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- Genre: Diaries
- Genre: Art--Study and teaching--Study and teaching (Higher)
- 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
We live in a world of rapidly changing technologies that bathe us in visual images and information, not only challenging us to find connections and make sense of what we are learning, but also allowing us to learn and to collaborate in new ways. Art educators are using one of these new technologies, virtual worlds, to create educational environments and curricula. This study looks at how post-secondary art educators are using Second Life in their undergraduate and graduate level curricula and what perceived benefits, challenges, and unique learning experiences they feel this new educational venue offers. This study uses qualitative and participant observation methodologies, including qualitative interviews, observations, and collection of generated works, to look at the practices of six art educators teaching university level undergraduate and graduate courses. Data are compared internally between the participants and externally by correlating to current research. Art education in Second Life includes many curricula activities and strategies often seen in face-to-face classes, including writing reflections, essays, and papers, creating presentations and Power Points, conducting research, and creating art. Challenges include expense, student frustration and anxiety issues, and the transience of Second Life sites. Among the unique learning experiences are increased opportunities for field trips, student collaboration, access to guest speakers, and the ability to set up experiences not practical or possible in the real world. The experiences of these six art educators can be used as a guide for art educators just beginning exploration of virtual world education and encouragement when looking for new ways to teach that may increase our students' understanding and knowledge and their access and connections to others.
ContributorsSchlegel, Deborah (Author) / Stokrocki, Mary (Thesis advisor) / Erickson, Mary (Committee member) / Young, Bernard (Committee member) / Arizona State University (Publisher)
Created2014
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