Music plays a pivotal role in most cultures and discussions as to its being a basic part of nature can be traced back to Plato (Gray et al., 2001). Nowadays in ...
Personality, emotion and the use of music in everyday life: Measurement, theory and neurophysiological aspects of a missing link
- First studies with the IAAM -
Richard von Georgi Department of Medical Psychology and Medical Sociology Department of Musical Sciences Justus-Liebig-University, Giessen Phillip Grant Institute of Anatomy and Cell Biology II Justus-Liebig-University, Giessen Susanne von Georgi Centre for Psychiatry Justus-Liebig-University, Giessen Stefan Gebhardt Department of Psychiatry and Psychotherapy Philipps-University, Marburg
1.
INTRODUCTION
1.1. 1.2. 1.3. 1.4.
PERSONALITY AND MUSICAL PREFERENCES AFFECT AND SITUATIONAL ASPECTS USE OF MUSIC AS CONSCIOUS ACTION FUNCTIONAL USE OF MUSIC AS THE MISSING LINK
5 5 10 12 13
2.
SUMMARY AND OVERVIEW OF STUDIES
17
3.
STUDY 1
19
3.1. 3.2. 3.3. 3.4. 3.4.1. 3.4.2. 3.5. 4.
STUDY 2
4.1. 4.2. 4.3. 4.4. 4.4.1. 4.4.2. 4.4.3. 4.5. 5. 5.1. 5.2. 6.
METHOD PARTICIPANTS AND PROCEDURE PSYCHOLOGICAL INVENTORIES RESULTS ITEM- AND SCALE-ANALYSES CORRELATION WITH PSYCHOLOGICAL MEASURES DISCUSSION STUDY 1
METHOD PARTICIPANTS AND PROCEDURE PSYCHOLOGICAL MEASURES RESULTS ITEM- AND SCALE-ANALYSES CORRELATIONS WITH PERSONALITY VARIABLES PERSONALITY, EMOTION MODULATION WITH MUSIC AND HEALTH DISCUSSION STUDY 2
STUDY 3
19 19 19 22 22 28 31 33 33 33 33 35 35 39 41 44 48
METHOD AND RESULTS DISCUSSION STUDY 3
48 49
GENERAL DISCUSSION
51
6.1. MEASUREMENT OF THE BASIC DIMENSIONS OF USING MUSIC 6.2. THEORY OF USING MUSIC AS MODULATION STRATEGY 6.2.1. THE NEUROPHYSIOLOGICAL MODEL OF GRAY & MCNAUGHTON 6.2.2. A NEUROPHYSIOLOGICAL MODEL OF USING MUSIC 6.2.3. AN ALTERNATIVE EXPLANATION OF USING MUSIC FOR AROUSAL-MODULATION 6.3. CONCLUSIONS
51 53 53 57 61 64
7.
64
EPILOGUE
REFERENCES
66
Introduction
5
1. INTRODUCTION
Music plays a pivotal role in most cultures and discussions as to its being a basic part of nature can be traced back to Plato (Gray et al., 2001). Nowadays in western societies music can be accessed by anyone at almost any time due to modern media as radio, TV, CD, MP3. Statistics show listening to music to be paramount amongst most people’s hobbies (Rentfrow & Gosling, 2003). The results raise the question which aspects are decisive for the choice of the respective music through various specific situations, as well as whether these choices are based on musical preferences. At second glance, this question cannot be answered unequivocally due to the difficulty of operationalisation of variables, as well as different hypotheses, depending on theoretical paradigms and leanings.
1.1.
PERSONALITY AND MUSICAL PREFERENCES
The
classical
trait-approach,
centred
on
genetically
determined
personality-characteristics, leads to the existence of various studies linking musical preferences to nomothetically measured properties. Research based on the theories of Eysenck (1967, 1990; Eysenck & Eysenck, 1985) and Zuckerman (1979, 1991, 1996) show Extraversion (E), Psychoticism (P), Sensation Seeking (SS) and related constructs to be linked to preference of arousal-stimulating music and musical characteristics. In accordance with Yerkes-Dodson’s law (1908), Eysenck’s theory supposes
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Extraverts to have lower cortical arousal compared to Introverts, wherefore they show more active behaviour and prefer more intensive stimuli in order to augment their cortical arousal and vice versa. These neurophysiological differences are attributed to the ascending reticulary activation-system (ARAS), a system of neurons and nuclei located diffusely throughout the entire brainstem and modulating cerebral activation and arousal and the intensity and duration of incoming sensory stimuli (e. g. Eysenck, 1967). Therefore in some studies E correlated with hard rhythm and upbeat tempi (Cattell & Saunders, 1954), as well as popular and energising-rhythmic music (Cawlings & Ciancarrelli, 1997; Rentfrow & Gossling, 2003). Experiments show Extraverts not only to achieve better memory performance with music (Furnham & Allass, 1999), but also preferences for music with aggregated bass (McCown et al., 1997).
Even though these musicpsychological studies appear to undermine the arousal-model, there are many contradictory results (Stelmack, 2004). It is well known that many other regions in the brain are involved in cortical and subcortical arousal-modulation apart from the ARAS (e.g. basal forebrain, ncl. reticularis thalami and the basal ganglia (Rammsayer, 2000)). It would therefore appear that Eysenck’s model of arousal modulation merely relates to a sub-aspect of the modulation of central arousal. Other authors emphasise various other systems (Pribram & McGuinness, 1975), integrate cognitive aspects (Sanders, 1983) or stress the respective involvement of regions otherwise involved in emotional stimulus-processing (Gray & McNaughton, 2000). In a nutshell various
Introduction
7
results lead to the conclusion that a simple single-dimensional arousalmodel for musical preferences, as for example suggested by Berlyne (1971) does not suffice in vivo (Sloboda & Juslin, 2005; Gembris, 2005), even though supporting data could be collected in experimental situations (see North & Hargreaves, 1997a, 1997b). Furthermore, many recent studies suggest the difference between Introverts and Extraverts not being a simple matter of overall cortical arousal, but rather a question of Introverts being more sensitive for stimuli. Amongst others, these results put the nigro-striatal and mesolimbic dopamine (DA)-systems in the centre of bio psychological interest (Depue & Collins, 1999; Depue & Lenzenweger, 2001; Rammsayer, 2000, 2004). Apart from the modulation of sensitivity regarding sensory stimuli (Le Moal & Simon, 1991) the DA-system is involved in susceptibility for positive reinforcement and approach-behaviour (Gray & McNaughton, 2000) and is therefore closely linked to positive affectivity.
Another reason for the rejection of the original arousal-concept are a small number of studies reporting significant linkages between musical preference and E. It would seem that E is rather involved in the capability of music to induce positive moods and motor activation, rather than the stimulation induced by musical genres. This would explain the preference of Extraverts for energising and rhythmic music rather than hard music (e.g. Heavy Metal) (Rentfrow & Gosling, 2003, 2006; von Georgi et al. 2004, 2005).
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Regarding Zuckerman’s SS and its related Eysenckian dimension P, the results are less ambiguous (Eysenck & Eysenck, 1976; Zuckerman, 1991, 1993, 1996; Roth & Hammelstein, 2003). The supposition of persons with high levels in SS also having lower cortical arousal lead to the assumption of preferences for dangerous sports and activities as well as proneness to drug abuse (Zuckerman, 1979). Again, results of many studies did not undermine this assumption, but indicated that high SS-persons did not seek extreme stimuli to augment their low cortical arousal, but were capable of enduring these stimuli better. These is in accordance with findings that SS and P correlate with preferences for hard music (e.g. Rock, Heavy Metal, Alternative) (Litle & Zuckerman, 1986; Rawlings et al., 1995; Robinson et al., 1996; Rawlings et al., 1998; Burst, 2003; Nater et al., 2005). Rawlings et al. (2000) found correlations between SSsubscales Experience Seeking and Dishinhibition with preferred listening to Hard Rock and Techno. Dillman Carpentier et al. (2003) found listening to music characterised by politically and corporately critical lyrics (Heavy Metal, Alternative, Rap), to be bound to Dishinhibion. One of only few experimental studies assessing the effects of Techno on different neuro-endocrine parameters in persons who seldom or never listened to music privately was conducted by Gerra et al. (1998). The authors showed that persons with high scores in Cloninger’s scale Novelty Seeking secreted significantly less norepinephrine (NE) (Cloninger, 1987). This allows for the conclusion that persons with high levels in SS or P are less prone to stress when confronted with highly arousing stimuli.
Introduction
9
The results regarding the personality dimension Neuroticism (N) or negative affectivity is less conclusively associated with musical preference (Payne, 1980). This may be explained through this dimension’s high dependence on situation and stimulus (Fahrenberg 1987, 1992).
Another dimension discussed as being linked to musical preference (Dollinger, 1993; Rawlings et al., 2000; Pearson & Dollinger, 2004) is Openess to Experience (OE) as part of the Big-Five model (Costa & McCrae, 1995; Aluja et al., 2002). Rentfrow & Gosling (2003) found distinct connections between scores in the NEO-FFI scale Openess to Experience and reflexive and complex music (Jazz, classical music). All in all the results are not, however, unambiguous also here. One possibility could bet hat OE is a heterogenous concept, wherefore no clear connections to musical preferences can be found. Garcia et al. (2005) showed that OE is linked to the constructs E, P, SS and Impulsivity. It may be assumed that OE partly overlaps with the construct of OpenEardness, which predicts changes in musical preference with age (Holbrook & Schindler, 1989; LeBlanc et al., 1996). Therefore the consistency of musical preference can be viewed as a personality characteristic of itself (Baumeister & Tice, 1988; Tellegen, 1988; Schmitt, 1990, 1992; Schmitz 1993).
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1.2.
von Georgi et al.
AFFECT AND SITUATIONAL ASPECTS
Correlations between personality and musical preferences are critically discussed in musical science (Gembris, 2002, 2005; Lehmann, 2005). Apart from problems concerning additional factors such as aspects of identity- and self-finding, self-socialisation as well as group processes and cultural specificity (e.g. Müller, 1995; Gembris, 2002; Connell & Gibson, 2003; Walker, 2004; Sloboda & Juslin, 2005), it has to be stressed that persons may well experience changes in musical preference with age (Dollase, 1997). Furthermore, cohort-effects may lead to confounding of links between personality and musical preference (Dollase, 2005). Mainly however, a simple causal relation of biologically determined personality characteristics and musical preference appears extremely deterministic (Lehmann, 2005), wherefore the prediction of situational personality specific behaviour and preference is very poor (Kemp, 1997). It is, however, in this context often forgotten that this approach follows a different research paradigm, the results of which in no way speaking in favour of dismissing the personality based approach (see also the discussion regarding the consistency debate or the attitude-research on the prediction of current behaviour (e.g. Fishbein & Ajzen, 1974; Rushton et al., 1983; Schwenkmezger, 1984; Eysenck & Eysenck, 1985; Epstein & O’Brien, 1985)). Even though, it has to be kept in mind that the effect and usage of music cannot be reduced to a single personality paradigm. Applied clinical research shows that music can be used in the therapy of psychological and psychiatric disorders. Situational application of music
Introduction
11
can modulate emotional states of arousal and tension as well as somatic processes. (Beck, 1991; Bartlett et al., 1993; Good, 1996; McCraty et al., 1996; Núñez et al., 2002; Phumdoung & Good, 2003; McDonald et al., 2003, Hanser & Mandel, 2005; Kemper & Danhauer, 2005). Unfortunately, these results are inconsistent, which could possibly be explained by the differential effects of various musical genres on different persons. The correlation of N, respectively of negative affectivity and anxiety, with a fill of psychological and somatic disorders as well as subjective bodily discomforts (e.g. Costa & McCrae, 1980; Costa, 1987; Fahrenberg, 1992; Brody et al., 1996; Hennig, 1998; Smith & Spiro, 2002; Johnson, 2003) is often overlooked and not controlled in experimental situations. This as basis, many authors view N and similar constructs as indicative of Disease-Prone Personality (Booth-Kewley & Friedman, 1987). Without supporting this extreme standpoint, the question still stands, why no clear link between N or anxiety and musical preference can be found on the one hand, whereas music is closely connected to the modulation of negative states like fear and pain on the other hand and could therefore indirectly even influence health and sickness processes.
This suggests that maybe important covariant variables could have been overlooked. Possibly N-related personality traits are connected to health and sickness and promote the genesis of strategies to influence negative affective states through music. Using modern neuro-imaging methods (PET, fMRI) the influence of music and different musical characteristics (e.g. tuning, dynamics, harmony, timbre etc.) on different areas of the brain, closely involved in positive and negative affectivity, could be
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shown (Blood et al., 1999; Blood & Zatorre, 2001; Tramo, 2001; Juslin & Sloboda, 2001; Panksepp & Bernatzky, 2002; Altenmüller et al., 2002; Kreutz et al., 2003; Menon & Levitin, 2005; Koelsch et al., 2006). Especially the finding that fast vegetative reactions on negative stimuli can take place without exact cortical representation (LeDoux, 2000) allows for the assumption that music may induce and modulate emotional states without higher central cognitive processes (Brown et al., 2004; Panksepp & Bernatzky, 2002; Winkielman & Berridge, 2004). This approach, even though appearing quite promising at first glance, must be criticised in not accounting for the problems of specificity of musical stimuli and the differential effects due to inter-individual differences. The neuroanatomical model by Tramo (2001), for example, includes the factors personality and preference, but limits these to genre, style and taste as well as social concepts like sub-culture, generation or collective ego (Tramo, 2001, p.55). As another example, the latest neurocognitive model of music perception by Koelsch & Siebel (2005, p.579) includes a series of important factors, but - with the exception of intelligence - interindividual differences are not considered.
1.3.
USE OF MUSIC AS CONSCIOUS ACTION
Finally, a theoretical aspect formerly ignored in music-psychological literature has to be mentioned. Common to all aforementioned theoretical and experimental approaches is the treatment of music as a simple “cause and effect”-relation. In fact, however, music represents an object of
Introduction
13
complex behaviours within an individual. Listening to music is therefore preceded by active behaviour connected to more or less conscious behavioural goals, specific to each individual. This approach is centred around the basic assumption that human behaviour represents active processes of dealing with internal and external information in order to realise existing motives or cognitive goals. In this context, an action does not simply follow a cognitive or stimulus-response approach, but rather represents a sequential process of individual actions, the major goal of which is the specific personal goal, for example to change a momentary affective state (Leontyev, 1978; Frese & Sabini, 1985; Hacker, 1986, 1994; Engström, 1999). It is important herefore that, apart from a perceptional representation, the individual has a cognitive representation of its state, whereby being able to reach an “ends and means”-decision as well as an inspection of success (Dörner & Sträudel, 1990; Dörner, Schaub, Sträudel & Strohschneider, 1988; Engström, 1999). Due to the fact that active listening to music is mostly preceded by more or less conscious and purposeful actions, theoretical approaches of the aforementioned kind should be taken into consideration.
1.4.
FUNCTIONAL USE OF MUSIC AS THE MISSING LINK
It is therefore apparent that a multitude of different approaches exit, explaining the relations between an individual and music, of which in this paper mainly the personal aspects are emphasised. Even though not mentioned here, a vast amount of studies exist regarding social and
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developmental psychology approaches. The empirical connection between personal and situational or context-variables, needed to relate all different methodological and theoretical approaches, is missing to date. Qualitative studies, done by DeNora (1999, 2000, 2001) and Hays & Minichiello (2005), give rise to a first possible idea of such a connection. In evaluating 52 interviews DeNora (1999) concludes that music is not merely a passive object, but often involves active behaviour (e.g. searching for or deciding on a specific CD or radio station etc.) and is used consciously and purposefully. She therefore writes: ‘In all of the above examples music is an active incredent in the organisation of self, the shifting of mood, energy level, conduct style, mode of attention and engagement with the world. In non of these examples, however, does music simply act upon individuals, like a stimulus.’ (DeNora, 1999, p. 44). On a psychological note, the aforementioned studies show music to be used for
a)
directly or indirectly influencing the emotional processing of existing states,
b)
modulation of momentary attention and concentration faculties and
c)
inducing or sustaining social relations.
It therefore seems reasonable to extend the appreciation of music from personal and situational or social contexts to its individual function regarding the interaction of these separate functions, whereby function is
Introduction
15
to be understood as more or less conscious planning and acting on the basis of emotional and cognitive operations. Other comparable findings result from questionnaire studies, showing explicit choice of music according to the interaction of personal and situational variables. Special emphasis is laid on regulation of self-identity and interpersonal relations as well as the modulation of affectivity through music (North & Heargreaves, 1996; Heargreaves & North, 1999; Sloboda et al., 2001; Sloboda & O’Neill, 2001; North, Hargreaves & Hargreaves, 2004; Juslin & Laukka, 2004; Vorderer & Schramm, 2004).
Due to the extensive illustration of frequency-analyses regarding single items or situations, these studies do not allow for empirically founded theory generation on various aspects of active music listening. The same can be said for the papers by Sloboda, O’Neill, Ivaldi (2000) and Sloboda & O’Neill (2001). Herein the importance of various strategies for the modulation of affectivity is analysed and discussed in dependence of their frequency. Not overly well known in anglo-american circles, even though very similar to works by Sloboda et al. (2001), the studies by Behne (1986) show similar problems. Examining a representative sample of 1224 students with a questionnaire on the effects of music he was able to discern eight different listener types, calling them motory, compensatory, vegetative, diffuse, emotional, sentimental, associative and distanced. These types were found through correlation- and cluster-analyses. Although this study supports the assumption of music serving various purposes, a test-theoretical conceptualisation was omitted.
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All critique notwithstanding, all of the aforementioned studies indicate the existence of nomothetic dimensions of the application of music, which could explain the different effects of specific type of music on different persons. Unfortunately, there are still no reliable or standardised psychometric inventories for measuring these dimensions. Lehmann (2005, p. 185) ascertains the theory generation in the musical sciences, regarding various receptional mechanisms connected to functional usage of music, to be far away from allowing a connection to classical personality traits.
Summary
17
2. SUMMARY AND OVERVIEW OF STUDIES
Over all, many different factors and theoretical approaches are involved in the functional usage of music (figure 1; p,18). On the personal basis there a three major areas: a) Personality and resulting possible preferences, b) situation dependent affectivity, arousal and cognitions and c) a persons more or less conscious behavioural planning resulting from these variables regarding the usage of certain music in specific situations. The latter functions as a form of manipulation of single situational variables or the entire situational and social context, thereby retroacting on the person. This operation is therefore not solely interesting psychologically as part of the stimulus-response approach, but also the basis of social-psychological and sociological explanations and research, e.g. juvenile and socialisation research (see also Dollase, 2005). All of these processes have been of interest in music sciences for a long time, however, the link between personality, affective states and contextual variables could not be assessed or theorised upon satisfactory to date, due to the lack of quantitative and standardised research on the topic.
Aim of the present studies was the empirical identification of a nomothetical model of basic dimensions of functional application of music in everyday life. On this basis a standardised and reliable instrument for the measurement of these dimensions was to be developed with the goal of identifying interindividual differences in the application of music and thereby creating a first theoretical model which allows the integration of the various theoretical and empirical approaches.
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Figure 1: Model of the role of music in everyday life
Primary goal of the first study was the identification of possible basic dimension and their relations to variables of personality and psychological and physical health. The results of this study were used to revise the developed instrument and analyse its test-theoretical properties anew in a second study. For reasons of construct-theoretical critique new personality inventories were included into the test array. Aim of the second study was also the prediction of overall health and its connections with personality and functional application of music. In a third study the temporal stability of the dimensions of the application of music in everyday life was investigated.
Study 1
19
3. STUDY 1
Using a constructed pool of items the basic dimensions were to be identified and respective scales constructed. In order to place these scales within an established continuum of personality dimensions the scores were correlated with other psychological properties.
3.1.
METHOD
3.2.
PARTICIPANTS AND PROCEDURE
233 students (160 female and 71 male) with a mean age of M=21.68 (SD=4.47) participated in this study. 180 of the participants were medical undergraduates (125 female, 55 male, mean age M=21,34 (SD=3,44)) and 55 students of musical sciences (37 female, 18 male, mean age M=22.80 (SD=6,72)). The participants were instructed to complete the entire array of tests.
3.3.
PSYCHOLOGICAL INVENTORIES
IAAM (Inventory for the Assessment of Activation- and Arousalmodulation through Music): An item pool of 145 items was constructed (IAAM:), measuring the application of and reaction on music in everyday life (e.g. „I listen to music and feel freed of all encumbrances “or „I listen to music when I have no escape from my problems “). The items were selected according to their redraftablility from existing personality and stress-
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coping inventories. Other items were constructed on the basis of expert opinions and current scientific literature regarding musical sciences. The instruction emphasised that questions should be answered in accordance with conscious usage of music in everyday life. All items were answerable on a five-point Likert scale ranging from “0=not at all” to “4=very often”.
SKI (Selfconcept Inventory): The SKI is a german inventory measuring personality as the result of social interaction. Five basic dimensions are analysed by means of 8 bipolar items each: Ego-strength (life- and selfcertitude), Attractiveness (potency in social groups), Confidence (openness in interpersonal communication), Orderliness (structuring of ambience) and Enforcement (assertiveness in social situations). Over various samples the SKI scales are highly internally consistent, retest reliable and Rasch-scalable. Intercorrelation with other personality instruments show Ego-strength to be negatively correlated with N and slightly positive with E. Attractiveness and Confidence appear to measure two different aspects of E. Orderliness is negatively correlated with the NEO-FFI
scales
Openness
to
Experience
and
positively
with
Conscientiousness. Enforcement is correlated with P, E and low N (von Georgi & Beckmann, 2004). In conclusion, the SKI results indicate a construct of dimensions rotated between 30-45° in relation to Eysenck’s personality model or the NEO-FFI-model by Costa & McCrae (1995) (von Georgi, 2006).
Study 1
21
PANAS (Positive and Negative Affect Schedule): PANAS (Watson et al., 1988; german version: Krohne et al., 1996) measures habitual and actual affectivity on the dimensions positive (PA) and negative affectivity/affect (NA) through 10 adjectives. NA and PA are supposed to be the basic dimensions of emotional experience (Watson & Tellegen, 1985). Watson (2000) understands these dimensions as higher order constructs of N and E. In this study only the trait-version of the PANAS was used, assuming that persons with high trait-scores will experience the respective affect or emotional state more often in specific and unspecific situations (Watson & Clark, 1992, 1997; Watson 2000).
SCL-90-R (Symptom Checklist by Derogatis): The Symptom-Checklist90-R (Derogatis, 1994; german version: Franke, 1995) measures a variety of psychological and psychopathological symptoms in different subscales via 90 items. All scales can be comprised into a single score, resulting in a total index of subjective experienced strain (GSI).
Music preferences: In addition, subjects were questioned regarding their primary musical preferences and grouped accordingly into the categories by Rentfrow & Gosling (2003): Reflexive & Complex (Jazz, Classic, Blues), Rhythmic & Energetic (Funk, Soul, Hip-Hop, Techno), Intense & Rebellious (Rock, Hard Rock, Heavy Metal, Alternative), Upbeat & Conventional (Pop, Oldies, Rock ’n’ Roll, Country, Soundtrack, German popular music).
22
3.4.
von Georgi et al.
RESULTS
3.4.1. ITEM- AND SCALE-ANALYSES
The first primary component analysis (PCA) yielded 24 factors with eigenvalues of 1 or above. The plot of eigenvalues was relatively homogenous, wherefore all possible factor-solutions as from
50%
variance-clearance (=12 factors) were calculated and analysed regarding their structural and contentual properties. The five-factor solution with 40% cumulated variance was statistically and theoretically most promising (see table 4 (p.26) and item texts in table 1).
The first factor unites items regarding physical and mental relaxation through music and was therefore entitled Relaxation (RX). The second dimension concerns mainly reflection on emotional and socio-emotional problems and was called Cognitive Problem Solving (CP). The third dimension deals with reducing negative emotional strain, wherefore it was named Reduction of Negative Activation (RA). Mainly items on stimulation in and of positive situations, also in social context comprised the fourth factor Fun Stimulation (FS). Items regarding the modulation of general arousal during learning and work loaded on the fifth factor entitled Arousal Modulation (AM) (see table 4 and table 1).
Study 1
23
Table 1: Results of the scale analyses Scale Fnoi Nnoi RX
CP
RA
FS
AM
Item text (I listen to music …)
71 1 and feel freed of all pressures 51 6 and reality is far away 52 11 and I am in my own world 72 16 and have better access to my inner self 37 21 and life makes sense again 53 X26 and gather strength to overcome future obstacles 59 31 and I forget all my little aches and pains 24 36 and everything seems filled with new hope 23 41 and feel strengthened and less ready to give in 47 46 and take everything more easy 26 X2 and think about ways to overcome odds 89 7 when I am disappointed with myself again 126 12 when I miss someone 98 17 when I am in love 78 22 when I have been hurt by others 67 27 and often think about my future 79 32 when I cannot go on and everything grows over my head 18 37 when I am feeling down 22 42 and contemplate situations that are on my mind 40 47 and try to solve my problems 82 3 when I am feeling irritable 141 8 when I have no other way to solve my problems 108 13 because everything is annoying me and list. to m. is the best way to loosen up 144 18 and really blow off steam 128 23 and I am really able to express my emotions 84 28 when I am feeling aggressive 127 33 and I am really able to let out my bottled up anger 81 38 to forget that I have a problem 116 X43 when something spins out of control 83 48 when I am nervous 129 X4 when spending time with my boyfriend/girlfriend 124 9 when I am on holiday 100 14 when I feel like dancing 122 19 when there is nothing good on TV 125 24 while I am cooking myself a good meal 106 29 when I get together with my friends 94 34 when I feel like partying 15 39 when I am together with my friends 80 44 to get into the mood before going to a party 99 49 when I am happy 34 5 and can get down to my work 64 10 and read at the same time 107 X15 when I am ill 85 20 to loosen up before exams 77 25 because it really helps me concentrate 74 30 and find it easier to go to sleep 35 35 and find it easier to work 118 40 when I am reading the newspaper 110 45 while working on the computer 63 50 because it helps me to retain facts in my memory
Mi
rc
d
1.56 1.35 1.63 1.58 1.23 1.57 1.43 1.97 2.16 1.66 1.90 1.35 2.52 2.72 1.73 2.13 1.73 2.36 2.44 1.63 1.72 1.95 1.62 1.61 1.72 1.71 1.71 1.27 1.04 1.60 2.34 2.43 2.88 2.29 1.86 2.46 2.70 2.90 2.43 2.98 1.42 1.14 1.96 1.44 1.46 1.52 1.72 1.12 1.91 1.29
.74 .67 .69 .72 .69 .77 .59 .71 .68 .69 .59 .68 .73 .57 .66 .66 .70 .66 .68 .76 .74 .66 .63 .72 .71 .76 .80 .62 .66 .66 .49 .47 .56 .41 .42 .54 .60 .52 .56 .47 .46 .65 .36 .57 .35 .43 .60 .42 .46 .71
.91 .91 .91 .91 .91 .91 .92 .91 .91 .91 .90 .90 .89 .90 .90 .90 .90 .90 .90 .89 .91 .91 .91 .91 .91 .91 .90 .92 .91 .91 .80 .80 .79 .81 .81 .80 .79 .80 .79 .80 .80 .78 .81 .79 .82 .81 .79 .81 .81 .78
RX: relaxation; CP: cognitive problem solving; RA: reduction of negative activation; FS: fun stimulation; AM: arousal modulation; Fnoi: number of Item in factor analysis (table 1; p. 26); Nnoi: new number of item for scale analysis; Mi: item mean; rc: corrected item total correlation; d: Cronbach’s if item deleted; X: item excluded in study 2 (see table 6; p.36)
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Items with factor loading of at least .40 on their respective factor were selected, with further regards to high theoretical relevance and low double loading. The resulting scales were examined via scale- and reliabilityanalyses. The last factor (AM) only having 10 items, the other scales where reduced stepwise to the best 10 items each accordingly, in order to produce equally sized constructs (see table 4 (p.26) and table 1 (p.23)). For each scale descriptive statistics for the entire sample and the subsamples (medical and musical science students) were computed, as well as Cronbach’s coefficients and split-half reliability (table 2). The subsamples show no differences. Normality tests showed all scale-scores to be almost normally distributed (p>.01). Estimations show all scales to have sufficient reliability. Table 2: Cronbach’s and split-half reliability for the constructed scales for total and sub-samples Scale RX PS RA FS AM
N 231 (176/55) 229 (174/55) 231 (176/55) 230 (175/55) 233 (178/55)
Descriptives statistics M SD 16.15 8.41 (16.31/15.62) (8.71/7.65) 2.10 8.53 (2.01/2.36) (8.62/8.33) 15.00 8.71 (14.65/15.93) (8.72/8.72) 26.27 7.35 (25.47/24.64) (7.45/7.07) 17.04 7.59 (17.17/16.64) (7.56/7.41)
pNorm .200 (.200/.084) .023 (.043/.538) .200 (.200/.608) .034 (.200/.629) .011 (.055/.091)
α .92 (.92/.92) .92 (.92/.92) .92 (.92/.92) .82 (.82/81) .82 (.83/.78)
Reliability Α1 α2 .85 .88
rsh .87
rSB .93
.85
.85
.73
.84
.85
.86
.83
.91
.76
.66
.63
.77
.78
.60
.66
.80
RX: relaxation; CP: cognitive problem solving; RA: reduction of negative activation; FS: fun stimulation; AM: arousal modulation; N: sample size; M: scale mean; SD: standard deviation; pNorm: significance of Kolmogorov-Smirnov test (n>55) and Shapiro-Wilks test (n=55) for normal distribution of scores; : Cronbach’s ; 1/2: Cronbach’s of splithalf tests; rsh: split-half reliability; rSB: Spearmen-Brown split-half reliability; values in parentheses refer to the subsamples of the medical and musical-science students. Levine-test for homogeneity of variances und t-test shows no significant differences between sub-samples.
Study 1
25
The reliability estimations within the groups of preference are also satisfying (table 3). This shows that all five IAAM-scales are also detectable in small samples of different musical preferences.
Table 3: Cronbach’s of the five IAAM-Scales with respect to musical preferences Scale Missing
RX PS RA FS AM
.93 (53) .91 (50) .92 (52) .80 (52) .81 (51)
Reflexive & Complex .95 (19) .95 (18) .80 (17) .90 (19) .87 (19)
Music preference Energetic Intense Upbeat Not & & & assignable Rhythmic Rebellious Conventional .92 (26) .88 (38) .92 (77) .90 (18) .93 (26) .92 (38) .91 (79) .92 (18) .93 (25) .86 (39) .93 (80) .93 (18) .71 (25) .70 (38) .84 (78) .92 (18) .69 (26) .84 (39) .84 (80) .76 (18)
RX: relaxation; CP: cognitive problem solving; RA: reduction of negative activation; FS: fun stimulation; AM: arousal modulation; Reflexive & Complex: Classic, Jazz, Blues; Energetic & Rhythmic: Funk, Soul, Hip-Hop, Techno; Intense & Rebellious: Rock, Hard-Rock, Heavy metal, Alternative; Upbeat & Conventional: Pop, Oldies, Rock ’n’ Roll, Country, German popular music.
26
von Georgi et al.
Table 4: Results of the factor analysis of study 1 Fnoi
RX
CP
RA
FS
AM
h2 %
Noi
RX
CP
RA
FS
AM
h2 %
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39
.19 -.06 .42 -.11 .05 .18 .33 .19 .36 .24 .57 .28 .12 .22 -.02 .11 .30 .18 .38 -.09 .11 .19 .60 .59 .47 .27 .16 .05 -.06 .60 .46 .43 .39 .26 .24 .58 .58 .41 .44
.39 .09 .42 -.13 -.02 .62 .45 .38 .13 .33 .17 .14 .36 .24 -.03 .27 .18 .54 .34 -.10 .09 .71 .36 .35 .18 .61 .71 .45 -.21 .16 .13 .25 .10 .08 -.02 .00 .27 .27 .08
.14 .07 .25 .22 -.02 .01 .18 .42 .04 .48 .03 .36 .54 .13 -.03 .45 .16 .40 .35 .32 .65 .12 .19 .18 .00 .11 .18 .17 .33 .24 .45 .36 .51 .19 .23 .24 .28 .46 .33
.03 .15 -.09 .24 .45 .20 -.06 .24 .25 -.02 .13 -.03 .13 .08 .58 .08 .11 .06 -.10 .38 .11 .07 .08 .08 .22 .06 -.02 .17 .22 .09 .01 -.04 .03 .07 .14 -.04 .09 -.20 .15
.08 .03 -.01 .24 .12 .14 -.23 .05 -.05 .00 -.01 .07 .01 .07 .12 .23 .11 -.02 -.12 -.11 .26 .08 .07 .02 .04 .23 .09 .00 -.04 -.04 .03 -.04 .16 .44 .54 .30 .08 -.10 .25
21.43 4.10 41.92 19.61 21.85 47.68 4.02 41.59 21.31 39.61 37.16 23.11 45.66 13.27 35.62 34.54 17.11 48.53 4.69 27.65 51.99 56.89 53.83 5.93 29.86 51.56 56.74 26.68 2.21 44.77 42.98 38.32 45.09 31.58 42.17 48.31 49.96 5.01 39.66
74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112
.24 .18 .44 -.09 .19 .29 .09 .36 .23 .23 .10 .25 .17 .05 .46 .26 .16 .15 .16 .24 .06 .33 .09 -.01 .19 .31 .19 .09 .12 .04 -.04 .06 -.01 .18 .13 .15 .20 .47 .20
.20 .11 .21 -.02 .54 .45 .09 .26 .13 .20 .14 .17 .26 .21 .28 .47 .11 .06 .12 .41 .00 .27 .40 .03 .53 .30 .15 .25 .06 .33 -.03 -.05 .01 .13 .41 .22 .07 .38 .37
.03 .00 .29 -.14 .49 .53 .04 .47 .65 .54 .72 .23 .03 .05 .35 .58 .24 .11 .20 .45 .00 .30 .18 .16 .09 .14 -.01 .15 .08 .26 .24 .21 .02 .14 .44 .02 .25 .15 .19
.22 .38 .22 .00 .13 .12 .59 .11 .13 .21 .13 .17 .18 .27 .05 -.02 .44 .38 .45 .27 .62 .33 .19 .48 .28 .47 .54 .10 .36 .16 .28 .06 .53 .28 .22 .35 .06 .02 .01
.45 .17 -.14 .51 -.20 -.20 -.15 -.03 .20 .25 .15 .46 .29 .16 .20 -.17 .07 -.02 .12 .11 -.17 .29 -.05 .24 -.02 .01 -.19 .08 .10 .27 .17 .10 .02 .40 .03 .23 .46 .15 .09
34.81 22.24 38.37 29.18 62.67 61.80 38.44 43.32 55.17 48.07 58.09 39.00 21.80 14.65 45.74 65.59 29.55 18.11 29.50 51.14 41.89 46.60 23.60 31.68 4.05 42.87 38.17 11.01 16.05 27.60 16.40 6.16 28.66 3.87 42.29 24.83 32.27 41.70 22.13
Study 1
27
Table 4 (continued)
Fnoi
RX
CP
RA
FS
AM
h2 %
Noi
RX
CP
RA
FS
AM
h2 %
.35 .33 .05 .15 63.90 113 .47 .27 .25 .02 .06 35.90 40 .62 41 .53 .20 .08 .10 .28 41.46 114 .54 .19 .35 .11 .26 52.24 42 .40 .31 .46 -.08 .07 48.25 115 .14 .06 .25 .20 .17 15.69 43 .37 .19 .49 .13 .08 44.26 .34 .28 .08 .17 6.99 116 .62 44 .49 .16 .21 .18 .12 36.17 117 .31 .42 .20 .13 -.14 34.92 45 .29 -.14 .28 .13 .04 19.99 .05 .28 .36 43.35 118 -.08 .46 46 .18 .21 .00 .23 .43 3.80 119 -.12 -.01 .16 .34 .21 2.02 .12 .12 .27 .11 56.63 120 .00 .04 .24 .31 .22 2.73 47 .67 48 .61 .05 .06 .21 .22 47.03 121 .17 .32 .13 .30 .02 24.04 49 .65 .15 .13 .02 .15 49.15 .24 .19 .10 .12 37.11 122 .50 50 .66 .10 .03 .14 .17 48.86 123 .09 .35 .11 .29 .12 23.93 .20 .15 -.01 .04 46.75 .18 .07 .26 39.62 51 .64 124 -.03 .54 .30 .17 -.08 -.06 52.52 .11 .11 -.03 .22 35.10 52 .63 125 .52 .21 .17 .03 .08 55.25 .24 .27 .18 -.01 55.66 53 .69 126 .63 54 .70 .08 .00 .12 .03 51.47 .25 .22 .11 .11 56.80 127 .66 55 .03 .40 -.02 .04 .09 16.96 .35 .40 .11 .00 6.57 128 .56 56 .55 .04 .04 .12 .22 37.34 .13 .13 .02 .01 25.52 129 .47 57 .07 -.03 -.03 .42 .05 18.30 130 .40 .24 .41 .21 .15 45.36 58 .24 .07 .11 .23 .01 12.53 131 .11 .08 .28 .43 .45 48.83 .16 .20 .07 .18 43.85 132 .21 .07 .21 .21 .18 17.07 59 .58 60 -.01 .15 .12 .20 .01 7.82 133 .05 .14 .35 .32 .26 3.88 61 .47 .18 .09 .09 .19 3.58 134 .27 .10 .32 .29 -.02 26.93 62 .09 .06 -.08 .49 .24 31.02 135 .17 .21 .15 .42 .12 29.28 .13 .10 .16 .08 51.83 136 .38 .34 .58 .10 .04 6.94 63 .68 .10 .11 .04 .17 41.16 137 .31 .17 .21 .53 .12 45.78 64 .60 65 .54 .21 .17 .19 .10 41.22 138 .35 .18 .15 .51 .13 45.94 66 .38 .47 .12 -.09 -.02 38.69 139 .15 .12 .23 .12 .09 11.03 .34 .11 .12 .13 55.65 140 .40 .20 .19 .41 .14 42.06 67 .63 68 .25 .48 .11 .12 .15 34.36 .31 .37 .07 .02 57.85 141 .59 69 .26 .52 .00 .23 .24 44.42 142 .57 .30 .26 .17 -.03 51.61 70 .33 .63 .06 .22 .16 57.94 143 .61 .22 .22 .32 -.13 59.11 .21 .11 .16 .09 53.73 .28 .22 .15 .15 49.50 71 .67 144 .57 .36 .26 .08 .06 55.64 145 .08 .34 .02 .05 .07 13.24 72 .59 73 .64 .02 .21 .15 -.04 47.17 a2% 12.30 9.59 7.58 6.49 4.62 F1: relaxation (RX); F2: cognitive problem solving (CP); F3: reduction of negative activation (RA); F4: fun stimulation (FS); F5: arousal modulation (AM); h2%: percent of sum of squared loadings; a2%: percent of variance after rotation; Fnoi: number of item in factor analysis (see table 1; p.23) ; bold marked loadings indicates items selected for IAAM-scales (see table 1)
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von Georgi et al.
3.4.2. CORRELATION WITH PSYCHOLOGICAL MEASURES
Table 5 (p.30) shows the intercorrelation of the IAAM-scales along with the correlations with the other personality scales for the sample, as well as the significant relations (p≤.05) within the groups of preference. All IAAM-scales, especially RX, CP and RA, correlate highly with each other. The correlations with the other personality dimensions, however, show the scales to be related to different aspects of personality. RX, CP and RA are all linked to NA, low Ego-Strength and exposure to psychological and physical ailments (GSI). These correlations are lower for RX, compared to CP and RA, and especially the ailments Somatisation, Anxiety and Hostility as well as the General Symptom Index (GSI) only correlate slightly with RX. The main difference between CP and RA is the higher correlation of CP with low Ego-Strength. Unlike the first three scales, FS is connected to positive affectivity, especially in social context. Also FS has significant relations to the SKI-scales Attractiveness and Confidence. Strangely the last scale AM has only low positive correlation to PA (table 5).
The grouping according to musical preference hints at possible differences in the application of the preferred music depending on personality. Persons preferring reflexive and complex music show connections of musical application between RX and high scores in the psychoticism scale of the SCL-90-R (depersonalisation) and Paranoid Ideation without further connection to variables of overall emotional instability (table 5). Persons within the group Rhythmic & Energetic use music primarily for cognitive
Study 1
29
problem solving (CP). This strategy is linked to high negative affectivity, low Ego-Strength and high scores in the scales Obsessive-Compulsive, Interpersonal
Sensitivity
and
Paranoid
Ideation
(SCL-90-R).
Characteristic for the group Intense & Rebellious is that reduction of negative arousal (RA) appears less important, music being mainly used for cognitive problem solving (CP) (table 5). Finally, within the group Upbeat & Conventional, depending on various negative traits, music appears to be important for CP and especially for the reduction of negative arousal (RA).
Table 5: Intercorrelations of the IAAM-scales with personality scales in total sample and preference sub-sample of study 1 Inventory IAAM
Scale
RX
Correlations within total group CP RA FS AM
R&C
Correlations within preferences E&R I&R
U&C
RX: relaxation CP: cognitive problem solving .73*** RA: reduction of negative activation .67*** .76*** FS: fun stimulation .34*** .37*** .34*** AM: arousal modulation .39*** .38*** .40*** .36*** PANAS negative affectivity .17* .37*** .28*** .11 .01 +CP +CP/RA +CP/RA positive affectivity .07 -.03 -.06 .13(*) .12(*) SKI ego-strength -.14* -.35*** -.19** -.03 .05 -CP -CP -RX/CP/RA attractiveness .01 -.03 -.03 .19** .01 confidence .02 .04 -.06 .25*** .01 +RA/FS/AM orderliness -.03 -.01 -.08 .00 -.04 enforceness .01 -.11 -.07 .01 .05 -RA SCL-90-R somatisation .10 .27*** .24*** .18** .04 +RX/FS +CP/RA/AM obsessive-compulsive .15* .29*** .22*** .11 .00 +CP +CP/-AM +CP/RA interpersonal sensitivity .14* .32*** .28*** .20** .05 +CP +CP +RX/CP/RA depression .15* .36*** .25*** .09 -.03 +CP +CP/RA anxiety .06 .19** .16** .15* -.01 +CP/-AM hostility .12(*) .21** .20** .15* .06 +FS +CP/RA phobic anxiety .14* .26*** .23*** .19** .05 +CP/RA paranoid ideation .14* .29*** .20** .15* .05 +RX/CP/FS +CP +CP +RX psychoticism .17* .34*** .24*** .15* .02 +RX/CP/RA +CP +CP/RA general symptom index (GSI) .15* .36*** .31*** .18* .00 +CP/RA +CP/RA R&C: Reflexive & Complex; E&R: Energetic & Rhythmic; I&R: Intensive & Rebellious; U&C: Upbeat and Conventional; IAAM: Inventory for Arousal and Activation Modulation with Music; PANAS: Positive and Negative Affect Schedule (trait form); SKI: Self-Construct Inventory; SCL-90-R: Symptom Checklist by Derogatis. (*): p
