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.
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Item Information Uncertainty Influences Learning Strategy from Sequentially Delayed Rewards(2023) Maulhardt, Sean Richard; Charpentier, Caroline; Psychology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The problem of temporal credit assignment has long been posed as a nontrivial obstacle to identifying signal from data. However, human solutions in complex environments, involving repeated and intervening decisions, as well as uncertainty in reward timing, remain elusive. To this end, our task manipulated uncertainty via the amount of information given in their feedback stage. Using computational modeling, two learning strategies were developed that differentiated participants’ updates of sequentially delayed rewards: eligibility trace whereby previously selected actions are updated as a function of the temporal sequence - and tabular update - whereby additional feedback information is used to only update systematically-related rather than randomly related past actions. In both models, values were discounted over time with an exponential decay. We hypothesized that higher uncertainty would be associated with (i) a switch from tabular to eligibility strategy and (ii) higher rates of discounting. Participants’ data (N = 142) confirmed our first hypothesis, additionally revealing an effect of the starting condition. However, our discounting hypothesis had only weak evidence of an effect and remains an open question for future studies. We explore potential explanations for these effects and possibilities of future directions, models, and designs.Item A FINITE MIXTURE MULTILEVEL STRUCTURAL EQUATION MODEL FOR UNOBSERVED HETEROGENEITY IN RANDOM VARIABILITY(2023) Feng, Yi; Hancock, Gregory R; Measurement, Statistics and Evaluation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Variability is often of key interest in various research and applied settings. Important research questions about intraindividual variability (e.g., consistency across repeated measurements) or intragroup variability (e.g., cohesiveness among members within a team) are piquing the interest of researchers from a variety of disciplines. To address the research needs in modeling random variability as the key construct, Feng and Hancock (2020, 2022) proposed a multilevel SEM-based modeling approach where variability can be modeled as a random variable. This modeling framework is a highly flexible analytical tool that can model variability in observed measures or latent constructs, variability as the predictor or the outcome, as well as the between-subject comparison of variability across observed groups. A huge challenge still remains, however, when it comes to modeling the unobserved heterogeneity in random variability. Given that no existing research addresses the methodological considerations of uncovering the unobserved sub-populations that differ in intraindividual variability or intragroup variability, or sub-populations that differ in the various processes and mechanisms involving intraindividual variability or intragroup variability, the current dissertation study aims to fill this gap in literature. In the current study, a finite-mixture MSEM for modeling unobserved heterogeneity in random variability (MMSEM-RV) is introduced. Bayesian estimation via MCMC is proposed for model estimation. The performance of MMSEM-RV with Bayesian estimation is systematically evaluated in a simulation study across varying conditions. An illustrative example with empirical PISA data is also provided to demonstrate the practical application of MMSEM-RV.Item Characterizing the Adventitious Model Error as a Random Effect in Item-Response-Theory Models(2023) Xu, Shuangshuang; Liu, Yang; Measurement, Statistics and Evaluation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)When drawing conclusions from statistical inferences, researchers are usually concerned about two types of errors: sampling error and model error. The sampling error is caused by the discrepancy between the observed sample and the population from which the sample is drawn from (i.e., operational population). The model error refers to the discrepancy between the fitted model and the data-generating mechanism. Most item response theory (IRT) models assume that models are correctly specified in the population of interest; as a result, only sampling errors are characterized, not model errors. The model error can be treated either as fixed or random. The proposed framework in this study treats the model error as a random effect (i.e., an adventitious error) and provides an alternative explanation for the model errors in IRT models that originate from unknown sources. A random, ideally small amount of discrepancy between the operational population and the fitted model is characterized using a Dirichlet-Multinomial framework. A concentration/dispersion parameter is used in the Dirichlet-Multinomial framework to measure the amount of adventitious error between the operational population probability and the fitted model. In general, the study aims to: 1) build a Dirichlet-Multinomial framework for IRT models, 2) establish asymptotic results for estimating model parameters when the operational population probability is assumed known or unknown, 3) conduct numerical studies to investigate parameter recovery and the relationship between the concentration/dispersion parameter in the proposed framework and the Root Mean Square Error of Approximation (RMSEA), 4) correct bias in parameter estimates of the Dirichlet-Multinomial framework using asymptotic approximation methods, and 5) quantify the amount of model error in the framework and decide whether the model should be retained or rejected.Item INVESTIGATING MODEL SELECTION AND PARAMETER RECOVERY OF THE LATENT VARIABLE AUTOREGRESIVE LATENT TRAJECTORY (LV-ALT) MODEL FOR REPEATED MEASURES DATA: A MONTE CARLO SIMULATION STUDY(2023) Houser, Ari; Harring, Jeffrey R; Human Development; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Over the past several decades, several highly generalized models have been developed which can reduce, through parameter constraints, to a variety of classical models. One such framework, the Autoregressive Latent Trajectory (ALT) model, is a combination of two classical approaches to longitudinal modeling: the autoregressive or simplex family, in which trait scores at one occasion are regressed on scores at a previous occasion, and latent trajectory or growth curve models, in which individual trajectories are specified by a set of latent factors (typically a slope and an intercept) whose values vary across the population.The Latent Variable-Autoregressive Latent Trajectory (LV-ALT) model has been recently proposed as an extension of the ALT model in which the traits of interest are latent constructs measured by one or more indicator variables. The LV-ALT is presented as a framework by which one may compare the fit of a chosen model to alternative possibilities or use to empirically guide the selection of a model in the absence of theory, prior research, or standard practice. To date, however, there has not been any robust analysis of the efficacy or usefulness of the LV-ALT model for this purpose. This study uses a Monte Carlo simulation study to evaluate the efficacy of the basic formulation of the LV-ALT model (univariate latent growth process, single indicator variable) to identify the true model, model family, and key characteristics of the model under manipulated conditions of true model parameters, sample size, measurement reliability, and missing data. The performance of the LV-ALT model for model selection is mixed. Under most manipulated conditions, the best-fitting of nine candidate models was different than the generating model, and the cost of model misspecification for parameter recovery included significant increases in bias and loss of precision in parameter estimation. As a general rule, the LV-ALT should not be relied upon to empirically select a specific model, or to choose between several theoretical plausible models in the autoregressive or latent growth families. Larger sample size, greater measurement reliability, larger parameter magnitude, and a constant autoregressive parameter are associated with greater likelihood of correct model selection.Item ESTIMATING THE Q-DIFFUSION MODEL PARAMETERS BY APPROXIMATE BAYESIAN COMPUTATION(2023) Tian, Chen; Liu, Yang; Human Development; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The Q-diffusion model is a cognitive process model that considers decision making as an unobservable information accumulation process. Both item and person parameters decide the trace line of the cognitive process, which further decides observed response and response time. Because the likelihood function for the Q-diffusion model is intractable, standard parameter estimation techniques such as the maximum likelihood estimation is difficult to apply. This project applies Approximate Bayesian Computation (ABC) to estimate parameters of the Q-diffusion model. Different from standard Markov chain Monte Carlo samplers that require pointwise evaluation of the likelihood function, ABC builds upon a program for data generation and a metric on the data space to gauge the similarity between imputed and observed data. This project aims to compare the performance of two criteria for gauging the similarity or distance. The limited-information criterion measures the distance in suitable summary statistics (i.e., variances, covariances, and means) between imputed and observed data. The enhanced limited information criterion additionally considers the dependencies among persons’ responses and response times. Bias, rooted mean squared error, and coverage of credible intervals were reported. Results show that when using posterior median as the point estimate, by jointly considering a person’s responses and response time, the enhanced criterion yielded less biased estimation on population scale of person power and slightly better item parameters. This SMC-ABC algorithm informs researchers about key data features that should be captured when determining the stopping rule for the algorithm.Item TESTING DIFFERENTIAL ITEM FUNCTIONING BY REGULARIZED MODERATED NONLINEAR FACTOR ANALYSIS(2022) Wang, Weimeng; Harring, Jeffrery R; Liu, Yang; Measurement, Statistics and Evaluation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Recent advancements in testing differential item functioning (DIF) have greatly relaxed restrictions made by the conventional multiple group item response theory (IRT) model with respect to the number of grouping variables and the assumption of predefined DIF-free anchor items. The application of the L1 penalty in DIF detection has shown promising results in identifying a DIF item without a priori knowledge on anchor items while allowing the simultaneous investigation of multiple grouping variables. The least absolute shrinkage and selection operator (LASSO) is added directly to the loss function to encourage variable sparsity such that DIF parameters of anchor items are penalized to be zero. Therefore, no predefined anchor items are needed. However, DIF detection using LASSO requires a non-trivial model selection consistency assumption and is difficult to draw statistical inference. Given the importance of identifying DIF items in test development, this study aims to apply the decorrelated score test to test DIF once the penalized method is used. Unlike the existing regularized DIF method which is unable to test the statistical significance of a DIF item selected by LASSO, the decorrelated score test requires weaker assumptions and is able to provide asymptotically valid inference to test DIF. Additionally, the deccorrelated score function can be used to construct asymptotically unbiased normal and efficient DIF parameter estimates via a one-step correction. The performance of the proposed decorrelated score test and the one-step estimator are evaluated by a Monte Carlo simulation study.Item Comparing the Validity & Fairness of Machine Learning to Regression in Personnel Selection(2022) Epistola, Jordan J; Hanges, Paul J; Psychology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In the realm of personnel selection, several researchers have claimed that machine learning (ML) can generate predictions that can out-predict more conventional methods such as regression. However, high-profile misuses of ML in selection contexts have demonstrated that ML can also result in illegal discrimination and/or bias against minority groups when developed improperly. This dissertation examined the utility of ML in personnel selection by examining the validity and fairness of ML methods relative to regression. Studies One and Two predicted counterproductive work behavior in Hanges et al.’s (2021) sample of Military cadets/midshipmen, and Study Three predicted job performance ratings of employees in Patalano & Huebner’s (2021) human resources dataset. Results revealed equivalent validity of ML to regression across all three studies. However, fairness was enhanced when ML was developed in accordance with employment law. Implications for the use of ML in personnel selection, as well as relevant legal considerations, are presented in my dissertation. Further, methods for further enhancing the legal defensibility of ML in the selection are discussed.Item Multilevel Regression Discontinuity Models with Latent Variables(2020) Morell, Monica; Yang, Ji Seung; Liu, Yang; Measurement, Statistics and Evaluation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Regression discontinuity (RD) designs allow estimating a local average treatment effect (LATE) when assignment of an individual to treatment is determined by their location on a running variable in relation to a cutoff value. The design is especially useful in education settings, where ethical concerns can forestall the use of randomization. Applications of RD in education research typically share two characteristics, which can make the use of the conventional RD model inappropriate: 1) The use of latent constructs, and 2) The hierarchical structure of the data. The running variables often used in education research represent latent constructs (e.g., math ability), which are measured by observed indicators such as categorical item responses. While the use of a latent variable model to account for the relationships among item responses and the latent construct is the preferred approach, conventional RD analyses continue to use observed scores, which can result in invalid or less informative conclusions. The current study proposes a multilevel latent RD model which accounts for the prevalence of clustered data and latent constructs in education research, allows for the generalizability of the LATE to individuals further from the cutoff, and allows researchers to quantify the heterogeneity in the treatment effect due to measurement error in the observed running variable. Models are derived for two of the most commonly used multilevel RD designs. Due to the complex and high-dimensional nature of the proposed models, they are estimated in one stage using full-information likelihood via the Metropolis-Hastings Robbins-Monro algorithm. The results of two simulation studies, under varying sample size and test length conditions, indicate the models perform well when using the full sample with at least moderate-length assessments. A proposed model is used to examine the effects of receiving an English language learner designation on science achievement using the Early Childhood Longitudinal Study. Implications of the results of these studies and future directions for the research are discussed.Item Investigating Uncertainty with Fungible Parameter Estimate Analysis(2020) Prendez, Jordan Yee; Harring, Jeffrey R; Measurement, Statistics and Evaluation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Researchers need methods for evaluating whether statistical results are worthy of interpretation. Likelihood functions contain large amounts of information regarding the support for differing estimates. However, maximum likelihood estimates (MLE) are typically the only set of estimates interpreted. Previous research has indicated that these alternative estimates can often be computed and represent data approximately as well as their MLE counterparts. The close fit between these alternative estimates are said to make them fungible. While similar in fit, fungible estimates are in some cases different enough (from the MLE) that they would support alternative substantive interpretations of the data. By calculating fungible parameter estimates (FPEs) one can either strengthen or weaken one’s inference by exploring the degree in which diverging estimates are supported. This dissertation has two contributions. First, it proposes a new method for generating FPEs under a broader definition of what should constitute fungible parameter estimates. This method allows for flexible computation of FPEs. Second, this method allows for an exploration of research inquiries that have been largely unexplored. What are the circumstances in which FPEs would convey uncertainty in the parameter estimates? That is, what are the causes of uncertainty that are measured by FPEs. Understanding the causes of this uncertainty are important for utilizing FPEs in practice. This dissertation uses a simulation study in order to investigate several factors that might be encountered in applied data analytic scenarios and affect the range of fungible parameter estimates including model misfit. The results of this study indicate the importance of interactions when examining FPEs. For some conditions, FPE ranges indicate that there was less uncertainty when the model was correctly specified. Under alternative conditions, FPE ranges suggest greater uncertainty for the correctly specified model. This example is mirrored in several results that suggest that a simple prediction of the level of uncertainty is difficult for likelihoods characterizing real world modeling scenarios.Item THE EFFECT OF DIFFERENT RELATIVE LOGISTIC REGRESSION GENERATED PROPENSITY SCORE DISTRIBUTIONS ON THE PERFORMANCE OF PROPENSITY SCORE METHODS(2020) An, Ji; Stapleton, Laura M; Human Development; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Much education research involves evaluating the causal effects of interventions. The propensity score (PS) method, which is often used to account for selection bias, has become a popular approach to facilitating causal inference in quasi-experimental designs. Because the success of the application of PS conditioning methods is dependent on the estimated propensity scores, the relative PS distribution between the treated and control groups could be an important yet not well-known factor. The primary goal of this dissertation was to explore, via a simulation study, the relations between the relative PS distributions and the performance of selected PS matching methods. The results indicated that PS weighting (without trimming) tends to be robust to a variety of data conditions and produces more accurate and trustworthy TE and SE estimates. The performance of the methods and conclusions were then illustrated through an empirical data analysis using data selected from the Early Childhood Longitudinal Study Kindergarten Class of 2010-11 study, assessing the effect of having home computers on first grade students’ math achievement.