By:

Laura M. Getz

Elizabethtown College, USA

Tomas Chamorro-Premuzic

Goldsmiths, University of London, UK

Michael M. Roy

Elizabethtown College, USA

Karendra Devroop

North-West University, Potchefstroom, South Africa

Abstract

The current study examined the relationship between individual differences in uses of music (i.e. motives for listening to music), music preferences (for different genres), and positive affect (PA) and negative affect (NA), thus linking two areas of past research into a more comprehensive model. A sample of 193 South African adolescents (ages 12–17) completed measures of the above constructs and data were analyzed via correlations and structural equation modeling (SEM). Significant correlations between affect and uses of music were tested using SEM; a model whereby PA influenced background and cognitive uses of music, NA influenced emotional use of music, and higher uses of music led to increased preferences for music styles was supported. Future research for uses of music and music preferences are discussed.

Keywords

adolescents, music preferences, PANAS, South Africa, uses of music.

There is no doubt that music is a ubiquitous force in our daily lives. We are bombarded with music at work, in restaurants, in waiting rooms, and at home on a daily basis. Annual sales

There is no doubt that music is a ubiquitous force in our daily lives. We are bombarded with music at work, in restaurants, in waiting rooms, and at home on a daily basis. Annual sales figures for compact disks, personal music players, and concert attendance climb into the billions each year (International Federation of the Phonographic Industry (IFPI), 2009; Schwartz & Fouts, 2003). This high level of accessibility means that music may be actively used for a variety of reasons in different settings (e.g. North, Hargreaves, & Hargreaves, 2004; Rana & North, 2007). Indeed, past research has shown music to be used for social interaction/iden­tity formation (Rentfrow & Gosling, 2006; Tekman & Hortaçsu, 2002), emotional regulation (Juslin & Laukka, 2003, 2004; Miranda & Claes, 2009; Saarikallio & Erkkilä, 2007), self-actualization (Tarrant, North, & Hargreaves, 2000), cognitive needs (North, Hargreaves & O’Neill, 2000), or simply out of habit (North et al., 2004). The use of music as a social function for identity formation may be even more pronounced in adolescents (Bakagiannis & Tarrant, 2006; North & Hargreaves, 1999; North et al., 2000; Tarrant et al., 2000).

Given this background, it comes as no surprise that psychologists have long taken an interest in individual differences in music usage and preferences (Cattell & Anderson, 1953a; Little & Zuckerman, 1986). In recent years, two main lines of research have emerged relating music to personality: uses of music and music preferences.

Uses of music

Previous research has examined multiple aspects of music usage in everyday life, such as where, when and why people listen to music (North et al., 2004; Rana & North, 2007). Motives for listening to music have been broken down into three major categories by Chamorro-Premuzic and Furnham (2007): manipulation or regulation of emotions (emotional use); rational or intellectual appreciation of music (cognitive use); and music as background to working, studying, socializing or performing other tasks (background use; see also Chamorro-Premuzic, Swami, Furnham, & Maakip, 2009; Chamorro-Premuzic, Gomà-i-Freixanet, Furnham & Muro, 2009). In relation to personality, emotional use of music has been found to positively correlate with neuroticism, explained in terms of higher emotional sensitivity among those high in neuroticism (Costa & McCrae, 1992; Juslin & Laukka, 2003; Juslin & Sloboda, 2001). In addition, background music has been found to positively correlate with extraversion, in line with Eysenck and Eysenck’s (1985) finding that extraverts are under-aroused compared to introverts and thus have a greater tolerance of background stimuli. Finally, cognitive use of music has been shown to positively correlate with openness, explained in terms of higher intellectual curiosity and need for cognition, as well as the positive link between openness and self-estimated and psychometrically measured intelligence (Chamorro-Premuzic & Furnham, 2005).

Music preferences

One of the earliest studies on music preferences was Cattell and Anderson’s (1953a, 1953b) I.P.A.T. Music Preference test, which interpreted preference factors as unconscious reflections of specific personality characteristics. Since then, research has focused on more explicit links between music preferences and personality. For example, Little & Zuckerman (1986) found a positive link between sensation seeking and preference for rock, heavy metal, and punk music, and McCown, Keiser, Mulhearn & Williamson (1997) found a positive correlation between extraversion, psychoticism, and preference for rap and dance music. Rentfrow and Gosling (2003) indicated that musical preferences could be organized from 14 genres into four inde­pendent dimensions: Reflective and Complex, Intense and Rebellious, Upbeat and Conventional, and Energetic and Rhythmic. Extensive links were found between each dimension and typical personality traits (see Rentfrow & Gosling, 2003, p. 1248–1249). This study was the first to suggest a ‘clear, robust, and meaningful structure underlying music preferences’ (p. 1250); however, they point out that future research should extend this research to other age groups and cultures in order to validate the present structure.

Present study

While research within these two fields may seem extensive, several important limitations exist in the present body of work. First, previous studies have tended to focus on either uses of music or music preferences in relation to personality, but no studies have combined these fields. The present study is therefore an attempt to unify research on uses of music and music preferences into a more comprehensive model.

Second, only a few studies have focused on non-western cultures (Chamorro-Premuzic, Swami et al., 2009; Rana & North, 2007). While music itself is a universal phenomenon, previ­ous research has suggested that there may be differences in music usage and perception as a function of ethnicity (Eerola, Himberg, Toiviainen & Louhivuori, 2006; Gans, 1974; Gregory & Varney, 1996; Saarikallio, 2008). A cross-cultural approach to music research can help to dis­cover common motives for listening to music as well as culturally specific uses. Therefore, the present study extended uses of music research by studying adolescents in South Africa. Though it is true that South Africa, like many other countries, has been strongly influenced by western cul­tures, considerable diversity still exists, with White, traditional African, and Southeast Asian eth­nic groups represented. Our current study examined a population of African and Southeast Asian participants from the KwaZulu-Natal province that could be categorized as ‘non-Western’ (Eerola et al., 2006 and Eerola, Louhivuori & Lebaka, 2009 use a similar descriptor).

Third, past studies using the Uses of Music inventory have only used university students (Chamorro-Premuzic & Furnham, 2007; Chamorro-Premuzic, Gomà-i-Freixanet et al., 2009), so the present study extended the inventory to the adolescent population. Because most adoles­cents consider music to be an integral part of their lives (Christenson & Roberts, 1998; Zillmann & Gan, 1997), it is important to understand their motives and preferences for listening to music. Previous research on adolescents’ music usage has shown strong links to socialization, enter­tainment and background use (Bakagiannis & Tarrant, 2006; North, & Hargreaves, 1999; North et al., 2000; Tarrant et al., 2000), and to coping and emotional regulation (Juslin & Sloboba, 2001; Miranda & Claes, 2009; North et al., 2000; Saarikallio, 2008; Saarikallio & Erkkilä, 2007; Tarrant et al., 2000), with less evidence for intellectual usage of music (Demorest & Serlin, 1997; Eerola et al, 2006; Krumhansl & Keil, 1982; North et al., 2000). It may be that adolescents’ cognitive use of music is lower because they are less capable than their older coun­terparts of critically analyzing rhythmic and melodic structure (Demorest & Serlin, 1997; Krumhansl & Keil, 1982); additionally, background and emotional uses of music may be used more by adolescents because of the important role music plays in identity formation (North & Hargreaves, 1999; North et al., 2000; Tarrant et al., 2000).

Finally, past research has mainly focused on the Big Five factors of personality as the basis for comparison. In the present study, we examined whether participants’ levels of positive affect (PA) and negative affect (NA), as measured by the PANAS (Watson & Tellegen, 1985; Watson, Clark & Tellegen, 1988), were predictive of their music use. The PANAS uses one-word responses and straightforward vocabulary, which was beneficial because the participants in this study were adolescents (ages 12–17) who were largely unfamiliar with personality scales and testing. Past research (see Lonigan, Hooe, David, & Kistner, 1999) has found the PANAS to be a reliable and valid measure of children’s (age range 9–17) affect, while reports of the Big Five with ado­lescents show significant age differences in coherence and differentiation of the factors between participants ages 10–20 (Soto, John, Gosling & Potter, 2008). Importantly, state temperament shows reliable links to overall disposition and personality (Costa & McCrae, 1980; Schmukle, Egloff & Burns, 2002; Watson & Clark, 1984). Since PA has been found to be highly correlated with extraversion and openness (DeNeve & Cooper, 1998; Lucas & Fujita, 2000; Mitte & Kämpfe, 2008), we expected that PA would be predictive of higher background and cognitive uses of music, while past positive links between NA and Neuroticism (Costa & McCrae, 1992; DeNeve & Cooper, 1998), would suggest that NA would be predictive of higher emotional use of music (Chamorro-Premuzic & Furnham, 2007, Chamorro-Premuzic, Gomà-i-Freixanet et al., 2009; Chamorro-Premuzic, Swami et al., 2009).

In sum, the current study examined the relationship between PA and NA, uses of music and music styles preferences. This study extended past research in these domains into a more com­prehensive model, using a novel age group and culture. Structural equation modeling (SEM) was used to test a model wherein affect (PA and NA) predicts uses of music, which in turn, pre­dict music preferences.

Method

Study site and participants

Pietermaritzburg, founded in 1838, is the capital of the KwaZulu-Natal province of South Africa. It is the second largest city in the province with a population of approximately 750,000 people. The city is a major producer of aluminum, timber and dairy products. Because of man­datory separation during the Apartheid, much inequality still exists among racial groups. Public schools in the KwaZulu-Natal province, catering largely to traditional African and Southeast Asian students, currently lack the resources necessary to provide students with for­mal musical instruction; therefore, the present study was completed as part of a music inter­vention service project at two Pietermaritzburg secondary schools.

Participants in the present study included 193 (81 males, 107 females, 5 not reported) sec­ondary school students from Pietermaritzburg, South Africa (age range 12–17, M = 13.77, SD = .85). The sample consisted of 77.2 % traditional Africans, 13.2 % of Southeast Asian descent and 7.4% mixed ethnicity (the remaining participants listed themselves in other ethnic groups). Nearly all of the African participants (84.9%) spoke Zulu at home, while the remaining partici­pants largely spoke English at home (88.4%). As all South African secondary school classes are taught in English, no translations of the scales were used. Despite a lack of formal music educa­tion, the majority of participants (75%) reported listening to music on a daily basis and to own­ing a radio or music playing device (75.6%).

Measures

Positive and Negative Affect Schedule (PANAS: Watson & Tellegen, 1985; Watson et al., 1988). This is a 20-item questionnaire measuring current positive and negative temperaments. Items are rated on a five-point scale (1 = Very slightly or not at all; 5 = Extremely), and participants are asked to rate to what extent they ‘feel this way right now, that is, at the present moment’. Watson et al. (1988) report that the scale items are internally consistent and have excellent convergent and discriminant correlations with lengthier measures of the underlying mood factors. In the present study, two items were changed to make the vocabulary more accessible to younger participants (Distressed became Worried, and Jittery became On edge). Cronbach’s a, M and SD for the two subscales (PA, NA) are reported in Table 2.

Uses of Music Inventory (Chamorro-Premuzic & Furnham, 2007). This is a 15-item scale measuring views regarding music, when it is listened to and why. Items are rated on a five-point scale (1 = Strongly disagree, 5 = Strongly agree) and the averages of certain items are computed to arrive at the three subscales of this inventory: Emotional use of music (M[emot], five items; sample item: ‘Listening to music really affects my mood’); Cognitive, intellectual, or rational use of music (M[cog], five items; sample item: ‘I often enjoy analyzing complex musical compositions); and, Background or social uses of music (M[back], five items; sample item: ‘I often enjoy listen­ing to music while I work’).

Because Cronbach’s alphas were low for M[emot] and M[cog] (a = .25 and .23, respectively), principal component analysis (PCA) was performed on each; there was one item forcing the subscale into two factors instead of fitting well with the rest of the items for both M[emot] and M[cog]. Therefore, ‘I am not very nostalgic when I listen to old songs (I used to listen to)’ was eliminated from M[emot], leaving four total items, and ‘I seldom like a song unless I admire the technique of the musicians’ was eliminated from M[cog], leaving four total items. While as remained somewhat lower than normal, possible reasons are discussed later. Cronbach’s a, M and SD for the three subscales are reported in Table 2.

Music in Everyday Life (North et al., 2004; Rana & North, 2007). Items about music usage andpreferences were adapted from North et al.’s (2004) ‘Uses of Music in Everyday Life’ survey. Participants rated on a five-point scale (1 = Never, 5 = Always) how often they listened to music with different people/groups of people (Listening Groups), to different music styles (Music Styles) and in different locations (Listening Locations). Several changes were made in the pres­ent study to make the survey age and culture appropriate; first, Spouse/partner was eliminated from the Listening Groups items. Second, on the Music Styles items, Blues and Country/folk were eliminated, ‘Golden oldies’ pop became Light/soft rock, and Non-Western pop and Non-Western traditional music became Kwaito and Traditional African, respectively. Finally, on the Listening Locations items, Driving and Pub/nightclub were eliminated, At home doing an intellec­tually demanding task became At home doing schoolwork, and Gym/exercising became Exercising/Playing sports. All three subscales were reduced using principal component analysis (see ‘Data reduction’ later).

Demographics. Participants completed a demographic questionnaire consisting of age, gender, race, language spoken at home, whom they live with, transportation method to school, and prior informal music experiences.

Procedure

All participants were recruited opportunistically by three of the authors of this study as part of a week-long service project that donated used band instruments to the schools. Data were col­lected over the course of two separate visits a year apart, with a music program started at each school in successive years. Testing took place in several large-group settings that were overseen by several experimenters and school teachers. Zulu-speaking teachers were available to answer any participant questions, and participants were instructed not to share answers with one another. All participants completed 12 pages of questionnaires consisting of demographics, Uses of Music inventory, Music in Everyday Life inventory, PANAS and several scales not ana­lyzed here. Participants were given unlimited time to complete the surveys, and all participants completed the surveys in about an hour.

Results

Data reduction

The 12 items of the Music Styles, the 13 items of the Listening Locations, and the 7 items of the Listening Groups inventories were reduced through PCA and factors were extracted based on eigenvalues larger than 1 and the results of a scree test. Varimax rotation (varimax with Kaiser normalizations) was performed on the data to obtain a clear solution and maximize loadings.

Four underlying factors were extracted to account for 57.5% of the variance in the Music Styles inventory. Items loadings on the component matrix (with factor eigenvalues and indi­vidual variance explained) are reported in Table 1; as seen in the table, the factor structure was clear, with only a few cross-loading genres.

Two underlying factors were extracted on the Listening Locations inventory; the overall amount of variance accounted for was 31.5% (Religious Worship was excluded because it did not load onto either factor). Two underlying factors were also extracted on the Listening Groups inventory; the overall amount of variance accounted for was 52.6% (Family and On My Own were excluded because they did not load onto either factor).

Descriptive statistics

Descriptive statistics (a, M, SD and number of items) for all target measures are reported in Table 2. Participants’ ratings on both PA and NA were similar to previous results using the

Table 1. Factor loadings for music style preferences

	Rock		African	Academic		Party
Rock	.84
Alternative rock	.83
Light rock	.67
Traditional African			.76
Kwaito		.71					.38
Pop		-.59					.43
Rap		-.46					.43
Western jazz				.80
Western classical				.79
Light instrumental		.35			.56
Dance						.68
R&B/soul				.		.63
	Eigenvalue = 2.22		Eigenvalue = 1.89	Eigenvalue = 1.52		Eigenvalue = 1.27
	% Variance = 18.52		% Variance = 15.72	% Variance = 12.72		% Variance = 10.69
	% overall variance explained = 57.55

Note: Loadings < .30 have been omitted.

Extraction method: principal component analysis. Rotation method: varimax with Kaiser normalization.

Table 2. Descriptive statistics for target measures

	α	Number of Items	M	SD
Uses of Music
Background use (M[back])	.60	5	3.71	.81
Emotional use (M[emot])	.34	4	3.82	.69
Cognitive use (M[cog])	.26	4	3.40	.65
Personality
PANAS positive (PA)	.72	10	3.90	.57
PANAS negative (NA)	.73	10	2.14	.67
Music Style Preferences
Rock	.70	3	2.14	.96
Academic	.57	3	2.58	1.00
African	.56	4	3.19	.87
Party	.30	2	3.41	.66
Music Usage
Listening Groups
With close others	.36	3	2.62	.78
With distant others	.41	2	2.31	.88
Listening Locations
Outside home	.63	8	3.29	.67
Inside home	.48	4	3.87	.74

PANAS with a similar age-group of American participants (Lonigan et al., 1999; Lonigan, Richey & Phillips, 2002). Ratings on the three Uses of Music factors (reported as averages in this study) were multiplied by the number of items to compare to past descriptive statis­tics; all three subscales were similar to past reports that used Spanish and Malaysian par­ticipants (Chamorro-Premuzic, Gomà-i-Freixanet et al., 2009; Chamorro-Premuzic, Swami et al., 2009).

Inter-correlations and structural equation modeling

Table 3 reports the inter-correlations among the target measures of personality, uses of music, and music preferences and usage. Significant correlations (p < .05) were tested using SEM carried out via AMOS 5.0 (Arbuckle, 2003). The choice to use SEM was driven by two main reasons. First, unlike regression analyses, SEM enables one to simultaneously treat variables as predictors and criteria; second, SEM also enables one to model latent or unob­served factors from observed variables (Byrne, 2006). In the present study, we modeled a latent endogenous factor (‘styles’) to represent the common variance underlying preferences for the four different music styles (i.e., rock, party, academic and African), as well as a latent mediator (‘uses’), which represented the common variance underlying the three uses of music (i.e., background, cognitive and emotional). Modeling latent factors is a useful tech­nique to simultaneously examine the role of general and specific factors in the model; for instance, if three observed variables are inter-correlated, removing the common variance among these variables enables one to examine the unique effects of each of the specific vari­ables, that is, variability that is not shared by any other variable. In addition, we included PA and NA as exogenous factors (in line with past models examining individual difference factors as determinants of music uses and preferences).

Table 3. Inter-correlations among target measures

	2	3	4	5	6	7	8	9	10	11	12	13
1. M[back]	.18*	.19**	.28**	-.04	.15*	.13	.12	.15*	.15*	.03	.36**	.43**
2. M[emot]		.29*	.15*	.19**	.16*	.18*	-.17*	.20**	.13	.04	.40**	.16
3. M[cog]			.27**	.04	.11	.22**	.16*	.04	.17*	.09	.14*	.07
4. PA				-.06	-.02	.15*	.00	.14	.10	.03	.28**	.12
5. NA					-.02	.10	.11	.04	.03	-.01	.01	.13
6. Rock						.09	-.18*	.18	.08	.03	.20**	.09
7. Academic							.03	.13	.18*	.16*	.16*	-.02
8. African								-.21	-.11	.09	-.24**	-.10
9. Party									.12	.14*	.24**	.17*
10. With close others										.07	.41**	.02
11. With distant others											.20*	-.02
12. Outside home												.21**
13. Inside home												.21**

Note: N = 193; *p < .05, **p < .01; M[back] = background use of music, M[emot] = emotional use of music, M[cog] = cognitive use of music, PA = positive affect, NA = negative affect.

The following paths were hypothesized: from PA onto background and cognitive use of music, from NA onto emotional use of music, from cognitive use of music onto preference for academic music, and from the latent ‘uses’ factor onto the latent ‘styles’ factor. The hypothe­sized model did not fit the data well:1 c2 (N = 192, d.f. = 24) = 37.6, p < .05; GFI = .96, AGFI = .92; CFI = .87; PGFI = .51; RMSEA = .05 (.01–.09). In line with modification indices, two paths were added to the model in order to improve fit; namely, from PA onto the latent ‘uses’ factor, and from cognitive use of music onto preference for African music. The modified model (shown in Figure 1), explained the data well: c2 (N = 192, d.f. = 22) = 18.3, p > .05; GFI = .98, AGFI = .96; CFI = 1.00; PGFI = .48; RMSEA = .00 (.00–.05).

As seen in Figure 1, PA had a significant positive effect on cognitive use of music and back­ground use of music, as well as an unpredicted positive effect on the latent ‘uses’ factor; NA had a significant positive effect on emotional use of music; cognitive use of music had a significant positive effect on preference for academic music and an unpredicted significant negative effect on preference for African music (since the loading of African music onto the latent ‘styles’ fac­tor is negative, the effect of cognitive use of music on liking of African music also becomes a negative relationship); and, the latent ‘uses’ factor had a significant positive effect on the latent ‘styles’ factor.

Discussion

The current study examined the relationship between positive and negative affect, uses of music and music style preferences. Therefore, we sought to bring together two lines of past research, namely, extending uses of music research (Chamorro-Premuzic & Furnham, 2007) to a new culture and age-group, as well as combining this research with music preferences research (Rentfrow & Gosling, 2003, 2006). In addition, this study used PA and NA as predic­tors of uses of music. Since the PANAS is a standard and highly reliable measure of disposition.

 

Figure 1. Modified SEM model for affect, uses of music, and music preferences.

Note: *p < .05, ** p < .01; solid lines represent hypothesized paths, dotted lines represent modified paths; PA = positive affect, NA = negative affect, M(back) = background use of music, M(emot) = emotional use of music, M(cog) = cognitive use of music.

(Watson & Tellegen, 1985), it was predicted to correlate well (and in a similar manner as the Big Five) with music uses. This study was unique in its use of African and Southeast Asian adolescents from South Africa and its attempt to unify uses of music and music preferences research into a single model in which affect would predict music use, which in turn, would predict music preferences.

We found that, as predicted, PA positively correlated with background and cognitive use of music, while NA positively correlated with emotional use of music. The positive associa­tion between NA and use of music as emotional regulation can be explained in that indi­viduals higher in neuroticism tend to experience a higher intensity of emotional affect, especially negative emotions (Costa & McCrae, 1992; DeNeve & Cooper, 1998). Past research has shown Extraversion to be consistently positively correlated with positive affect (DeNeve & Cooper, 1998; Lucas & Fujita, 2000); therefore, the link between PA and social or back­ground use of music is consistent with previous findings. Finally, several PA scale items (i.e., Interested, Inspired, Determined, Attentive) have shown past links to openness (Mitte & Kämpfe, 2008), and therefore, the link between PA and intellectual uses of music is consis­tent with past findings.

In regards to music preferences research, the current study maps on well to previous find­ings by Rentfrow and Gosling (2003). In their analysis of undergraduates’ music preferences, four style factors were extracted: Reflective/Complex, Intense/Rebellious, Upbeat/Conventional and Energetic/Rhythmic. Our ‘Academic’ factor includes similar loadings to Reflective/Complex (i.e., western jazz, western classical), ‘Rock’ includes similar loadings to Intense/Rebellious (i.e., rock, alternative), and ‘Party’ includes similar loadings to Energetic/Rhythmic (i.e., R&B/soul, dance). ‘Traditional’ did not match previous style loadings because the genres (i.e., Kwaito, African) were unique to the South African population; however, Upbeat/Conventional (‘genres that emphasize positive emotions and are structurally simple’, 2003, p. 1241) still describe these genres. Obviously, there are numerous genres that were not explicitly tested in the present study; however, this study shows that the structure underlying music preferences identified by Rentfrow and Gosling (2003), if not the exact genres themselves, are able to be replicated across both age groups and cultures.

In the current SEM model, we did not predict any specific relationships between affect and musical style preferences; this is also in line with Rentfrow and Gosling (2003, 2006), who sug­gested that chronic emotional states (PA and NA) may not have a strong effect on music prefer­ences, but rather, songs within each dimension can capture different emotional states. Therefore, the use of SEM allowed us to test whether uses of music are associated with affect, and in turn, whether uses in general are linked to higher preferences for all music styles. Indeed, not only were PA and NA related to the subscales of the Uses of Music inventory, but the latent ‘uses’ factor was significantly positively correlated with the latent ‘styles’ factor. While this gen­eral correlation between uses and preferences was found, we were not able to predict any spe­cific correlations except cognitive use of music having a significant positive effect on preference for Academic music. Therefore, one direction for future research would be to examine the rela­tionship between uses of music and music genre preferences more closely to establish how spe­cific uses of music relate to preferences for specific genres.

Our results also included several measures of music usage: with whom (Listening Groups) and where (Listening Locations) participants listened to music (North et al., 2004). These factors were excluded from the final SEM analysis, in part because of lower than desired internal consistencies and in part to simplify the final model. Because these categories were developed for this study and not previously validated, we did not feel confident using these categories for further analysis. Although Cronbach’s alphas for emotional and cognitive uses of music were also lower than desired, these subscales have been used repeatedly in past research with consistently reliable results (Chamorro-Premuzic & Furnham, 2007; Chamorro-Premuzic, Gomà-i-Freixanet et al., 2009; Chamorro-Premuzic, Swami et al., 2009), and therefore were included in the present analysis. Although Listening Groups and Listening Locations were excluded from the final model, an interesting point exists in relation to these findings: more music usage factors showed signifi­cant correlations to background use of music (3 out of 4 significant) than emotional or cognitive uses of music (1 and 2 out of 4 significant, respectively). It may be that the music usage items and the factors extracted are more likely to lend themselves to a background use than other uses; for example, listening to music with close others and listening outside the home suggest an inherent background or social quality, whereas other items that showed weak loadings, such as listening by oneself or deliberately listening to music at home, would be more likely to correlate to emo­tional or cognitive uses of music. Future research should address this issue and produce a music usage scale that equally lends itself to background, cognitive and emotional functions of music.

A final note about the present results is in regards to the low Cronbach’s alphas for emo­tional and cognitive use of music. Because no previous studies of the Uses of Music Inventory have focused on adolescents, it may be that the social function of music overwhelms other uses for this age group. Previous research shows that adolescents use music for identity formation, socialization, and entertainment (Bakagiannis & Tarrant, 2006; North, & Hargreaves, 1999; North et al., 2000; Tarrant et al., 2000), but there has been less consistency in showing that adolescents use music for intellectual stimulation, which may be because of a lower capacity for musical analysis (Demorest & Serlin, 1997; Eerola et al, 2006; Krumhansl & Keil, 1982; North et al., 2000). If students are incapable of understanding music from a cognitive perspec­tive, it makes sense that the internal consistency of the M[cog] items would be lower. Additionally, while much research exists to support an emotional use of music among adolescents (Juslin & Sloboba, 2001; Miranda & Claes, 2009; North et al., 2000; Saarikallio & Erkkilä, 2007; Saarikallio, 2008; Tarrant et al., 2000), the Uses of Music inventory does not distinguish between positive or negative mood regulation, which could cause lower internal consistency of results. In addition to a novel age group, a new cultural group was used as well. It is possible that since none of the participants had experienced formal music study before, the concept of music as an intellectual activity was foreign to them. Yet despite a lack of formal music educa­tion, the majority of participants in this study reported listening to music daily outside of school and owning a personal radio or music-playing device; so clearly, background use of music seems to be an important part of the South African culture. However, further research will need to be done to more fully examine the emotional and cognitive use of music both in South Africa and in the adolescent population.

Because of the novelty of the age and culture used in the present study, a number of limita­tions existed. First, the study relied only on self-reports of music use and preferences, which may not translate to actual music use and preferences in real life; this method only assumes that indi­viduals accurately report on their uses and preferences for music. This limitation is compounded by the fact that participants completed the surveys in one sitting, and the results of this model are therefore only hypothetical and not verified across a longitudinal study. Further research could overcome this limitation by including actual music usage and preferences across a variety of settings for a more accurate depiction of uses of music and style preferences.

Second, although the present results were generally consistent with past research, it is important to note that we are using data from a non-western sample to support findings from western cultures. Although cross-cultural differences in the uses of music are likely to be minor (Rana & North, 2007; Chamorro-Premuzic, Swami et al., 2009), we cannot rule out that differ­ences in the uses of music (for example, low internal consistencies) were the result of cross-cultural differences. The present findings also showed consistency to Rentfrow and Gosling’s (2003) music preferences categories; however, future research should examine the role of cul­ture in uses of music and music preferences more thoroughly.

Finally, despite all efforts made to ensure the surveys were age and culture appropriate, it is still possible that participants did not have a complete understanding of the surveys. As previ­ously mentioned, several of the surveys were adapted to make the items easier to understand, and Zulu-speaking teachers were available to answer participants’ questions. Of course, how­ever, we have no guarantee that participants understood all of the items, and this could explain some of the differences found in the present study. On the other hand, multiple studies have shown that adolescents from African cultures were able to complete tasks and surveys with little difficulty (Eerola et al, 2006; Saarikallio, 2008). Additionally, extensive cross-cultural studies using participants unfamiliar to the psychological testing process have proven to be suc­cessful (Scherer, 1997a, 1997b; Scherer & Wallbott, 1994). It may still be beneficial for future research to address this issue by developing a Uses of Music inventory that is written in straight­forward language with simple vocabulary that would be more appropriate for adolescents and non-native English speakers.

These limitations notwithstanding, the current study adds to the literature on both uses of music and music preferences, providing a link between these two bodies of music research. Indeed, the current study suggests that previous research on uses of music and music prefer­ences does generalize across cultures and age groups, and also provides support for the growing literature suggesting that variations in music use and preferences are to some extent related to individual differences in temperament, particularly emotional dispositions.

Acknowledgements

The work of Laura Getz, Michael Roy, and Karendra Devroop on this project was supported by two Collaborative Interdisciplinary Scholarship Program Grants and a Professional Development Grant, all awarded by Elizabethtown College, USA. As part of these grants, concert band programs were initiated at two secondary schools in Pietermaritzburg, South Africa.

Notes

1. The following fit indexes were used: c2 (Bollen, 1989), which tests whether an unconstrained model fits the covariance/correlation matrix as well as the given model (although non-significant c2 values indicate good fit, well-fitting models often have significant c2 values); the goodness-of-fit index (GFI) measures the percent of observed covariances explained by the covariances implied by the model; the AGFI adjusts for the degrees of freedom in the specified model; for both the GFI and AGFI (Hu & Bentler, 1999), values close to 1.00 are indicative of good fit; the CFI (Bentler, 1990) compares the hypothesized model with a model based on zero-correlations among all variables (values around .90 indicate very good fit); the parsimony goodness-of-fit indicator (PGFI; Mulaik et al., 1989) measures power and is optimal around .50; and for the root-mean-square error of approximation (RMSEA; Browne & Cuddeck, 1993), values < .08 indicate good fit.

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Acknowledgements

The work of Laura Getz, Michael Roy, and Karendra Devroop on this project was supported by two Collaborative Interdisciplinary Scholarship Program Grants and a Professional Development Grant, all awarded by Elizabethtown College, USA. As part of these grants, concert band programs were initiated at two secondary schools in Pietermaritzburg, South Africa.

Biographies

Laura M. Getz is a first-year Cognitive Psychology graduate student at the University of Virginia, USA. The data presented here were collected while she was an undergraduate at Elizabethtown College and analyzed while working in collaboration with Dr Chamorro-Premuzic at Goldsmiths, University of London, UK. Her research interests include the combination of music and psy­chology, both from a personality and cognitive perspective.

Tomas Chamorro-Premuzic is a world-wide expert in personality, intelligence, human perfor­mance, and psychometrics. He is a Reader at Goldsmiths, Research Fellow at UCL, and Visiting Professor at NYU in London.

Dr Chamorro-Premuzic has published more than 100 scientific articles and 5 books, covering a wide range of social and applied topics, such as human intelli­gence and genius, consumer and media preferences, educational achievement, musical prefer­ences, creativity and leadership, and he frequently appears in the media to provide psychological expertise to a wide audience. His current interests include online dating, employability, film preferences, and entrepreneurship.

Michael M. Roy is an Assistant Professor of Psychology at Elizabethtown College, USA. The majority of his research is in the area of social cognition. In addition, he is a drummer and has been an active performer for a number of years.

Karendra Devroop is Associate Professor of Music and Director of the School of Music and Conservatory at North West University in Potchefstroom, South Africa. His major research area is the occupational development of professional and amateur musicians.

Note* This is reprint article . It has been published earlier at

http://pom.sagepub.com/

http://pom.sagepub.com/content/40/2/164

Published by: http://www.sagepublications.com

By: Prapanna Lahiri

Network marketing, also called Multilevel Marketing, is a strategy that some direct sales companies use to encourage their existing distributors to recruit new distributors by paying the existing distributors a percentage of their recruits’ sales. The recruits are known as a distributor’s “down line.” All distributors also make money through direct sales of products to customers1. It is a model of business that is very popular with people looking for part-time remunerative engagement and flexible business opportunities.
Way back in 1930s a man called Carl Rehnborg started distributing diet supplement products in America by his company, Nutralite. In doing so he pioneered a strategy called Multi Level Marketing (MLM) to boost his sales. The power of this strategy giving exponential growth to sales was unearthed by two distributors of the Nutralite products, Jay Van Andel and Rich DeVos, who later founded another MLM company called Amway which eventually took over the business of Nutralite. Today, Amway is one of the largest companies.
Network Marketing has become a fast growing phenomenon and some analysts have called it the Business of the 21st Century. Various reasons, why it shows a noticeable growth spurt in popularity today, are analysed below:
1. ​Unemployment: Historically, popularity trends of network marketing have shown a direct relationship to prevailing unemployment rates. Therefore, during recessions, this direct selling industry proves to be rather resilient.
2. ​Job security no longer exists. The job market is shrinking with the global population explosion. Taking for granted a ‘job for life’ and the ‘security’ from working for a large corporation, are disappearing fast.
3.​ Flexibility: This unique business opportunity is very popular with people looking for part-time, flexible businesses that provide more money and more free time. MLM companies have traditionally presented an excellent opening to stay-at-home mothers and wives to earn a little side cash to empower themselves.
4. ​Self employment Network Marketing: the new age mantra for Marketing: MLM offers job seekers the prospect of working from home and ‘earning an income’ for themselves rather than having ‘job’ and working for someone else.
5. ​Low Investment: Network marketing schemes feature a low upfront investment ― an investment good enough for the purchase of a product sample kit, lending the opportunity to sell a product line directly to friends, family and other personal contacts. Thus it is a “Low Cost Franchise business”.
6. ​‘Downline’: In network marketing programmes participant distributors recruit and train their own sales representatives that constitute their ‘down line.’ Sales achieved by down line generate income for those above them in the programme chain.
7. ​Technological progress: Developments in Information technology has made automated record keeping, reward calculation, processing, electronic funds transfer, mailing etc very accessible, affordable and effective. Improved Communications technology like voice mail, email, internet telephony, video conference calls, auto-responders etc have helped eliminate serious obstacles relating to time, distance and cost ― essential to maintain the personal touch so vital in such kind of networking business.
8. ​Marketing strategy: It allows an ingenious new entrant to strategise marketing effort by being able to positively visualise increased earnings with recruitment of a larger network making use of social networking sites.
9. ​Attraction Marketing the New Mantra: Like in any other marketing strategy, knowing one’s products and communicating their benefits to the prospective customer is the key in MLM too. But the new mantra which many are calling ‘attraction marketing’ is the approach of “building relationship first, business second.” It envisages establishing a relationship with people that gradually over time will cause them to come to know you, like you, and trust you. Assuming identical product/ service offerings, a buyer would choose to buy from someone he/ she knew, liked, and trusted over someone who just rang the doorbell.
Network Marketing is the future of business. In corporate, capitalistic model, large companies profit from sales, and then distribute the surplus to externalised share holders while in MLM the shareholders are the internal, integrated, non-salaried distributors. Worldwide annual sales in the network marketing industry are approaching $90 billion with 150,000 network marketers joining the industry globally per week2. The growth potential of this industry is reflected in the words of Bill Gates, “If I would be given a chance to start all over again, I would choose NETWORK MAKETING.”
Reference:
http://www.investopedia.com/terms/m/multi-level-marketing.asp
https://arnelzion.wordpress.com/2014/04/02/network-marketing-new-mantra/

By: Shaifali Rachna Puri

 It is a universally accepted truth that the actions emphasize a particular concept better than words or it can be said; that what is experienced practically makes one learn the best. The learning hence, gained by individual stays with him through out the life. Perhaps, it is due to this fact that most of the teaching community prefers to use such teaching processes that involve a repeated use of practical aids. Cinema is one of such most practical aids, frequently used for the purpose of teaching. When some one ponders over the question, as to what cinema is, the answers that readily come to mind are “An art form”, “A means of telling stories”, “A business”, “An entertainment” and possibly several other similar phrases but of course, these are not the real definitions of cinema. In fact these are the theories or the concepts that one develops while watching cinema. It is these pseudo definitions that nurture whole set of attitudes towards cinema and mask out important issues.

“A novel is a narrative that organizes it self into a world where as a film is a world that organizes itself into a narrative” says Mitry, Jean

One more opinion that cannot be left unquoted is by David, Bordwell. It goes, “A film I suggest, does not position anybody. A film was spectator to execute a definite variety of operations”.

If one looks at the definitions quoted above he will find quite a different type of impression coming up. When one looks at often vague and pseudo-definitions of cinema one feels that the simple evocation of cinema arouses deeply personal resonance. But the cinema does not perform only this function. Apart from arousing feeling cinema also plays a very important role in day to day social as well as moral life of people. Cinema is such a subject, the study of which provides a peep into the life and atmosphere of the society in which it is produced. It is such a media that effects the changes in the life style of the society and is within the reach of all.

It is not only the most important means of communicating information and entertainment but it also casts significant influence on the behaviour and attitude of the society. Cinema only is responsible for improvement as well as deterioration of tastes in public

 “Much of our knowledge, many of our attitudes, the topic of conversation at work the next day, the brand of soap we pick up at the grocery store, the cartoon characters on our children’s clothes, our aspiration- almost every conceivable aspect of our lives is touched by this medium: yet, like eating or sleeping, we accept it as just another part of our daily routine, uncritically and unknowingly.”

All the opinions given above with their true reactions, wild allegations, emotional overtones and ill-defined terms suggest that cinema is the object of ideological confrontation. There is no better initiation to socialize then the study of cinema. While the cinema permits the audience to probe the collective psyche of a society, it also helps them to understand their individual psyche and personality. It is so because the cinema involves the widest range of physical, mental and social development of ones personality.

At this point amidst the discussion one feels a strong need for the explanation of the term “Development”. The sole aim of education is to bring an all round development in the personality of a human being. Human life starts form a fertilized cell. The constant interaction of the human being results in the encouragement and the outlet to innate capacity, abilities and potentialities. It refers to the changes in the quality or character rather than in quantitative aspect.

Development is a continuous process that goes from womb to tomb. It does not end with the end in a particular phase of life.

The term development refers to the overall changes in an individual taking place with the passage of time and causing such a positive difference that makes him worth the society wherein he lives.

Therefore it becomes clear that the development takes place in ones life regularly and it is only due to this development that one finds the personality of human being undergoing a change at different phases of life.

Deep within lies in every individual a powerful urge to grow and blossom out as a PERSON – to be distinguished from sub-human species. Born as a helpless infant, totally dependant upon it’s mother for survival, the human body grows, matures and develops, learns and comes to be reckoned as a PERSON. Among several persons in this world, the quest of everyone is to become a person as Carl Rogers says and develop ones personality.

Some psychologists trace the origin of the term PERSONALITY to a Latin form PERSONA – a kind of mask used by Greek actors while staging plays in bygone days. For the theological meaning, personal tic philosophers agree that

 (I) Personality is of supreme value.

(II) Persons are to be distinguished Meta physically from things.

(III) The subjective experience is the final psychological court of appeal.

According to Browne “Personality can never be constructed as a product or a compound; it can be only experienced as a fact”.

On the other hand, Stern though a persona list, does not deny corporal significance to personality but declares a person to be “Psychologically Neutral” multi form dynamic unity.

Philosophically, Locke defines a person as-“A thinking intelligent being that has a reason and reflection and considers self as itself”.

Psychologically speaking, perhaps the commonest type of definition says-“Personality is the some total of all the biological, innate dispositions, impulses, tendencies, appetites and instincts of the individual, and the acquired dispositions and tendencies-acquired by experience”.

According to Allport, neither a theologian, nor a psychologist can monopolize PERSONALITY. According to him, the following amplifications seem to serve the purpose better – “Personality is the dynamic organization within the individual of those psychophysical systems that determine his unique adjustment to his environment”.

Regard less of the diversity of descriptions, one does find a few commonalities; namely-ones personality is unique, organized, multidimensional and consistent. No two individuals are alike and that is what makes life interesting and exciting.

One notices that the personality of a human being can be viewed from various aspects but basically there are only few angels which when properly studied give a full view of what personality actually is. These are:

(1) Physical development

(2) Intellectual development

(3) Social development

(4) Emotional development

Physical development: It refers to a process which brings bodily and psychological changes-internal as well as external. The physical development effects the development of the other aspects of ones personality too. The physical development of a child or an individual greatly depends on few factors such as the atmosphere at the time of delivery, mental health of mother, the living conditions, social circle, the place of interaction, influences on psyche etc. Thus one finds that both environment and hereditary condition the physical development of an individual.

Intellectual development: The growth and development of the mental abilities and capacities which help an individual to adjust his behavior to the ever changing environmental conditions or to enable him to accomplish a task that needs complex cognitive abilities is referred to as mental or intellectual development. The process of mental development includes the development of various cognitive, reasoning and conceptual abilities.

Social Development:  The process of the development of such qualities and abilities in a person, which bring desirable changes in his social behavior, is referred to as social development.

According to Sorenson “By social growth and development we mean increasing ability to get along well with oneself and others”.

Emotional Development: Emotion is a ‘stirred up” or moved state of mind of an organism. It is a reaction of an individual towards any particular object, act or surrounding. These are not present in a human being at the time of birth rather these need to be infused in a child with the passage of time. How, when and where to give expression to these emotions need a great exercise and that is what we call emotional development. If one studies in detail the process of development of the personality one comes across the fact that the personality of an adolescent is the most complicated one. Adolescent community is such a part of society, which can be very easily effected by any and every incident around. This point gets proved easily when one looks at the daily activity of the adolescents around him.

Amandeep kaur

Professor, Khalsa College of Nursing, Amritsar, Punjab, India

In industrialized countries every fourth death is caused by cancer. Scarcely any family or circle of acquaintance is spared the sad fate of watching while a loved one slowly succumbs to this disease. Those who have faced the knowledge that their body is carrying a tumor which is threatening to spread may well ask “What can I expect from the future? Must I give in without a fight or are there practical and promising methods for tackling the situation?”

Not all health problems are avoidable, but you have more control over your health than you may think. Research shows that a large percentage of cancer-related deaths are directly linked to lifestyle choices such as smoking, drinking, a lack of exercise and an unhealthy diet. Avoiding cigarettes, limiting alcohol, reaching a healthy weight, and getting regular exercise are a great start to preventing cancer. Cancer treatments are designed to kill cancer cells. But these treatments can also damage healthy cells. Damage to healthy cells can cause side effects. Some of these side effects can lead to eating/ appetite problems. But to best support your health, you also need to look at your eating habits. What you eat and don’t eat has a powerful effect on health, including the risk of cancer. Without knowing it, you may be eating many foods that fuel cancer, while neglecting the powerful foods and nutrients that can protect you. For example, a daily serving of processed meat increases your risk of colorectal cancer, whereas eating soy foods such as tofu can help reduce your risk of breast cancer and eating more fruits and vegetables can lower your risk for a variety of common cancers. By making small changes to your diet and behaviors, you can lower your risk of disease.  If you’ve already been diagnosed, eating a healthy diet can help boost your mood and outlook as well as support your medical treatment at this challenging time. External factors affecting nutritional status include the environmental and social contexts within which an individual exists. These contexts encompass the overall health of the country’s economy, which has an impact on transportation, access to food shopping, availability of different nutrients, adequacy of housing and food preparation facilities and availability of programs that offer food assistance. Environmental factors influence the individual, who possesses cultural beliefs and attitudes about nutrition and eating behaviors. Internal factors that influence a person’s tendency to develop nutritional deficiencies include age, body image, past history of food fads or eating disorders, social support, educational level, alcohol or tobacco intake, and presence of comorbid diseases. Much more research in this area is needed before individuals at risk can be reliably identified. Cancer-related factors include the type of cancer that affects the probability of malnutrition. Individuals with breast cancer or leukemia are at low risk, whereas 31% to 48% of patients with non-Hodgkin’s lymphoma have significant weight loss. Moreover, unfavorable histology is correlated with higher weight loss. Individuals with cancers of the aero digestive (upper respiratory and digestive) and gastrointestinal (GI) tracts are at special risk for under nutrition from mechanical obstruction and physiological dysfunction due to local tumor compression. Host responses to the cancer and the cancer itself cause changes in metabolism and energy needs and may explain why those individuals with advanced disease are more likely to have nutritional problems. People with cancer often need to follow diets that are different from what they think of as healthy. For most people, a healthy diet includes: Lots of fruits and vegetables, and whole grain breads and cereals, modest amounts of meat and milk products, small amounts of fat, sugar, alcohol and salt. Cancer clients need to eat to keep up strength to deal with the side effects of treatment. When you are healthy, eating enough food is often not a problem.  But when you are dealing with cancer and treatment, this can be a real challenge. In cancer, one may need extra protein and calories. At times, diet may need to include extra milk, cheese, and eggs. Sometimes, one may need to eat low-fiber foods instead of those with high fiber. Common eating problems during cancer treatment include the following:

1. Appetite loss
2. Changes in sense of taste or smell
3. Constipation
4. Diarrhea
5. Dry mouth
6. Lactose intolerance
7. Nausea
8. Sore mouth
9. Sore throat and trouble swallowing
10. Vomiting
11. Weight gain
12. Weight loss.

Therefore nutrition counseling is required to maintain health of the client which includes

1. Diet: It covers selection and rejection of what to eat and what not to eat respectively.
2. Exercise & activity: This helps to increase the strength of your body so that the body can fight against all the forces that are causing a threat to it.
3. Behavior modification and managing acute side effects: Cancer treatment is not side effect free. You cannot get rid of the side effects but you can surely manage them. Nutrition counseling helps to develop diets that will enable to control such side effects.

In conclusion, it can be pointed out that the main purpose of nutrition counseling for a cancer patient is to provide the patient (on whom it is being done) with not only physical strength but also with mental strength to fight against the odds. It helps one to develop an eating habit that is safe.

“The food you eat can be either the safest and most powerful form of medicine or the slowest form of poison.”

                                                                                                                                                     – Ann Wigmore