In this study, we suggest a typology of stage entrance behaviour of performers, based on audience members’ first impression of selected video recordings of an international violin competition. For the first time in performance research, we have focused on the stage entrance, which commences with the performers’ first appearance and lasts until the first tone is played. Against the theoretical background of the social interaction theory by Goffman, with its emphasis on the role of impression management, we assume that performance evaluation can only be understood as an interaction between expectations from audience sub-classes and behaviour groups (types) of performers. We conducted an analysis of performer behaviour in three steps: (a) Using methods of classical test theory and item response theory, we presented a selection of six items that describe performer’s impression management with regard to the audience’s impression formation (nodding, direction of gaze, touching one’s self, stance width, step size, resolute impression); (b) by means of a multi-level latent class analysis, we came up with three latent classes of the audience’s first impression judgments (appropriate, acceptable and inappropriate stage entrance behaviour) resulting in two latent groups (types) of performers’ stage entrance behaviour evaluation (match or mismatch to the audience’s expectations); (c) the association between audience first impression classes and the audience’s motivation for performance continuation was used as an indicator for a more in-depth engagement with a particular performance. Our finding of a statistical model-based typology allows the integration of a performer behaviour that had previously only been marginalized as irrelevant. We argue that the evaluation of audio-visual music performance remains within the framework of general rules of social interaction. Consequently, for an adequate understanding of music performance, we suggest a model of performance elaboration as an alternative to models of musical communication.
AdamsR. J.GonzalesE. J. (1996). The TIMSS test design. In MartinM. O.KelleyD. L. (Eds.), Third international mathematics and science study. Technical report (Vol. I: Design and development, pp. 1–36). Chestnut Hill, MA: Boston College.
2.
Adobe Systems Incorporated (2011). Adobe Audition (CS5.5) [Computer software]. San Jose, CA: Adobe Systems Incorporated.
3.
AldenderferM. S.BlashfieldR. K. (1984). Cluster analysis. Newbury Park, CA: SAGE.
4.
American Psychological Association. (2007). APA dictionary of psychology. Washington, DC: American Psychological Association.
5.
AndersenE. B. (1973). A goodness of fit test for the Rasch model. Psychometrika, 38(1), 123–140.
6.
AndersenE. B. (1977). Sufficient statistics and latent trait models. Psychometrika, 42(1), 69–81.
7.
BeatonA. E. (1987). Implementing the new design: The NAEP 1983–84 technical report. Princeton, NJ: Educational Testing Service.
8.
BerloD. K.LemertJ. B.MertzR. J. (1969). Dimensions for evaluating the acceptability of message sources. Public Opinion Quarterly, 33(4), 563–576.
9.
BogenK. (1996, May). The effect of questionnaire length and response rates: A review of literature. Paper presented at the Proceedings of the Section on Survey Research Methods, Alexandria, Virginia.
10.
BondT. G.FoxC. M. (2007). Applying the Rasch model: Fundamental measurement in the human sciences (2nd ed.). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.
11.
BoyleJ. D.RadocyR. E. (1987). Measurementand evaluation of musical experiences. New York, NY: Schirmer Books.
12.
BozdoganH. (1987). Model selection and Akaike’s Information Criterion (AIC): The general theory and its analytical extensions. Psychometrika, 52(3), 345–370.
13.
BurgoonJ. K.BirkT.PfauM. (1990). Nonverbal behaviors, persuasion, and credibility. Human Communication Research, 17, 140–169.
14.
BurnhamK. P. (2004). Multimodel inference: Understanding AIC and BIC in model selection. Sociological Methods & Research, 33(2), 261–304.
15.
CernyB. A.KaiserH. F. (1977). A study of a measure of sampling adequacy for factor-analytic correlation matrices. Multivariate Behavioral Research, 12(1), 43–47.
16.
CliffN. (1988). The Eigenvalues-Greater-Than-One rule and the reliability of components. Psychological Bulletin, 103(2), 276–279.
17.
CohenJ. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). New York, NY: Psychology Press.
18.
CookN. (2008). Beyond the notes. Nature, 453(26), 1186–1187.
19.
CostelloA. B.OsborneJ. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation, 10(7), 1–9.
20.
DavidsonJ. W. (1993). Visual perception of performance manner in the movements of solo musicians. Psychology of Music, 21(2), 103–113.
21.
DavidsonJ. W.CorreiaJ. S. (2002). Body movement. In ParncuttR.McPhersonG. E. (Eds.), The science & psychology of music performance (pp. 237–250). New York, NY: Oxford University Press.
22.
DaviesS. (1994). Musical meaning and expression. Ithaca, NY: Cornell University Press.
23.
de AyalaR. J. (2009). The theory and practice of item response theory. New York, NY: The Guilford Press.
24.
DuntemanG. H. (1989). Principal component analysis. Newbury Park, CA: SAGE.
25.
DziubanC. D.ShirkeyE. C. (1974). When is a correlation matrix appropriate for factor analysis?Psychological Bulletin, 81(6), 358–361.
26.
EdwardsP.RobertsI.ClarkeM.DiGuiseppiC.PratapS.WentzR.KwanI. (2002). Increasing response rates to postal questionnaires: Systematic review. BMJ, 324, 1183.
27.
EmbretsonS. E.HershbergerS. L. (1999). Summary and future of psychometric methods in testing. In EmbretsonS. E.HershbergerS. L. (Eds.), The new rules of measurement: What every psychologist and educator should know (pp. 243–254). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.
28.
EricssonK. A.KrampeR. T.Tesch-RömerC. (1993). The role of deliberate practice in the acquisition of expert performance. Psychological Review, 100(3), 363–406.
29.
FabrigarL. R.WegenerD. T.MacCallumR. C.StrahanE. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299.
30.
FischerL.BraunsD.BelschakF. (2002). Zur Messung von Emotionen in der angewandten Forschung. Analysen mit den SAMs – Self-Assessment-Manikin [The measurement of emotions in applied science: Analyses with the SAM - Self-Assessment-Manikin]. Lengerich, Germany: Pabst Science Publishers.
31.
FiskeS. T.LinM.NeubergS. L. (1999). The continuum model: Ten years later. In ChaikenS.TropeY. (Eds.), Dual process theories in social psychology (pp. 231–254). New York, NY: Guilford.
32.
FiskeS. T.TaylorS. E. (1991). Social cognition. Reading, MA: Addison-Wesley.
33.
GabrielssonA. (1999). Music performance. In DeutschD. (Ed.), The psychology of music (2nd ed., pp. 501–602). San Diego, CA: Academic Press.
34.
GabrielssonA. (2003). Music performance research at the millennium. Psychology of Music, 31(3), 221–272.
35.
GoffmanE. (1959). The presentation of self in everyday life. London, UK: Penguin Books.
36.
GriffithsN. K. (2008). The effects of concert dress and physical appearance on perceptions of female solo performers. Musicae Scientiae, 12(2), 273–290.
37.
HabermanS. J. (1979). Analysis of qualitative data: Vol. 2. New developments. New York, NY: Academic Press.
38.
HagenaarsJ. A.HalmanL. C. (1989). Searching for ideal types: The potentialities of latent class analysis. European Sociological Review, 5(1), 81–96.
39.
HigginsE. T. (1997). Beyond pleasure and pain. American Psychologist, 52(12), 1280–1300.
40.
HovlandC. I.JanisI. L.KelleyH. H. (1953). Communication and persuasion. New Haven, CT: Yale University Press.
41.
HuronD. (2006). Sweet anticipation: Music and the psychology of expectation. Cambridge, MA: The MIT Press.
42.
JuslinP. N. (2005). From mimesis to catharsis: Expression, perception, and induction of emotion in music. In HargreavesD. J.MacDonaldR.MiellD. (Eds.), Musical communication (pp. 85–115). New York, NY: Oxford University Press.
43.
KaiserH. F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20(1), 141–151.
44.
KaiserH. F. (1970). A second generation Little Jiffy. Psychometrika, 35(4), 401–415.
45.
KlicksteinG. (2009). The musician’s way. Oxford, UK: Oxford University Press.
46.
KnapeJ. (2003). Persuasion. In UedingG.WalterJ. (Eds.), Historisches Wörterbuch der Rhetorik (Vol. 6). Tübingen, Germany: Niemeyer.
47.
LazarsfeldP. F. (1950). The logical and mathematical foundation of latent structure analysis and the interpretation and mathematical foundation of latent structure analysis. In StoufferS. A., et al. (Eds.), Measurement and prediction (pp. 362–472). Princeton, NJ: Princeton University Press.
48.
LehmannA. C.SlobodaJ. A.WoodyR. H. (2007). Psychology for musicians. Understanding and acquiring the skills. Oxford, UK: Oxford University Press.
49.
LemanM. (2008). Embodied music cognition and mediation technology. Cambridge, MA: MIT Press.
50.
MagidsonJ.VermuntJ. K. (2004). Latent class models. In KaplanD. (Ed.), The SAGE handbook of quantitative methodology for the social sciences (pp. 175–198). London, UK: SAGE.
51.
MairP.HatzingerR. (2007). Extended Rasch modeling: The eRm package for the application of IRT models in R. Journal of Statistical Software, 20(9), 1–25.
52.
McLachlanG.PeelD. (2000). Finite mixture models. New York, NY: John Wiley & Sons.
53.
McPhersonG. E.SchubertE. (2004). Measuring performance enhancement in music. In WilliamonA. (Ed.), Musical excellence: Strategies and techniques to enhance performance (pp. 61–82). New York, NY: Oxford University Press.
54.
NandakumarR.AckermanT. (2004). Test modeling. In KaplanD. (Ed.), The SAGE handbook of quantitative methodology for the social sciences (pp. 93–105). London, UK: SAGE.
55.
O’ConnorB. P. (2000). SPSS and SAS programs for determining the number of components using parallel analysis and Velicer’s MAP test. Behavior Research Methods, Instruments, & Computers, 32(3), 396–402.
56.
OllenJ. E. (2006). A criterion-related validity test of selected indicators of musical sophistication using expert ratings. Dissertation Abstracts International: Section A. The Humanities and Social Sciences Collection, 67(10), 3238158.
57.
PettyR. E.CacioppoJ. T. (1986). The elaboration likelihood model of persuasion. Advances in Experimental Social Psychology, 19, 123–205.
58.
PettyR. E.WegenerD. T. (1999). The Elaboration likelihood model: Current status and controversies. In ChaikenS.TropeY. (Eds.), Dual-process theories in social psychology (pp. 41–72). New York, NY: The Guilford Press.
59.
PlatzF.KopiezR. (2012). When the eye listens: A meta-analysis of how audio-visual presentation enhances the appreciation of music performance. Music Perception, 30(1), 71–83.
60.
PonocnyI. (2001). Nonparametric goodness-of-fit tests for the Rasch model. Psychometrika, 66(3), 437–460.
61.
R Development Core Team. (2011). R: A language and environment for statistical computing [Computer software]. Vienna, Austria. Retrieved from http://www.R-project.org/
62.
RaschG. (1960). Probabilistic models for some intelligence and attainment tests. Copenhagen, Denmark: Danmarks Pædagogiske Institut.
63.
ReipsU.-D. (2002a). Internet-based psychological experimenting: Five dos and five don’ts. Social Science Computer Review, 20(3), 241–249.
64.
ReipsU.-D. (2002b). Standards for Internet-based experimenting. Experimental Psychology, 49(4), 243–256.
65.
RizopoulosD. (2006). ltm: An R package for latent variable modeling and item response theory analyses. Journal of Statistical Software, 17(5), 1–25.
66.
RostJ.LangeheineR. (1997). A guide through latent structure models for categorical data. In RostJ.LangeheineR. (Eds.), Applications of latent trait and latent class models in the social sciences (pp. 13–37). Münster, Germany: Waxmann Verlag.
67.
SchumannR. (1985). Gesammelte Schriften über Musik und Musiker [Collected writings on music and musicians] (Vols. 3 & 4). Wiesbaden, Germany: Breitkopf & Härtel. (Original work published 1854)
68.
SchwarzG. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461–464.
69.
SeashoreC. E. (1938). Psychology of music. Columbus, OH: McGraw-Hill.
70.
SnijdersT. A. B.BoskerR. J. (2011). Multilevel analysis. An introduction to basic and advanced multilevel modeling (2nd ed.). Thousand Oaks, CA: AGE.
71.
StanleyM.BrookerR.GilbertR. (2002). Examiner perceptions of using criteria in music performance assessment. Research Studies in Music Education, 18(1), 46–56.
72.
StevensS. S. (1946). On the theory of scales of measurement. Science, 103(2684), 677–680.
73.
ThompsonS.WilliamonA. (2003). Evaluating evaluation: Musical performance assessment as a research tool. Music Perception, 21(1), 21–41.
74.
ThompsonS.WilliamonA.ValentineE. (2007). Time-dependent characteristics of performance evaluation. Music Perception, 25(1), 13–29.
75.
TsayC.-J. (2012, July). The impact of visual cues on the judgment and perceptions of music performance. Paper presented at the ICMPC – ESCOM 2012 Joint Conference, Thessaloniki, Greece. Abstract retrieved from http://icmpc-escom2012.web.auth.gr/?q=node/67
76.
UNESCO. (1997). International standard classification of education: ISCED 1997. UNESCO.
77.
VermuntJ. K. (2003). Multilevel latent class models. Sociological Methodology, 33, 213–239.
78.
VermuntJ. K. (2008). Multilevel latent variable modeling: An application in education testing. Austrian Journal of Statistics, 37(364), 285–299.
79.
VermuntJ. K. (2010). Latent class models. In PetersonP.BakerE.McGawB. (Eds.), International Encyclopedia of Education (Vol. 7, pp. 238–244). Oxford, UK: Elsevier.
80.
VermuntJ. K.MagidsonJ. (2004a). Latent variable. In Lewis-BeckM. S.BrymanA.LiaoT. F. (Eds.), The SAGE encyclopedia of social sciences research methods (pp. 555–556). Thousand Oaks, CA: SAGE.
81.
VermuntJ. K.MagidsonJ. (2004b). Local independence. In Lewis-BeckM. S.BrymanA.LiaoT. F. (Eds.), The SAGE encyclopedia of social sciences research methods (pp. 580–582). Thousand Oaks, CA: SAGE.
82.
VermuntJ. K.MagidsonJ. (2004c). Nonparametric random-effects model. In Lewis-BeckM. S.BrymanA.LiaoT. F. (Eds.), The Sage encyclopedia of social sciences research methods (pp. 733–734). Thousand Oaks, CA: SAGE.
VermuntJ. K.MagidsonJ. (2005b). Technical guide for Latent GOLD 4.0: Basic and advanced. Belmont, MA: Statistical Innovations Inc.
85.
von DavierM. (1997). Bootstrapping goodness-of-fit statistics for sparse categorical data: Results of a Monte Carlo study. Methods of Psychological Research Online, 2(2), 29–48.
86.
WaldA. (1943). Test of statistical hypotheses concerning several parameters when the number of observations is large. Transactions of the American Mathematical Society, 54(3), 426–482.
87.
WapnickJ.CampbellL.Siddell-StrebelJ.DarrowA.-A. (2009). Effects of non-musical attributes and excerpt duration on ratings of high-level piano performances. Musicae Scientiae, 13(1), 35–54.
88.
WapnickJ.RyanC.CampbellL.DeekP.LemireR.DarrowA.-A. (2005). Effects of excerpt tempo and duration on musicians’ ratings of high-level piano performances. Journal of Research in Music Education, 53(2), 162–176.
89.
WilsonE. J.SherrellD. L. (1993). Source effects in communication and persuasion research: A meta-analysis of effect size. Journal of the Academy of Market Science, 21(2), 101–112.
YbarraO. (2001). When first impressions don’t last: The role of isolation and adaptation processes in the revision of evaluative impressions. Social Cognition, 19(5), 491–520.
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