By: Aamarpali Puri

Dear Readers please check the link to the presentation below.

Online Publication of Manuscript

Karendra Devroop*

*School of Music, North-West University, Private Bag X6001, Internal Box 124, Potchefstroom 2520, South Africa.

Abstract:

Within the literature there exists a large volume of research studies attesting to the positive relationships between studying music and various psychological and sociological variables. A close examination of these studies reveals that only a handful were conducted on disadvantaged populations. Accordingly, it remains unclear to what extent these findings hold true for disadvantaged students. The purpose of this study was to investigate the social-emotional impact of instrumental music instruction on disadvantaged South African students. The two specific questions addressed in this study were (1) what impact did instrumental music instruction have on student’s self-esteem, optimism, sense of happiness and perseverance and (2) do any relationships exist between instrumental music instruction and the variables under investigation? The results indicated that there were generally increased levels of self-esteem, optimism, happiness and perseverance after participation in an instrumental music programme. There was also an increase in subject’s optimism and sense of happiness. There were moderate to moderately strong positive relationships between participation in instrumental music and self-esteem, optimism, happiness and perseverance.

Keywords: disadvantaged students; instrumental music performance; social emotional impact.

The role of the arts in education

Several researchers and leading international organisations suggest that the arts play a critical role in the development of youth. UNESCO, one of the champions of policy initiatives on culture and education, appealed to arts organisations and practitioners to foster the development of arts education in the UNESCO Road Map for Arts Education (UNESCO 2006). Several researchers have indicated that involvement in the arts has been associated with improved scores in math and reading and elevated verbal, cognitive and spatial reasoning skills (Burton, Horowitz, and Abeles 1999). Researchers (Brown, Benedett, and Armistead 2010) have also indicated that the arts provide a suitable avenue for school readiness skills for children from diverse backgrounds.

The arts have been shown to play a critical role in the development of at-risk children and those facing poverty-related stressors. It has been suggested that the arts could provide regulation of emotions and behaviour for students from economically disadvantaged backgrounds while increasing their cultural awareness through education (Brown, Benedett, and Armistead 2010). The arts could in essence become a focal point for early intervention of developmental deficiencies in children facing economic and other social challenges including lack of parental support, drug abuse and crime.

The importance of music in the educational curriculum

Within the literature there exists a large volume of research studies attesting to the positive relationships between studying music and various social-emotional variables (Costa-Giomi 1999; Fitzpatrick 2006; Marjoribanks and Mboya 2004; Michel and Farrell 1973; North, Hargreaves, and O’Neill 2000; Taetle 1999; Trusty and Oliva 1994; Young 1975). Asmus (2005) conducted an intensive literature review of approximately 270 electronic databases with the aim of identifying research studies that investigated the impact of music education on various samples. He categorized the research studies into three large all-encompassing groups that investigated specific variables. These included: (1) home environment and learning (socio-economic status, enrichment, parental attitude, genetics, motivation, attitude, creativity, intellectual development and confidence), (2) school (grades, motivation, student learning, self-esteem, social status and language development) and (3) community (social development, emotional development, behaviour, self-expression and social skills). In the majority of these studies, positive relationships were established between studying music and the variables under investigation. In some studies, researchers were able to identify specific variables that accounted for as much as 80% of the variance in learning. Several researchers have provided evidence of strong positive correlations between instrumental music performance and specific social-emotional constructs. These include but are not limited to self-esteem, self-discipline, perseverance, motivation, leadership, attitude and cooperation (Adderley, Kennedy, and Berz 2003; Costa-Giomi 2004; Hietolahti and Kalliopuska 1990; Lillemyr 1983; McDowell 2002; MENC 2009; Scott 1992; Zehr 2003). Additionally researchers (Fitzpatrick 2006; Young 1975) have identified strong positive relationships between music and brain activity (cognition, spatial temporal reasoning and communication), music and the development of fine motor skills and music and improved test scores. Given the abundance of research findings in this area, it can be argued that studying a musical instrument positively impacts the development of various psychological and sociological skills. A close examination of these studies reveals that only a handful was conducted on disadvantaged populations. Accordingly, it remains unclear to what extent these findings hold true for disadvantaged students. According to Young (1975), disadvantaged students have less musical ability than their counterparts at the point of which they enter school. He further suggests that this disparity becomes greater as they progress through school due to differences in learning rate. However, Gordon (1970) hypothesises that disadvantaged students would achieve the same academic standards if they were afforded the same educational opportunities as their counterparts. According to Gordon, achievement levels of disadvantaged students and their counterparts may be different but their aptitude is essentially the same. He validated this hypothesis in a series of studies that followed over the past three decades.

The current status of music education in South Africa

The current music education environment in the public school system in South Africa is facing tremendous challenges. Since 1994, many public school music programmes have been abolished for reasons ranging from: a greater focus on math and science, lack of facilities, curriculum constraints, lack of suitably qualified teachers and little to no financial resources to support music programmes. A very small number of schools around the country offer instrumental music instruction due primarily to a lack of human and financial resources. The vocal tradition continues to sustain itself with many schools offering a school choir as the only form of exposure to music. String, wind and percussion programmes are practically non existent in most public schools. Many schools face challenges other than the lack of music education programmes. These included poor and deteriorating facilities, lack of financial resources, lack of qualified teachers and overcrowding. Given the tremendous challenges facing most schools in the country, it may be understandable why so few music programmes exist. The need for studies focusing on the benefits of studying music in South Africa is of vital importance given the large population of economically disadvantaged students within the country. The benefits of research studies investigating the effect of music instruction on disadvantaged South African youth could serve a dual purpose: (1) justifying the need for music instruction within the public school system and the expansion of current offerings within the arts and (2) investigating a potentially novel mechanism for addressing the learning disparity between disadvantaged youth and their counterparts. The latter would be justified if the findings from such studies were to substantiate the current findings in the literature. The current study is the second in a series of studies addressing the impact of instrumental music instruction on disadvantaged South African youth. In a previous study by this researcher (Devroop 2009), the effects of instrumental music instruction on the career plans of disadvantaged South African youth were investigated. The purpose of this study was to investigate the social-emotional impact of instrumental music performance on economically disadvantaged South African students. The two specific questions addressed in this study were (1) what impact did instrumental music performance have on student’s self-esteem, optimism, sense of happiness and perseverance? and (2) do any relationships exist between instrumental music performance and the variables under investigation?

The South African Music Outreach Project

This study was conducted in conjunction with the South African Musical Outreach Project (SAMOP). The SAMOP is an international outreach project that creates sustainable music programmes in the form of performing instrumental ensembles (concert bands) at disadvantaged public schools in South Africa. Over the past four years, the SAMOP has created instrumental music ensembles comprising approximately 45 musicians each via grants and donations of musical instruments from the USA. The project was founded by Dr Karendra Devroop in 2007 and is administered in conjunction with faculty from North-West University (Potchefstroom campus), Tshwane University of Technology and the University of KwaZulu-Natal (Pietermaritzburg campus). The goal of the SAMOP is to establish instrumental music ensembles specifically wind ensembles for economically disadvantaged students at public schools in South Africa. The majority of students who benefit from the SAMOP have no prior experience studying or performing music. The majority of students come to the programme with no experience or training in music. Due to the severe economic and social challenges that face these students, many cannot afford a musical instrument. Educationally the SAMOP approaches music making in a large ensemble setting thereby trying to accommodate as many students as possible. The ensembles established by the SAMOP are modelled on the typical US middle and high school concert band which comprise woodwind, brass and percussion instruments. Students study the fundamentals of wind performance while developing their instrumental proficiency and playing through various wind literature. Instruction generally occurs one to two times per week in a ‘full band’ setting and also once a week in sectional rehearsals. The band director at each school is the primary instructor and is occasionally assisted by a part time instructor. To date the SAMOP has positively affected hundreds of students from elementary to high school and more recently to university-level students. In addition to the service component of the SAMOP, there exists a research component in which faculty and students conduct individual and collaborative interdisciplinary research studies. To date several studies (Bogdanov 2009; Devroop 2008, 2009; Getz et al. 2012; Sandifer 2008) have been published and presented at conferences in the US, UK and South Africa. The current study is the second in a series of longitudinal studies aimed at addressing the efficacy of the intervention.

Method

Subjects for this study (N_84) consisted of students from the two instrumental ensembles established by the SAMOP in the Kwazulu-Natal province during 2008 and 2009. Each ensemble consisted of approximately 42 students who had never played a musical instrument before. Students in the two ensembles had participated in the SAMOP project for approximately two years. Prior to participating in the SAMOP project, none of the students could read music and the majority had never been exposed to a musical instrument. Over 95% of the students had never performed in a music ensemble and this was their very first formal instruction and exposure to an instrumental music ensemble. The ensembles were similar to concert bands that currently exist in US middle and high schools and included flutes, clarinets, saxophones, trumpets, trombones and percussion instruments. The average age for subjects within the sample was 13 years (M_13.65, SD_0.77) with a range of 12_16 years. All subjects were in the eighth grade. There was a greater number of females (62%) compared to males (38%). Racial distribution revealed that 76% of the sample was comprised of Black/African students followed by 19% of Coloured and 4% of Indian students. White/Caucasian students accounted for a little less than 1% of the total sample. Due to the high percentage of orphaned and homeless children in South Africa, subjects were required to provide information on their family and home environment. Approximately 38% of the total group indicated that they lived with both parents at home. A quarter of the total sample (25%) indicated that one or both parents were deceased and 42% indicated that they lived with a single parent or guardian. Many students were impacted by AIDS, crime and poverty and lacked parental supervision. Due to the lack of parental supervision, many students were drawn into drugs and gangs from a very young age. Approximately 20% of respondents indicated that they lived with someone other than a parent or guardian. School administrators were asked to provide insight into this finding and the general consensus was that the majority of these students were classified as ‘head of household’. In essence, these students lived by themselves while attempting to provide a stable living environment for their younger siblings. Administrators cited the death of parents due to HIV/AIDS, an over-burdening of the government social support infrastructure and the need for parents to work in other parts of the country in order to sustain an income as the primary reasons for the lack of parental supervision of these students. A survey instrument was developed specifically for this study after review of surveys conducted on similar populations in other countries. Items on the questionnaire were measured on a 5-point Likert scale ranging from ‘strongly disagree’ to ‘strongly agree’. Subjects were required to respond to a series of statements such as ‘participation in band has made me feel happier’ and ‘participation in band makes me feel like I can be a leader’. Each of the constructs (self-esteem, optimism, happiness and perseverance) was measured by obtaining a composite score of several sub-questions and/or statements. This was consistent with the manner in which previous researchers measured such constructs. Administration of the questionnaire was done by the researcher in a joint rehearsal of both ensembles. This ensured that all participants were available to complete the questionnaire, resulting in a 100% return rate. Prior to administration, the questionnaire was submitted for review to an expert familiar with questionnaire construction to ensure that content was appropriate for the target group. The questionnaire was revised for content and language and subsequently administered to the target group. Completed questionnaires were coded and entered into a statistical database (SPSS 16.0) for analysis. All information remained confidential and were stored on the investigator’s personal computer. The data were analysed using (1) descriptive statistics that included frequencies, percentages, means and standard deviations and (2) correlational statistics that included Pearson’s r and Spearman’s Rho. Correlation statistics measure relationships between variables such that changes in one variable are generally accompanied by similar changes in the correlated variable.

Results

The first question that this study sought to answer was the impact of instrumental music performance on student’s self-esteem, optimism, sense of happiness and perseverance. These variables were included by researchers in several prior studies on similar populations. Accordingly, they were included in the current study. Table 1 presents the composite mean scores of these variables. Changes in self-esteem, optimism, happiness and perseverance were measured by asking students to respond to a series of questions that related to each of the constructs.

Table 1. Mean distribution of change in self-esteem, optimism, happiness and perseverance.

Subjects were required to respond to questions on a 5-point Likert scale, with lower scores indicating that they strongly disagreed with the statement and higher scores indicating they strongly agreed with the statement. The results indicated that there were generally increased levels of self-esteem, optimism, happiness and perseverance after participation in the instrumental music programme. Mean scores ranged from 4.43 to 4.56 for the four main constructs. Considering that mean scores could range from 1 to 5, it can be deduced that the mean scores were extremely high thereby alluding to the notion that students’ participation in instrumental music had positively impacted their self-esteem, optimism, sense of happiness and perseverance. Interpretation of mean scores was consistent with model surveys upon which the current survey was based, such that higher mean scores reflected increased levels and vice versa. Accordingly, a mean score of 4.45 (range 1_5) on self-esteem suggested that there was an increase in student’s self-esteem after participation in instrumental music instruction. Similarly, there was an increase in subject’s optimism and sense of happiness with mean scores at 4.56 and 4.43, respectively. The final construct attempted to measure perseverance. Within the literature there exists a number of studies that suggest confidence levels increase when individuals persevere and succeed at challenges that are presented to them. This in turn motivates individuals to persevere at greater challenges in life due to their success with overcoming smaller challenges. The researcher included this construct in an effort to measure whether the perseverance that students exhibited in this environment would transfer to other greater challenges in life, given that these students faced daunting challenges on a daily basis. Subjects were asked questions such as ‘having learned to play an instrument, how likely are you to succeed at other challenges in life?’ A mean score of 4.40 was obtained on this item with a range of 1_5 where higher scores reflected greater perseverance. The high composite mean score suggested that students felt more inclined to persevere at some of the challenges they face on a daily basis due to overcoming the challenge of learning to play a musical instrument and function collaboratively within a performing ensemble. The results from this series of analyses affirm that student’s participation in instrumental music performance positively impacted their self-esteem, optimism, sense of happiness and perseverance. The mean scores for these variables suggest that students ‘agreed’ to ‘strongly agreed’ that their participation in instrumental music performance positively impacted their self-esteem, optimism, sense of happiness and perseverance. To address the second research question, a series of analyses were conducted to determine the possible relationships between student’s participation in instrumental music performance and the four constructs under investigation. To determine potential relationships between variables, correlation statistics were utilised. These statistics generally provide the strength (on a range of _1 to _1) and direction (positive or negative) of the relationships such that changes in one variable are generally accompanied by similar changes in the correlated variable. The correlation matrix in Table 2 indicates that there were moderate to moderately strong positive relationships between participation in instrumental music and self-esteem, optimism, happiness and perseverance. The strongest relationship (0.50) existed between participation in music and sense of happiness, followed by participation in music and perseverance (0.48) and participation in music and optimism (0.47). The weakest relationship was identified between participation in music and self-esteem (0.38). Although this relationship would be classified as small to moderate in size, the relationship remained positive. It is important to note that there were no negative relationships between variables. The fact that all of the relationships (albeit some were small) were positive indicates that the overall impact of students’ participation in instrumental music performance had a positive effect on their self-esteem, optimism, sense of happiness and perseverance. One can infer that continued participation in instrumental music performance would ultimately strengthen and enhance these social-emotional constructs due to the positive relationships between these constructs and students’ participation in instrumental music performance. The analysis of relationships between variables excluding participation in music revealed the strongest relationship between happiness and optimism (0.56) and the weakest relationship between self-esteem and perseverance (0.28). Almost all relationships were statistically significant at the 0.01 alpha, except the relationship between self-esteem and perseverance which was statistically significant at the 0.05 alpha level.

Discussion

The results from this study need to be analysed and interpreted with caution due to the relatively small number of studies conducted in this area in South Africa. Furthermore, sample size and sample selection does not allow for generalisations to the entire population of disadvantaged students within the country. However, the results do provide baseline data and initial insight into this unique yet understudied group.

The results from this study provide evidence that self-esteem, optimism, happiness and perseverance increased after participation in the instrumental programme. This finding is validated in the literature with several researchers substantiating these findings in previous studies. Costa-Giomi (2004) and Hietolahti and Kalliopuska (1990) found increases in self-esteem in studies that focused on the effect of instrumental music instruction on young children. These studies also found positive correlations between music and self-esteem, thereby validating the positive correlation between music and self-esteem in this study. Increases in levels of happiness and a positive correlation between participation in music and happiness have been identified in previous research studies. In a recent study that investigated the relationships between musical experiences of religious musicians and happiness, researchers Hills and Argyle (1998) established increased levels of happiness and a correlation between music and happiness. The findings by Hills and Argyle lend credibility to the findings from the current study. While the findings from the current study may be justified, the issue of greater importance is the applicability of the findings to the current sample and potentially to the broader population of students within the country. Given the circumstances that disadvantaged South African youth face on a daily basis including poverty, crime, hunger and lack of a stable home social environment, the importance of elevating students’ levels of happiness cannot be overstated. Researchers have found that increased levels of happiness tend to impact psychological health, thereby potentially impacting the general psychological well-being of the individual. The ramifications of all South African youth having good psychological health could have far-reaching consequences for the nation as a whole.Relationships between participation in music, optimism and perseverance have not been widely studied. In fact a very small number of studies were found to address these issues. Optimism and perseverance were included in this study due to their importance to the population under investigation. In research studies unrelated to music but based on South African youth, researchers (Mathombela 1997; Watson et al. 1997) have alluded to the importance of investigating optimism and perseverance of disadvantaged South African youth. Accordingly, these variables were included in this study. Results indicated that mean scores were higher for optimism and perseverance after participation in the instrumental music programme. In fact the mean score for optimism was the highest when viewed in relation to the other variables under investigation. Correlations between participation in music and optimism and perseverance were moderately strong and positive. These findings were supported in studies by Scott (1992) and Priest (2006) who found higher levels of optimism and perseverance after participation in music.Similar to the variable happiness, increases in optimism and perseverance need to be viewed in light of the broader social-emotional development of economically disadvantaged South African youth. With high rates of temporary withdrawal, grade repetition and dropout within the public school system in South Africa, increased levels of optimism and perseverance become pivotal building blocks to a psychologically and socially balanced and healthy generation of youth. The findings from this study indicate that participation in music may play a vital role in addressing these issues. Subsequent research would need to be conducted to validate this claim. Irrespective of the potential social-emotional benefits to studying music, the need to engage in instrumental music instruction and performance is justified as an entity within itself.

 Notes on contributor

Dr. Karendra Devroop is Professor of Music and Director of the School of Music and Conservatory at North-West University in South Africa. His primary area of research is on the career development of amateur and professional musicians. He has published and presented his research in several countries including the US, Canada, Mexico, UK, Italy, Germany, Taiwan, Thailand and South Africa. In addition to his research and teaching, he is a professional jazz saxophonist with a string of recordings and performances at several international jazz festivals.

 References

Adderley, C., M. Kennedy, and W. Berz. 2003. A Home Away from Home: The World of the High School Music Classroom. Journal of Research in Music Education 51: 190_205.

Asmus, E.P. 2005. The impact of music education on home, school and community. http://www.uncg.edu/mus/SoundsOfLearning/HomeSchoolCommunity.pdf (accessed December 29, 2009).

Bogdanov, J. 2009. The effect of an instrumental music program on South African student’s cultural enrichment. Research paper presented at Scholarship and Creative Arts Day,

Elizabethtown College, PA. Brown, E.D., B. Benedett, and M.E. Armistead. 2010. Arts enrichment and school readiness for children at risk. Early Childhood Research Quarterly 25: 112_24.

Burton, J., R. Horowitz, and H. Abeles. 1999. Learning in and through the arts: Curriculum implications. In Champions of change: The impacts of the arts on learning, ed. E.B. Fiske, 35_45. Washington, DC: Arts Education Partnership.

Costa-Giomi, E. 1999. The effects of three years of piano instruction on children’s cognitive development. Journal of Research in Music Education 47: 198_212.

Costa-Giomi, E. 2004. Effects of three years of piano instruction on children’s academic achievement, school performance and self-esteem. Psychology of Music 32, no. 2: 139_52.

Devroop, K. 2008. Changing lives through participation in band: The South African Musical Out reach Project. National Band Association Journal 49, no. 2: 60_1.

Devroop, K. 2009. The effect of instrumental music instruction on disadvantaged South African student’s career plans. MUSICUS 37, no. 2: 7_12.

Fitzpatrick, K. 2006. The effect of instrumental music participation and socioeconomic status on Ohio fourth-, sixth-, and ninth-grade proficiency test performance. Journal of Research in Music Education 54: 73_84.

Getz, L., T. Chamorro-Premuzic, M. Roy, and K. Devroop. (2012). Cross-cultural differences in music use within a South African secondary school. Psychology of Music 40: 164_78.

Gordon, E. 1970. First-year results of a five-year longitudinal study of the musical achievement of culturally disadvantaged students. Journal of Research in Music Education 18: 195_213.

Hietolahti, A.M., and M. Kalliopuska. 1990. Self-esteem and empathy among children actively involved in music. Perceptual and Motor Skills 71: 1364_6.

Hills, P., and M. Argyle. 1998. Music and religious experiences and their relationship to happiness. Personality and Individual Differences 25, no. 2: 91_102.

Lillemyr, O.F. 1983. Achievement motivation as a factor in self-perception. Norwegian Research Council for Science and the Humanities 17: 245_8.

Marjoribanks, K., and M. Mboya. 2004. Learning environments, goal orientations, and interest in music. Journal of Research in Music Education 52, no. 2: 155_66.

Mathombela, E.N. 1997. Educational and occupational aspirations of high school students in South Africa. PhD thesis, University of Iowa, Iowa City, IA.

McDowell, C. 2002.Why music in schools? Students’ written responses: A descriptive analysis. General Music Today 16, no. 1: 8_14.

MENC. 2009. Smart students Study Music [online]. http://www.menc.org/v/higher_education_admin/smart-students-study-music (accessed December 29, 2009).

Michel, D.E., and D.M. Farrell. 1973. Music and self-esteem: Disadvantaged problem boys in an all-black elementary school. Journal of Research in Music Education 21, no. 1: 80_4.

North, A.C., D.J. Hargreaves, and S.A. O’Neill. 2000. The importance of music to adolescents. The British Journal of Education Psychology 70: 255_72.

Priest, T. 2006. Self-evaluation, creativity and musical achievement. Psychology of Music 34, no. 1: 47_61.

Sandifer, K. 2008. A comparison of South African student’s formal and informal music making. Research session presented at the Pennsylvania Music Educators 75th Annual Conference, April 2008, in Hershey, PA.

Scott, L. 1992. Attention and perseverance behaviors of preschool children enrolled in Suzuki violin lessons and other activities. Journal of Research in Music Education 40: 225_35.

Taetle, L. 1999. The relationship between fine arts participation and daily school attendance at the secondary level. Contributions to Music Education 26, no. 1: 50_66.

Trusty, J., and G. Oliva. 1994. The effect of arts and music education on students’ self-concept. Update: Applications of Research in Music Education 13, no. 1: 23_8.

Watson, M., C. Foxcroft, M. Horn, and G. Stead. 1997. Occupational aspirations of black South African adolescents. Psychological Reports 80: 627_35.

Young, W. 1975. Efficacy of a self-help in music for disadvantaged preschools. Journal of Research in Music Education 23: 99_108.

Zehr, M. 2003. Mariachi’s encore. Teacher Magazine 14, no. 7: 14_7.

Note* This is reprint article .

It has been published earlier with Routledge Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK ISSN 1461-3808 print/ISSN 1469-9893 online, # 2012 Taylor & Francis, http://dx.doi.org/10.1080/14613808.2012.685456. http://www.tandfonline.com.

Amandeep Kaur

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

INTRODUCTION: 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 illness. 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. Therefore nutrition counseling is required to maintain health of the client which includes; diet, exercise & activity, behavior modification and managing acute side effects.

DEFINITION: Nutrition counseling is an ongoing process in which a health professional, usually a registered dietitian, works with an individual to assess his or her usual dietary intake and identify areas where change is needed.

FACTORS RELATED TO NUTRITIONAL DISTURBANCES

1. External and internal factors
2. Cancer related factors
3. Treatment related factors
4. External and internal factors: External factors 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 co morbid diseases. Much more research in this area is needed before individuals at risk can be reliably identified.
5. Cancer-related factors: The type of cancer 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 histologies are correlated with higher weight loss. Individuals with cancers of the aerodigestive (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.
6. Treatment related factors: All cancer therapies have the potential to cause nutritional deficiency. The magnitude of the treatment-related risk depends on the area of treatment, type of treatment, number of therapeutic modalities used, dosages of therapy used, and length of treatment. Surgery itself alters function. Major aero digestive resections may produce taste alterations, dysgeusia, or impaired swallowing, resulting in reduced intake. Radiation therapy can alter nutritional status by exerting both systemic and local effects. The extent of the alteration varies with the area of the body being treated, the size of the area being treated, and the duration of treatment. Radiation alters function in the treatment area and poses particular problems for patients with aero digestive or GI cancers. Acute effects are transient and include anorexia, diarrhea, bleeding, nausea, vomiting, weight loss, mucositis, esophagitis, gastritis, xerostomia, and changes in taste. Local desquamation reactions can temporarily increase energy needs. Some of these changes—especially xerostomia, taste changes, and diarrhea—can become chronic. Chemotherapy has a number of direct and indirect effects on nutrition. Direct effects include alteration of the absorptive surface of the GI tract, excitation of the chemoreceptor trigger zone and true vomiting center, and interference with specific metabolic and enzymatic reactions. The majority of chemotherapeutic agents, because of the damage they cause to frequently reproducing cells, alter the length and surface area of intestinal villi. The reduced ability of the gut to absorb nutrients and water production that results can induce diarrhea and malabsorption.

CANCER-INDUCED ALTERATIONS IN NUTRIENT INTAKE

• Appetite loss
• Constipation
• Diarrhea
• Dry mouth
• Nausea
• Sore throat and trouble swallowing
• Vomiting

 Appetite Loss refers to when you do not want to eat or do not feel like eating very much. One may have appetite loss for just 1 or 2 days, or throughout your course of treatment. Reasons may include: the cancer itself, fatigue, pain, feelings such as stress, fear, depression & anxiety, Cancer treatment side effects such as nausea, vomiting, or changes in how foods taste or smell.

Suggested intervention to improve appetite are: after food choice, increase oral hygiene; avoid sight, smell of food; eat sour foods; eat cold foods; use straw; increase seasoning; use plastic utensils; small amount of alcohol.

Constipation: Constipation occurs when bowel movements become less frequent and stools become hard, dry, and difficult to pass. Client may have painful bowel movements, feel bloated, or have nausea. Chemotherapy, the location of the cancer, pain medication, and other medicines can cause constipation. Increase liquid intake; eat more fiber; eat more fruit; exercise; take laxative; drink hot beverages; add bran to foods.

Diarrhea: Diarrhea occurs when there are frequent bowel movements that may be soft, loose, or watery. Foods and liquids pass through the bowel so quickly that body cannot absorb enough nutrition, vitamins, minerals, and water from them. Diarrhea can be caused by cancer treatments such as radiation therapy to the abdomen or pelvis, chemotherapy, or biological therapy.

Take medicine; increase fluids; drink rehydration fluids; low-residue diet; avoid spices and caffeine, avoid milk products; take soluble-fiber supplement; eat low-fat diet.

Dry Mouth: Dry mouth occurs when there is less saliva than it is used to. This can make it harder to talk, chew, and swallow food. Dry mouth can also change the way food tastes.  Chemotherapy and radiation therapy to the head or neck area can damage the glands that make saliva. Biological therapy and some medicines can also cause dry mouth.

Take prescribed medicine; increase fluids; chew gum; suck on sugarless candy; blend foods; avoid acid, salty, or spicy foods; moisten food, humidify air; apply oil to oral cavity.

Nausea: Nausea occurs when the client feels queasy or sick to stomach. It may be followed by vomiting (throwing up), but not always. Nausea can keep from getting the food and nutrients the client needs. Not everyone gets nausea and those who do may get it right after a treatment or up to 3 days later. Nausea almost always goes away once treatment ends. Nausea can be a side effect of surgery, chemotherapy, biological therapy, and radiation therapy to the abdomen, small intestine, colon, or brain. It can also be caused by certain types of cancer or other illnesses.

Take medicine; alter diet, practice relaxation; listen to music; rest after meals; avoid sight, smell of food; eat cold foods; increase oral hygiene; eat small frequent meals; eat slowly; get fresh air; drink clear liquids; keep busy/distracted; chew food well; drink between meals; eat crackers; breathe through the mouth: eat sour foods; eat low-fat foods; avoid spicy foods; eat sweet foods.

Sore Throat and trouble swallowing: Chemotherapy and radiation therapy to the head and neck can make the lining of your throat inflamed and sore. It may feel as if one has a lump in throat or that chest or throat is burning. There may also be trouble in swallowing. These problems may make it hard to eat and cause weight loss. Some types of chemotherapy and radiation to the head and neck can harm fast-growing cells, such as those in the lining of your throat.

Take prescribed medicine; apply cold (ice) to oral cavity during chemotherapy administration; increase oral hygiene; drink liquids; use soft toothbrush; avoid spicy food; humidify air, avoid use of gravy; use baking soda mouthwash; apply mucosa-adhesive film, avoid alcohol and tobacco; use straws; use supplements; use glutamine.

Vomiting: Vomiting is another way to say “throwing up.”Vomiting may follow nausea and be caused by cancer treatment, food odors, motion, an upset stomach, or bowel gas. Some people vomit when they are in places (such as hospitals) that remind them of cancer. Vomiting, like nausea, can happen right after treatment or 1 or 2 days later. Biological therapy, some types of chemotherapy, and radiation therapy to the abdomen, small intestine, colon, or brain can cause nausea, vomiting, or both. Often, this happens because these treatments harm healthy cells in your digestive tract.

Take prescribed medicine; practice relaxation; rest after meals; drink clear liquids; avoid sight, smell of food; eat slowly; eat crackers; eat cold foods; get fresh air; chew mint candy; eat room-temperature foods; alter diet; increase oral hygiene; eat small frequent meals; eat low-fat diet; avoid spicy foods.

PHYSICAL ACTIVITY

Physical inactivity can lead to muscle atrophy, contributing to loss of cardio respiratory fitness and fatigue. Weight loss that occurs secondary to catabolic activity or cytokine mediated changes in metabolism or corticosteroid use can also contribute significantly to decreased muscle mass. The structure and function of muscle and bone depend on physical activity combined with appropriate nutritional intake and a hormonal milieu that supports anabolism. An evolving body of knowledge supports the role of physical activity in enhancing a number of clinical outcomes. Improvements have been documented in functional capacity, fatigue, medication requirements, self-esteem, and mood, sense of control and well-being, and immunological parameters. Physical activities might include walking programs, stretching, and use of resistance bands, swimming, cycling, or dancing, as tolerated.

VERBAL COUNSELING AND EDUCATION

Verbal counseling can be extremely effective in assisting patients to choose calorie-dense foods and treat symptoms that interfere with oral intake. A number of self-care actions have been proposed for the treatment of cancer induced nutritional problems. Patient education material commonly includes interventions related to decreased appetite, nausea, vomiting, constipation, taste changes, and mucositis. Little research has explored the effectiveness of most of these actions. Of those studies that employed an experimental approach, the majority have included non pharmacological interventions. Much of what is suggested to patients regarding treatment of side effects is not based on scientific evidence or systematic review of patient experience. Moreover, some of the interventions are global in nature. For example, counseling and psycho educational approaches have benefited patients having nausea. However, the actual content of the counseling and psycho educational interventions has not been standardized, so research application is difficult. Much more research is needed before health personnel can accurately predict which intervention will prove effective for a specific patient in a given situation. Patients and their families may identify self-care activities that differ from those commonly suggested in the cancer patient education literature.

.

By Dr Bhajan Singh Lark

Retd Prof (Chemistry) Guru Nanak Dev University, Amritsar, Punjab, India.

 While solving problems in science we many a times have to use t– ratios say like sin of certain angle. Naturally we look up for tables of t- ratios. For example while solving problems on X- rays we have to use sin of certain angle in Bragg’s (1) equation

                                         nʎ = 2d sin θ.                                                         … (1)

But the table is not easily available. Here I am suggesting a method to find t- ratios of any angle quite precisely (and that too) without using a table.

This method involves mainly the following trigonometric corollary that:

Sin θ = θ when θ is small (θ is measured in radians)

How small should be the angle for this corollary to hold good.  It will depend upon as to how accurately we need the value of sin θ. Generally we use the value of sin θ correct up to 4 places of decimal which are given usually in mathematical tables(2). Here the procedure to determine the value of sin θ of any angle between 0-90^o is explained. 

We know that π radians equal to 180^o. Thus the value of a radian in degrees will depend upon the value of π. For accurate value of sin of an angle up to 4 places of decimal the value of π = 3.1416 1.e., taken up to 4 places of decimal works very well.

Understandably if 3.1416 (π) radians  =  180^o

Then one radian     = 180^o/3.1416 = 57.296^o.

Let us check if 1/10th of a radian which is equal to 5.7296^o rounded to = 5.730 is small or not for the above relation 1 to hold.

Sin of 0.1 radian should be equal to 0.1.  As given by scientific calculator, the value of sin 5.730 is 0.0998 which is the same as given in 4 fig. sin Tables. Thus the present method gives the value of sin 5.73, 0.0002 more than the table value. It is clear form table I that the sin of any angle up to 4^o agrees very well with that predicted by the relation 1.Further on this relation predicts a value a little higher than the table or the calculator i.e. real value of sin θ and as the angle becomes higher and higher, the deviation becomes bigger and bigger.

Table 1.  Comparison of predicted values of sin θ with the calculator values

Below I give a method to know the value of sin θ of any angle having θ greater than 4^o 

accurate up to 4 or more places after the decimal.  The method utilizes the following identities.

sin2θ = 2 sinθ cos θ  = 2sin θ (1-sin² θ)^0.5                               (2)

And when θ  is small, i.e. sin θ  = θ   therefore relation 2.

becomes,                   sin2θ = 2 θ (1- θ ²)^0.5                                                (3)

sin 3θ = 3sinθ – 4sin³θ =  3θ – 4θ³                                                  (4)

sin(A+B) = sinAcosB+ cosAsinB

=  A(1-B² )^0.5 + B(1-A² )^0.5                                                  (5)

sin(A-B) = sinAcosB- cosAsinB

=  sinA(1-B² )^0.5 + cosA (1-A² )^0.5                                   (6)

 The method involves breaking up the given angle so as to have angles smaller than preferably 3 or 4 degrees so that the sin value is known accurately up to 5 places of decimals and then by the help of any of the identity or of a judicious combination of these gives the sin of the desired angle . Let us find the value of sin 5. It can be arrived as follows,

                              sin 5 = 3sin (5/3) – 4sin³ (5/3)

Angle 5/3 when expressed in radians = 0.01745x 5/3

Thus 3 sin 5/3 degrees = 3x 0.01745x 5/3 =  5×0.01745= 0.08725

Similarly 4sin³ (5/3)   = 4x(0.01745)³  =  0.00002

                          And thus  sin 5 =  0.08725     –  0.00002

= 0.08723 = 0.0872 (up to 4 places of decimals) and this value agrees quite well with the table value of 0.0872 and the calculator value of .08716

Below we give calculated values for sin of certain angles and compare them with the table values and refer to the identity used.

Table 2

 For values of sin θ of angles higher than 10 degrees we can use the values of angles up to 10 degrees as given in table 2 and then use  any of the relations 3-5 given above.

For angles – 30,45,60,90 the values of sin θ are straight forward and for any angle near these angles, any of the standard relations

        sin(A+B) = sinA cosB + cos A sin B

 may be used.     Here (angle) A = 30, 45,60  i.e. the angles for which the  t values  are known and B is a small angle less than 4 for which sin θ = θ. When θ is in radians.

sin θ values for some selected angles are compared with the table values  in Table 3.

Table 3.

Thus we see that we can calculate quite precisely sin θ of any angle and certainly then we can calculate from that value, the value of any of the other t ratio/s of the given angle by usual trigonometric relations.

References:

1.States of Matter by B.S.lark and S. Joseph, Vishal Pub. Co. Jallandhar . 2011.

2.The Spectrum of Mathematics, by K.K. Gupta and others, Sharma Publications VII Edition,1997.

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

Note: Loadings < .30 have been omitted.

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

Table 2. Descriptive statistics for target measures

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

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.

References

Arbuckle, J. L. (2003). AMOS 5.0 Update to the AMOS user’s guide. Chicago, IL: Smallwaters.

Bakagiannis, S., & Tarrant, M. (2006). Can music bring people together? Effects of shared musical prefer­ence on intergroup bias in adolescence. Scandinavian Journal of Psychology, 47, 129–136.

Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238–246.

Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley.

Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136–162). Newbury Park, CA: Sage.

Byrne, B. M. (2006). Structural equation modeling with EQS: Basic concepts, applications, and programming (2nd ed.). Mahwah, NJ: Erlbaum.

Cattell, R. B., & Anderson, J. C. (1953a). The measurement of personality and behavior disorders by the I.P.A.T. music preference test. Journal of Applied Psychology, 37, 446–454.

Cattell, R. B., & Anderson, J. C. (1953b). The I.P.A.T. Music Preference Test of Personality. Champaign, IL: Institute for Personality and Ability Testing.

Chamorro-Premuzic, T., & Furnham, A. (2005). Personality and intellectual competence. Mahwah, NJ: Lawrence Erlbaum.

Chamorro-Premuzic, T., & Furnham, A. (2007). Personality and music: Can traits explain how people use music in everyday life? British Journal of Psychology, 98, 175–185.

Chamorro-Premuzic, T., Gomà-i-Freixanet, M., Furnham, A., & Muro, A. (2009). Personality, self-estimated intelligence and uses of music: A Spanish replication and extension using structural equa­tion modeling. Psychology of Aesthetics, Creativity, and the Arts, 3, 149–155.

Chamorro-Premuzic, T., Swami, V, Furnham, A., & Maakip, I. (2009). The Big Five personality traits and uses of music in everyday life: A replication in Malaysia using structural equation modeling. Journal of Individual Differences, 30, 20–27.

Christenson, P. G., & Roberts, D. F. (1998). It’s not only rock & roll: Popular music in the lives of adolescents. Cresskill, NJ: Hampton Press.

Costa, P. T., Jr., & McCrae, R. R. (1980). Influence of extraversion and neuroticism on subjective well-being: Happy and unhappy people. Journal of Personality and Social Psychology, 38, 668–678.

Costa, P. T., Jr., & McCrae, R. R. (1992). NEO PI-R and NEO-FFI professional manual. Odessa, FL: Psychological Assessment Resources, Inc.

Demorest, S. M., & Serlin, R. C. (1997). The integration of pitch and rhythm in musical judgment: Testing age-related trends in novice listeners. Journal of Research in Music Education, 45, 67–79.

DeNeve, K. M., & Cooper, H. (1998). The happy personality: A meta-analysis of 137 personality traits and subjective well-being. Psychological Bulletin, 124, 197–229.

Eerola, T., Himberg, T., Toiviainen, P., & Louhivuori, J. (2006). Perceived complexity of western and African folk melodies by western and African listeners. Psychology of Music, 34, 341–375.

Eerola, T., Louhivuori, P., & Lebaka, E. (2009). Expectancy in Sami yoiks revisited: The role of data-driven and schema-driven knowledge in the formation of melodic expectations. Musicae Scientiae, 12, 231–272.

Eysenck, H. J., & Eysenck, M. W. (1985). Personality and individual differences: A natural science approach. New York: Plenum Press.

Gans, H. J. (1974). Popular culture and high culture: An analysis and evaluation of taste. New York: Basic Books.

Gregory, A. H., & Varney, N. (1996). Cross-cultural comparisons in the affective response to music. Psychology of Music, 24, 47–52.

Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55.

IFPI. (2009, January). Digital Music Report 2009. Retrieved 20 October 20 2009, from http://www.ifpi.org/content/library/DMR2009. pdf

Juslin, P. N., & Laukka, P. (2003). Communication of emotions in vocal expression and music perfor­mance: Different channels, same code? Psychological Bulletin, 129, 770–814.

Juslin, P. N., & Laukka, P. (2004). Expression, perception, and induction of musical emotions: A review and a questionnaire study of everyday listening. Journal of New Music Research, 33, 217–238.

Juslin, P. N., & Sloboda, J. A. (2001). Music and emotion: Theory and research. Oxford: Oxford University Press.

Krumhansl, C., & Keil, F. C. (1982). Acquisition of the hierarchy of tonal functions in music. Memory & Cognition, 10, 243–251.

Little, P., & Zuckerman, M. (1986). Sensation seeking and music preferences. Personality and Individual Differences, 7, 575–577.

Lonigan, C. J., Hooe, E. S., David, C. F., & Kistner, J. A. (1999). Positive and negative affectivity in children: Confirmatory factor analysis of a two-factor model and its relation to symptoms of anxiety and depres­sion. Journal of Consulting and Clinical Psychology, 67, 374–386.

Lonigan, C. J., Richey, J. A., & Phillips, B. P. (2002, November). Factor Structure of the Positive Affect Negative Affect Schedule (PANAS) in a child, adolescent and young adult sample. Poster Paper pre­sented at the annual convention of the Association for the Advancement of Behavior Therapy (AABT), Reno, NV.

Lucas, R. E., & Fujita, F. (2000). Factors influencing the relation between extraversion and pleasant affect. Journal of Personality and Social Psychology, 79, 1039–1056.

McCown, W., Keiser, R., Mulhearn, S., & Williamson, D. (1997). The role of personality and gender in preferences for exaggerated bass in music. Personality and Individual Differences, 23, 543–547.

Miranda, D., & Claes, M. (2009). Music listening, coping, peer affiliation and depression in adolescence. Psychology of Music, 37, 215–233.

Mitte, K., & Kämpfe, N. (2008). Personality and the four faces of positive affect: A multitrait-multimethod analysis using self- and peer-report. Journal of Research in Personality, 42, 1370–1375.

Mulaik, S. A., James, L. R., van Alstine, J., Bennett, N., Lind, S., & Stilwell, C. D. (1989). Evaluation of goodness-of-fit indices for structural equation models. Psychological Bulletin, 105, 430–445.

North, A. C., & Hargreaves, D. J. (1999). Music and adolescent identity. Music Education Research, 1, 75–92.

North, A. C., Hargreaves, D. J., & Hargreaves, J. J. (2004). Uses of music in everyday life. Music Perception, 22, 41–77.

North, A. C., Hargreaves, D. J., & O’Neill, S. A. (2000). The importance of music to adolescents. British Journal of Educational Psychology, 70, 255–272.

Rana, S. A., & North, A. C. (2007). The role of music in everyday life among Pakistanis. Music Perception, 25, 59–73.

Rentfrow, P. J., & Gosling, S. D. (2003). The do re mi’s of everyday life: The structure and personality cor­relates of music preferences. Journal of Personality and Social Psychology, 84, 1236–1256.

Rentfrow, P. J., & Gosling, S. D. (2006). Message in a ballad: The role of music preferences in interpersonal perception. Psychological Science, 17, 236–242.

Saarikallio, S. (2008). Cross-cultural investigation of adolescents’ use of music for mood regulation. In Proceedings of the 10th International Conference on Music Perception and Cognition, August 25–29, 2008, Sapporo, Japan.

Saarikallio, S., & Erkkilä, J. (2007). The role of music in adolescents’ mood regulation. Psychology of Music, 35, 88–109.

Scherer, K. R. (1997a). Profiles of emotion-antecedent appraisal: Testing theoretical predictions across cultures. Cognition and Emotion, 11, 113–150.

Scherer, K. R. (1997b). The role of culture in emotion-antecedent appraisal. Journal of Personality and Social Psychology, 73, 902–922.

Scherer, K. R., & Wallbott, H. G. (1994). Evidence for universality and cultural variation of differential emotion response patterning. Journal of Personality and Social Psychology, 66, 310–328.

Schmukle, S. C., Egloff, B., & Burns, L. R. (2002). The relationship between positive and negative affect in the Positive and Negative Affect Schedule. Journal of Research in Personality, 36, 463–475.

Schwartz, K. D., & Fouts, G. T. (2003). Music preferences, personality style, and developmental issues of adolescents. Journal of Youth and Adolescence, 32, 205–213.

Soto, C. J., John, C. P., Gosling, S. D., & Potter, J. (2008). The developmental psychometrics of Big Five self-reports: Acquiescence, factor structure, coherence, and differentiation from ages 10 to 20. Journal of Personality and Social Psychology, 94, 718–737.

Tarrant, M., North, A. C., & Hargreaves, D. J. (2000). English and American adolescents’ reasons for lis­tening to music. Psychology of Music, 28, 166–173.

Tekman, H. G., & Hortaçsu, N. (2002). Music and social identity: Stylistic identification as a response to musical style. International Journal of Psychology, 37, 277–285.

Watson, D., & Clark, L. A. (1984). Negative affectivity: The disposition to experience aversive emotional states. Psychological Bulletin, 96, 465–490.

Watson, D., & Tellegen, A. (1985). Toward a consensual structure of mood. Psychological Bulletin, 98, 219–235.

Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS Scales. Journal of Personality and Social Psychology, 47, 1063–1070.

Zillmann, D., & Gan, S. (1997). Musical taste in adolescence. In D. J. Hargreaves & A. C. North (Eds.), The social psychology of music (pp. 161–187). Oxford: Oxford University Press.

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

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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.
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Reference:
http://www.investopedia.com/terms/m/multi-level-marketing.asp
https://arnelzion.wordpress.com/2014/04/02/network-marketing-new-mantra/