The difficulties associated with measuring the complex construct of musicianship have received considerable attention in the music psychology literature. Multiple measures exist for various constructs, yet the need for the careful replication and documentation of the use of these measures remains an area of critical importance. Here, we describe the replication of the Goldsmiths Musical Sophistication Index (Gold-MSI) in a sample of 346 university students, drawn from both a school of music and a department of psychology. The original approach to modeling the Gold-MSI was followed as closely as possible, and the results replicated well overall. Issues were noted, however, with the characteristics of the sample, the skew of some of the individual items, and the overall use of the bifactor structure. These findings are discussed in relation to the state of measuring musicianship in the current literature, as well as in relation to the larger theoretical concerns surrounding the modeling of complex psychological and musical constructs.
BakerD. J.MüllensiefenD. (2017). Perception of leitmotives in Richard Wagner’s Der Ring des Nibelungen. Frontiers in Psychology, 8(662). doi:10.3389/fpsyg.2017.00662.
2.
BeaujeanA. A. (2014). Latent variable modeling using R: A step-by-step guide. New York: Routledge.
3.
BentleyA. (1966). Musical ability in children and its measurement. London: Harrap.
4.
BlackingJ. (1974). How musical is man?Seattle: University of Washington Press.
5.
BoyleJ. D.RadocyR. E. (1987). Measurement and evaluation of musical experiences. New York: Schirmer Books.
6.
ChinT.CoutinhoE.SchererK. R.RickardN. S. (2018). MUSEBAQ: A modular tool for music research to assess musicianship, musical capacity, music preferences, and motivations for music use. Music Perception, 35, 376–399.
7.
ChinT.RickardN. S. (2012). The music use (muse) questionnaire: An instrument to measure engagement in music. Music Perception, 29, 429–446.
8.
ColleyI. D.KellerP. E.HalpernA. R. (2017). Working memory and auditory imagery predict sensorimotor synchronization with expressively timed music. The Quarterly Journal of Experimental Psychology, 71(8), 1–49.
9.
CorrigallK. A.SchellenbergE. G.MisuraN. M. (2013). Music training, cognition, and personality. Frontiers in Psychology, 4(222). doi:10.3389/fpsyg.2013.00222.
10.
De LeeuwJ. R. (2015). jsPsych: A JavaScript library for creating behavioral experiments in a web browser. Behavior Research Methods, 47(1), 1–12.
11.
DalyH. R.HallM. D. (2018). Not all musicians are created equal: Statistical concerns regarding the categorization of participants. Psychomusicology: Music, Mind, and Brain, 28, 117–126.
12.
FarrugiaN.JakubowskiK.CusackR.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.
13.
FrielerK.MüllensiefenD.FischingerT.SchlemmerK.JakubowskiK.LothwesenK. (2013). Replication in music psychology. Musicae Scientiae, 17, 265–276.
14.
GembrisH. (1997). Historical phases in the definition of musicality. Psychomusicology: A Journal of Research in Music Cognition, 16(1–2), 17–25.
15.
GordonE. (1989). Manual for the advanced measures of music audiation. Chicago, IL: GIA Publications.
16.
GreenbergD. M.MüllensiefenD.LambM. E.RentfrowP. J. (2015). Personality predicts musical sophistication. Journal of Research in Personality, 58, 154–158.
17.
HarrisonP. M.CollinsT.MüllensiefenD. (2017). Applying modern psychometric techniques to melodic discrimination testing: Item response theory, computerised adaptive testing, and automatic item generation. Scientific Reports, 7(1), 3618.
HenrichJ.HeineS. J.NorenzayanA. (2010). Most people are not weird. Nature, 466(7302), 29–29.
20.
HuL.-T.BentlerP. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.
21.
IoannidisJ. P. (2005). Why most published research findings are false. PLoS Medicine, 2(8), e124. doi:10.1371/journal.pmed.0020124.
22.
IrwingP.HughesD. J. (2018). Test development. In IrwingP.BoothT.HughesD. J. (Eds.), The Wiley handbook of psychometric testing: A multidisciplinary reference on survey, scale and test development (pp. 3–48). Hoboken, NJ: Wiley Blackwell.
23.
JaschkeA. C.HoningH.ScherderE. J. A. (2018). Longitudinal analysis of music education on executive functions in primary school children. Frontiers in Neuroscience, 12(103). doi:10.3389/fnins.2018.00103.
24.
KlineR. B. (2015). Principles and practice of structural equation modeling. New York: Guilford Publications.
25.
KovacsK.ConwayA. R. (2016). Process overlap theory: A unified account of the general factor of intelligence. Psychological Inquiry, 27, 151–177.
26.
LawL. N.ZentnerM. (2012). Assessing musical abilities objectively: Construction and validation of the profile of music perception skills. PloS ONE, 7(e52508). doi:10.1371/journal.pone.0052508.
27.
LevitinD. J. (2012). What does it mean to be musical?Neuron, 73, 633–637.
28.
LinH.KopiezR.MüllensiefenD.WolfA. (2018, July). The Chinese version of the Gold-MSI: Adaptation and validation of an inventory for the measurement of musicality in a Taiwanese sample. Talk presented at the International Conference for Music Perception and Cognition 15, Graz, Austria.
29.
LoehlinJ. C.BeaujeanA. A. (2016). Latent variable models: An introduction to factor, path, and structural equation analysis. Milton Park, UK: Taylor & Francis.
30.
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.
31.
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.
32.
McPhersonG. E.HallamS. (2016). Musical potential. In HallamS.CrossI.ThautM. (Eds.), Oxford handbook of music psychology (pp. 433–448). Oxford, UK: Oxford University Press.
33.
MeinzE. J.HambrickD. Z. (2010). Deliberate practice is necessary but not sufficient to explain individual differences in piano sight-reading skill: The role of working memory capacity. Psychological Science, 21, 914–919.
34.
MorinA. J.ArensA. K.MarshH. W. (2016). A bifactor exploratory structural equation modeling framework for the identification of distinct sources of construct-relevant psychometric multidimensionality. Structural Equation Modeling: A Multidisciplinary Journal, 23(1), 116–139.
35.
MüllensiefenD.GingrasB.MusilJ.StewartL. (2014). The musicality of non-musicians: An index for assessing musical sophistication in the general population. PLoS ONE, 9, e89642. doi:10.1371/journal.pone.0089642.
36.
MüllensiefenD.HarrisonP.CapriniF.FancourtA. (2015). Investigating the importance of self-theories of intelligence and musicality for students’ academic and musical achievement. Frontiers in Psychology, 6(1702). doi:10.3389/fpsyg.2015.01702.
37.
MurphyC. (1999). How far do tests of musical ability shed light on the nature of musical intelligence?British Journal of Music Education, 16(1), 39–50.
38.
NicholsB. E.WöllnerC.HalpernA. R. (2018). Score one for jazz: Working memory in jazz and classical musicians. Psychomusicology: Music, Mind, and Brain, 28(2), 101–107.
Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349(6251). doi:10.1126/science.aac4716.
41.
PeretzI. (2006). The nature of music from a biological perspective. Cognition, 100(1), 1–32.
42.
R Core Team. (2017). R: A language and environment for statistical computing [Computer software manual]. Vienna, Austria. Retrieved from https://www.R-project.org/
43.
RavenJ.RavenJ.CourtJ. (1998). Manual for Raven’s progressive matrices and vocabulary scales. Oxford, UK: Oxford Psychologists Press.
44.
RevelleW. (2014). psych: Procedures for personality and psychological research. Northwestern University, Evanston. R package version1.1.
45.
RitchieS. (2015). Intelligence: All that matters. London, UK: Hodder & Stoughton.
46.
RosseelY. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36. Retrieved from http://www.jstatsoft.org/v48/i02/
47.
SaffranJ. R.JohnsonE. K.AslinR. N.NewportE. L. (1999). Statistical learning of tone sequences by human infants and adults. Cognition, 70(1), 27–52.
48.
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 for measuring musical sophistication in a German population]. Musicae Scientiae, 18, 423–447.
49.
SeashoreC. E. (1915). The measurement of musical talent. The Musical Quarterly, 1(1), 129–148.
50.
SeashoreC. E. (1919). Manual of instructions and interpretations for measures of musical talent. London, UK: Columbia Graphophone Company.
51.
SeashoreC. E.LewisD.SaetveitJ. G. (1960). Measures of musical talents [test]. New York: Psychological Corporation. (Earlier version published 1919, 1939)
52.
SwaminathanS.SchellenbergE. G.KhalilS. (2017). Revisiting the association between music lessons and intelligence: Training effects or music aptitude?Intelligence, 62, 119–124.
53.
SchellenbergE. G. (2004). Music lessons enhance IQ. Psychological Science, 15, 511–514.
54.
SchmidJ.LeimanJ. M. (1957). The development of hierarchical factor solutions. Psychometrika, 22(1), 53–61.
55.
SchneiderW.EschmanA.ZuccolottoA. (2002). E-prime: User’s guide. Pittsburgh, PA: Psychology Software Inc.
56.
SpearmanC. (1904). “General intelligence” objectively determined and measured. The American Journal of Psychology, 15, 201–292.
57.
TabachnickB. G.FidellL. S.OsterlindS. J. (2001). Using multivariate statistics. Harlow, UK: Pearson Publishing.
58.
TalaminiF.AltoèG.CarrettiB.GrassiM. (2017). Musicians have better memory than nonmusicians: A meta-analysis. PLoS ONE, 12, e0186773.
59.
TarrantM.NorthA. C.HargreavesD. J. (2002). Youth identity and music. Musical Identities, 13, 134–150.
60.
UnsworthN.HeitzR. P.SchrockJ. C.EngleR. W. (2005). An automated version of the operation span task. Behavior Research Methods, 37, 498–505.
61.
VenturaJ. (2018). Predicting working memory and fluid intelligence from measures of musicality. Unpublished Master’s Thesis, Louisiana State University. Retrieved from https://digitalcommons.lsu.edu/gradschool_theses/4755/
62.
WallentinM.NielsenA. H.Friis-OlivariusM.VuustC.VuustP. (2010). The Musical Ear Test, a new reliable test for measuring musical competence. Learning and Individual Differences, 20, 188–196.
63.
WingH. D. (1968). Tests of Musical Ability and Appreciation (2nd ed.). Cambridge, UK: Cambridge University Press.
64.
WolfA.KopiezR. (2018). Development and validation of the Musical Ear Training Assessment (META). Journal of Research in Music Education, 66(1), 53–70.
