What mood matters: The relationship between mood
state and music selection
Zalfa Farah 12034150 Master’s Thesis
University of Amsterdam Graduate school of Communication Master’s Program: Communication Science
Supervisor: Rinaldo Kühne
Abstract
Music is experienced by billions of people every day. Previous studies have shown that the music we listen to affects the mood that we experience. However, few studies have investigated this relationship the other way around. This research aimed to understand how individual’s current mood could predict their recent music listening behaviors, via an online questionnaire study. This research tested the hypothesis that participants mood state
(dysphoric or angry) would relate to their heavy music listening frequency (heavy-metal and hip-hop/rap). It was found that those who were more dysphoric or angry had recently listened to more heavy-metal music, but not hip-hop/rap music compared to those who were not experiencing these mood states. Although, participants general likeability of heavy metal music worsened this relationship. It was also found that the importance of lyrics to the participants and their trait extroversion, did not moderate the above relationships. These findings partially support a mood congruent selection perspective and refute the theory of mood management. Taken together, these findings will benefit parents and therapists who can further understand their child’s or patient’s behaviors via their music habits.
Introduction
Music transgresses through people from all walks of life, to spread togetherness or individuality, taking the listener on a personalized journey. It gather’s people; whether it be a tribe calling for its people, a concert for entertainment, or a therapeutic tool. An individual’s experience of music is unique; this is what makes music and understanding its personalization interesting.
It is interesting to know more about why people consume music, particularly how this can change depending on their mood and how they feel during the time of consumption. For instance, it would be beneficial for the music industry to know more about their audience’s mood and how this affects the purchasing of their media. Moreover, this research could raise awareness for parents and therapists who may learn to appreciate the role that music plays in their son or daughter’s mental health and their mood management. Furthermore, this study will validate theoretical perspectives regarding mood management, including mood
management theory (Zillmann, 1988a) and mood congruent selection. Music may be selected to accompaniment a mood state or either be used as a tool to alter and adapt their current mood state. These perspectives could explain why an individual’s mood state can influence their music selection and to validate which of these frameworks better explains this.
The present study investigated whether mood would significantly predict participants recent music genre listening frequency. In addition, the research attempted to establish the role that lyrical importance and extroversion would have in the above relationship. Therefore, the following research questions were formulated:
Theory
Musical Preference
Given the popularity of music listening amongst emerging adults, researchers have investigated how music relates to a range of behaviors and mental processes in emerging adults; such as self-identity forming (Elves, 2016), mood (Stewart, Garrido, Hense, McFerran, 2019) and emotional regulation (Ginsborg, 2019). It is interesting to know why people select certain music and what circumstances change their music preferences. A genre is a style of music (Aucouturier & Pachet, 2003). Given the number of musical genres, researchers have attempted to categorize these based on certain characteristics. For example, Delsing et. al, (2008) used the categories of; intense and rebellious, upbeat and conventional and energetic and rhythmic categories. Whereas Lacourse, Claes and Villeneuve (2001) proposed five categories of music; metal, soul, pop, classical and electronic. For the purpose of this study, the musical framework of Schwarz (2003) will be adopted. The musical genres can be divided in two categories, either heavy or light music. This simplified framework was adopted as the research was not interested in the arousal categories used by Delsing. Light music is
considered to be of a positive nature, including genres such as pop and soundtrack, whereas arguably more negative genres such as heavy metal and hip-hop fall under heavy music.
Mood State: Dysphoria and anger
Mood can be defined as the temporary state of a person’s feeling which arises from ones positive or negative experience (Nettle & Bateson, 2012). It is susceptible to the influence of numerous factors and can impact our behavior. For example, dysphoria can complement feelings of depression, however unlike depression, feelings of dysphoria may occur more regularly amongst people (DSM V, 2013). When feeling dysphoric, an individual will commonly experience dissatisfaction and anxiety. Anger on the other hand can be
defined as an emotion described by negative feelings toward something or someone (Berkowitz, 2005). They will be investigated as recent mood states (a mood that has been lasting for two weeks) in order to establish their role in the individual’s selection on recent music listening frequency.
Mood Management Theory
According to mood management theory (Zillmann & Bryant, 1985), individuals have certain desires to remove negative moods by selecting media content that would improve their mood for the better (mood enhancement). For example, Vella and Mills (2016) found that during periods of unpleasant mood states, participants listened to music to regulate emotion. This is evident through-out all media content; audiences select media that suits their mood and to improve the current state they are in. Mares, Oliver, and Cantor (2008) found that older adults (50+) had more interest in watching media that had positive valence, compared to negative valence. This is in line with Mood Management. It is possible that older adults dealt with more life issues, and were more likely to tune into media that would enhance their mood. Stewart, Garrido, Hense and McFerran (2019) found that younger adults tired to alter their mood state by using music. This confirms the prediction of the mood management theory, that individuals are seeking to enhance their mood, by altering the media they are listening to, and how long they listen to it for, although not all studies how found results in this direction (Friedman, Gordis, & Förster, 2012). Knobloch and Zillmaan (2002) found that participants who were in a bad mood, chose to listen to joyful music for a longer period of time, compared to participants who were in a good mood. Another study by McFerran, Garrido, O’Grady, Grocke and Sawyer (2015), found that individuals who were sad and distressed to begin with, were more likely to regulate their mood by listening to an increased frequency of positive, uplifting music. Furthermore, a qualitative study found that the importance of music is intrinsically related to enjoyment and positive experiences (Saarikallio & Erkkila, 2007).
They described music to bring out a strong form of sensation, revival, discharge and mental work and solace. The participants argued that through music they were able to change their mood which resulted in a positive outcome. In a more generalizable larger sample, Lonsdale and North (2011), found that the primary reason students listen to music was to
manage/regulate their moods. Taken together these findings, suggest that individuals will likely select media/music to enhance their mood supporting the theory of mood management.
Based on the above, the first hypothesis (H1a1, H1b1)of the current study would predict that those who have feelings of dysphoria or anger will enhance their mood by selecting more light music (i.e. pop).
Mood Congruent Selection
The framework of mood congruent selection (Bower, 1981), originates from the general theory of mood congruency, which argues that individual’s cognitive ability and behaviors are aligned with their current mood state. Therefore, the mood congruent selection perspective posits that individuals select media that maintains their current/recent mood. For example, when feeling down/sad, an individual may desire to listen to music will most likely be congruent to the mood that they are in. Thoma, Ryf, Mohiyeddini, Ehlert and Nater (2012) suggest that listeners use music in order to regulate current emotional states. Indeed, it has been shown that music is functional in regulating individual’s moods (Gallop and Casetllii 1989). For example; “Exposure of the annoyed individual to materials featuring hostile and aggressive behavior perpetuates his or her elevated state of excitation” (Zillmann, 1979, p. 213). It can be argued that media acts as a scape goat for individuals who are feeling a sense of not belonging, using music to further detach from the outside world, hence heightening their unhappy mood state. In the present study, it would be that angry individuals might select heavy music in order to maintain their mood, as this category is more closely related to their current mood. This is because heavy music is loud, dense and could be perceived as
aggressive. Hip-hop/rap and heavy-metal music contain lyrics that are typically more violent than other genres, therefore it is plausible that an individual in a state of anger would seek such music to accompany their feelings of anger.
Shwartz (2003), found that adolescents who had a preference for heavy metal music were likely to feel rejected or misunderstood by others. In this case, listening to heavy music likely reflects the characteristic and intensity of their mood state. Others have argued that it is the congruency in valence of mood and a musical genre that will result in selection of these specific media. McFerran, et al. (2015) found that participants who were feeling sad or distressed were more likely to listen to negatively valence music (heavy-metal) and deliberately seek the expression of anger within this music. These findings support mood congruent selection, as participants were feeling sad, and sought media that was congruent to their mood. Some studies found that participants in a negative mood state listened to heavy metal, to give them listeners stress relief (Arnett, 1991; Van Goethem & Sloboda, 2011).In a series of experimental studies Taylor and Friedman (2014) found that individuals who were in a sad mood tended to avoid happy music (i.e. pop and funk) and continued to listen to more sad music. Additionally, Oliver (2008) found that individuals sought to maintain their sad mood state by listening to more sad music instead of more happy music, especially when they were undergoing social obligations. Similarly, a study by Garrido and Schubert (2015) found that individuals who were clinically depressed, tended to listen to music which intensified their symptoms of depression, further supporting mood congruent selection. Finally, Chen, Zhou, and Bryant (2007) found that participants who were in a sad mood were more likely to listen to distressing music. However, after a certain amount of time in this study, participants would generally seek more upbeat music, which ironically supports the mood management perspective. Taken together, the above research suggests that the music that individuals seek can often maintain their current mood state. If the findings of the current study support this
theory, then it will suggest that participants were selecting media that corresponds with their mood (dysphoria and anger) within the two last weeks, hypothesis (H1a2, H1b2).
Lyrical Value
Some people believe the lyrics are the most fundamental part of a song, while others disagree. For example, it is possible that in hip-hop/rap music, people pay more attention to lyrics than other elements of the track. In an article by Travis (2012), rap music served a voice of empowerment to form meaningful identities to urban communities, particularly in the African-American culture. Whereas, in more conventional music such as pop, the lyrics tend to be more positive, upbeat and inspirational and some lyrics will be more influential than others.
Fischer and Greitemeyer (2006), asked participants to listen to either neutral or misogynist lyrics to see if they would affect aggressive behavior. Interestingly, male participants were more likely to behave aggressively towards females having heard
misogynous lyrics as opposed to neutral lyrics. It should be noted that the misogynist lyrics came from rap or rock songs. This suggests that lyrics in these genres have the ability to boost aggressive acts, at least in males. This finding could have occurred because males value lyrics more than females in these specific genres. Support for this notion was found in a study by Miranda and Claes (2008), who explored the role of lyrical importance/value in the
relationship of mood and music preferences. They found that adolescent boys who gave more importance to lyrics were more inclined to like heavy-metal music compared to girls. On the other hand, with some genres, i.e. soul music, they found that gender did not moderate the relationship between music preference and lyrical value. Furthermore, this research is interested to find out whether the value a person gives to the lyrics of a song, moderates the relationship between their mood state and their music genre listening frequency. In other words, those who are feeling angry or dysphoric, and pay more attention to lyrics, will
increase their heavy music genre listening frequency (heavy-metal and hip-hop/rap), compared to those who do not value the lyrics as much.
Extroversion
A study by Rawlings and Ciancerlli (1997) found that extroversion was one of the strongest predictors of music preferences from the Big Five Personality Dimension (Costa & McCrae, 1992). Moreover, a study by North et al. (2005), showed that extroverts tend to prefer hip-hop/rap and R&B, supported by Delsing et al. (2008) who also found this in a sample of adolescents. It has been argued that extroverts seek motivations from music in order to reach optimal arousal (Dollinger, 1993). Therefore, this suggests that musical genres may contain such stimuli in order for extroverts to choose this type of music. Similarly, Rentfrow and Gosling, (2003), found that extroverts had a greater preference for upbeat and conventional music (e.g, pop), which can be linked to light music. Delsing et al., argues that extroverts may enjoy this type of musical genre as it is commonly linked with socializing and partying, which are two behaviors that extroverts participate in. To further this, Vella and Mills (2016) and Ferwerda, Tkalcic and Schedl (2017) found those who were more extroverted were also more inclined to listen to more hip-hop/rap compared to introverts.
Although the relationship between extroversion and preference for musical genres has been researched, little is known about the role that extroversion plays in the relationship between mood state and music genre listening frequency. Therefore, the present study aims to determine whether there are any differences in the relationship between mood and music genre listening frequency between extroverts and introverts. The reason for including extroversion was to further explore individual differences amongst partcipants. It was expected that the relationship between mood state and heavy music listening frequency, would be weaker for those who were extroverted. This is because extroverted individuals would be more likely to remedy their angry or dysphoric mood by being amongst people, and
listen to music less than somebody who is less extroverted; who is likely to stay more indoors and use mood maintenance.
Current study
This study aimed to determine the influence of two negative mood states; dysphoria and anger, on music genre listening frequency. The reason for choosing partcipants’ mood state (over the past 2 weeks) as opposed to mood trait, was because it was easier for partcipants to remember their feeling over this short period of time. Secondly, most of the research has focused on trait mood, therefore, a gap in the research was to explore state mood. Additionally, this research wanted to include mood states that could potentially be congruent with heavy music, therefore, dysphoria and anger were chosen. Therefore, the following research questions and hypotheses were formulated:
RQ 1- Does recent dysphoric or angry mood predict music listening frequency in the last two weeks?
To answer this question, this study tested two competing theoretical predictions. Firstly, mood management theory would predict that participants will regulate feeling dysphoric or angry by enhancing their mood by listening to more positive/light music in the last two weeks (e.g. pop music). On the other hand, mood congruent selection would predict that participants who were feeling more dysphoric or angry, would have listened to music that is congruent to these states, being more negative/heavy music (e.g. heavy-metal music, hip-hop/rap), (see figure 1). It was expected that these relationships would remain when
controlling for age, gender and music likeability.
H1a1: Participants who have been in a dysphoric mood state will have listened to more
light/positive music (mood management theory).
H1a2: Participants who have been in a dysphoric mood state will have listened to more heavy
Mood State
Music Genre Preference
(heavy-metal music)
Music Listening Frequency
Music Genre Preference (heavy-metal music)
H1b1: Participants who have been in an angry mood state will have listened to more
light/positive music (mood management theory)
H1b2: Participants who have been in an angry mood state will have listened to more heavy
music that consistent with their mood (mood congruent selection).
RQ 2- Does lyrical value moderate the relationship between mood and music listening frequency?
The present study aimed to see if lyrics would be important to individuals when experiencing mood states and their music listening frequency (heavy music), (see figure 2).
H2a: It is predicted that the relationship between dysphoria and heavy music listening
frequency will be stronger for those who place more importance on lyrical value.
H2b: It is predicted that the relationship between anger and heavy music listening frequency
will be stronger for those who place more importance on lyrical value.
RQ 3- Does extroversion moderate the relationship between mood and music genre listening frequency?
The personality trait (extroversion) was included to determine the role it played in the relationship between mood state and music genre listening frequency (heavy music), (see figure 3).
H3a: It is predicted that the relationship between dysphoria and heavy music listening
frequency will be stronger for less extroverted participants.
H3b: It is predicted that the relationship between anger and heavy music listening frequency
will be stronger for less extroverted participants.
Mood State (Dysphoria)
Music Genre Preference (heavy-metal music)
Music Listening Frequency
Music Genre Preference (heavy-metal music)
(Control Variables)
Music Genre Preference (heavy-metal music)
Lyrical ValueMusic
Genre LlLyPreference (heavy-metal music) ExtroversionMusic Genre Preference (heavy-metal music)
Mood State (Anger)
Music Genre Preference (heavy-metal music)
Music Listening Frequency
Music Genre Preference (heavy-metal music)
(Control Variables)
Music Genre Preference (heavy-metal music)
Figure 2. Conceptual model
Figure 3. Conceptual model
Note: The control variables represent age, gender and likeability of music.
Method Design
As this research did not attempt to prove causality, a cross-sectional survey design was deemed appropriate as this research investigated subjective variables (music listening
frequency) and self-reported mood state. Surveys acquire numerous benefits, they enable the researcher to gather a large sample, which further increases the generalizability of the
findings. However, the validity of the survey may be affected, as individuals might not answer truthfully or miss questions.
Sampling
Non-probability convenience sampling was used. This was because the research centered on understanding emerging adults, their moods and music genre listening
frequencies. As Etikan (2016) argues, the study’s findings may not be generalizable when using non-probability sampling, however, it was the most affordable and least
time-consuming method. Moreover, due to the opportunity of accessibility to Lebanon, it seemed wiser to also attain a sample of Lebanese and non-Lebanese living in Lebanon. There was originally 162 recorded partcipants however, 20 partcipants data were removed due to having incomplete data or being over 30 years of age, resulting in a final sample of 142 participants. According to, Schonbrodt and Preugini (2018), correlations will be established with sample sizes anywhere from 80-100. In the case of this research, 142 seemed to be decent participant response number (See table 1, appendix 1).
Procedure
Once ethical approval was obtained from the University of Amsterdam, participants took part in an online survey using Qualtrics. Once consent was given, they were asked to complete a series of questionnaires via Facebook and email, which were then scored individually. The questionnaires took about 10 min to complete. The survey consisted 5 questionnaires that asked participants about their feelings of dysphoria and anger over the past
2 weeks, their personality traits (including extroversion), their music genre listening
frequency in the past two weeks, and overall music genre likeability. Finally, they were asked one question about the value of lyrics in music. Demographics were also recorded.
Participants were debriefed about the nature of the research. Please see appendix for full survey.
Observed Variables Dysphoria
Dysphoria was measured using the 7-item dysphoria subscale from the “Hospital Anxiety and Depression Scale (HADS)” (Zigmond, & Snaith, 1983). The sub-scale included statements such as “I feel as if I have slowed down” and “I feel life is not worth living”. The scale ranges from 1 = strongly disagree to 5 = strongly agree, with high scores representing greater feelings of dysphoria. The Cronbach’s alpha for the HADS subscale in the present study showed very strong reliability, α = .81.
Anger
Anger over the past two weeks was measured using the state anger scale from the “State-Trait Anger Expression Inventory 1 (Speilberger, 1994)”. Participants respond to 20 statements such as “I feel Calm”, on a five-point Likert scale, ranging from 1 = strongly disagree to 5 = strongly agree. The higher the score, the angrier the individual is. The
Cronbach’s alpha for the STAXI subscale in the present study showed very strong reliability, α =.94.
Extroversion
To measure extroversion, the 2-item sub scale from the “Ten Items Personality Indicator (TIPI)” was used (Gosling, Rentfrow & Swann, 2003), which includes the
5-point Likert scale was used for responses (1 = strongly disagree to 5 = strongly agree). In order to disguise the fact that the research is interested in extroversion, participants completed questions related to other personality traits such as neuroticism and agreeableness. In this case, higher scores suggested more extraversion. The Cronbach’s alpha for the TIPI extroversion subscale in the present study showed very good reliability, α = .72.
Lyrical Value
To measure lyrical value, a question with a 5-point scale (1 = prefer the music instead of lyrics to 5 = strongest preference for lyrics), asked participants how much importance they place on the value of lyrics in music.
Music Listening Frequency and Music Likeability
Music listening frequency over the last two weeks was measured using an adapted version of the “Short Test of Music Preferences (STOMP)” (Rentfrow & Gosling, 2003). Participants indicated on a 5-point scale (1 = not listening at all to 5 = listening all the time) the extent to which they listened to. See appendix 2 for genre list. Three genres were removed from the original questionnaire. In addition, the original version of the STOMP was used to assess the participants’ general likeability of the different music genres listed in the appendix Participants responded using a 5 point Likert scale (1 = strongly dislike to 5 = strongly like).
Results
The data was analyzed using IBM SPSS (version 25). Prior to the main analysis, the assumptions of normal distribution and linearity of the variables were checked in order to proceed with regression. Descriptive statistics were reported, (see table 2, appendix 1). First, a correlational analysis was run between all musical frequency variables and mood (see table 3, appendix 1). To explore hypothesis 1, a series of simple regressions were conducted to explore whether mood states (dysphoria & anger) would predict the variance in heavy music (heavy-metal and hip-hop/rap). Further to this, the following variables were controlled for;
music genre likeability, age and gender. To test Hypothesis 2, that lyrical value will moderate the relationship between mood and heavy music listening frequency (heavy-metal and
rap/hip-hop), three moderation analyses was conducted using Process (version 3.3). In the first moderation analysis, the independent variable was dysphoria with the dependent variable was heavy-metal music listening frequency. In the second moderation analysis, the
independent variable was anger with the dependent variable was heavy-metal music listening frequency. In the third moderation analysis, the independent variable was anger with the dependent variable of hip-hop/rap music listening frequency. Age, gender, and likeability were entered in as control variables. The relationship between dysphoria and hip-hop/rap music was not entered into a moderation analysis, as there was no correlation between
dysphoria and hip-hop/rap music. Finally, to test hypothesis 3, that extroversion will moderate the relationship between mood and heavy music listening frequency (heavy-metal and hip-hop/rap), three moderation analyses were conducted using Process (version 3.3). The
independent variables, dependent variables and control variables were the same as those used in the previous analysis, however, the moderator was now extroversion. The variables were mean centered in the moderation analysis.
Mood and Heavy Music Listening Frequency Dysphoria & heavy-metal
There was a linear relationship between dysphoria and heavy-metal listening frequency. Furthermore, the sample included more that 20 cases for the predictor, the residuals were normally distributed and the data showed homoscedasticy therefore the data was suitable for a simple regression. In order to test hypothesis 1a, simple regression was run whilst controlling for age, gender and heavy metal likeability. The regression showed that the first model comprised of the control variables explained 57 percent of the variance in heavy-metal listening frequency (R2= .57). Dysphoria explained an additional 12 percent of variance. The
final model containing dysphoria and all control variables was significant, F(4, 132) = 45.87, p < .001. When controlling for age, gender and heavy-metal music likeability, dysphoria did not significantly predict heavy-metal music listening frequency, although this was almost significant, b = 0.11 , t(140) = 1.91 , p = .06 , 95% CI [-0.01, 0.06]. Incidentally, heavy-metal likeability was the only control variable which predicted heavy-metal music listening
frequency, b = 0.71 , t(140) = 11.95 , p < .001, 95% CI [0.42, 0.59].
Anger & Heavy-metal
There was a linear relationship between anger and heavy-metal listening frequency. Furthermore, the sample included more that 20 cases for the predictor, the residuals were normally distributed and the data showed homoscedasticy therefore the data was suitable for a simple regression. In order to test hypothesis 1b, a simple regression was run whilst
controlling for age, gender and heavy-metal likeability. The regression showed that the first model comprised of the control variables explained 56.5 percent of the variance in heavy-metal listening frequency (R2= .57). Anger explained an additional 3 percent of variance in heavy-metal listening frequency. The final model containing anger and all control variables was significant, F(4, 134) = 44.04 , p < .001. When controlling for age, gender and heavy-metal music likeability, anger did not significantly predict heavy-heavy-metal music listening frequency, b = 0.06 , t(140) = 0.99 , p = .321 , 95% CI [-0.01, 0.02]. Heavy-metal likeability
b = 0.70 , t(140) = 11.68 , p < .001, 95% CI [0.42, 0.59] and gender b = 0.12 , t(140) =
-1.95 , p = .053 , 95% CI [-0.58, 0.00] , predicted heavy-metal music listening frequency.
Anger & Hip-hop/rap
There was a linear relationship between anger and hip-hop/rap listening frequency. Furthermore, the sample included more that 20 cases for the predictor, the residuals were normally distributed and the data showed homoscedasticy therefore the data was suitable for a simple regression. In order to further test hypothesis 1b, that anger would significantly predict
hip-hop/rap listening frequency, a simple regression was run whilst controlling for age, gender and hip-hop/rap likeability. The regression showed that the first model comprised of the control variables explained 50 percent of the variance in hip-hop/rap listening frequency (R2= .50). Anger failed to explain any additional variance in hip-hop/rap listening frequency. The final model containing anger and all control variables was significant, F(4, 133) = 33.83,
p < .001. When controlling for age, gender and hip-hop/rap likeability, anger did not
significantly predict hip-hop/rap music listening frequency, b = -0.03 , t (140) = -0.46 , p = .46, 95% CI [-0.01, 0.01]. Hip-hop/rap likeability b = 0.71 , t(140) = 10.99 , p < .001 , 95% CI [0.51, 0.73] was the only control variable which predicted hip-hop/rap listening frequency.
Role of Lyrical Value
In order to test hypothesis 2, three separate moderation analyses were conducted to establish the role of lyrical value in the above relationships, whilst controlling for age, gender and music likeability.
Dysphoria, heavy-metal and lyrical value
The model explained 60 percent of the variance in heavy-metal listening frequency (R2= .60), F(6, 129) = 31.86 , p < .001 . The moderation analysis found that dysphoria did not significantly predict heavy-metal listening frequency when controlling age, gender and likeability, b = 0.02 , t(140) = 1.60, p = .116, 95% CI [-0.01, -0.05]. However, lyrical value did significantly predict heavy-metal listening frequency b = 0.14 , t(140) = 2.15, p < .05, 95% CI [0.01, 0.27]. Finally, the interaction between dysphoria and lyrical value was not significant b = -0.01 , t(140) = -0.91 , p = .367, 95% CI [-0.03, 0.01]. The only control variable that significantly predicted heavy-metal listening frequency was heavy-metal likeability, b = 0.51 , t(140) = 12.03 , p < .001 , 95% CI [0.42, 0.59].
The model explained 59 percent of the variance in heavy-metal listening frequency (R2= .59), F(6, 131) = 30.84 , p < .001 .The moderation analysis found that anger did not significantly predict heavy-metal listening frequency scores when controlling age, gender and likeability b = 0.001 , t(140) = 0.88, p = .379, 95% CI [-0.00, 0.02]. Moreover, lyrical value did significantly predict heavy-metal listening frequency b = 0.16, t(140) = 2.41, p = .017, 95% CI [0.03, 0.29]. Finally, the interaction between anger and lyrical value was not
significant b = -0.002 , t(140) = -0.37, p = .715, 95% CI [-0.01, 0.01]. The control variables that significantly predicted heavy-metal listening frequency were heavy-metal likeability b = 0.50 , t = 11.66 , p < .001, 95% CI [0.42, 0.59] and gender b = -0.31 , t(140) = -2.12 , p = .036, 95% CI [-0.60, -0.02].
Anger, hip-hop/rap and lyrical value
The model explained 53 percent of the variance in hip-hop/rap listening frequency (R2= .53), F(6, 130) = 24.42 , p < .001 .The moderation analysis found that anger did not significantly predict hip-hop/rap listening frequency scores when controlling age, gender and likeability b = -0.002 , t(140) = -0.30, p = .762, 95% CI [-0.01, 0.01]. Moreover, lyrical value did not significantly predict hip-hop/rap listening frequency b = 0.10 , t(140) = 1.49, p = .140 , 95% CI [-0.03, 0.24]. Finally, the interaction between anger and lyrical value was not significant b = -0.003 , t(140) = -0.68, p = .501, 95% CI [-0.01, .01]. The only control variable that significantly predicted hip-hop/rap listening frequency was hip-hop/rap likeability b = 0.62 , t(140) = 11.16 , p < .001, 95% CI [0.51 , 0.73].
Role of Extroversion
In order to test hypothesis 3, three separate moderation analyses were conducted to establish the role of extroversion in the above relationships, whilst controlling for age, gender and music likeability.
The model explained 58 percent of the variance in heavy-metal listening frequency (R2= .58), F(6, 130) = 30.90 , p < .001. The moderation analysis found that dysphoria did significantly predict heavy-metal listening frequency scores when controlling age, gender and likeability b = 0.03 , t(140) = 2.00 , p < .05, 95% CI [0.00, 0.07]. However, extroversion did not significantly predict heavy-metal listening frequency b = 0.03 , t(140) = 0.65 , p = .519 , 95% CI [-0.06 , 0.11]. Finally, the interaction between dysphoria and lyrical value was not significant b = 0.003 , t(140) = 0.48, p = .634 , 95% CI [-0.01 , 0.02]. The only control variable that significantly predicted heavy-metal listening frequency was heavy-metal likeability b = 0.51 , t(140) = 11.92 , p < .001 , 95% CI [0.43 , 0.60].
Anger, heavy-metal and extroversion
The model explained 57 percent of the variance in heavy-metal listening frequency (R2= .57), F(6, 132) = 28.96 , p < .001. The moderation analysis found that anger did not significantly predict heavy-metal listening frequency scores when controlling age, gender and likeability b = 0.01, t(140) = 0.92, p = .363 , 95% CI [-0.01 , 0.02]. Moreover, extroversion did not significantly predict heavy-metal listening frequency b = 0.001 , t(140) = 0.32 , p = .975 , 95% CI [-0.08 , 0.09]. Finally, the interaction between anger and extroversion was not significant b = 0.001 , t(140) = 0.32 , p = .754 , 95% CI [-0.00 , 0.01].The only control variable that significantly predicted heavy-metal listening frequency was heavy-metal likeability b = 0.51 , t(140) = 11.60 , p < .001 , CI [0.42 , 0.60].
Anger, hip-hop/rap and extroversion
The model explained 51 percent of the variance in hip-hop/rap listening frequency (R2= .51), F(6, 131) = 22.35 , p < .001The moderation analysis found that anger did not significantly predict hip-hop/rap listening frequency scores when controlling age, gender and likeability b = -0.004 , t(140) = -0.60, p = .550 , 95% CI [-0.02 , 0.01]. Moreover,
-0.37, p = .71 , 95% CI [-0.11 , 0.072]. Finally, the interaction between anger and
extroversion was not significant b = -0.001, t(140) = -0.50 , p = .619 , 95% CI [-0.01 , 0.01]. The only control variable that significantly predicted hop/rap listening frequency was hip-hop/rap likeability b = 0.62 , t(140) = 10.92 , p < .001 , 95% CI [0.51, 0.73].
Discussion
The goal of this study was to find out whether an individual’s recent mood state had a relationship to their music genre listening frequency. An additional aim of this study was to explore the role of lyrical value and extroversion in the above relationship. Firstly, it was found that in general, mood and music listening frequency were related. As expected, those participants who were feeling more dysphoric and angry within the last two weeks, were those who were listening to more heavy-metal. Moreover, those who were feeling angry also stated that they had been listening to more hip-hop/rap in the past two weeks. However, these relationships were lost once heavy-metal and hip-hop/rap likeability were controlled for. Finally, lyrical value and extroversion did not moderate the relationship between mood and music genre listening frequency.
Relationship between mood and music listening frequency
The current study found evidence to support the first overall hypothesis that mood would relate to participant recent music genre listening frequency. It was expected that in the last two weeks participants would either be seeking to select music to enhance their mood (H1a1 & H1b1) or select music congruent to their mood (H1a2 & H1b2). It was found that participants who had been feeling dysphoric recently had been listening to more heavy-metal music in this time frame, supporting hypothesis H1a2. Moreover, these same participants had stated they had they had been listening to less pop music. These genres fit into different categories, heavy vs. light, Schwarz (2003). Heavy music includes genres like heavy-metal, hip-hop/rap, which may seem aggressive and loud. By contrast, light music contains genres
like pop, which are non-aggressive and mainstream. The correlation between dysphoria and heavy-metal music listening frequency supports previous research (McFerran, et al., 2015; Oliver, 2008), who also found that participants feeling sad or distressed were more likely to listen to negative music (heavy-metal). One possible explanation for the relationship between dysphoria and heavy-metal music listening frequency is that participants were seeking music congruent to their mood state. When feeling dysphoric, an individual may experience
depression, anxiety, despair, which seems appropriate that they would select a heavy genre of music, and one that resonates with their current negative situation. This is in line with mood congruent selection, which argues that individuals will select media that is congruent to their current mood and feelings. This finding makes sense as according to Bower (1981), our behavior is congruent on the mood state we are experiencing at the time (e.g, if we are sad, we recall more negative information). Another possibility is that dysphoric individuals will use media to regulate their emotions, in this through mood maintenance (Gallup & Castelli, 1989). As no significant relationship was found between Dysphoria and pop music listening frequency, this would suggest these participants were not seeking lighter music, contradicting hypothesis H1a1. This evidence contradicts the prediction of mood management theory (Zillmann & Bryant, 1985) individuals will attempt to enhance their current mood by selecting lighter media/music e.g pop. However, this lack of relationship between dysphoria and pop music is in line with Taylor and Friedman’s (2014) study, which found that
individuals who were in a sad mood tended to avoid lighter music (i.e. pop) and continued to listen to more sad music.
It was surprising that there was no relationship found between dysphoria and hip-hop/rap music listening frequency. Although both these genres can be considered as heavy music (Schwarz, 2003), there are clear differences in the musical properties of these genres. For example, hip-hop is arguably more melodic than heavy-metal, and the lyrics tend to be
much clearer. Although we can try to objectify the valence of hip-hop/rap, or indeed any genre, it must be taken into consideration, hip-hop could be a genre that is positive to some and negative to others, or, heavy to some and light to others. Although Schwarz’s framework categorizes hip-hop/rap as heavy music, it is possible that dysphoric participants do not perceive hip-hop in that way. However, it was found that those who were dysphoric listened to more rock music in the last two weeks, which is also classified as heavy music (Schwarz, 2003). It would seem that a person’s dysphoric mood does increase the likelihood that they would listen to certain heavy music, that coincides with their mood. Finally, these findings corroborate Garrido and Schubert’s (2015) research, which showed that partcipants who were clinically depressed sought out more heavy music, which in return intensified their symptoms of depression, further supporting mood congruent selection.
The finding that anger significantly predicted recent music listening frequency was in line with the research’s hypothesis H1b. Specifically, it was expected that participants would either be seeking to select music to enhance their mood (H1b1) or select music congruent to their mood (H1b2). It was found that participants who had been feeling angry recently had been listening to more heavy-metal and less pop music in this time frame, supporting
hypothesis H1b2. These findings could imply that participants were seeking music congruent to their current negative mood and perhaps deliberately seek the expression of anger within this music. This gives further evidence for mood congruent selection. Alternatively, angry participants could have used heavy-metal music to regulate their emotions, through mood maintenance (Gallup & Castelli, 1989). Additionally, this finding adds support to previous studies that have found a relationship between negative mood state and heavy-metal music (Arnett, 1991; Van Goethem & Sloboda, 2011). The fact that participants were less likely to seek pop music, contradicts the predictions of mood management theory. It was surprising that participants who were feeling angry had chosen to listen to significantly less hip-hop/rap
in the last two weeks. As mentioned earlier, hip-hop/rap can be categorized as heavy music, and arguably hip-hop/rap is more melodic than other types of heavy music (e.g. heavy-metal) and the lyrics are much clearer.
Role of lyrical value
Lyrical value played no role in the relationship between mood and heavy music listening frequency (heavy-metal & hip-hop/rap), therefore rejecting hypotheses 2a and 2b. Surprisingly, this age group did not give importance to the lyrics of these music genres, suggesting that perhaps other elements of the music are more important to for mood maintenance. It could also be that the lyrics included in these genres are simply not that important and influential. However, a study by Travis (2012), argued that rap music served a voice of empowerment to form meaningful identities, particularly in the African-American culture. However, the current sample contained no African-American partcipants, therefore the lyrics of hip-hop/rap music may not have such influence on them. Furthermore, Miranda and Claes (2008) found that adolescent boys who gave more importance to lyrics were more inclined to like heavy metal music compared to girls. This was supported in the present study, where it was found that lyrical value predicted heavy-metal music listening, as those
partcipants who value lyrics more, were those that were listening to more heavy-metal music.
Role of extroversion
It was found that extroversion did not play a role in the relationship between mood and heavy music listening frequency, therefore rejecting hypotheses 3a and 3b. Specifically, this research predicted that the relationship between mood and heavy music listening
frequency, would be more apparent in those who were less extroverted. This is because the assumption behind the hypothesis was that extroverts are more likely to cure their anger or dysphoric mood by being amongst others, and therefore listen to music less than somebody who introverted. These findings are not in line with previous research, that has shown that
extroversion predicts general music preference for hip-hop/rap (Delsing et al., 2008; Ferwerda et al., 2017; North et al., 2005). However, in the present study, the research was interested in frequency of listening to hip-hop/rap whereas the other studies were concerned with how extroversion predicts general likeability of hip-hop/rap. Perhaps, extroversion is more applicable to the general likeability of music, not with the listening frequency of music genres. It appears that the partcipants mood was more dominant in their media selection as opposed to their personality(extroversion).
Scientific and Social Relevance
Taken together the findings of this study, have partially supported and validated the mood congruent selection perspective, because it was found that participants who were feeling dysphoric or angry were seeking out more heavy music, in the last two weeks. However, this relationship no longer seemed to exist particularly when controlling for the participants general likeability for heavy music. Furthermore, the findings have invalidated the mood management theory as it would appear that participants were not interested in listening to genres that would enhance their mood; i.e. lighter, positive music. However, the difficulty of objectification of positive/light and negative/heavy music may play a role in these findings, hence making it harder to test both theories.
This study has high social relevance given that music is a popular media choice. Understanding the relationship between mood and music preference/listening frequency could potentially help therapists by highlighting that their patient’s music choice is playing a role in their well-being. The more the therapists learn about the listening habits (frequency and likeability) of their patients, could be an indication about their current mood, which could in turn, highlight a potential issue about their mental health. Furthermore, listening habits of children may play a big role in the formation of their identity, which is something that arguably most parents would be interested to find out. This could raise awareness for parents
to understand the link between their children’s behavior and mood. Finally, this research could be of interest to the music industry, because they like to know more about their consumers habits, which in turn can affect their preferences and purchasing activities.
Strengths & Limitations
One limitation of the study is the possible categorization of heavy and light music. As mentioned previously, choosing Schwarz’s (2003) framework, was because of the simplicity of it (compared to other frameworks). Although both categorized as heavy, the findings of this study showed that effects of hip-hop/rap and heavy-metal music were not the same. Other researchers have attempted to categorize these music genres in different ways, for instance, Delsing (2008), would place hip-hop/rap under the category of energetic and rhythmic. This could imply that the genre is not as heavy as first thought and may explain why both
dysphoria and anger have predicted an increase in heavy-metal but not in hip-hop/rap. For future research, it would be wise to treat each separately instead or use different theoretical perspective.
Secondly, it is important to note that music is extremely subjective and what one person recognizes as heavy music (e.g. heavy-metal) or negative music (e.g. shouting lyrics), another person may recognize as more positive music. Therefore, if someone was feeling dysphoric and listening to heavy-metal music, it could be that this person is actually trying to enhance their mood (mood management) with a genre they feel to be more positively valence, as opposed to selecting heavy music for the reason that it is negative and heavy, just like the mood they are experiencing.
A major strength of this study was the diversity and size of the sample. This improves the generalizability and population validity. Music is a universal language, and therefore all future studies when researching such a topic, should recruit a diverse sample. As outlined earlier, this study has high level of social and scientific relevance. Finally, this study was the
first to investigate the link between mood state and specifically music listening frequency in a sample of emerging adults.
Future research
As this research was unable to determine causality, it would be wise for future studies to adopt an experimental approach. Additionally, longitudinal research could be conducted to see whether or not these effects last over a longer period of time. This could help us
understand more about the effects of mood on media choice. Furthermore, although the research included a range of musical genres, only two were explored in more detail, therefore, future studies should explore the effects of different mood states on different genres. It would be unwise to assume that personality does not play a role in the relationship between mood and music, when this study only looked at extroversion. Therefore, future studies could explore a range of personality factors.
Conclusion
The main aim of this study was to find out to what extent mood state would predict recent music listening frequency, in a sample of emerging adults. As expected, the level of heavy music listening frequency (heavy-metal and hip-hop/rap) was predicted by individuals who had been feeling dysphoric and angry in the last two weeks, initially supporting mood congruent selection. However, these relationships no longer existed when controlling for likeability of these genres. This suggests that participants likability of the music genre in the first place is more prevalent and should be taken into consideration. Furthermore, this study has established that both lyrical value and extroversion do not matter in the relationship between mood state and music listening frequency. This study has high social relevance as it caters to parents, therapists and the music industry.
References
Arnett, J. J. (2006). Emerging adulthood: Understanding the new way of coming of age. Emerging adults in America: Coming of age in the 21st century, 3-19. doi:10.1037/11381-001
Aucouturier, J., & Pachet, F. (2003). Representing musical genre: A state of the art. Journal
of New Music Research, 32(1), 83-93. doi:10.1076/jnmr.32.1.83.16801
Beck, A. T., Steer, R. A., & Brown, G. (1996). Beck depression inventory–II. PsycTESTS
Dataset. doi:10.1037/t00742-000
Berkowitz, L. (2005). Anger. Handbook of Cognition and Emotion, 409-428. doi:10.1002/0470013494.ch20
Bower, G. H. (1981). Mood and memory. American Psychologist, 36(2), 129-148. doi:10.1037/0003-066x.36.2.129
Bryant, J., & Zillmann, D. (2002). Media Effects: Advances in Theory and Research. Oxfordshire, England: Taylor & Francis.
Chen, L., Zhou, S., & Bryant, J. (2007). Temporal changes in mood repair through music consumption: Effects of mood, mood salience, and individual differences. Media
Psychology, 9(3), 695-713. doi:10.1080/15213260701283293
Diagnostic and statistical manual of mental disorders, 5th Edition. (2013). doi:10.1176/appi.books.9780890425596.893619
Dollinger, S. J. (1993). Research note: personality and music preference: extraversion and excitement seeking or openness to experience? Psychology of Music, 21(1), 73-77. doi:10.1177/030573569302100105
Elliott, D., Polman, R., & Taylor, J. (2012). The effects of relaxing music for anxiety control on competitive sport anxiety. European Journal of Sport Science, 14(sup1), S296-
S301. doi:10.1080/17461391.2012.693952
Etikan, I. (2016). Comparison of convenience sampling and purposive sampling. American
Journal of Theoretical and Applied Statistics, 5(1), 1.
doi:10.11648/j.ajtas.20160501.11
Ferwerda, B., Tkalcic, M., & Schedl, M. (2017). Personality traits and music
genres. Proceedings of the 25th Conference on User Modeling, Adaptation and
Personalization - UMAP '17. doi:10.1145/3079628.3079693
Festinger, L. (1962). A Theory of Cognitive Dissonance. Redwood City, CA: Stanford University Press.
Fischer, P., & Greitemeyer, T. (2006). Music and aggression: The impact of sexual- aggressive song lyrics on aggression-related thoughts, emotions, and behavior toward the same and the opposite sex. Personality and Social Psychology
Bulletin, 32(9), 1165-1176. doi:10.1177/0146167206288670
Fisher, S., & Greenberg, R. P. (1972). Selective effects upon women of exciting and calm music. Perceptual and Motor Skills, 34(3), 987-990. doi:10.2466/pms.1972.34.3.987 Friedman, R. S., Gordis, E., & Förster, J. (2012). Re-exploring the influence of sad mood on
music preference. Media Psychology, 15(3), 249-266. doi:10.1080/15213269.2012.693812
Gallup, G. J., & Castelli, J. (1989). The people’s religion. New York, NY: Macmillan. Garrido, S., and Schubert, E. (2015a). Moody melodies: Do they cheer us up? A study of
the effect of sad music on mood. Psychol. Music 43, 244–261. doi: 10.1177/0305735613501938
Gosling, S. D., Rentfrow, P. J., & Swann, W. B. (2003). A very brief measure of the big-five personality domains. Journal of Research in Personality, 37(6), 504-528.
Knobloch, S., & Zillmann, D. (2002). Mood management via the digital jukebox. Journal of
Communication, 52(2), 351-366. doi:10.1111/j.1460-2466.2002.tb02549.x
Lonsdale, A. J., & North, A. C. (2011). Why do we listen to music? A uses and gratifications analysis. British Journal of Psychology, 102(1), 108-134.
doi:10.1348/000712610x506831
Mares, M., Oliver, M. B., & Cantor, J. (2008). Age differences in adults' emotional motivations for exposure to films. Media Psychology, 11(4), 488-511. doi:10.1080/15213260802492026
McFerran, K. S., Garrido, S., O’Grady, L., Grocke, D., & Sawyer, S. M. (2014). Examining the relationship between self-reported mood management and music preferences of Australian teenagers. Nordic Journal of Music Therapy, 24(3), 187-203.
doi:10.1080/08098131.2014.90894
Miranda, D., & Claes, M. (2008). Personality traits, music preferences and depression in adolescence. International Journal of Adolescence and Youth, 14(3), 277-298. doi:10.1080/02673843.2008.9748008
Nettle, D., & Bateson, M. (2012). The evolutionary origins of mood and its
disorders. Current Biology, 22(17), R712-R721. doi:10.1016/j.cub.2012.06.020 North, A. C., Desborough, L., & Skarstein, L. (2005). Musical preference, deviance, and
attitudes towards music celebrities. Personality and Individual Differences, 38(8), 1903-1914. doi:10.1016/j.paid.2004.11.016
Oliver, M. B. (2008). Tender affective states as predictors of entertainment preference. Journal of Communication, 58(1), 40-61. doi:10.1111/j.1460- 2466.2007.00373.x
Pascoe, G. (2014). Research matters, sampling, juta company, cape town, south africa, 131-146 Powell, S., 2007,‘orginisational marketing, identity and the creative brand. Journal of Brand Management, 15(1), 41-56.
Rawlings, D., & Ciancarelli, V. (1997). Music preference and the five-factor model of the NEO personality inventory. Psychology of Music, 25(2), 120-132.
doi:10.1177/0305735697252003
Rentfrow, P. J., & Gosling, S. D. (2003). Short test of musical preferences. PsycTESTS
Dataset. doi:10.1037/t08222-000
Robazza, C., Macaluso, C., & D'Urso, V. (1994). Emotional reactions to music by gender, age, and expertise. Perceptual and Motor Skills, 79(2), 939-944.
doi:10.2466/pms.1994.79.2.939
Rubin, A. M., West, D. V., & Mitchell, W. S. (2001). Differences in aggression, attitudes toward women, and distrust as reflected in popular music preferences. Media
Psychology, 3(1), 25-42. doi:10.1207/s1532785xmep0301_02
Schönbrodt, F. D., & Perugini, M. (2018). Corrigendum to “at what sample size do correlations stabilize?” [J. Res. Pers. 47 (2013) 609–612]. Journal of Research in
Personality, 74, 194. doi:10.1016/j.jrp.2018.02.010
Schwartz, K. D., & Fouts, G. T. (2003). Music preferences, personality style, and
developmental issues of adolescents. Journal of Youth and Adolescence, 32(3), 205 213. doi:10.1023/a:1022547520656
Spielberger, C. D. (2010). State-trait anger expression inventory. The Corsini Encyclopedia
of Psychology. doi:10.1002/9780470479216.corpsy0942
Stewart, J., Garrido, S., Hense, C., & McFerran, K. (2019). Music use for mood regulation: Self-Awareness and conscious listening choices in young people with tendencies to depression. Frontiers in Psychology, 10. doi:10.3389/fpsyg.2019.01199
Taylor, C. L., & Friedman, R. S. (2014). Sad mood and music choice: Does the self- relevance of the mood-eliciting stimulus moderate song preference? Media
Psychology, 18(1), 24-50. doi:10.1080/15213269.2013.826589
Thoma, M. V., Ryf, S., Mohiyeddini, C., Ehlert, U., & Nater, U. M. (2012). Emotion regulation through listening to music in everyday situations. Cognition &
Emotion, 26(3), 550-560. doi:10.1080/02699931.2011.595390
Travis, R. (2012). Rap music and the empowerment of today’s youth: Evidence in
Everyday Music Listening, Music Therapy, and Commercial Rap Music. Child and
Adolescent Social Work Journal, 30(2), 139-167. doi:10.1007/s10560-012-0285-x
Van Goethem, A., & Sloboda, J. (2011). The functions of music for affect
regulation. Musicae Scientiae, 15(2), 208-228. doi:10.1177/1029864911401174 Vella, E. J., & Mills, G. (2016). Personality, uses of music, and music preference: The
influence of openness to experience and extraversion. Psychology of Music, 45(3), 338-354. doi:10.1177/0305735616658957
Zigmond, A. S., & Snaith, R. P. (1983). Hospital anxiety and depression scale. PsycTESTS
Dataset. doi:10.1037/t03589-000
Zillmann, D. (1988). Mood management through communication choices. American
Appendix 1
Table 1- Demographic information of the participants
Demographics Percent Frequencies
Gender Male 41.5 Female 58.5 Nationality Lebanese 50 Dutch 14.8 American 2.2 Other 33 Age Mean (SD) 24.59(3.96) Range 18-30
Table 2. Descriptive statistics of the study’s variables
HADS= The Hospital Anxiety and Depression Scale STAXI= State-Trait Anger Expression inventory TIPI= Ten Items Personality Indicator
Variables M SD Min-Max Skewness Kurtosis
HADS 17.718 5.009 8-29 .344 -.539
STAXI 57.3475 13.636 25 -98 .048 -.318
TIPI 6.532 1.973 2-10 -.091 -.718
Table 3. Correlations
V= Variables, 1= Dysphoria, 2= Anger, 3=Listening frequency classical, 4= Listening frequency country, 5= Listening frequency Dance/Electronica, 6= Listening frequency hip-hop/rap, 7= Listening frequency soul/funk, 8= Listening frequency alternative, 9= Listening frequency jazz, 10= Listening frequency rock, 11=Listening frequency heavy-metal, 12= Listening frequency soundtracks/theme songs, 13= Listening frequency pop
V 1 2 3 4 5 6 7 8 9 10 11 12 13 1 2 3 4 5 6 7 8 9 10 11 12 13 - .762 - -.224** -.226** - .006 -.003 .261** - -.122 -.181* -.075 .058 - -.102 -.184* -.181 -.115 .280** - -.317** -.386** .142 .011 .244** .335** - .150 .082 .038 .107 .276** .138 .340** - -.234** -.209* .334** .071 .041 .074 .485** .273** - .193* .208* -.215* .083 -.040 .088 -.024 .304** .060 - .294** .261** -.140 .027 .016 .160 -.130 .224** -.059 .561** - .038 -.047 .356** .338** .048 -.097 .096 .162 .187* .018 .082 - -.141 -.124 .232** .234** -.132 .051 .154 -.065 .109 -.004 -.110 .412** -
Appendix 2
Mood, Music and Personality
Q1 Music, Mood and Personality
Thank you for your interest in this study conducted by Zalfa Farah, a Master’s student at the University of Amsterdam (supervised by Rinaldo Kühne). This study partially fulfills the requirements for a Master’s degree at the University of Amsterdam. This study is designed to learn more about the relationship between personality and moods of young adults (18 to 30 years). If you agree to take part, you will be asked to complete an online questionnaire. The questionnaire includes questions about your personality, musical preferences, and your mood. In total, the study will take up to 20 minutes. Taking part in this research is completely
voluntary. Any information that you provide will remain completely anonymous. We will ask you to indicate your age, gender, and education. No other personally identifiable information will be collected. You are free to stop the study at any time, without any negative
consequences. Participating in this research will not entail you being subjected to any appreciable risk or discomfort. You will not be exposed to any explicitly offensive material. Furthermore, you will be fully debriefed about the purpose of the study once you have finished your participation. No rewards are be provided for participation in this study. If you would like to know the results of the questionnaire or if you have any questions during or after the study, please feel free to contact Zalfa Farah (Zalfa.farah@gmail.com). In the case that you are not in the age group of 18 to 30, please do not participate in this study. Should you have any complaints or comments about this research, you can contact the Ethics Committee representing ASCoR, at the following address:
ASCoR Secretariat, Ethics Committee, University of Amsterdam, PO Box 15793, 1001 NG Amsterdam 020-525 3680 ascor-secr-fmg@uva.nl
Any complaints or comments will be treated in the strictest confidence.
o
I consent, begin the study (1)o
I do not consent, I do not wish to participate (2) Q3 AgeQ16 Nationality
________________________________________________________________
Q4 Gender
o
Male (4)o
Female (5)o
Prefer not to say (6)o
Other (7)Page Break
Q5 The next statements are about how you felt during the last two weeks. Please indicate how much you agree with the single statements:
disagree (1) agree nor disagree (3) agree (5) I wake early and then sleep badly
for the rest of the night. (1)
o
o
o
o
o
I feel miserable and sad. (2)o
o
o
o
o
I have lost interest in things (3)o
o
o
o
o
I have a good appetite. (4)o
o
o
o
o
I feel life is not worth living. (5)o
o
o
o
o
I still enjoy the things I used to. (6)o
o
o
o
o
I feel as if I have slowed down. (7)o
o
o
o
o
Q6 Please indicate the extent to which you agree or disagree with that statement. You should rate the extent to which the pair of traits applies to you, even if one characteristic applies more strongly than the other. I see myself as:
Strongly Disagree (1) Disagree (2) Neither agree nor disagree (3) Agree (4) Strongly agree (5) Extraverted, enthusiastic (1)
o
o
o
o
o
Critical, quarrelsome (2)o
o
o
o
o
Dependable, self-disciplined (3)o
o
o
o
o
Anxious, easily upset (4)o
o
o
o
o
Open to new experiences, complex (5)o
o
o
o
o
Reserved, quiet (6)o
o
o
o
o
Sympathetic, warm (7)o
o
o
o
o
Disorganized, careless (8)o
o
o
o
o
Calm, emotionally stable (9)o
o
o
o
o
Conventional, uncreative (10)o
o
o
o
o
Page BreakQ7 Please indicate how much you like to following music the genres: Strongly dislike (1) Dislike (2) Neither like or Dislike (4) Like (5) Strongly like (6) Classical (1)
o
o
o
o
o
Country (2)o
o
o
o
o
Dance/Electronica (3)o
o
o
o
o
Rap/hip-hop (4)o
o
o
o
o
Soul/funk (5)o
o
o
o
o
Alternative (6)o
o
o
o
o
Jazz (7)o
o
o
o
o
Rock (8)o
o
o
o
o
Heavy Metal (9)o
o
o
o
o
Soundtracks/theme songs (10)o
o
o
o
o
Pop (11)o
o
o
o
o
Page BreakQ8 Please indicate the frequency of listening time for each genre below within the last two weeks: Not listening at all (1) Infrequently (2) About the same (3) More Frequently (4) Listening all the time (5) Classical (1)
o
o
o
o
o
Country (2)o
o
o
o
o
Dance/Electronica (3)o
o
o
o
o
Rap/hip-hop (4)o
o
o
o
o
Soul/funk (5)o
o
o
o
o
Alternative (6)o
o
o
o
o
Jazz (7)o
o
o
o
o
Rock (8)o
o
o
o
o
Heavy Metal (9)o
o
o
o
o
Soundtracks/theme songs (10)o
o
o
o
o
Pop (11)o
o
o
o
o
Page BreakQ9 To what extent do you agree with this statement; The lyrics are the most important part of the song?
o
Strongly disagree (1)o
Disagree (2)o
Neither agree nor disagree (3)o
Agree (4)o
Strongly agree (5) Page BreakQ10 Within the last two weeks including today, please indicate on the scale below how you have felt for each statement:
Strongly Disagree (1) Disagree (2) Neither agree nor disagree (3) Agree (4) Strongly agree (5) I feel calm (1)
o
o
o
o
o
I feel secure (2)o
o
o
o
o
I am tense (3)o
o
o
o
o
I feel strained (4)o
o
o
o
o
I feel at ease (5)o
o
o
o
o
I feel upset (6)o
o
o
o
o
I am presently worrying over possible misfortunes (7)o
o
o
o
o
I feel satisfied (8)o
o
o
o
o
I feel frightened (9)o
o
o
o
o
I feel comfortable (10)o
o
o
o
o
Page BreakQ17 Within the last two weeks including today, please indicate on the scale below how you have felt for each statement:
Strongly Disagree (1) Disagree (2) Neither agree nor disagree (3) Agree (4) Strongly agree (5) I feel self-confident (1)
