Matching Items (3)
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- Genre: Diaries--Psychological aspects.
- Genre: Art--Study and teaching--Technological innovations.
- 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
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 present study explored the use of augmented reality (AR) technology to support cognitive modeling in an art-based learning environment. The AR application used in this study made visible the thought processes and observational techniques of art experts for the learning benefit of novices through digital annotations, overlays, and side-by-side comparisons that when viewed on mobile device appear directly on works of art.
Using a 2 x 3 factorial design, this study compared learner outcomes and motivation across technologies (audio-only, video, AR) and groupings (individuals, dyads) with 182 undergraduate and graduate students who were self-identified art novices. Learner outcomes were measured by post-activity spoken responses to a painting reproduction with the pre-activity response as a moderating variable. Motivation was measured by the sum score of a reduced version of the Instructional Materials Motivational Survey (IMMS), accounting for attention, relevance, confidence, and satisfaction, with total time spent in learning activity as the moderating variable. Information on participant demographics, technology usage, and art experience was also collected.
Participants were randomly assigned to one of six conditions that differed by technology and grouping before completing a learning activity where they viewed four high-resolution, printed-to-scale painting reproductions in a gallery-like setting while listening to audio-recorded conversations of two experts discussing the actual paintings. All participants listened to expert conversations but the video and AR conditions received visual supports via mobile device.
Though no main effects were found for technology or groupings, findings did include statistically significant higher learner outcomes in the elements of design subscale (characteristics most represented by the visual supports of the AR application) than the audio-only conditions. When participants saw digital representations of line, shape, and color directly on the paintings, they were more likely to identify those same features in the post-activity painting. Seeing what the experts see, in a situated environment, resulted in evidence that participants began to view paintings in a manner similar to the experts. This is evidence of the value of the temporal and spatial contiguity afforded by AR in cognitive modeling learning environments.
Using a 2 x 3 factorial design, this study compared learner outcomes and motivation across technologies (audio-only, video, AR) and groupings (individuals, dyads) with 182 undergraduate and graduate students who were self-identified art novices. Learner outcomes were measured by post-activity spoken responses to a painting reproduction with the pre-activity response as a moderating variable. Motivation was measured by the sum score of a reduced version of the Instructional Materials Motivational Survey (IMMS), accounting for attention, relevance, confidence, and satisfaction, with total time spent in learning activity as the moderating variable. Information on participant demographics, technology usage, and art experience was also collected.
Participants were randomly assigned to one of six conditions that differed by technology and grouping before completing a learning activity where they viewed four high-resolution, printed-to-scale painting reproductions in a gallery-like setting while listening to audio-recorded conversations of two experts discussing the actual paintings. All participants listened to expert conversations but the video and AR conditions received visual supports via mobile device.
Though no main effects were found for technology or groupings, findings did include statistically significant higher learner outcomes in the elements of design subscale (characteristics most represented by the visual supports of the AR application) than the audio-only conditions. When participants saw digital representations of line, shape, and color directly on the paintings, they were more likely to identify those same features in the post-activity painting. Seeing what the experts see, in a situated environment, resulted in evidence that participants began to view paintings in a manner similar to the experts. This is evidence of the value of the temporal and spatial contiguity afforded by AR in cognitive modeling learning environments.
ContributorsShapera, Daniel Michael (Author) / Atkinson, Robert K (Thesis advisor) / Nelson, Brian C (Committee member) / Erickson, Mary (Committee member) / Arizona State University (Publisher)
Created2016