Matching Items (4)
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- Genre: Diaries--Psychological aspects.
- Genre: Conference papers and proceedings
- Genre: Music--Arizona--21st century--History and criticism.
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
ABSTRACT A cultural overview of the so-called "early music movement" in Arizona, specifically the musicians who performed early music in the mid-to-late twentieth century, has never been undertaken. In applying ethnographic methods to Western art music, Kay Kaufman Shelemay suggests, in her 2001 article, "Toward an Ethnomusicology of the Early Music Movement," that a musical anthropology "would seem to hold great potential for the study of `Western music.'" In this paper I analyze and discuss issues related to "early music" in Arizona from roughly 1960 to 2008. In focusing primarily on the musicians themselves, I address issues in three primary areas: 1) the repertory and the so-called "early music revival;" 2) specific types of early music which have been presented in Arizona and the effects of economic factors; and 3) Arizona musicians' attitudes toward the repertory and their motivations for specializing in it. I then analyze Arizona musicians' involvement with both the early music repertory itself and with the community, identifying how musicians were exposed to early music and whether or not those first exposures began a long-lasting involvement with the repertory. In this section I also describe ways in which musicians define early music for themselves as well as analyze more critical areas such as musicians' formation of an "early music identity." I also asked informants to discuss how they see early music as being fundamentally different from other types of "classical" music and how they view their own places in that community of "difference." Finally, I compare musicians' thoughts on the "transformative" effect that some early music can have on performers and listeners and how that effect compares with similar phenomena in other types of Western art music.
ContributorsDe Fazio, James M (Author) / Haefer, John R (Thesis advisor) / Solis, Theodore (Committee member) / Saucier, Catherine (Committee member) / Arizona State University (Publisher)
Created2010
Description
Human team members show a remarkable ability to infer the state of their partners and anticipate their needs and actions. Prior research demonstrates that an artificial system can make some predictions accurately concerning artificial agents. This study investigated whether an artificial system could generate a robust Theory of Mind of human teammates. An urban search and rescue (USAR) task environment was developed to elicit human teamwork and evaluate inference and prediction about team members by software agents and humans. The task varied team members’ roles and skills, types of task synchronization and interdependence, task risk and reward, completeness of mission planning, and information asymmetry. The task was implemented in MinecraftTM and applied in a study of 64 teams, each with three remotely distributed members. An evaluation of six Artificial Social Intelligences (ASI) and several human observers addressed the accuracy with which each predicted team performance, inferred experimentally manipulated knowledge of team members, and predicted member actions. All agents performed above chance; humans slightly outperformed ASI agents on some tasks and significantly outperformed ASI agents on others; no one ASI agent reliably outperformed the others; and the accuracy of ASI agents and human observers improved rapidly though modestly during the brief trials.
ContributorsFreeman, Jared T. (Author) / Huang, Lixiao (Author) / Woods, Matt (Author) / Cauffman, Stephen J. (Author)
Created2021-11-04
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