresses multiple-group issues from a latent class perspective. The remainder of the book covers 'special topics' (chapters on interrupted time series approaches to LGM analyses, growth modeling with ordered categorical outcomes, Missing data models, a latent variable framework for LGM power analyses and Monte Carlo estimation, and latent growth interaction models). The zipfile is quite large (1MB) since it contains all files for the various software programs.