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Ensemble pitch and rhythm error discrimination : the identification and selection of predictors

University of British Columbia

This study investigated relationships between 36 predictor variables and ensemble pitch and rhythm error discrimination ability. Precollege musical background and other demographic data were collected by means of the Musical Background Questionnaire. Musical achievement was measured by the Aliferis-Stecklein Music Achievement Test, College Midpoint Level. Undergraduate musical coursework data were obtained from transcripts. The criterion variables were measured by the Ramsey-Vincent Test of Instrumental Error Detection; a test of aural-visual pitch and rhythm error discrimination for full-score band music of medium difficulty. All three instruments were administered to 82 undergraduate music students. Subjects represented three Canadian universities and two community colleges. Pearson product-moment correlation tests were used to identify variables significantly related to musical ensemble error discrimination at the .10 level of significance. Eighteen variables were found to be significantly related to ensemble pitch error discrimination. Fourteen variables were found to be significantly related to ensemble rhythm error discrimination. Regression procedures were performed for each of the significant variables. These variables were then organized into blocks representing precollege musical background, other demographic variables, musical achievement, and undergraduate coursework. Regressions were performed for each of the blocks. Musical achievement, precollege musical background, demographic, and undergraduate coursework blocks of variables accounted for 5, 15, 35, and 21 percent of the variance in ensemble pitch error discrimination scores respectively. Musical achievement, precollege musical background, demographic, and undergraduate coursework blocks of variables accounted for 21, 16, 19, and 12 percent of the variance in ensemble rhythm error discrimination scores respectively. Combinations of variables from these blocks produced a linear model comprised of five demographic variables plus precollege choral experience that accounted for 42 percent of the variance in ensemble pitch error discrimination scores. Combinations of variables from the four blocks produced a linear model of ensemble rhythm error discrimination comprised of rhythmic discrimination, choice of a band instrument as one's major performance medium, composition as one's program major, and precollege band or orchestral experience. These four variables accounted for 32 percent of the variance in ensemble rhythm error discrimination scores. The variables selected for use in this study accounted for a substantial portion of the variance in error discrimination scores. To improve the predictive power of future studies, other variables need to be identified and included in the model. Ten conclusions were made regarding the prediction of ensemble error prediction ability. Three recommendations were made for improving error discrimination training and seven recommendations were made for future research in ensemble error discrimination.

Text

2011-03-14

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http://hdl.handle.net/2429/32443

Education

University of British Columbia

Graduate