Music training and the academic achievement of Hong Kong students

Abstract

Many Hong Kong Chinese parents believe that music training enhances the academic achievement of their children. The current study investigates the relationship between the extent and outcome of students’ music training, their perceptions of the value of the subjects they study and their academic achievement. A total of 286 students in Primary 4, 5 and 6 from one school in Hong Kong reported the extent and outcome of their music training, including the number of instruments they studied, the number of years spent training, highest grade and highest level achieved. For value, students completed a subscale of the Achievement Task Value Questionnaire to measure their liking and interest, perceived importance and usefulness of their school subjects. A nested structural equation model showed that, for both boys and girls, the extent and outcome of music training positively predicts academic achievement in Chinese, English and mathematics. Furthermore, the model shows that for both boys and girls, students’ perceived value of their school subjects negatively predicts academic achievement in Chinese, and age has a direct and negative effect on mathematics achievement. For girls, age positively moderates the extent and outcome of music training on academic achievement whereas the moderator effect of age on students’ perceived value of academic subjects is non-significant. For boys, in contrast, the moderator effect of age on extent and outcome of music training is non-significant, whereas its effect on students’ perceived value of academic subjects is negative. In broad terms, the results show that parents are justified in believing that music training is positively related to academic achievement. However, the results differ for boys and girls in connection to the role of age in this relationship.

Keywords

academic achievement age Hong Kong students motivation music training

There is a growing interest amongst researchers on the relationship between children’s musical education and their academic success, including Hong Kong Chinese students (Leung & McPherson, 2010, 2011; Tai, Phillipson & S. Phillipson, 2018a; 2018b). On the one hand, research suggests that musical education is positively linked to academic achievement. For example, several recent reviews of the research have concluded that there are clear links between musical education and general academic achievement (Hallam & MacDonald, 2013; Wetter, Koerner, & Schwaninger, 2009).

On the other hand, commentators in Hong Kong are increasingly vocal regarding the negative effects of extracurricular activities on Hong Kong’s students. They argue that parental preoccupation with activities such as music training is excessive and detrimental (cf. Kammerer, 2016; Wu, 2013; Zhao, 2014). As argued by Zhao, many parents “push” their children into learning music in order to add to their child’s “achievement” portfolio, thereby supporting their child’s enrolment application in their preferred schools. For other parents, music training is believed to enhance their children’s “mental, motor and academic development” (Choi, Tse, Ni So, & Yeung, 2005, p. 118), or that it makes their children “smarter” (Chen-Hafteck, 2013).

It has become a truism that Chinese parents, including those from Hong Kong, have high expectations for their children’s academic achievement. A large corpus of research has focused on the Confucian basis of these expectations as a way to explain the exceptional achievements of students from East Asia in international tests such as PISA and TIMSS (Chen & Wong, 2014; E. Ho & Kwong, 2013; Leung & McPherson, 2011; Phillipson, Stoeger & Ziegler, 2013; Tao, 2016; Tao & Hong, 2014; To, 2013). As well as their role in communicating values such as filial piety, parents in Confucian-heritage cultures generally believe that they are actively responsible for their children’s development and not just wishing their son to be a “dragon” (龍) or their daughter a “phoenix” (鳳) (Tai & Phillipson, 2012).

The focus of the current research is to test the predictive relationship between students’ music training and their academic success. Moreover, our emphasis is on the experiences of Hong Kong Chinese students studying in schools that have adopted the Hong Kong curriculum. Specifically, we model the relationship between students’ perceived value of academic subjects, the extent and outcome of their music training on their academic achievement. In focusing on students in Hong Kong, we note that music training is a particular form of musical education, and argue that the Hong Kong educational milieu is unique in that the cultural context is relatively homogenous. Hence, our findings cannot ignore the context of the Hong Kong Chinese culture.

We begin with a brief outline of the research linking the variability in musical training with academic success, drawing on international research as well as the Hong Kong context. Next, we outline the variability in musical training in Hong Kong as the basis for the research in the current study. Finally, we describe the methods used in the current study and our findings.

Music training and academic achievement

A seminal study investigating the relationship between musical participation and academic achievement amongst middle-school US students found that student participation in either band or choral ensembles was positively related to achievement scores in mathematics, science and reading (Kinney, 2008). Furthermore, Kinney found that the effects are partially accounted for by students’ socio-economic status. Based on the analytical techniques used in the research, Kinney could not establish predictive relationships, concluding that high achieving students could be attracted to musical ensembles.

Similarly, several studies showed that musical ability could predict linguistic skills in second language learning. Slevc and Miyake (2006) focused on 50 Japanese adult learners of English, and found a relationship between musical ability and second language skills in receptive and productive phonology, showing that musical expertise is associated with second language learning. Furthermore, Milovanov and colleagues (Milovanov, Huotilainen, Valimaki, Esquef, & Tervaniemi, 2008; Milovanov & Tervaniemi, 2011) established correlations between musical skills and linguistic ability.

Several recent research reviews generally concluded that there are both direct and indirect links between musical education and general academic achievement (e.g., Wetter et al., 2009). In a preamble to a special issue devoted to current perspectives in music psychology and education research, Hallam and MacDonald (2013) concluded that the relationship between mathematics achievement and music “engagement” (p. 84) is equivocal, however, with the relationship dependent on the type of music training and the type of mathematics problem. For example, children learning rhythm instruments score better on part–whole mathematics problems compared to other types of musical learning.

The link between music education and enhanced cognitive performance represents an indirect link between music training and academic achievement. As Hallam and MacDonald (2013) pointed out, the association between music training and enhanced spatial reasoning appears “strong and reliable” (p. 84). Other research has also investigated the indirect link between music education and enhanced cognitive performance (Costa-Giomi, 2015; Moreno et al., 2011; Schellenberg, 2004, 2011a, 2011b). Although these studies generally showed correlations between music education and intelligence, they did not explicitly establish causal relationships between music education and intelligence.

From a neuroscientific perspective, Miendlarzewska and Trost (2013) concluded that the effects of music education enhanced the “near transfer skills” (p. 5), including fine motor skills, listening skills, temporal processing and attentional orientation. In addition, music education enhanced “far transfer skills” (p. 5), including verbal memory, listening and reasoning, social skills and the executive functions of general IQ. Again, Miendlarzewska and Trost cautioned that it is difficult to establish a cause and effect relationship between music education and any of these skills because of the correlational nature of the research design.

In a review focusing specifically on the impact of music education on intelligence and cognitive abilities, Costa-Giomi (2015) concluded that there are short-term effects on cognitive abilities but little evidence to establish long-term effects on intelligence. Of importance—according to Costa-Giomi—are the factors that lead to musical education in the first place, and the impact of practice.

Although the cited research did not specifically mention the “Chinese” context, Costa-Giomi (2015) referred to the importance of “mediating variables” that may explain the impact of musical education on intelligence. These variables include those that relate to the child and include initial interest in music lessons, perseverance for music lessons, levels of achievement and personality. Variables that relate to the family include family income, parental education, family structure and, of course, the provision of an initial opportunity (Costa-Giomi, 2015).

At first glance it appears that Chinese Hong Kong parents are justified in their belief regarding the value of their children’s music training in enhancing their children’s academic achievement. To date, however, few studies have established predictive relationships between music training and academic achievement. The next section describes recent research focusing on music in the Hong Kong context.

“Music” variability in Hong Kong

Music plays a role in both the Hong Kong curriculum as well as the wider community. For students moving from primary to secondary school, several recent studies have highlighted an apparent paradox between its diminishing place in the regular curriculum and its importance as an extracurricular activity (see Leung & McPherson, 2010, 2011; Tai et al., 2018a; 2018b). Furthermore, research has begun to understand students’ and parents’ motives toward the study of music. This section focuses solely on music from a student’s perspective.

The majority of schools in Hong Kong have adopted the Hong Kong educational curriculum, including those schools identified as Government Schools, Aided Schools and Direct Subsidy Schools, with students up to Secondary 3 required to study music as part of the regular school curriculum (Education Bureau, 2013). However, the number of students studying music declines steadily after Secondary 3 (Leung & McPherson, 2010; Tai et al., 2018a; 2018b). In parallel with the regular curriculum is a robust industry focusing on the provision of music education outside of school. Although the precise number is unknown, recent estimates place the number of secondary students studying music outside of school as high as 21% (Tai et al., 2018a; 2018b).

There is a growing body of research focusing on musical education in Hong Kong (Chen-Hafteck, 2013; Choi et al., 2005; W. C. Ho, 2009; Leung & McPherson, 2010, 2011; Tai et al., 2018a; 2018b). In particular, Leung and McPherson (2011) interviewed high achieving music students regarding their motivations to learn music. Although Leung and McPherson did not distinguish between school-based or extracurricular music education, they concluded that students’ musical development began on the suggestion of their parents and continued in order to meet their parents’ expectations. In some instances, children reported being forced to continue against their wishes.

Leung and McPherson’s (2011) findings may not be generalisable to the wider population for two reasons: first, the students were identified using the criteria of high grades in the subject music and outstanding achievement in learning, and instrument and ensemble playing. Second, schools were asked to nominate students to be interviewed. Clearly, the views of high achieving students may not represent the views of “average” students, and the nomination process itself would bias against those students with strongly adverse views towards music.

In a related research, Hallam (2013) found that amongst a cohort of committed instrumental students in the UK, enjoyment of musical activities (p. 185) was the highest motivation for their studies, followed by attitudes toward their instrument and the value of music. Furthermore, Hallam found gender-based differences, with boys enjoying music more than girls. Similar to the Leung and McPherson (2011) study, students were nominated by their teachers according to the researcher’s criteria and so may not be representative of all instrumental learners.

A complete census that provides information regarding the instruments studied by Hong Kong’s students appears to be non-existent. Leung and McPherson’s (2011) research on musical participation of high achievers reported that the most popular instruments were piano and string instruments, with many of them learning more than two instruments. Similarly, Choi et al. (2005) reported that most students in their study received instructions in piano, violin, wind instruments and/or choir.

A study involving a convenience sample of 20 parents and their children in Hong Kong showed that the range of instruments studied includes Western instruments such as piano, clarinet, double bass, singing, flute, handbell, recorder, celesta, xylophone and Western percussion (W. C. Ho, 2009). The range of Chinese instruments studied includes pipa, liuqin, dizi, Chinese percussion, erhu and guzheng. Many students had studied more than one instrument, with the maximum being five. Furthermore, W. C. Ho (2009) reported that 4 out of 21 (19%) students had studied more than three instruments, albeit not always simultaneously. Similarly, Leung and McPherson (2011) reported that at least one of the 24 students (> 4%) had studied up to four instruments. Again, Leung and McPherson did not distinguish between instruments that had once been studied and dropped and those currently being studied.

W. C. Ho’s (2009) research also documented the length of time that students had studied their instrument, with the period ranging from a few months to 7 years. However, it is difficult to draw any firm conclusions from the data given that the ages of the children ranged from 8 to 18 years. Finally, Ho did not report any information regarding the grade level or level of attainment reached by the students in their instruments.

The distinction between the study of music in the regular curriculum and music as an extracurricular activity is important. The experiences of these students outside of their regular school has been referred to using a number of terms, including “informal education” (Choi et al., 2005), “community music education” (Leung & McPherson, 2010, 2011), “out-of-school experience” (W. C. Ho, 2009) and “music training” (Tai et al., 2018a; 2018b). Whatever the term used, the industry operates in parallel with the schools to provide students with a range of “musical” opportunities, including dance, choir, drama and music tuition, for example. For reasons outlined in Tai et al. (2018a; 2018b), the current research uses the term music training to refer to music as an extracurricular activity.

Recently, Tai et al. (2018b) reported the validation of the Achievement Task Value Questionnaire (ATVQ) (Leung & McPherson, 2010; McPherson & O’Neill, 2010) with a cohort of Hong Kong Chinese students. The ATVQ is based on the expectancy-value theory articulated by Eccles and her colleagues (Eccles, 1983; Eccles, O’Neill, & Wigfield, 2005; Eccles, Wigfield, & Schiefele, 1998; Wigfield & Cambria, 2010). In using Rasch modelling (Rasch, 1980) to establish the measurement properties of the ATVQ, Tai et al. (2018b) showed that the instrument can distinguish between students’ interest in, perceived importance of and usefulness of music in comparison to other subjects such as mathematics, thereby extending the utility of the instrument beyond that reported in Leung and McPherson (2010).

Culture and academic success

Culture is an important environmental “engine” for academic achievement (King & McInerney, 2014). Within Hong Kong, Confucianism is the dominant cultural influence, followed by Buddhism and Taoism (Sun, 2008). Hence, it is likely that culture plays a role in children’s motivations to learn music. Taking a broad sociocultural perspective (S. Phillipson, Ku, & Phillipson, 2013; S. Phillipson & Yick, 2013), this influence occurs indirectly via parents and other close members of the family, and/or directly on the child.

In Hong Kong, the influence of Confucianism in explaining the exceptional achievements of East Asian students has been explored within a sociocultural perspective (S. Phillipson et al., 2013; Phillipson et al., 2013). Recent research, however, has started to examine more closely the specific aspects of Confucian culture on Hong Kong students’ motivations and achievement. For example, the important role of filial piety in academic achievement is beginning to be investigated using structural equation modelling. Chen and Wong (2014) found that for university students from Macau and Hong Kong, reciprocal views of filial piety¹ were associated with incremental beliefs of intelligence and, ultimately, academic achievement. Similarly, Chinese university students from Singapore and Macau viewed academic achievement as an obligation to their parents, helping to explain their feelings of guilt and sense of failure when unsuccessful (Tao & Hong, 2014). These findings help to explain students’ socially-oriented motivations for academic achievement (SOAM) (Tao, 2016).

The motivations of Hong Kong’s secondary school students appear to have a similar origin. Hui, Sun, Chow, and Chu’s (2011) research involving the students from one secondary school in Hong Kong concluded that filial piety played an important role in predicting academic success. Given the core role played by filial piety in the behaviours of Chinese children (Ryan, 2013; To, 2013), it is reasonable to expect that the influence of filial piety would include music training. However, a search of the published research to date showed that this line of research is yet to be developed. Future research should address the influence of parental factors in the relationship between a child’s music training and academic achievement.

In linking Hong Kong students’ motivation to study music (Leung & McPherson, 2011) with the Confucian context, Chinese Hong Kong students appear to begin their music training in response to their parents’ wishes. Their obligation to please their parents, “powers” their training until either the parent no longer desires music training, or the child’s expertise sustains both the child’s and their parent’s interest and motivation for music.

Approach in current research

In broad terms, the current research investigates the general belief amongst Hong Kong Chinese parents that music training enhances their children’s academic achievement. In particular, we test the hypothesis that the extent and outcome of music training positively predicts academic achievement. We also test the hypothesis that student motivation values (basically how they find academic subjects interesting, useful and important) affect their academic achievement.

In testing the hypothesis, we use structural equation modelling (SEM) to model the relationships between student music training and motivation variables in predicting academic achievement in three academic subjects. Given some evidence for time-effects (Costa-Giomi, 2015) and gender-based differences in the perceptions of the perceived importance and usefulness of music (McPherson, Osborne, Barrett, Davidson, & Faulkner, 2015), we also investigate differences in the relationships based on the age and gender of the students. Details of the methodology used in investigating the hypothesis of this study are described next.

Methods

Participants

All students in Primary 4 to 6 from one aided primary school in the mid-Kowloon area were invited to participate in the study. The school is one of the 572 primary schools in Hong Kong, and, therefore, care must be taken when attempting to generalise the findings to all Hong Kong Chinese students. However, the impact of school culture on academic achievement is minimised when participants are derived from the same school.

In accordance with the Hong Kong school music curriculum, all students at this school are required to learn the recorder. The school also offers students opportunities to participate in the school’s three choirs, with each choir taking up to 60 students at any one time. However, the school allows the school facilities to be used by students and their tutors for their music training, with any fees being paid directly to the tutor. All music training at this school or elsewhere occurs outside of the regular school day.

Ethics approval was obtained for this research with the authors’ university. Following parental and school consent, students participated as fully informed and consenting volunteers.

Instruments

Students responded to a questionnaire asking for their date of birth, gender (male or female) and grade at school (Primary 4, 5 or 6). Information regarding their date of birth was used to calculate their age (years) at time of testing. Student gender was coded as 1=Boy and 2=Girl.

The school provided the most up-to-date examination results in mathematics, English and Chinese with results reported as a percentage. These raw scores were not transformed but used as reported. Pre-allocated codes were used in order to avoid the possibility of identifying the students.

Students also responded to questions regarding the extent and outcome of their music training, including:

Are you participating in musical training? If yes, what musical training do you have? Options include no musical training, piano/organ, guitar, string (violin, viola, cello, etc.), woodwind (flute, clarinet, saxophone, etc.), brass (trumpet, trombone, etc.), percussion, Chinese instrument (dizi, erhu, guzheng, etc.) or “other”. (Note that students were asked to refer to the instruments they studied outside of their regular school.) The total number of instruments they studied was calculated and used in SEM. The possible responses for this question ranged from 0 to 6.

At what age did you start to participate in music training? Options include Age 2 years or below, Age 3–5 years, Age 6–8 years, Age 9–11 years, and Age 12 and above. For SEM, responses were coded as 1, 2, 3, 4 and 5, respectively.

How many years in total have you had music training? Options include One year or below, 2–3 years, 4–5 years, 6–7 years, and 8 years and above. For SEM, responses were coded as 1, 2, 3, 4 and 5, respectively. In modelling the responses to age first started and number of years of music training, we note that the responses will co-vary.

What is the highest grade of instrumental exam that you have participated in with a result? (For example, Associated Board of the Royal Schools of Music (ABRSM) or Trinity College London examinations.) Options include Not applicable, Grades 1–2, Grade 3–4, Grade 5–7, and Grade 8 or above. For SEM, responses were coded as 1, 2, 3, 4 and 5, respectively.

What is the highest level (result) of the graded music examination? Options include Not applicable, Fail, Pass, Merit, Distinction. For SEM, responses were coded as 1, 2, 3, 4 and 5, respectively.

The ATVQ distinguishes between student perceptions of their interest in, importance of, and perceived usefulness of music in comparison with other subjects (Tai et al., 2018b). Following an examination of its measurement properties using Rasch modelling (Author 1 et al.), the final form of the instrument consisted of 10 items, each with six options relating to the subjects they study at school, including Music, Chinese, PE/Gym, Math, English, Art. See Appendix 1 for details.

Procedures

Information and consent forms were sent to parents via their children after approval was obtained from the school’s Principal. After signed consent was obtained, pre-coded copies of the Chinese version of the ATVQ and other instruments were completed during school at a time agreed to by their teachers. The first author explained the questionnaire and assisted the students when necessary. However, students completed the ATVQ independently of each other and returned the questionnaire directly to the researcher. Responses were collated onto a spreadsheet for further initial checking.

Analysis

In the first instance, descriptive statistics were generated for student age and academic achievement, and the responses relating to the extent and outcome of music training. Although tabulated against gender and grade level, no attempt was made to identify significant differences in the responses in this descriptive analysis. However, the responses to the variables related to the extent and outcome of music training were summed and coded as relevant, and used in further analysis.

After establishing the fit of the responses from the ATVQ to the Rasch model, the next step in the analysis was to obtain estimates of person ability for each of the three subscales using the software program Winsteps (v.3.74.0). These estimates of ability are based on an interval-level measurement scale and, hence, assist in fulfilling the assumptions of parametric tests of significance and SEM.

Two main comparative analyses were completed. First, independent samples t-tests were conducted to compare the functions of variables between boys and girls in this study. Effect sizes in the form of Cohen’s d were computed for each of the variable t-tests output.

Second, a nested structural equation model for boys and girls was built using AMOS version 20.0.1 (Arbuckle, 2011) and maximum likelihood estimate method. Traditional and non-traditional model fit indices reported include non-significant χ² (p > .001), with a ratio values of less than 2.00 in a χ²/df, Goodness of Fit Index (GFI), Comparative Fit Index (CFI), the Root Mean Square Error of Approximation (RMSEA) and PCLOSE. These indexes were chosen because they provide stringent measures of fit in consideration of sample variances and have been frequently quoted as the sufficient indicators of fit along with the traditional chi-squares (see Hu & Bentler, 1995). The nested SEM model for boys and girls represented the relationships between student motivation values as one latent variable predicting academic achievement scores, and student music training as the other latent variable predicting academic achievement scores. Age of students was added into the model as the moderating variable onto both student motivation values and student music training.

Results

Of the 421 students invited to complete the ATVQ, a complete data set consisting of 286 student responses was obtained, including 133 boys and 153 girls. The distribution of students across each of the three grade levels, their gender and mean (SD) age are reported in Table 1. Almost 60% of the respondents in Primary 4 are girls, with the proportions approaching 50% for respondents from Primary 5 and 6. Overall, the proportion of boys responding to the questionnaire was 47%, with girls being 53%. The mean (SD) age of the boys and girls appears to be distributed uniformly.

Table 1.

Gender, age distribution and number of instruments studied by students responding to the Achievement Task Value Questionnaire (N = 286).

	Grade level
	Primary 4		Primary 5		Primary 6		Total
	Boys	Girls	Boys	Girls	Boys	Girls	Boys	Girls
Mean (SD) age (years)	10.3 (.4)	10.4 (.4)	11.4 (.5)	11.3 (.3)	12.6 (.5)	12.4 (.5)	11.5 (1.0)	11.3 (1.0)
N (%)	38 (40%)	57 (60%)	43 (49%)	45 (51%)	52 (50%)	51 (50%)	133 (47%)	153 (53%)
Number of instruments studied^a	Boys	Girls	Boys	Girls	Boys	Girls	Total	%
0	3	3	4	2	5	1	18	6.3
1	20	24	16	19	24	15	118	41.3
2	11	17	16	16	16	16	92	32.2
3	2	11	4	8	5	14	44	15.4
4	2	2	3	0	2	3	12	4.2
5	0	0	0	0	0	2	2	0.7
6	0	0	0	0	0	0	0	0.0
Total	38	57	43	45	52	51	286	100

Students were asked to report the number the instruments that they studied.

Descriptive results

Number of instruments studied

The number of instruments studied by students ranged from 0 to 5, with the modal number being one instrument (41%), followed by two (32%), three (15%), and four instruments (4%) (Table 1). Less than one per cent of students studied five instruments and 6% reported that they did not study an instrument. It is important to note that when asking students how many instruments they studied, we could not determine whether the instruments were being learned simultaneously. Nevertheless, the results are in broad agreement with the findings of W. C. Ho (2009) and Leung and McPherson (2011), where 19% students had studied at least three instruments and at least 4% had studied up to four instruments. Although not formally tested, the number of instruments studied by boys did not appear to differ to that studied by girls, except for three instruments where nearly three times as many girls (22%) studied three instruments compared to boys (8%).

The most common instrument studied is a woodwind instrument (38%), followed by piano/organ (16%), voice (14%), stringed instrument (12%), followed by percussion (10%) and Chinese instruments (6%) (see Table 2). Finally, 3% of students studied guitar, with brass instruments being the least studied (2%).

Table 2.

Instruments studied by boys and girls at Primary 4, 5 and 6.

		Grade
		Primary 4		Primary 5		Primary 6		Total (%)
		Boys (%)	Girls (%)	Boys (%)	Girls (%)	Boys (%)	Girls (%)
Type of instrument studied	Piano/organ	7 (18.4)	17 (29.8)	12 (27.9)	12 (26.7)	11 (21.2)	20 (39.2)	79 (15.9)
	Guitar	0 (0)	4 (7.0)	3 (7.0)	3 (6.7)	2 (3.8)	2 (3.9)	14 (2.8)
	String	9 (23.7)	9 (15.8)	7 (16.3)	7 (15.6)	9 (17.3)	16 (31.4)	57 (11.5)
	Woodwind	19 (50.0)	39 (68.4)	27 (62.8)	26 (57.8)	41 (78.8)	36 (70.6)	188 (37.8)
	Brass	2 (5.3)	0 (0)	2 (4.7)	2 (4.4)	4 (7.7)	2 (3.9)	12 (2.4)
	Percussion	7 (18.4)	11 (19.3)	5 (11.6)	5 (11.1)	4 (7.7)	15 (29.4)	47 (9.5)
	Chinese instrument	7 (18.4)	7 (12.3)	6 (14.0)	6 (13.3)	2 (3.8)	2 (3.9)	30 (6.0)
	Voice	5 (13.2)	12 (21.1)	15 (34.9)	14 (31.1)	6 (11.5)	18 (35.3)	70 (14.1)
	Other	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0.0)
	Total	56	99	77	75	79	111	497 (100)

Note. Students were asked to report what instruments they studied outside of their regular school. Totals (and percentages) exceed the number of students (and 100%) because many students studied more than one instrument (see Table 1).

The instruments that are studied appear to differ across gender. In terms of instruments studied, the major discrepancies are piano/organ, where 23% of boys report that they study this instrument compared to 32% of girls (Table 2). Furthermore, 20% of boys report that they study voice, compared to 29% of girls, and 6% of boys study brass compared to 3% of girls. Finally, 12% of boys report that they study percussion, compared to 20% of girls.

On the other hand, the proportions of students studying guitar are similar, with the proportion of boys being 4% of boys compared to 6% of girls (Table 2). In terms of stringed instruments, 65% of boys report that they studied this instrument compared to 66% of girls. Finally, 11% of boys report that they study Chinese instruments compared to 10% of girls.

The instruments that students study appear to differ across Grade level (Table 2). More boys (24%) study stringed instruments than girls (16%) in Primary 4. However, more girls (30%) study piano/organ, woodwind (68%) and voice (21%), compared to boys with proportions of 18%, 50% and 13%, respectively.

The proportions in Primary 5 are very similar, with values of 28% of boys and 27% of girls reporting that they study piano/organ, for example (Table 2). In Primary 6, however, the proportions of girls studying piano/organ (39%), stringed instruments (31%), percussion (29%) and voice (35%) exceed the proportion of boys studying the same instrument, with values of 21%, 17% and 12%, respectively. No attempt was made to verify the statistical significance of these differences.

Age first began music training

Summary statistics of student self-reports of the age they first began music training are shown in Table 3. The most common age for boys in Primary 4 and 5 is between 6–8 years, with proportions ranging from 58% and 54%, respectively. For 42% of boys in Primary 6 the most common age is 9–11 years. However, between 23% and 26% of boys report the first age as being between 3–5 years.

Table 3.

Student self-reports of the extent and outcomes of their Music Training.

	Response categories	Grade level
	Response categories	Primary 4		Primary 5		Primary 6
		Boys (%)	Girls (%)	Boys (%)	Girls (%)	Boys (%)	Girls (%)
Age first began MT	< 2years	1 (2.6%)	1 (1.8%)	0 (0%)	1 (2.2%)	0 (0%)	1 (2.0%)
	3–5years	10 (26.3%)	20 (35.1%)	10 (23.3%)	13 (28.9%)	12 (23.1%)	18 (35.3%)
	6–8years	22 (57.9%)	31 (54.4%)	25 (58.1%)	22 (48.9%)	18 (34.6%)	23 (45.1%)
	9–11years	5 (13.2%)	5 (8.8%)	8 (18.6%)	9 (20%)	22 (42.3%)	8 (15.7%)
	> 12years	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (2.0%)
	N (%)	38 (100%)	57 (100%)	43 (100%)	45 (100%)	52 (100%)	51 (100%)
Number of yearsof MT	NA	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (2.0%)
	< 1year	8 (21.1%)	6 (10.5%)	2 (4.7%)	5 (11.1%)	4 (7.7%)	2 (3.9%)
	2–3years	16 (42.1%)	22 (38.6%)	17 (39.5%)	17 (37.8%)	24 (46.2%)	10 (19.6%)
	4–5years	10 (26.3%)	20 (35.1%)	18 (41.9%)	14 (31.1%)	8 (15.4%)	14 (27.5%)
	6–7years	3 (7.9%)	9 (15.8%)	4 (9.3%)	8 (17.8%)	12 (23.1%)	15 (29.4%)
	> 8years	1 (2.6%)	0 (0%)	2 (4.7%)	1 (2.2%)	4 (7.7%)	9 (17.6%)
	N (%)	38 (100%)	57 (100%)	43 (100%)	45 (100%)	52 (100%)	51 (100%)
Highest grade completed	NA	28 (73.7%)	31 (54.4%)	22 (51.2%)	25 (55.6%)	31 (59.6%)	24 (47.1%)
	Grade1–2	7 (18.4%)	15 (26.3%)	2 (4.7%)	8 (17.8%)	7 (13.5%)	7 (13.7%)
	Grade 3–4	3 (7.9%)	4 (7.0%)	15 (34.9%)	8 (17.8%)	6 (11.5%)	8 (15.7%)
	Grade 5–7	0 (0%)	5 (8.8%)	4 (9.3%)	4 (8.9%)	7 (13.5%)	7 (13.7%)
	> Grade 8	0 (0%)	2 (3.5%)	0 (0%)	0 (0%)	1 (1.9%)	5 (9.8%)
	N (%)	38 (100%)	57 (100%)	43 (100%)	45 (100%)	52 (100%)	51 (100%)
Highest level achieved	NA	28 (73.7%)	31 (54.4%)	22 (51.2%)	25 (55.6%)	31 (59.6%)	24 (47.1%)
	Fail	0 (0%)	0 (0%)	0 (0%)	1 (2.2%)	1 (1.9%)	0 (0%)
	Pass	3 (7.9%)	8 (14.0%)	12 (27.9%)	8 (17.8%)	9 (17.3%)	7 (13.7%)
	Merit	7 (18.4%)	14 (24.6%)	7 (16.3%)	9 (20.0%)	8 (15.4%)	18 (35.3%)
	Distinction	0 (0%)	4 (7.0%)	2 (4.7%)	2 (4.4%)	3 (5.8%)	2 (3.9%)
	N (%)	38 (100%)	57 (100%)	43 (100%)	45 (100%)	52 (100%)	51 (100%)

Note. Students were asked to report the extent and outcomes of their music training, values are frequency (and percentage). Highest grade completed refers to music examinations offered by either the Associated Board of the Royal Schools of Music (ABRSM), Trinity College London or similar. Highest level refers to the result achieved at the highest grade. The highest percentage in each category is bolded.

For girls in Primary 4, 5 and 6, the most common age for beginning music training is 6–8 years, with proportions of 54%, 49% and 45%, respectively. Again, up to one third of girls report beginning their music training aged between 3–5 years. Given that the overall aim of the current research was to model the relationship between these and other variables, no attempt was made to determine the significance of any differences in the reported proportions.

Although the age first started is interesting, it is possible that students began music training but then ceased training for whatever reason. Hence, the impact of music training is better estimated using the total number of years undergoing music training. Accordingly, the responses to this question were not used in the main comparative analyses.

Number of years of music training

Given that approximately 80% of students began their music training aged between 3 and 8 years of age, the total number of years of music training is likely to reflect the age they first begin music training. In asking this question, however, we take into account the possibility that some students began but did not always continue their music training.

For boys in Primary 4, 42% report that they had 2–3 years of music training (Table 3). This figure corresponds broadly with the proportion of 58% reporting 6–8 years as the age they first begin music training. Furthermore, 26% of Primary 4 boys report that they have received 4–5 years of music training, corresponding with the 26.3% reporting between 3 and 5 years as the age first beginning music training. For girls in Primary 4, 73% report between 2 and 5 years of music training, again corresponding broadly with the proportion of girls reporting 3–5 as the age first beginning music training.

For students in Primary 5 and 6, the number of years of music training varies uniformly, particularly for girls. For example, the proportion of girls in Primary 5 reporting 2–3 years, 4–5 years or 6–7 years of music training is 11%, 38% and 18%, respectively. For girls in Primary 6, the proportions are 20%, 28%, and 29% for 2–3 years, 4–5 years or 6–7 years of music training. Furthermore, 18% of girls report more than 8 years of music training. The responses from boys in Primary 5 and 6 vary similarly. These distributions were not assessed for their statistical significance.

Highest music grade achieved

Students were asked to report the highest grade they have achieved in their music training, irrespective of their instrument (Table 3). For boys in Primary 4, the highest grade is Grade 3–4 (8%), with 18% reporting a Grade of 1–2. On the other hand, the highest grade achieved by girls ranged more widely, with 26% completing Grade 1–2, 7% completing Grade 3–4 and 9% completing Grade 5–7.

However, 74% of boys and 54% of girls in Primary 4 report not applicable for highest grade reached. Similarly, the proportions of boys and girls in Primary 5 and 6 reporting not applicable, did not drop below 51% and 47%, respectively. These distributions were not assessed for their statistical significance.

Highest music level achieved

As well as highest grade completed, students were asked the highest level (result) achieved, with options including Not Applicable, Fail, Pass, Merit and Distinction (Table 3). As expected, the proportion of responses reporting not applicable matched the proportion in highest grade achieved, indicating a high degree of reliability in the responses to these two questions.

For all students, the most common level is Merit. The only exceptions were boys in Primary 5 and Primary 6, where the most common level being Pass. Nevertheless, variability in the levels was reported, including Fails and Distinctions. Again, these distributions were not assessed for their statistical significance.

Comparison between boys and girls

Independent samples t-tests

Initially, the student responses to the ATVQ were Rasch analysed and the person measures, reported as logits, were used for the main comparative analyses. Table 4 reports the results of the independent samples t-tests for the ATVQ subscales, academic achievement, age of students, number of instrument studied, number of years, highest grade and highest level. For boys, the mean (SD) response to the interest, importance and useful subscales are .4 (.77), .93 (.84) and .84 (.77), respectively. For girls, the values are .50 (.75), 1.01 (.66) and 1.05 (.77), respectively. The only significance difference is found between responses to perceived usefulness (t₂₈₆ = -2.25, p = .03) with effect size being small (Cohen’s d = -.27).

Table 4.

Independent samples t-tests (equal variances not assumed) of measures compared between male and female students from a total sample of 286 students in this study.

	Mean (SD)		t	p	95% CI Mean Difference		Cohen’s d^a
	Boy(n = 133)	Girl(n = 153)			Lower	Upper
Chinese achievement (%)	78.59 (9.50)	79.02 (8.31)	−.67	.50	−2.81	1.38	−.05
English achievement (%)	76.85 (17.19)	79.02 (11.64)	−1.23	.22	−5.64	1.31	−.15
Mathematics achievement (%)	81.28 (13.94)	78.28 (14.21)	1.80	.07	−0.28	6.29	.21
Age	11.52 (1.02)	11.34 (.95)	1.51	.13	−0.05	0.41	.18
Interest^b	0.40 (.77)	0.50 (.75)	−1.17	.24	−0.28	0.07	−.13
Importance^b	0.93 (.84)	1.01 (.66)	−.93	.35	−0.26	0.09	−.11
Useful^b	0.84 (.77)	1.05 (.77)	−2.25	.03	−0.38	−0.03	−.27
Number of instruments	1.56 (.96)	1.86 (.99)	−2.65	.01	−0.53	−0.08	−.34
Years in MT	2.61 (1.03)	2.83 (1.08)	−1.77	.08	−0.47	0.03	−.21
Highest Grade achieved	0.74 (1.07)	0.92 (1.23)	−1.36	.18	−0.45	0.08	−.16
Highest Level achieved	1.02 (1.34)	1.32 (1.46)	−1.84	.07	−0.63	0.02	−.21

Cohen’s d is a measure of effect size. Values of effect size are estimated from t and the number of cases in each group.

Variables are Rasch person measures (logit).

The mean (SD) academic achievement of each student in mathematics, Chinese and English and their age is summarised in Table 4. Evaluation of the differences between boys and girls in academic achievement of each student in mathematics, Chinese and English showed no significant differences. Finally, the Mean (SD) age of boys and girls is 11.52 (1.02) and 11.34 (.95), respectively. However, the observed differences are not significant.

The mean (SD) of the coded responses to the number of instruments studied, number of years participating in music training, highest grade and highest level is also reported in Table 4. The only significant difference between boys and girls is in the number of instruments studied, with more instruments being studied by girls than boys (t₂₈₆ = -2.65, p = .01) with effect size being small (Cohen’s d = −.37). The results also show that the number of years that girls undergo music training is greater than boys and girls achieve higher levels than boys. However, the difference is not significant with a small effect size. Though the effect sizes reported are considered small, they are “not so small as to be trivial” (Sullivan & Feinn, 2012, p. 281), and therefore, the differences should be considered for the purposes of this paper.

Nested structural equation models for boys and girls

Multigroup nested SEM model was built for boys and girls, with latent variables of Student Music Training and Student Motivation Values as moderated by students’ Age. The latent variable Student Music Training is indicated by the four observed variables of Number of Instruments, Year of Music Training, Highest Grade achieved and Highest Level achieved. The latent variable Student Motivation Values is indicated by the observed variables of Interest, Useful and Important.

The nested model was an over-identified recursive model with χ²/df = 2.70, being slightly more than the acceptable value of 2.00, and the GFI is .92 and CFI estimated at .94. The RMSEA’s value was moderate at .06 with a poor PCLOSE of .16. This nested model had paths between the latent variables and moderating variable to the independent variables of academic achievement. This initial model showed that Student Motivation Values significantly predicted boys’ Chinese and English achievement (r = -.20), whereas girls Student Motivation Values did not predict any of their academic subjects significantly. Student Music Training, on the other hand, significantly predicted academic achievement of three subjects for both boys and girls. Age was found only to predict mathematics for both boys and girls.

Misspecifications indices identified co-variances between the academic subject (Chinese, English and mathematics) variables’ error measures and also between Highest Grade and Highest Level variables’ error measures. Hence, these co-variances were added to the model. To obtain a fully parsimonious model, all non-significant pathways existing for both boys and girls were removed after chi-square difference tests indicated no effects on model fit.

The fully saturated nested model is an over-identified recursive model with χ²/df = 1.52, being less than the acceptable value of 2.00, and the GFI is .95 and CFI estimated at .98. The RMSEA is also good with a value of .03 with a PCLOSE of 1.00. Unstandardised direct and indirect path estimates for both boys and girls are shown in Table 5.

Table 5.

Unstandardised significant direct and indirect path estimates of nested model for boys and girls.

Male students (n = 133)			Unstandardised estimates	Direct (D)/Indirect (I)
Student Motivation Values	←	Age	−.14	D
Mathematics	←	Age	−2.77	D
Number of instruments	←	Student Music Training	.94	D
Years	←	Student Music Training	1.02	D
Highest grade	←	Student Music Training	.96	D
Highest level	←	Student Music Training	1.00	D
Mathematics	←	Student Music Training	8.11	D
English	←	Student Music Training	12.27	D
Chinese	←	Student Music Training	6.42	D
Interest	←	Student Motivation Values	1.00	D
Importance	←	Student Motivation Values	1.58	D
Useful	←	Student Motivation Values	1.55	D
Chinese	←	Student Motivation Values	−2.55	D
Useful	←	Age	−.22	I
Importance	←	Age	−.23	I
Interest	←	Age	−.14	I
Female students (n = 153)			Unstandardised estimates	Direct (D)/Indirect (I)
Student Music Training	←	Age	.19	D
Mathematics	←	Age	−5.04	D
Mathematics	←	Student Music Training	10.53	D
English	←	Student Music Training	10.61	D
Chinese	←	Student Music Training	5.48	D
Highest level	←	Student Music Training	1.26	D
Highest grade	←	Student Music Training	1.31	D
Years	←	Student Music Training	1.27	D
Number of instruments	←	Student Music Training	1.03	D
Chinese	←	Student Motivation Values	−2.50	D
Useful	←	Student Motivation Values	1.29	D
Importance	←	Student Motivation Values	1.22	D
Mathematics	←	Age	1.98	I
English	←	Age	2.00	I
Chinese	←	Age	1.16	I
Highest level	←	Age	.24	I
Highest grade	←	Age	.25	I
Years	←	Age	.24	I
Number of instruments	←	Age	.19	I

Note. Standardised path estimates are shown in graphic models as in Figures 1 and 2.

p= two-tail significance as validated by bootstrap bias-corrected percentile intervals.

Overall, for both boys and girls, Student Music Training significantly and positively predicted all three academic subjects, whereas Student Motivation Values had a significant but negative effect on Chinese achievement. Furthermore, Age predicted mathematics for both boys and girls; however, the path estimate is negative.

Boys’ SEM model

The model shows that 27% of Chinese, 28% of English and 21% of mathematics achievement is explained by the variables in the model (Figure 1). An examination of the 95% bias corrected bootstrap confidence intervals of indirect effects in the model showed non-zero confidence intervals instances only for the observed variables of Student Motivation Values, indicating that boys’ Age negatively moderates their perceived Motivation Values of their school subjects (Interest, Importance and Useful). There are no other moderating effects of Age on boys’ academic achievement. Age also does not affect Student Music Training at all for boys, though their music training does impact directly on their academic achievement.

Figure 1.

Full parsimonious nested model with standardised estimates for boys showing relationships between their music training and motivation as moderated by their age in predicting academic achievement. Non-significant path is shown as NS.

Girls’ SEM model

The model shows that 19% of Chinese, 34% of English and 24% of mathematics achievement is explained by the variables in the model (Figure 2). Unlike the boys, Age impacted significantly and positively on girls’ Student Music Training. Also different to the boys, girls’ Age does not relate to their Motivation Values. The examination of the 95% bias corrected bootstrap confidence intervals of indirect effects for the girls’ model showed a number of instances of non-zero confidence intervals that indicate presence of moderator effects. Age positively and significantly moderated girls’ Student Music Training. Age also moderated all three academic subjects in a positive way with strong indirect regression paths ranging from 1.16 to 2.00.

Figure 2.

Full parsimonious nested model with standardised estimates for girls showing relationships between their music training and motivation as moderated by their age in predicting academic achievement. Non-significant path is shown as NS.

Discussion and conclusion

Amongst the Hong Kong Chinese, the role of parents in the academic success of their children is rooted in their Confucian culture (King & McInerney, 2014). Recent research has linked filial piety and academic achievement using SEM (Chen & Wong, 2014). Although there is a general perception amongst Hong Kong Chinese parents that music training is positively related to the academic achievement of their children, this study is among the first to directly test this perception. Rather than attending to the compulsory study of music within their regular curriculum, this study focuses on the formal instruction they receive outside of their regular school.

The results show that 94% of students reported that they study an instrument outside of their school. Despite being asked to focus on instruments studied outside of school, this result needs to be viewed cautiously as some students may have included the recorder, an instrument that all students study as part of their regular curriculum, or viewed their involvement in choir as “voice” training. Furthermore, some students are part of the school woodwind ensemble and this would further inflate the proportion. If the proportion is estimated from students who study two or more instruments, this means that in excess of 50% of Primary 4, 5 and 6 students at this school study music outside of their regular school.

The possible inclusion of recorder in student responses means that the most commonly studied instrument needs also to be interpreted cautiously. If both woodwind and voice are excluded, the most commonly studied instrument is piano/organ (16%), followed by strings (12%) and percussion (10%). This frequency is broadly consistent with previous estimates (Choi et al., 2005; W. C. Ho, 2009; Leung & McPherson, 2011).

In reporting the extent and outcome of their music training, the students reported considerable variability in the number of years they spent in music training, indicating that students focused on their music training experiences as expected. However, a considerable proportion of students across all grades reported “Not applicable” to questions regarding the highest grade and level reached, indicating that a high proportion of students undertook music training without the associated formal examinations.

One main focus of this research hypothesis concerned the relationship between extent and outcome of music training and academic achievement. For students at this one school, the results indicate that parents are justified in their belief that music training for both boys and girls is positively related to academic achievement in Chinese, English and mathematics, supporting earlier speculation by some studies (e.g., Hallam, 2013; Kinney, 2008). However, there are also some important differences in this relationship when considering the influence of the students’ age and the value they place on their regular school subjects. The results indicate that the mean age of the boys does not differ to that of the girls, thus we can confidently compare the relationships between boys and girls in terms of their age effect.

For girls, the moderator effect of age on the relationship is positive, meaning that as girls become older, the impact of music training on academic achievement is enhanced. This finding confirms conclusions reached by Costa-Giomi (2015) that highlighted the effect of time music training has on achievement. However, the moderator effect found in this study is more profound with Chinese and English rather than mathematics, given that as girls become older, age negatively predicts mathematics achievement. In other words, the decline in mathematics achievement for girls is lessened through music training. This result therefore extends studies that have shown indirect links between musical training and achievement (Costa-Giomi, 2015; Moreno et al., 2011; Schellenberg, 2004, 2011a, 2011b).

For boys, the model tells a slightly different story. Though the path between music training and academic achievement is significant, their age does not have any significant effect on their music training. This finding means that as boys become older, the impact of music training on academic achievement does not change. Furthermore, a finding of a direct and negative path between age and mathematics achievement means that music training does not ameliorate the decline in mathematics achievement. Such a result is unique in the research literature where time-effect is seen as having an indirect effect on achievement (e.g., Costa-Giomi, 2015). Nevertheless, it is important to note that music training still has a direct impact on academic achievement for boys in this study, hence broadly supporting and justifying Hong Kong Chinese parents’ perception that music training is crucial for their children’s academic success.

We also tested the hypothesis that student motivation values (basically how they find academic subjects interesting, useful and important) affect their academic achievement. The results showed that there was no relationship between perceived value and academic achievement in both English and mathematics for either boys or girls. On the other hand, the perceived value of their school subjects negatively influenced academic achievement in Chinese. This finding is interesting as it highlights tensions around students’ perceived value of Chinese, raising speculation around the importance of Chinese in relation to learning music and other subjects in the context of filial piety (Hui et al., 2011).

The moderator effect of age on perceived value differed between boys and girls. For girls, the perceived value of their school subjects does not change as they grow older. For boys, on the other hand, the perceived value lessens as they become older. However, a moderator pathway between age, perceived value and academic achievement does not exist, therefore showing that perceived motivation values has a lesser role in predicting achievement irrespective of students’ gender. This result points to external factors such as culture playing a dominant role in dictating students’ academic achievement, supporting the important role parents play in desiring their children’s music training as a facilitator of academic success (S. Phillipson & Phillipson, 2012).

In conclusion, the models show that music training predicts around 20% of the variability in academic achievement for Hong Kong Chinese students at this one school, providing support for parents’ belief of the importance of music for their children’s academic success. In contrast to boys, however, the effects of music training increases as the girls become older. Interestingly, the perceived value of their subjects does not appear to influence academic achievement for these students. Although future research is expected to confirm this finding more generally, efforts should focus on modelling the impact of parents in the relationship between students’ music training and academic achievement.

Footnotes

Appendix

Appendix 1.

The Achievement Task Value Questionnaire.

Factor	Item	Description
Like and Interest	1	At school, how much do you like learning [subject]? ¹ a) Music, b) Chinese, c) PE/Gym, d) Math, e) English, f) Art.
	2	At school, how interesting do you find [subject]? ² a) Art, b) Music, c) Chinese, d) PE/Gym, e) Math, f) English
Importance	3	For you, how important is it to learn [subject]? ³ a) English, b) Art, c) Music, d) Chinese, e) PE/Gym, f) Math
	4	For you, how important is it to be good at [subject]? ³ a) Math, b) English, c) Art, d) Music, e), Chinese, f) PE/Gym
	5	For you, how important is it to get good school results in [subject]? ³ a) PE/Gym, b) Math, c) English, d), Art, e), Chinese, f) Music
Usefulness and worthiness	6	In general, how useful is what you learn in each of these subjects? ⁴ a) PE/Gym, b) Math, c) English, d) Art, e) Chinese, f) Music
	7	How useful are these subjects compared to your other activities? ⁴ a) Art, b) Chinese, c), PE/Gym, d) Math, e) English, f) Music
	8	How useful do you think learning the following subjects will be for you when you leave school and get a job? ⁴ a) Music, b) Chinese, c) PE/Gym, d) Math, e) English, f) Art.
	9	How useful is learning the following subjects for your daily life outside school? ⁴ a) Art, b) Music, c) Chinese, d) PE/Gym, e) Math, f) English
	10	How worthwhile for you is the amount of effort it takes to do the following [subjects]? ⁵ a) English, b) Art, c) Music, d) Chinese, e) PE/Gym, f) Math

Possible responses range between 1=I don’t like it and 4=I like it a lot.

Possible responses range between 1=Not interesting and 4=Very interesting.

Possible responses range between 1=Not important and 4=Very important.

Possible responses range between 1=Not useful and 4=Very useful.

Possible responses range between 1=Not worthwhile and 4=Very worthwhile.

Note that each of the 10 items had six options (Music, Chinese, PE/Gym, Math, English and Art) and that the order of the subjects varied for each item. Hence, participants responded to 60 questions.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Notes

ORCID iD

Shane Phillipson

Author biographies

Dianne M. Tai is an educational consultant working in Hong Kong. Her research focuses on the impact of music education and academic achievement.

Shane N. Phillipson is an associate professor. His research interests include the relationship between cultural contexts and academic achievement.

Sivanes Phillipson is Professor of Education and Associate Dean International. Her research investigates the relationship of parental expectations and involvement with children’s development and education.

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