This study presents the Chinese adaptation of the Goldsmiths Musical Sophistication Index (Gold-MSI), an instrument for measuring individual differences in musical ability and skilled musical behaviour. Its psychometric properties were examined with a Taiwanese sample. The Gold-MSI inventory was translated into Chinese following recommendations from the literature on cross-cultural test development. Subsequently, the psychometric properties of the Chinese Gold-MSI self-report inventory, including the Melody Memory Task and the Beat Alignment Perception Task, were evaluated using an online survey with 1,065 participants. Results of confirmatory factor analysis suggest that the original factor structure of the Gold-MSI inventory showed an acceptable fit with the data from the Chinese-speaking sample. In addition, the Chinese Gold-MSI inventory shows good reliability. The Melody Memory Task and the Beat Alignment Perception Task also have sufficient test-retest reliability. Finally, correlations between the Chinese Gold-MSI inventory and the Musical-Rhythmic Intelligence subscale of the Eight Multiple Intelligences Questionnaire as well as the two additional music tests provide evidence for convergent and divergent validity. Overall, the data suggest that the Chinese Gold-MSI has good psychometric properties. Percentile norms for the Gold-MSI inventory and the music tests from the present sample are reported for use in future studies. The present study thus makes a valuable contribution to cross-cultural research in music psychology by enabling the comparison between Chinese and Western studies of individual differences in musical ability.
AbellN.SpringerD. W.KamataA. (2009). Developing and validating rapid assessment instruments. New York: Oxford University Press.
2.
AllenM.GlenJ. C.MüllensiefenD.SchwarzkopfD. S.FardoF.FrankD.ReesG. (2017). Metacognitive ability correlates with hippocampal and prefontal microstructure. NeuroImage, 149, 415–423.
3.
BakerD. J.VenturaJ.CalamiaM.ShanahanD.ElliottE. M. (2018). Examining musical sophistication: A replication and theoretical commentary on the Goldsmiths Musical Sophistication Index. Musicæ Scientiæ. Advance online publication. doi: 10.1177/1029864918811879
4.
BauerA.-K. R.KreutzG.HerrmannC. S. (2015). Individual musical tempo preference correlates with EEG beta rhythm. Psychophysiology, 52, 600–604.
5.
BeaujeanA. A. (2014). Latent variable modeling using R: A step-by-step guide. New York: Routledge.
6.
BentleyA. (1966). Measures of musical abilities. London, UK: George A. Harrap.
7.
BentleyA. (1969). Measurement and development of musical abilities. Journal of Research in Music Education, 17, 41–46.
8.
BillrothT. (1895). Wer ist musikalisch? [Who is musical?] Berlin, Germany: Paetel.
9.
BouwerF. L.WernerC. M.KnetemannM.HoningH. (2016). Disentangling beat perception from sequential learning and examining the influence of attention and musical abilities on ERP responses to rhythm. Neuropsychologia, 85, 80–90.
10.
BrownT. A. (2015). Confirmatory factor analysis for applied research (2nd ed.). New York: The Guilford Press.
11.
ChaoH.-C. (2013). Arts and humanities of sixth graders: Music appreciation computer quiz questions preparation (master’s thesis). National Taichung University of Education, Taichung, Taiwan.
12.
ChenP.-H.LinY.-L.HsuC.-L.YenY.-C. (2007). The development of multidimensional computerized adaptive testing for multiple intelligence. Paper presented at the IMPS 2007, the 15th International and 72nd Annual Meeting for the Psychometric Society, Tokyo, Japan.
13.
ChenY.-T. (2003). Development of music aptitude tests for primary school students (Master’s thesis). National Taichung University of Education, Taichung, Taiwan.
14.
ChouT.-C. R. (2006). Analysis of reliability and validity of Eight Multiple Intelligences Questionnaire. Bulletin of Educational Psychology, 37, 215–229.
15.
DegraveP.DedonderJ. (2019). A French translation of the Goldsmiths Musical Sophistication Index, an instrument to assess self-reported musical skills, abilities and behaviours. Journal of New Music Research48, 138–144.
16.
DeMarsC. E. (2018). Classical test theory and item response theory. In IrwingP.BoothT.HughesD. J. (Eds.), The Wiley handbook of psychometric testing: A multidisciplinary reference on survey, scale and test development (pp. 49–73). Hoboken, NJ: Wiley Blackwell.
17.
FarrugiaN.JakubowskiK.RhodriC.StewartL. (2015). Tunes stuck in your brain: The frequency and affective evaluation of involuntary musical imagery correlate with cortical structure. Consciousness and Cognition, 35, 66–77.
18.
FiedlerD.MüllensiefenD. (2015). Validierung des Gold-MSI-Fragebogens zur Messung Musikalischer Erfahrenheit von Schülerinnen und Schülern der Sekundarstufen an allgemeinbildenden Schulen [Validation of the Gold-MSI questionnaire for measuring musical sophistication of pupils at secondary level in general-education schools]. Musikpädagogische Forschung, 36, 199–220.
19.
FiedlerD.MüllensiefenD. (2017). Musical self-concept and musical sophistication influence the development of interest in music as a school subject. An empirical long-term study of German students at secondary schools. Research in Music Education, 38, 217–235.
20.
GardnerH. (1985). Frames of mind: The theory of multiple intelligences. New York: Basic Books.
21.
GembrisH. (1997). Historical phases in the definition of musicality. Psychomusicology: Music, Mind & Brain, 16, 17–25.
GordonE. E. (1965). Musical aptitude profile. Boston, MA: Houghton Mifflin.
24.
GordonE. E. (1986). The nature, description, measurement, and evaluation of music aptitudes. Chicago, IL: GIA Publications.
25.
GordonE. E. (1989). Manual for the Advanced Measures of Music Audiation. Chicago, IL: G. I. A. Publications.
26.
HallamS. (2010). 21st century conceptions of musical ability. Psychology of Music, 38, 308–330.
27.
HallamS. (2016). Musicality. In McPhersonG. E. (Ed.), The child as musician: A handbook of musical development (2nd ed., pp. 67–80). Oxford, UK: Oxford University Press.
28.
HallamS.PrinceV. (2003). Conceptions of musical ability. Research Studies in Music Education, 20(1), 2-22.
29.
HalpernA. R.BartlettJ. C.DowlingW. J. (1995). Aging and experience in the recognition of musical transpositions. Psychology and Aging, 10, 325–342.
30.
HansonJ. (2019). Meta-analytic evidence of the criterion validity of Gordon’s music aptitude tests in published music education research. Journal of Research in Music Education. Advance online publication. doi: 10.1177/0022429418819165
31.
HarrisonP. M. C.MusilJ. J.MüllensiefenD. (2016). Modelling melodic discrimination tests: Desciptive and explanatory approaches. Journal of New Music Research, 45, 265–280.
32.
HoM.-h. R.StarkS.ChernyshenkoO. (2012). Graphical representation of structural equation models using path diagrams. In HoyleR. H. (Ed.), Handbook of structural equation modelling. New York: The Guilford Press.
33.
HoY.-C. (2004). The development of a music achievement test for junior high school students. Unpublished master’s thesis, National Taiwan University of Science and Technology, Taipei, Taiwan.
34.
HwangY.-J. (2009). The suitability of the New International Occupational Scale in Taiwan’s educational research: Consideration of indigenization and internationalization. Journal of Research in Education Sciences, 54, 1–27.
IversenJ. R.PatelA. D. (2008). The Beat Alignment Test (BAT): Surveying beat processing abilities in the general populationProceedings of the 9th International Symposium on Computer Music Modelling and Retrieval (pp. 311–318). Queen Mary University of London, London, UK.
37.
JakubowskiK.MüllensiefenD. (2013). The influence of music-elicited emotions and relative pitch on absolute pitch memory for familiar melodies. Quarterly Journal of Experimental Psychology, 66, 1259–1267.
38.
JansenM.LüdtkeO.SchroedersU. (2016). Evidence for a positive relation between interest and achievement: Examining between-person and within-person variation in five domains. Contemporary Educational Psychology, 46, 116–127.
39.
KarmaK. (2007). Musical aptitude definition and measure validation: Ecological validity can endanger the construct validity of musical aptitude tests. Psychomusicology: A Journal of Research in Music Cognition, 19, 79–90.
40.
KeithT. Z. (2015). Multiple regression and beyond: An introduction to multiple regression and structural equation modeling (2nd ed.). New York: Routledge.
41.
KomstaL.NovomestkyF. (2015). moments: Moments, cumulants, skewness, kurtosis and related tests (R package version 0.14). Retrieved from https://CRAN.R-project.org/package=moments
42.
KopiezR.LeeJ. I. (2008). Towards a general model of skills involved in sight reading music. Music Education Research, 10, 41–62.
43.
KopiezR.WolfA.PlatzF.MonsJ. (2016). Replacing the orchestra? – The discernibility of sample library and live orchestra sounds. PLoS ONE, 11, e0158324.
44.
KorkmazS.GoksulukD.ZararsizG. (2014). MVN: An R package for assessing multivariate normality. The R Journal, 6, 151–162.
45.
KriegbaumK.BeckerN.SpinathB. (2018). The relative importance of intelligence and motivation as predictors of school achievement: A meta-analysis. Educational Research Review, 25, 120–148.
46.
LauS.RoeserR. W. (2002). Cognitive abilities and motivational processes in high school students’ situational engagement and achievement in science. Educational Assessment, 8, 139–162.
47.
LawL. N. C.ZentnerM. (2012). Assessing musical abilities objectively: Construction and validation of the Profile of Music Perception Skills. PLoS ONE, 7, e52508.
48.
LeinerD. J. (2014). Sosci Survey (Version 2.6.00-i) [Computer software]. Available at http://www.soscisurvey.com
49.
LevitinD. J. (2012). What does it mean to be musical?Neuron, 73, 633–637.
50.
LeysC.LeyC.KleinO.BernardP.LicataL. (2013). Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median. Journal of Experimental Social Psychology, 49, 764–766.
51.
LimaC. F.CorreiaA. I.MüllensiefenD.CastroS. L. (2018). Goldsmiths Musical Sophistication Index (Gold-MSI): Portuguese version and associations with socio-demographic factors, personality and music preferences. Psychology of Music. Advance online publication.
52.
LinW.-C. (2013). Fifth grade arts and humanities computerized music achievement test. Unpublished master’s thesis, National Taichung University of Education, Taichung, Taiwan.
53.
LuC.-M.ChenS.-M.ChenL.-C. (1981). A study on the musical aptitude of Chinese student. Bulletin of Educational Psychology, 14, 149–160.
54.
MarshH. W.HauK.-T.GraysonD. (2005). Goodness of fit in structural equation models. In Maydeu-OlivaresA.McArdleJ. J. (Eds.), Contemporary psychometrics: A festschrift for Roderick P. McDonald (pp. 275–340). Mahwah, NJ: Lawrence Erlbaum Associates.
55.
MarshH. W.LüdtkeO.MuthénB.AsparouhovT.MorinA. J. S.TrautweinU.NagengastB. (2010). A new look at the Big Five factor structure through exploratory structural equation modeling. Psychological Assessment, 22, 471–491.
56.
MartinsM. D.GingrasB.Puig-WaldmuellerE.FitchW. T. (2017). Cognitive representation of “musical fractals”: Processing hierarchy and recursion in the auditory domain. Cognition, 161, 31–45.
57.
MatthewsT. E.ThibodeauJ. N. L.GuntherB. P.PenhuneV. B. (2016). The impact of instrument-specific musical training on rhythm perception and production. Frontiers in Psychology, 7.
58.
MichaelisC. F. (1805). Über die Prüfung musikalischer Fähigkeiten [On the testing of musical abilities]. Berlinische Musikalische Zeitung, 56– 68, 222–230.
59.
MotaG. (1997). Detecting young children’s musical aptitude: A comparison between standardized measures of music aptitude and ecologically valid musical performances. Bulletin for the Council of Research in Music Education, 133, 89–94.
60.
MüllensiefenD.GingrasB.MusilJ. J.StewartL. (2014). The musicality of non-musicians: An index for assessing musical sophistication in the general population. PLoS ONE, 9, e89642.
OllenJ. E. (2006). A criterion-related validity test of selected indicators of musical sophistication using expert ratings. Unpublished doctoral dissertation, The Ohio State University. Retrieved from https://etd.ohiolink.edu/
63.
OreckB. A.OwenS. V.BaumS. M. (2003). Validity, reliability, and equity issues in an observational talent assessment process in the performing arts. Journal for the Education of the Gifted, 27, 62–94.
64.
PérezE.BeltraminoC.CupaniM. (2003). Inventario de autoeficacia para inteligencias múltiples: Fundamentos teóricos y estudios psicométricos [Self-report inventory of multiple intelligences: Theoretical foundations and psychometric studies]. Evaluar, 3, 35–60.
65.
PlatzF.KopiezR.LehmannA. C.HasselhornJ.BüringM.WolfA., . . . Estrada RodriguezL. A. (2015, 17–22August2015). Requiem for Edwin Gordon’s “Advanced Measures of Music Audiation” (AMMA)? – A critical evaluation. Paper presented at the Ninth Triennial Conference of the European Society for the Cognitive Sciences of Music, Manchester, UK.
66.
PutnickD. L.BornsteinM. H. (2016). Measurement invariance conventions and reporting: The state of the art and future direction for psychological research. Developmental Review, 41, 71–90.
67.
R Core Team. (2017). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://www.R-project.org
68.
RevelleW. (2017). psych: Procedures for personality and psychological research (Version 1.7.5). Evanston, IL: Northwestern University. Retrieved from https://CRAN.R-project.org/package=psych
69.
RosseelY. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48, 1–36.
70.
RStudio Team. (2016). RStudio: Integrated development for R. Boston, MA: RStudio, Inc. Retrieved from http://www.rstudio.com/
71.
RutkowskiJ. (1996). The effectiveness of individual/small-group singing activities on kindergartners’ use of singing voice and developmental music aptitude. Journal of Research in Music Education, 44, 353–368.
72.
SchaalN. K.BauerA.-K. R.MüllensiefenD. (2014). Der Gold-MSI: Replikation und Validierung eines Fragebogeninstrumentes zur Messung Musikalischer Erfahrenheit anhand einer deutschen Stichprobe [The Gold-MSI: Replication and validation of a questionnaire instrument for measuring musical sophistication, based on a German sample]. Musicæ Scientiæ, 18(4), 423–447.
73.
SchaalN. K.KrauseV.LangeK.BanissyM. J.WilliamsonV. J.PollokB. (2015). Pitch memory in nonmusicians and musicians: Revealing functional differences using transcranial direct current stimulation. Cerebral Cortex, 25, 2774–2782.
74.
SeashoreC. E. (1919). The psychology of musical talent. Boston, MA: Silver, Burdett and Company.
75.
SeashoreC. E.LewisD.SaetveitJ. G. (1960). A second revision of the manual of instructions and intrepretations for the Seashore Measure of Musical Talents (1939 revision). New York: The Psychological Corporation.
76.
SorkinR. D. (1999). Spreadsheet signal detection. Behavior Research Methods, Intruments, & Computers, 31, 46–54.
77.
StevensC. J.ByronT. (2016). Universals in music processing. In HallamS.CrossI.ThautM. (Eds.), The Oxford handbook of music psychology (2nd ed., pp. 19–31). Oxford, UK: Oxford University Press.
78.
TabachnickB. G.FidellL. S. (2013). Using multivariate statistics (6th ed.). Boston, MA: Perason.
79.
TranT. V.NguyenT. H.ChanK. T. (2017). Developing cross-cultural measurement in social work research and evaluation (2nd ed.). New York: Oxford University Press.
80.
UllénF.MosingM. A.HolmL.ErikssonH.MadisonG. (2014). Psychometric properties and heritability of a new online test for musicality, the Swedish Musical Discrimination Test. Personality & Individual Differences, 63, 87–93.
UNESCO Institute for Statistics. (2015). ISCED 2011 Operational manual: Guidelines for classifying national education programmes and related qualifications. Retrieved from http://dx.doi.org/10.1787/9789264228368-en
83.
van de VijverF. J. R.LeungK. (2011). Equivalence and bias: A review of concepts, models, and data analytic procedures. In MatsumotoD.van de VijverF. J. R. (Eds.), Cross-cultural research methods in psychology (pp. 17–45). New York: Cambridge University Press.
84.
WenY.-C.TsaiC.-G. (2017). The effect of harmonization on cortical magnetic responses evoked by music of rapidly changing tonalitites. Psychology of Music, 45(1), 22–35.
85.
WingH. D. (1939). Standardised tests of musical intelligence. The Mere, UK: National Foundation for Educational Research.
86.
WingH. D. (1961). Standardised tests of musical intelligence. The Mere, UK: National Foundation for Educational Research.
87.
WolfA.KopiezR. (2018). Development and validation of the Musical Ear Training Assessment (META). Journal of Research in Music Education, 66, 53–70.
88.
WolfA.KopiezR.PlatzF. (2018). Thinking in music: An objective measure of notation-evoked sound imagry in musicians. Psychomusicology: Music, Mind and Brain, 28, 209–221.
89.
WolfA.KopiezR.PlatzF.LinH.-R.MützeH. (2018). Tendency towards the average? The aesthetic evaluation of a quantitatively average music performance: A successful replication of Repp’s (1997) study. Music Perception, 36, 98–108.
90.
WuW.-T. (2008). Chinese version of multiple intelligences developmental assessment scales form-C, revised from C. Branton Shearer’s Multiple Intelligences Developmental Assessment Scales. Taipei, Taiwan: Psychological.
91.
YangH.MaW.GongD.HuJ.YaoD. (2014). A longitudinal study on children’s music training experience and academic development. Scientific Reports, 4, 5854.
92.
YehY.-C.PengY.-Y.YehY.-H. (2009). The development of “the Inventory of Preschoolers’ Multiple Intelligences”. Early Childhood Education, 296, 2–21.
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