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- Genre: Diaries
- Genre: Short stories
- 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
DescriptionA collection of stories as viewed through the lens of Oulipo methodology.
ContributorsHyde, Allegra (Author) / McNally, Thomas (Thesis advisor) / Ison, Tara (Committee member) / Pritchard, Melissa (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
Description
The year is 1982 and the Mayan genocide is at its bloodiest. Ava, the daughter of a Ladino meat-shop owner and his bed-ridden wife, marries a Mayan radish farmer known as “K.” After K disappears alongside thousands of indigenous Maya, Ava hides with their daughter, Olivia, inside their cornstalk house in the town of Peña Blanca. When Olivia is infected with lesions, Ava must venture outside for the first time in months and bear witness to the lingering spirit of the disappeared. Inspired by the unrelenting immigrant spirit and one nation’s own brokenness, The Quiet Yellowing of Birds is a novel interspersed between Ava’s privileged past and her harrowing present, between the highlands of Guatemala, the refugee camps of Campeche, and the cacti-lined cul-de-sacs of Arizona. Written in both the past and present tense, this novel is a reflection of Guatemala’s fractured narrative, of the nonlinear immigrant experience.
ContributorsAlvarez, María Isabel (Author) / Rios, Alberto A. (Thesis advisor) / Bell, Matt D. (Committee member) / Ison, Tara (Committee member) / Arizona State University (Publisher)
Created2017