Matching Items (3)
Filtering by
- Genre: Diaries
- Genre: Music--Instruction and study--Social aspects.
- 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
In this study, I uncover the coded meanings of "urban" within the music education profession through an exploration and analysis of the discourse present in two prominent music education journals, Music Educators Journal (MEJ) and The Journal of Research in Music Education (JRME). Using critical discourse analysis (CDA), I investigate how the term "urban" is used in statements within a twenty-year time span (1991-2010), and how the words "inner-city," "at-risk," "race," and "diversity" are used in similar ways throughout the corpus. An in-depth examination of these five terms across twenty years of two major publications of the profession reveals attitudes and biases within the music education structure, uncovering pejorative themes in the urban music education discourse. The phrase "urban music education" is rarely defined or explained in the corpus examined in this study. Rather, the word "urban" is at times a euphemism. Based on a CDA conducted in this study, I suggest that "urban" is code for poor, minority, and unable to succeed. Relying on the philosophical ideas of Michel Foucault, I uncover ways in which the profession labels urban music programs, students, and teachers and how the "urban music education" discourse privileges the White, suburban, middle class ideal of music education. I call for an evaluation of the perceptions of "success" in the field, and advocate for a paradigm shift, or different methods of knowing, in order to provide a more just teaching and learning space for all music education actors.
ContributorsFarmer, Dawn Marie (Author) / Stauffer, Sandra L. (Thesis advisor) / Schmidt, Margaret (Committee member) / Solis, Theodore (Committee member) / Sullivan, Jill M. (Committee member) / Tobias, Evan (Committee member) / Arizona State University (Publisher)
Created2015
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