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- Genre: Diaries--Psychological aspects.
- Genre: Creative nonfiction
- 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
This dissertation is both creative and scholarly, engaging in the technique of "narrative scholarship," an increasingly accepted technique within the field of ecocriticism. The project is framed by my experiences with Spanish and Latino actors as well as activists involved with the 15-M movement in and around Madrid. It takes a "material ecocritical" approach, which is to say that it treats minds, spirits and language as necessarily "bodied" entities, and creates an absolute union between beings and the matter that constructs them as well as their habitat. I apply the lens of Jesper Hoffmeyer's Biosemiotics, which claims that life is at its most essential levels a communicative process. In other words, I will explore how "all matter is 'storied' matter," as well as how the "semiosphere," which is an important concept in biosmiotics, signaling a semiotic environment that predicts and defines all biological bodies/life, the human, the plant and the animal as beings who are made of and involved in semiotic activity, can serve as a basis for union amongst all bodies and provide a model of cooperation rooted in "storytelling." My project aims to embody what Wendy Wheeler describes as ecocriticism's, "syntheses between the sciences and the humanities" It is my strong opinion that creative writing has the power to offer the general public insight into the reasons why new research in biosemiotics is so important to the work that activists are doing to raise awareness of how humans can live responsibly on the only planet that is our home. This will help readers of creative writing and cultural studies scholars understand why they ought to embrace science, especially in literary and cultural studies, as a path to better understanding of the role of the humanities in an increasingly scientifically oriented world.
ContributorsDay, Timothy (Author) / Adamson, Joni (Thesis advisor) / Gutkind, Lee (Committee member) / Flys-Junquera, Carmen (Committee member) / Arizona State University (Publisher)
Created2016
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