UMD Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/3
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.
More information is available at Theses and Dissertations at University of Maryland Libraries.
Browse
6 results
Search Results
Item SUBSCORE REPORTING FOR DOUBLE-CODED INNOVATIVE ITEMS EMBEDDED IN MULTIPLE CONTEXTS(2018) Li, Chen; Jiao, Hong; Measurement, Statistics and Evaluation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Reporting subscores is a prevalent practice in standardized tests to provide diagnostic information for learning and instruction. Previous research has developed various methods for reporting subscores (e.g. de la Torre & Patz, 2005; Wainer et al., 2001; Wang, Chen, & Cheng, 2004; Yao & Boughton, 2007; Yen, 1987). However, the existing methods are not suitable for reporting subscores for a test with innovative item types, such as double-coded items and paired stimuli. This study proposes a two-parameter doubly testlet model with internal restrictions on the item difficulties (2PL-DT-MIRID) to report subscores for a test with double-coded items embedded in paired-testlets. The proposed model is based on a doubly-testlet model proposed by Jiao and Lissitz (2014) and the MIRID (Butter, De Boeck, & Verhelst, 1998). The proposed model has four major advantages in reporting subscores— (a) it reports subscores for a test with double-coded items in complex scenario structures, (b) it reports subscores designed for content clustering, which is more common than subscores based on construct dimensionality in standardized tests, (c) it is computationally less challenging than the Multidimensional Item Response Theory (MIRT) models when estimating subscores, (d) it can be used to conduct Item Response Theory (IRT) based number-correct scoring (NCS, Yen, 1984a). A simulation study is conducted to evaluate the model parameter recovery, subscore estimation and subscore reliability. The simulation study manipulates three factors: (a) the magnitude of testlet effect variation, (b) the correlation between testlet effects for the dual testlets and (c) the percentage of double-coded items in the test. Further, the study compares the proposed model with other underspecified models in terms of model parameter estimation and model fit. The result of the simulation study has shown that the proposed 2PL-DT-MIRID yields more accurate model parameter and subscore estimates, in general, when the testlet effect variation is small, the dual testlets are weakly correlated and there are more double-coded items in a test. Across the study conditions, the proposed model outperforms other competing models in model parameter estimation. The reliability yielded from models ignoring dual testlets are spuriously inflated, the 2PL-DTMIRID produces higher overall score reliability and subscore reliability than models ignoring double-coded items, in most study conditions. In terms of model fit, none of the model fit indices investigated in this study (i.e. AIC, BIC and DIC) can achieve satisfactory rates of identifying the proposed true model as the best fitting model.Item STRATEGIC MONETIZATION AND UPGRADING DECISIONS FOR MOBILE APPLICATIONS(2017) Lee, Seoungwoo; Zhang, Jie; Wedel, Michel; Business and Management: Marketing; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The mobile applications (apps) market has been growing steadily, propelled by rapid technological developments and consumer adoption of smartphones and tablet personal computers. In this dissertation research, I study app publishers’ strategic monetization and upgrading decisions. The first essay studies app publishers’ dynamic forward-looking decisions on offering different versions of an app: free, paid, or both (i.e., freemium), and investigates alternative commission schemes which could benefit both app publishers and an app platform. My findings lead to recommendations on how one may improve the current commission structure to achieve mutual benefits for both the platform and app publishers. The second essay examines strategic upgrading decisions of mobile apps by taking into consideration of their interconnections with versioning decisions and between the free and paid versions of an app. Our joint model of versioning and upgrading decisions provides estimates of various revenues and costs associated with the two decisions, and our policy simulations based on the model estimates examine the soundness of certain current practices and identifies opportunities to improve app publishers’ profits, the app distribution platform’ revenue, and the eco-system payoff. This dissertation research provides a range of policy recommendations to key players in the mobile app industry.Item ACOUSTIC EMISSION-BASED STRUCTURAL HEALTH MANAGEMENT AND PROGNOSTICS SUBJECT TO SMALL FATIGUE CRACKS(2014) Keshtgar, Azadeh; Modarres, Mohammad; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)One of the major concerns in structural health management (SHM) is the early detection of growing crack. Using this, future consequential damage due to crack propagation can be reduced or eliminated by scheduling maintenance which can prevent costly downtime. Early crack detection can also be used to predict the remaining useful life of a system. Acoustic Emission (AE) is a non-destructive testing (NDT) method with potential applications for locating and monitoring fatigue cracks during SHM and prognosis. The research presented in this dissertation focuses on the structural health monitoring using AE. In this research a correlation between AE signal characteristics and crack growth behavior is established, and a probabilistic model of fatigue crack length distribution based on certain AE signal features is developed. In order to establish the AE signal feature versus the fatigue crack growth model and study the consistency and accuracy of the model, several standard fatigue experiments have been performed using standard test specimens subjected to cyclic loading with different amplitude and frequencies. Bayesian analysis inference is used to estimate the parameters of the model and associated model error. The results indicate that the modified AE crack growth model could be used to predict the crack growth rate distribution at different test conditions. In the second phase of this research, an AE signal analysis approach was proposed in order to detect the time of crack initiation and assess small crack lengths, which happen during the early stages of damage accumulation. Experimental investigation from uniform cyclic loading tests indicated that initiation of crack could be identified through the statistical analysis of AE signals. A probabilistic AE-based model was developed and the uncertainties of the model were assessed. In addition, a probabilistic model validation approach was implemented to validate the results. The developed models were properly validated and the results were accurate. It was shown that the updated model can be used for detection of crack initiation as well as prediction of small crack growth in early stages of propagation. It was found that the novel AE monitoring technique facilitates early detection of fatigue crack, allows for the original life predictions to be updated and helps to extend the service life of the structure. Finally, a quantification framework was proposed to evaluate probability of failure of structural integrity using the observed initial crack length. The outcome of this research can be used to assess the reliability of structural health by estimating the probability density function of the length of a detected crack and quantifying the probability of failure at a specified number of cycles. The proposed method has applications in on-line monitoring and evaluation of structural health and shows promise for use in fatigue life assessment.Item A MIXTURE RASCH MODEL WITH A COVARIATE:A SIMULATION STUDY VIA BAYESIAN MARKOV CHAIN MONTE CARLO ESTIMATION(2009) Dai, Yunyun; Mislevy, Robert J; Measurement, Statistics and Evaluation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Mixtures of item response theory models have been proposed as a technique to explore response patterns in test data related to cognitive strategies, instructional sensitivity, and differential item functioning (DIF). Estimation proves challenging due to difficulties in identification and questions of effect size needed to recover underlying structures. In particular, the impact of auxiliary variables, or covariates, for examinees in estimation has not been systematically explored. The goal of this dissertation is to carry out a systematically designed simulation study to investigate the performance of mixture Rasch model (MRM) under Bayesian estimation using Markov Chain Monte Carlo (MCMC) method. The dependent variables in this study are (1) the proportion of cases in which the generating mixture structure is recovered, and (2) among those cases in which the structure is recovered, the bias and root mean squared error of parameter estimates. The foci of the study are to use a flexible logistic regression model to parameterize the relation between latent class membership and the examinee covariate, to study MCMC estimation behavior in light of effect size, and to provide insights and suggestions on model application and model estimation.Item Latent Failures and Mixed Distributions: Using Mixed Distributions and Cost Modeling to Optimize the Management of Systems with Weak Latent Defect Subpopulations(2008-11-21) Touw, Anduin E; Sandborn, Peter; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Under most reliability model assumptions, all failures in a population are considered to come from the same distribution. Each individual failure time is assumed to provide information about the likely failure times of all other devices in the population. However, from time to time, process variation or an unexpected event will lead to the development of a weak subpopulation while other devices remained durable. In this paper, estimation techniques for this situation are explored. Ideally, when such situations arise, the weak subpopulation could be identified through determination of root cause and sequestering of impacted devices. But many times, for practical reasons, the overall population is a mixture of the weak and strong subpopulations; there may be no non-destructive way to identify the weak devices. If the defect is not inspectable, statistical estimation methods must be used, either with or without root cause information, to quantify the reliability risk to the population and develop appropriate screening. The accuracy of these estimates may be critical to the management of the product, but estimation in these circumstances is difficult. The mixed Weibull distribution is a common form for modeling latent failures. However, estimation of the mixed Weibull parameters is not straightforward. The number of parameters involved, and frequently the sparseness of the data, can lead to estimation biases and instabilities that produce misleading results. Bayesian techniques can stabilize these estimates through the priors, but there is no closed-form conjugate family for the Weibull distribution. This dissertation, using Monte Carlo simulation, examines bias and random error for three estimation techniques: standard maximum likelihood estimation, the Trunsored method, and Bayesian estimation. To determine how errors in the estimation methods impacts decisions about screening, a cost model was developed by generalizing existing screening cost models through the addition of the impact of schedule slippage cost and capacity. The cost model was used in determining the optimal screen length based on total life-cycle cost. The estimated optimal screen length for each method was compared to the true optimal screen length. Recommendations about when each estimation method is appropriate were developed.Item The Multidimensional Generalized Graded Unfolding Model for Assessment of Change across Repeated Measures(2008-05-13) Cui, Weiwei; Roberts, James S; Dayton, Chan M; Measurement, Statistics and Evaluation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)A multidimensional extension of the generalized graded unfolding model for repeated measures (GGUM-RM) is introduced and applied to analyze attitude change across time using responses collected by a Thurstone or Likert questionnaire. The model conceptualizes the change across time as separate latent variables and provides direct estimates of both individual and group change while accounting for the dependency among latent variables. The parameters and hyperparameters of GGUM-RM are estimated by fully Bayesian estimation method via WinBUGS. The accuracy of the estimation procedure is demonstrated by a simulation study, and the application of the GGUM-RM is illustrated by the analysis of attitude change toward abortion among college students.