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Daily dairies and other intensive measurement methods are increasingly used to study the relationships between two time varying variables X and Y. These data are commonly analyzed using longitudinal multilevel or bivariate growth curve models that allow for random effects of intercept (and sometimes also slope) but which do not

Daily dairies and other intensive measurement methods are increasingly used to study the relationships between two time varying variables X and Y. These data are commonly analyzed using longitudinal multilevel or bivariate growth curve models that allow for random effects of intercept (and sometimes also slope) but which do not address the effects of weekly cycles in the data. Three Monte Carlo studies investigated the impact of omitting the weekly cycles in daily dairy data under the multilevel model framework. In cases where cycles existed in both the time-varying predictor series (X) and the time-varying outcome series (Y) but were ignored, the effects of the within- and between-person components of X on Y tended to be biased, as were their corresponding standard errors. The direction and magnitude of the bias depended on the phase difference between the cycles in the two series. In cases where cycles existed in only one series but were ignored, the standard errors of the regression coefficients for the within- and between-person components of X tended to be biased, and the direction and magnitude of bias depended on which series contained cyclical components.
ContributorsLiu, Yu (Author) / West, Stephen G. (Thesis advisor) / Enders, Craig K. (Committee member) / Reiser, Mark R. (Committee member) / Arizona State University (Publisher)
Created2013
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Description
ABSTRACT

The early music revival in Paris, which came into full swing in the 1890s, had a defining impact on the composer Claude Debussy. Among the leaders of this movement were the Chanteurs de Saint Gervais under the direction of Charles Bordes and the Schola Cantorum, a school Bordes founded for

ABSTRACT

The early music revival in Paris, which came into full swing in the 1890s, had a defining impact on the composer Claude Debussy. Among the leaders of this movement were the Chanteurs de Saint Gervais under the direction of Charles Bordes and the Schola Cantorum, a school Bordes founded for the study and performance of early music in Paris. Debussy wrote admiringly of the performances of the Chanteurs and opera productions he saw at the Schola. He also spoke of the revelatory nature of performances of Renaissance masses that he heard in Italy after he won the Prix de Rome. Finally, he most likely visited Solesmes, important in the revival of plainchant. Hitherto unknown documents raise questions about the date of that visit, which most likely took place in 1892 or 1893.

A powerful manifestation of the influence of early music on Debussy’s compositional style is a melodic gesture that he referred to as “arabesque.” Debussy made many comments about the “divine arabesque,” which he related to the “primitives,” Palestrina, Victoria, and di Lasso. Further, Debussy connected those composers’ use of the arabesque to plainchant: “They found the basis of [the arabesque] in Gregorian chant, whose delicate tracery they supported with twining counterpoints.”

Debussy’s writings on early music provide a deeper context for understanding how plainchant, as well as music from the Renaissance, contributed to his compositional style, specifically in his use of modes and his notion of the arabesque. These influences are especially apparent in his only a cappella choral work, Trois chansons de Charles d’Orléans.

Until now, analysis of the Trois chansons has not sufficiently considered the importance of either plainchant or the arabesque and their influence on the style and character of this work. Viewing Debussy’s musical aesthetic through the lens of plainchant and the arabesque brings his music to life in a new and exciting way, resulting in a richer understanding and more informed performance practice, especially in the Trois chansons de Charles d’Orléans.
ContributorsRynex, Carolyn Rose (Author) / Schildkret, David (Thesis advisor) / Kopta, Anne (Committee member) / Saucier, Catherine (Committee member) / Arizona State University (Publisher)
Created2016
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Description
The comparison of between- versus within-person relations addresses a central issue in psychological research regarding whether group-level relations among variables generalize to individual group members. Between- and within-person effects may differ in magnitude as well as direction, and contextual multilevel models can accommodate this difference. Contextual multilevel models have been

The comparison of between- versus within-person relations addresses a central issue in psychological research regarding whether group-level relations among variables generalize to individual group members. Between- and within-person effects may differ in magnitude as well as direction, and contextual multilevel models can accommodate this difference. Contextual multilevel models have been explicated mostly for cross-sectional data, but they can also be applied to longitudinal data where level-1 effects represent within-person relations and level-2 effects represent between-person relations. With longitudinal data, estimating the contextual effect allows direct evaluation of whether between-person and within-person effects differ. Furthermore, these models, unlike single-level models, permit individual differences by allowing within-person slopes to vary across individuals. This study examined the statistical performance of the contextual model with a random slope for longitudinal within-person fluctuation data.

A Monte Carlo simulation was used to generate data based on the contextual multilevel model, where sample size, effect size, and intraclass correlation (ICC) of the predictor variable were varied. The effects of simulation factors on parameter bias, parameter variability, and standard error accuracy were assessed. Parameter estimates were in general unbiased. Power to detect the slope variance and contextual effect was over 80% for most conditions, except some of the smaller sample size conditions. Type I error rates for the contextual effect were also high for some of the smaller sample size conditions. Conclusions and future directions are discussed.
ContributorsWurpts, Ingrid Carlson (Author) / Mackinnon, David P (Thesis advisor) / West, Stephen G. (Committee member) / Grimm, Kevin J. (Committee member) / Suk, Hye Won (Committee member) / Arizona State University (Publisher)
Created2016