The present study investigated estimation of the variance of the cross-classified factors’ random effects’ interaction for cross-classified data structures. Results for two different three-level cross-classified random effects model (CCREM) were compared: Model 1 included the estimation of this variance component and Model 2 assumed the value of this variance component was zero and did not estimate it. The second model is the model most commonly assumed by researchers utilizing a CCREM to estimate cross-classified data structures. These two models were first applied to a real world data set. Parameter estimates for both estimating models were compared. The results for this analysis served as a guide to provide generating parameter values for the Monte Carlo simulation that followed. The Monte Carlo simulation was conducted to compare the two estimating models under several manipulated conditions and assess their impact on parameter recovery. The manipulated conditions included: classroom sample size, the structure of the cross-classification, the intra-unit correlation coefficient (IUCC), and the cross-classified factors’ variance component values. Relative parameter and standard error bias were calculated for fixed effect coefficient estimates, random effects’ variance components, and the associated standard errors for both. When Model 1 was used to estimate the simulated data, no substantial bias was found for any of the parameter estimates or their associated standard errors. Further, no substantial bias was found for conditions with the smallest average within-cell sample size (4 students). When Model 2 was used to estimate the simulated data, substantial bias occurred for the level-1 and level-2 variance components. Several of the manipulated conditions in the study impacted the magnitude of the bias for these variance estimates. Given that level-1 and level-2 variance components can often be used to inform researchers’ decisions about factors of interest, like classroom effects, assessment of possible bias in these estimates is important. The results are discussed, followed by implications and recommendations for applied researchers who are using a CCREM to estimate cross-classified data structures. / text

The current study investigates the influence of auditory cues on the localization of briefly presented peripheral visual stimuli. Because the brief presentation of peripheral visual stimuli often leads to mislocalization (Binda, Morrone, & Burr, 2010; Bocianski, Musseler, & Erlhagen, 2008; Musseler, Heijden, Mahmud, Dubel, & Ertsey, 1999) these types of stimuli are the most commonly studied and represent the basis of the current study. Musseler et al. (1999) found that peripheral mislocalization toward the fovea occurred during asynchronous presentations of a pair of visual stimuli in retinal periphery, but not during synchronous presentations of stimuli. The current project is an investigation of how sound influences mislocalization of briefly presented peripheral stimuli. If the mechanism of mislocalization is an increased variability of responses when the peripheral stimuli are presented asynchronously, could sound reduce the variability of localization judgments and thus, reduce or eliminate the mislocalization effect? Does sound influence peripheral mislocalization in some other way? This study found that during a relative judgment task, a brief, laterally presented sound leads to mislocalization of a target stimulus toward the direction of the sound (Experiment 1). During an absolute judgment task, however, the influence of the brief, laterally presented sound no longer evokes mislocalization in the direction of the sound. Rather, stimulus onset asynchrony elicits mislocalization similar to the results of Musseler et al. (Experiment 2). When a dynamic sound stimulus occurs prior to the onset of the target stimulus during an absolute judgment task, however, sound idiosyncratically influences the localization of a target stimulus toward the onset of the sound stimulus or direction of the apparent motion of the sound stimulus (Experiment 3).

This thesis consists of three chapters on the statistical adjustment, calibration, and uncertainty quantification of complex computer models with applications in engineering. The first chapter systematically develops an engineering-driven statistical adjustment and calibration framework, the second chapter deals with the calibration of potassium current model in a cardiac cell, and the third chapter develops an emulator-based approach for propagating input parameter uncertainty in a solid end milling process. Engineering model development involves several simplifying assumptions for the purpose of mathematical tractability which are often not realistic in practice. This leads to discrepancies in the model predictions. A commonly used statistical approach to overcome this problem is to build a statistical model for the discrepancies between the engineering model and observed data. In contrast, an engineering approach would be to find the causes of discrepancy and fix the engineering model using first principles. However, the engineering approach is time consuming, whereas the statistical approach is fast. The drawback of the statistical approach is that it treats the engineering model as a black box and therefore, the statistically adjusted models lack physical interpretability. In the first chapter, we propose a new framework for model calibration and statistical adjustment. It tries to open up the black box using simple main effects analysis and graphical plots and introduces statistical models inside the engineering model. This approach leads to simpler adjustment models that are physically more interpretable. The approach is illustrated using a model for predicting the cutting forces in a laser-assisted mechanical micromachining process and a model for predicting the temperature of outlet air in a fluidized-bed process. The second chapter studies the calibration of a computer model of potassium currents in a cardiac cell. The computer model is expensive to evaluate and contains twenty-four unknown parameters, which makes the calibration challenging for the traditional methods using kriging. Another difficulty with this problem is the presence of large cell-to-cell variation, which is modeled through random effects. We propose physics-driven strategies for the approximation of the computer model and an efficient method for the identification and estimation of parameters in this high-dimensional nonlinear mixed-effects statistical model. Traditional sampling-based approaches to uncertainty quantification can be slow if the computer model is computationally expensive. In such cases, an easy-to-evaluate emulator can be used to replace the computer model to improve the computational efficiency. However, the traditional technique using kriging is found to perform poorly for the solid end milling process. In chapter three, we develop a new emulator, in which a base function is used to capture the general trend of the output. We propose optimal experimental design strategies for fitting the emulator. We call our proposed emulator local base emulator. Using the solid end milling example, we show that the local base emulator is an efficient and accurate technique for uncertainty quantification and has advantages over the other traditional tools.

Classroom observations have been increasingly used for teacher evaluations, and thus it is important to examine the measurement quality and the use of observation ratings. When a teacher is observed in multiple classrooms, his or her observation ratings may vary across classrooms. In that case, using ratings from one classroom per teacher may not be adequate to represent a teacher’s quality of instruction. However, the fact that classrooms are nested within teachers is usually not considered while classroom observation data is analyzed. Drawing on the Measures of Effective Teaching dataset, this dissertation examined the variation of a teacher’s classroom observation ratings across his or her multiple classrooms. In order to account for the teacher-level, school-level, and rater-level variation, a cross-classified random effects model was used for the analysis. Two research questions were addressed: (1) What is the variation of a teacher’s classroom observation ratings across multiple classrooms? (2) To what extent is the classroom-level variation within teachers explained by observable classroom characteristics? The results suggested that the math classrooms shared 4.9% to 14.7% of the variance in the classroom observation ratings and English Language and Arts classrooms shared 6.7% to 15.5% of the variance in the ratings. The results also showed that the classroom characteristics (i.e., class size, percent of minority students, percent of male students, percent of English language learners, percent of students eligible for free or reduced lunch, and percent of students with disabilities) had limited contributions to explaining the classroom-level variation in the ratings. The results of this dissertation indicate that teachers’ multiple classrooms should be taken into consideration when classroom observation ratings are used to evaluate teachers in high-stakes settings. In addition, other classroom-level factors that could contribute to explaining the classroom-level variation in classroom observation ratings should be investigated in future research.

Explaining a phenomenon often requires identification of an underlying relationship between two variables. However, it is common practice in psychological research to sample only a few values of an independent variable. Young, Cole, and Sutherland (2012) showed that this practice can impair model selection in between-subject designs. The current study expands that line of research to within-subjects designs. In two Monte Carlo simulations, model discrimination under systematic sampling of 2, 3, or 4 levels of the IV was compared with that under random uniform sampling and sampling from a Halton sequence. The number of subjects, number of observations per subject, effect size, and between-subject parameter variance in the simulated experiments were also manipulated. Random sampling out-performed the other methods in model discrimination with only small, function-specific costs to parameter estimation. Halton sampling also produced good results but was less consistent. The systematic sampling methods were generally rank-ordered by the number of levels they sampled.

Reduced eye fixation and deficits in emotion identification accuracy have been commonly reported in individuals with autism spectrum disorder (AS), but are not ubiquitous. There is growing evidence that emotion processing deficits may be better accounted for by comorbid alexithymia (i.e., difficulty understanding and describing one's emotional state), rather than AS symptoms per se. Another possible explanation is anxiety, which is often comorbid with AS; emotion processing difficulties, including attentional biases, have also been observed in anxiety disorders, suggesting that anxiety symptoms may also influence emotion processing within AS. The purpose of the current study was to test the role of dimensional symptoms of autism, anxious apprehension (AA), and alexithymia in mediating eye fixation across two different facial processing tasks with three adult samples: adults diagnosed with autism (AS; n = 30), adults with clinically-elevated anxiety without autism (HI-ANX; n = 29), and neurotypical adults without high anxiety (NT; n = 46). Experiment 1 involved participants completing an emotion identification task involving short video clips. Experiment 2 was a luminescence change detection task with an emotional-expression photo paired with a neutral-expression photo. Joy, anger, and fear video and photo stimuli were used. Dimensional, mixed-effects models showed that symptoms of autism, but not alexithymia, predicted lower eye fixation across two separate face processing tasks. There were no group differences or significant dimensional effects for accuracy. Anxious apprehension was negatively related to response time in Experiment 1 and positively related to eye fixation in Experiment 2. An attentional avoidance of negative emotions was observed in the NT and HI-ANX group, but not the AS group. The bias was most pronounced at lower levels of AS symptoms and higher levels of AA symptoms. The results provide some evidence for a possible anxiety-related subtype in AS, with participants endorsing high autism symptoms, but low anxious apprehension, demonstrating more classic emotion processing deficits of reduced eye fixation.

Fifteen or so years into what is said to be the game changer of our time there are many fields of science  focusing  their  attention  towards  the  online  market  in  attempts  to  describe  its implications for the traditional, offline markets. Where most of the literature on economics of ecommerce focus on pricing mechanisms and growth little attention has been directed towards more general market structure effects. This thesis adopts techniques, empirical and theoretical models  from  the  search  cost  and  market  structure  literature  in  order  to  examine  the relationships between ecommerce and offline market structures in the retail industry through regional employment and establishment data. The literature reviewed and used focus only on the US market whereas this thesis shifts the attention to the European regions. The results are convincing and in general corresponding to previous research results. As ecommerce usage increase and the consumer search costs thereby gets lower inefficient firms drop out of the market resulting in a decline in local establishment counts. The opposite effect is seen for pure online retailing establishments that thrive in the presence of local ecommerce usage. The effect of   ecommerce  on  traditional  offline  establishments  seems  to  be  aggregated  phenomena whereas  the  effect  on  pure  online  firms  seems  to  be  of  a  more  local  nature.  Focus  of policymakers and company management therefore might consider looking at the two effects in their respective aggregation level to best sort out how to react in the presence of increased competition from ecommerce usage.

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<div>Prediction of player performance is a key component in the construction of baseball team rosters. Traditionally, the problem of predicting seasonal plate appearance outcomes has been approached univariately. That is, focusing on each outcome separately rather than jointly modeling the collection of outcomes. More recently, there has been a greater emphasis on joint modeling, thereby accounting for the correlations between outcomes. However, most of these state of the art prediction models are the proprietary property of teams or industrial sports entities and so little is available in open publications.</div><div><br></div><div>This dissertation introduces a joint modeling approach to predict seasonal plate appearance outcome vectors using a mixed-effects multinomial logistic-normal model. This model accounts for positive and negative correlations between outcomes both across and within player seasons. It is also applied to the important, yet unaddressed, problem of predicting performance for players moving between the Japanese and American major leagues.</div><div><br></div>This work begins by motivating the methodological choices through a comparison of state of the art procedures followed by a detailed description of the modeling and estimation approach that includes model t assessments. We then apply the method to longitudinal multinomial count data of baseball player-seasons for players moving between the Japanese and American major leagues and discuss the results. Extensions of this modeling framework to other similar data structures are also discussed.<br>

Criminology’s most recent theoretical tradition involves examination of the developmental onset, continuity, and desistance from offending behavior across the life course. A prominent life course perspective organized around social bonding was proffered by Robert J. Sampson and John H. Laub in dual volumes that include Crime in the Making: Pathways and Turning Points Through Life (1993), and Shared Beginnings, Divergent Lives (2003). Because Sampson and Laub’s age-graded theory is based on a sample of White males born in the 1920s and 1930s, and matured during a historical period of vast economic growth, the universal theoretical processes emphasized in their theory may be overstated. Such assumptions may not generalize to more heterogeneous samples that includes minorities and individuals that vary in their levels of offending. The present research evaluates the generalizability of the age-graded theory through examination of data collected from a representative and contemporary sample of adolescents followed into adulthood. In addition, this study seeks to examine an alternate turning point from deviant conduct, specifically religiosity/spirituality. Building on prior studies that explore the role of religiosity on change processes across race and ethnicity (Chu & Sung, 2009; Stansfield, 2017), the current investigation addresses open questions relating to the nature of the religion-desistance relationship. Multilevel mixed effects models are utilized to estimate over time the separate impact of religious behavior and religious beliefs on deviant conduct, to further assess a religious turning point effect across subgroups disaggregated by race/ethnicity, and to evaluate the influence of religiosity on change from deviant outcomes characterized as violations of secular and ascetic standards. Analyses of religiosity/spirituality on these differing forms of deviance across race/ethnicity are also conducted. In contrast to the hypothesized relationships, study findings reveal very little evidence of a religious/spiritual turning point effect in enacting change from deviant behaviors in the main models. Similar results indicate that religiosity indicates minimal differences in change from deviant conduct when the sample is disaggregated across race and ethnicity. Findings point to the nuances of the religion-desistance relationship, and depends upon processes that may involve attendance to church services or spiritual beliefs, and may be conditional on the type of deviance outcome examined—whether in violation of a secular or ascetic standard. Along with a discussion of these findings, limitations of the study, directions for future research, and implications for policy are provided.