Matching Items (3)
Filtering by
- Genre: Diaries
- Genre: Music--Social aspects--Brazil--Salvador.
- 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
Geography, and the social sciences more broadly, have long operated within what is arguably a paradigm of the visual. Expanding the reach of geographical consideration into the realm of the aural, though in no way leaving behind the visual, opens the discipline to new areas of human and cultural geography invisible in ocular-centric approaches. At its broadest level, my argument in this dissertation is that music can no longer be simply an object of geographical research. Re-conceptualized and re-theorized in a geographical context to take into account its very real, active, and more-than-representational presence in social life, music provides actual routes to geographic knowledge of the world. I start by constructing a theoretical framework and methodological approach for studying music beyond representation. Based on these theoretical and methodological arguments, I present four narratives that unfold at the intersections of race and music in the northeast Brazilian city of Salvador. From the favelas of Rio de Janeiro to the troubled neighborhood of the Pelourinho, from the manic tempos of samba to the laid back grooves of samba-reggae, and in the year-round competition between the oppressive forces of ordinary time and the fleeting possibility of carnival, music emerges as a creative societal force with affects and effects far beyond the realm of representation. Together, these narratives exemplify the importance of expanding geographical considerations beyond a strictly visual framework. These narratives contribute to the musicalization of the discipline of geography.
ContributorsFinn, John C (Author) / McHugh, Kevin (Thesis advisor) / Lukinbeal, Christopher (Committee member) / Bolin, Bob (Committee member) / Price, Patricia (Committee member) / Arizona State University (Publisher)
Created2011
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