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Some of the material in is restricted to members of the community. By logging in, you may be able to gain additional access to certain collections or items. If you have questions about access or logging in, please use the form on the Contact Page.
Multivariate meta-analysis methods typically assume the dependence of effect sizes. One type of experimental-design study that generates dependent effect sizes is the multiple-endpoint study. While the generalized least squares (GLS)...
This work develops both the theoretical foundation and the practical application of random Sobol' analysis with two goals. The first is to provide a more general and accommodating approach to global sensitivity analysis, in which the...
In structural equation modeling (SEM), researchers use the model chi-square statistic and model-fit indexes to evaluate model-data fit. Root mean square error of approximation (RMSEA), comparative fit index (CFI), and Tucker-Lewis index ...
In this dissertation, methods of estimating the sensitivity of complex exotic options, including options written on multiple assets, and have discontinuous payoffs, are investigated. The calculation of the sensitivities (Greeks) is based...
The Impact of Rater Variability on Relationships among Different Effect-Size Indices for Inter-Rater Agreement between Human and Automated Essay Scoring
Since researchers investigated automatic scoring systems in writing assessments, they have dealt with relationships between human and machine scoring, and then have suggested evaluation criteria for inter-rater agreement. The main...
DIF detection methods identify the difference between the performances of subgroups when the subgroups are matched by examinees' ability level or a proxy variable, such as total test score (Holland & Wainer, 1993). Log-linear Models (LLM...
Currently, more sophisticated techniques such as factor analyses are frequently applied in primary research thus may need to be meta-analyzed. This topic has been given little attention in the past due to its complexity. Because factor...
In factor analysis, determining the number of factors underlying measurement indicators is important. An incorrect decision on the number of factors may mislead practitioners in terms of estimating parameters in factor analysis, ...
Measurement invariance analysis is important when test scores are used to make a group-wise comparison. Multiple-group IRT modeling is one of the commonly used methods for measurement invariance examination. One essential step in the...
Checking that models adequately present data is an essential component of applied statistical inference. Psychometricans increasingly use complex models to analyze test takers responses. The appeal of using complex cognitive diagnostic...
Novel Numerical Analysis Methods, Using the WENO and WENO-Z Algorithms, for Combining Observational Data with Model Predictions for Improving Forecasts
“Data assimilation is a mathematical discipline that seeks to optimally combine theory (usually in the form of a numerical model) with observations.” — Wikipedia Strong constraint 4D-Variational data assimilation (4D-Var) seeks to find...
This thesis is to identify the underlying structures of multivariate time series and propose a methodology to construct predictive VAR models. Due to the complexity of high dimensions in multivariate time series, forecasting a target...
Microfiltration is one of the most efficient filtration techniques used to remove pathogenic protozoa, bacteria, and other colloidal matter from feed water. During the filtration process these pollutants accumulate on the filter surface...
Because of the advent of massively parallel computing resources, which is due to the fact that today’s processors are reaching the clock speed limit, parallel computing is becoming more prevalent when computing solutions to partial...
The Impact of Unbalanced Designs on the Performance of Parametric and Nonparametric DIF Procedures: A Comparison of Mantel Haenszel, Logistic Regression, SIBTEST, and IRTLR Procedures
The current study examined the impact of unbalanced sample sizes between focal and reference groups on the Type I error rates and DIF detection rates (power) of five DIF procedures (MH, LR, general IRTLR, IRTLR-b, and SIBTEST). Five...
Over the years a variety of indices have been proposed to summarize regression analyses. Unfortunately the proposed indices are only appropriate when meta-analysts want to understand the role of a single predictor variable in predicting...
Primary studies are using complex models more and more. Slopes from multiple regression analyses are reported in primary studies, but few scholars have dealt with how to combine multiple regression slopes. One of the problems in...
Option pricing by the Fourier method has been popular for the past decade, many of its applications to Lévy processes has been applied especially for European options. This thesis focuses on exponential convergence Fourier method and its...
This study introduces a longitudinal diagnostic classification model, called the LTA+HDCM, which is a fusion of latent transition analysis (LTA; Collins & Flaherty, 2002; Collins & Wugalter, 1992) and the hierarchical diagnostic...
Many popular global model-data fit indices (GFIs), such as Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residuals (SRMSR) are proposed and...
Mature male African elephants are known to periodically enter a temporary state of heightened aggression called "musth, often linked with increased androgens, particularly testosterone. Sexually mature males are capable of entering...
Estimating a treatment effect is problematic when selection bias exists. This dissertation sought to elucidate the problem of estimating the treatment effect size of the Response-to-Intervention (RTI) policy when selection bias exists....
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