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- Genre: Diaries--Psychological aspects.
- Genre: Music--Peru--History and criticism.
- Creators: Arizona State University
Description
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
Description
Due to the recent inclusion of a semi-regular "News from Latin America" column since 2007 in The Clarinet magazine and an increased emphasis on world music genre performances at the International Clarinet Association's annual ClarinetFest, Latin American clarinet compositions have become increasingly popular. Consequently, Latin American performers and composers are receiving more attention and recognition than ever before. The contemporary repertoire for clarinet increasingly includes works highlighted at the ClarinetFest international festivals, and many clarinetists express interest in finding new Latin American compositions. In order to supplement this growing Latin American repertoire and to introduce the life and works of Peruvian composer Armando Guevara Ochoa (1926-2013), this project presents a brief biography of the composer, a discussion of his musical style, and new editions of his popular works transcribed for clarinet. A recording of these works is included in an appendix to this document. Prior to this research, much of the scholarship written about Guevara Ochoa was in Spanish. While most sources and scholars relate that Guevara Ochoa composed over 400 works, the whereabouts of fewer than 200 are currently known. This project will supplement Guevara Ochoa's clarinet literature and raise awareness of his compositions in English-speaking countries.
ContributorsPalmer, Katherine H (Author) / Spring, Robert (Thesis advisor) / Micklich, Albie (Committee member) / Norton, Kay (Committee member) / Solís, Ted (Committee member) / Hill, Gary (Committee member) / Arizona State University (Publisher)
Created2013
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 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.
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