UMD Theses and Dissertations
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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 Accuracy and consistency in discovering dimensionality by correlation constraint analysis and common factor analysis(2009) Tractenberg, Rochelle Elaine; Hancock, Gregory R; Measurement, Statistics and Evaluation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)An important application of multivariate analysis is the estimation of the underlying dimensions of an instrument or set of variables. Estimation of dimensions is often pursued with the objective of finding the single factor or dimension to which each observed variable belongs or by which it is most strongly influenced. This can involve estimating the loadings of observed variables on a pre-specified number of factors, achieved by common factor analysis (CFA) of the covariance or correlational structure of the observed variables. Another method, correlation constraint analysis (CCA), operates on the determinants of all 2x2 submatrices of the covariance matrix of the variables. CCA software also determines if partialling out the effects of any observed variable affects observed correlations, the only exploratory method to specifically rule out (or identify) observed variables as being the cause of correlations among observed variables. CFA estimates the strengths of associations between factors, hypothesized to underlie or cause observed correlations, and the observed variables; CCA does not estimate factor loadings but can uncover mathematical evidence of the causal relationships hypothesized between factors and observed variables. These are philosophically and analytically diverse methods for estimating the dimensionality of a set of variables, and each can be useful in understanding the simple structure in multivariate data. This dissertation studied the performances of these methods at uncovering the dimensionality of simulated data under conditions of varying sample size and model complexity, the presence of a weak factor, and correlated vs. independent factors. CCA was sensitive (performed significantly worse) when these conditions were present in terms of omitting more factors, and omitting and mis-assigning more indicators. CFA was also found to be sensitive to all but one condition (whether factors were correlated or not) in terms of omitting factors; it was sensitive to all conditions in terms of omitting and mis-assigning indicators, and it also found extra factors depending on the number of factors in the population, the purity of factors and the presence of a weak factor. This is the first study of CCA in data with these specific features of complexity, which are common in multivariate data.Item Finite Mixture Model Specifications Accommodating Treatment Nonresponse in Experimental Research(2009) Wasko, John A.; Hancock, Gregory R; Measurement, Statistics and Evaluation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)For researchers exploring causal inferences with simple two group experimental designs, results are confounded when using common statistical methods and further are unsuitable in cases of treatment nonresponse. In signal processing, researchers have successfully extracted multiple signals from data streams with Gaussian mixture models, where their use is well matched to accommodate researchers in this predicament. While the mathematics underpinning models in either application remains unchanged, there are stark differences. In signal processing, results are definitively evaluated assessing whether extracted signals are interpretable. Such obvious feedback is unavailable to researchers seeking causal inference who instead rely on empirical evidence from inferential statements regarding mean differences, as done in analysis of variance (ANOVA). Two group experimental designs do provide added benefit by anchoring treatment nonrespondents' distributional response properties from the control group. Obtaining empirical evidence supporting treatment nonresponse, however, can be extremely challenging. First, if indeed nonresponse exists, then basic population means, ANOVA or repeated measures tests cannot be used because of a violation of the identical distribution property required for each method. Secondly, the mixing parameter or proportion of nonresponse is bounded between 0 and 1, so does not subscribe to normal distribution theory to enable inference by common methods. This dissertation introduces and evaluates the performance of an information-based methodology as a more extensible and informative alternative to statistical tests of population means while addressing treatment nonresponse. Gaussian distributions are not required under this methodology which simultaneously provides empirical evidence through model selection regarding treatment nonresponse, equality of population means, and equality of variance hypotheses. The use of information criteria measures as an omnibus assessment of a set of mixture and non-mixture models within a maximum likelihood framework eliminates the need for a Newton-Pearson framework of probabilistic inferences on individual parameter estimates. This dissertation assesses performance in recapturing population conditions for hypotheses' conclusions, parameter accuracy, and class membership. More complex extensions addressing multiple treatments, multiple responses within a treatment, a priori consideration of covariates, and multivariate responses within a latent framework are also introduced.Item Testing for Differentially Functioning Indicators Using Mixtures of Confirmatory Factor Analysis Models(2009) Mann, Heather Marie; Hancock, Gregory R; Measurement, Statistics and Evaluation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Heterogeneity in measurement model parameters across known groups can be modeled and tested using multigroup confirmatory factor analysis (CFA). When it is not reasonable to assume that parameters are homogeneous for all observations in a manifest group, mixture CFA models are appropriate. Mixture CFA models can add theoretically important unmeasured characteristics to capture heterogeneity and have the potential to be used to test measurement invariance. The current study investigated the ability of mixture CFA models to identify differences in factor loadings across latent classes when there is no mean separation in both the latent and measured variables. Using simulated data from models with known parameters, parameter recovery, classification accuracy, and the power of the likelihood-ratio test were evaluated as impacted by model complexity, sample size, latent class proportions, magnitude of factor loading differences, percentage of noninvariant factor loadings, and pattern of noninvariant factor loadings. Results suggested that mixture CFA models may be a viable option for testing the invariance of measurement model parameters, but without impact and differences in measurement intercepts, larger sample sizes, more noninvariant factor loadings, and larger amounts of heterogeneity are needed to distinguish different latent classes and successfully estimate their parameters.Item Investigating the Role of Personality in (Sport) Consumer Behavior(2008-11-17) Mahan III, Joseph Edward; McDaniel, Stephen R; Kinesiology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation is presented as three empirical investigations examining the state of personality research in consumer behavior (CB). Each study supports the notion that the use of established personality theory can serve to better inform CB research (e.g., Baumgartner, 2002). Study one builds upon previous research in evaluating and comparing the validity and reliability of the Impulsive Sensation Seeking (ImpSS) scale with the more established Sensation Seeking Scale, Form V (SSS-V) and a third measure of Optimum Stimulation Level (OSL) in both homogenous and heterogeneous samples. Findings suggest ImpSS to be a valid and reliable alternative to SSS-V. Structural Equation Modeling (SEM) results point to concurrent validity of ImpSS and SSS-V. In addition, the predictive validity of ImpSS compares favorably to both SSS-V and CSI in the context of high-risk behavioral correlates (i.e., gambling, smoking, and drinking). Consumer use of imagery to process advertising messages has received much attention in the literature (e.g., Thompson and Hamilton 2006) yet little is known about its underlying structure. Study two adopts a hierarchical personality approach (cf. Mowen and Spears 1999) in examining the influence of certain traits on an individual's processing style. Results suggest that variance in preferences for a visual processing style may be explained by interplay among some higher-order personality traits (i.e., Openness to Experience and fantasy-proneness) but not others (i.e., ImpSS). The findings of study two also provide a platform for the third investigation by demonstrating that a theoretically-grounded personality trait (i.e., fantasy proneness) appears to play a role in mode of processing. The third study examines the role of personality in the imagery processing of sport marketing stimuli. Specifically, this investigation explores the effects of fantasy proneness on processing and response to print ads containing varying levels of sport-related imagery. While the research hypotheses are not supported, this study follows existing imagery-processing literature (e.g., Petrova & Cialdini, 2005) in that manipulation of imagery-eliciting ad elements (i.e., ad copy) can lead to increased processing and more favorable ad response. Results of post hoc regression analyses also imply that fantasy proneness may, in fact, play a small role in consumer processing.Item Evaluation of a Culturally Inclusive Model of Sexual Minority Identity Formation(2008-05-19) Risco, Cristina Maria; Fassinger, Ruth E.; Counseling and Personnel Services; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In the current work, the reliability and validity of a measure of sexual minority identity formation (the Same-Sex Orientation Identity Questionnaire; SSOIQ) was assessed with a racially/ethnically diverse sample. The SSOIQ was developed to measure one's location in a sexual minority identity formation process. The measure was derived from the Fassinger and colleagues (McCarn & Fassinger, 1996)) dual-trajectory model that hypothesizes two separate but reciprocal processes of individual sexual identity development and group membership identity development. Estimates of internal consistency reliability were assessed through Cronbach's alpha. A preliminary evaluation of the theoretical model underlying the measure was conducted by examining the interrelationships of the conceptually distinct phases of the model. Convergent validity was partially established through relationships of the measure to measures of identity confusion, internalized homonegativity, same group orientation, and outness. Discriminant validity was partially established using a measure of dogmatism.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.Item Planning for Career and Family: An Instrument Development Study(2008-04-10) Ganginis, Heather Victoria; O'Brien, Karen; Psychology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The purpose of the present study was to develop a scale to measure the extent to which people take into consideration future children and romantic relationships when deciding on a career (i.e. The Planning for Career and Family Scale) and to assess the psychometric properties of this instrument. Participants included 325 women. Data suggested that two subscales comprise the measure, the Incorporating Future Family Scale and the Choosing a Career Independent of Family Scale. Internal consistency estimates of subscales ranged from .78 to .83. Convergent and discriminant validity was supported for the Incorporating Future Family in Career Plans subscale and the Choosing a Career Independent of Future Family subscale. Test-retest reliability estimates were adequate, suggesting stability regarding the measurement of these constructs. Directions for future research and the limitations of this study are discussed.Item MULTIDIMENSIONALITY IN THE NAEP SCIENCE ASSESSMENT:SUBSTANTIVE PERSPECTIVES, PSYCHOMETRIC MODELS, AND TASK DESIGN(2008-03-05) Wei, Hua; Mislevy, Robert J; Measurement, Statistics and Evaluation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Educational assessments are characterized by the interplay among substantive theories, task design, and measurement models. Substantive theories define the nature of inferences to be made about students and types of observations that lend support to the targeted inferences. Task design represents the schemes for the design of tasks and extraction of evidence from student behaviors in the task situations. Measurement models are the tools by which observations of students' performances are synthesized to derive the targeted inferences. This dissertation elaborates on the interplay by specifying the entities that are involved and how they work in concert to produce an effective assessment and sound inferences. Developments in several areas are contributing to interest in more complex educational assessments: Advances in cognitive psychology spark interest in more complex inferences about students' knowledge, advances in technology make it possible to collect richer performance data, and advances in statistical methods make fitting more complex models feasible. The question becomes how to construct and analyze assessments to take advantage of this potential. In particular, a framework is required for understanding how to think about selecting and reasoning through the multivariate measurement models that are now available. Illustrations of the idea are made through explicating and analyzing the 1996 National Assessment of Educational Progress (NAEP) Science Assessment. Three measurement models, each of which reflects a particular perspective for thinking about the structure of the assessment, are used to model the item responses. Each model sheds light on a particular aspect of student proficiencies, addresses certain inferences for a particular purpose, and delivers a significant story about the examinees and their learning of science. Each model highlights certain patterns at the expense of hiding other potentially interesting patterns that reside in the data. Model comparison is conducted in terms of conceptual significance and degree of fit. The two criteria are used in complement to check the coherence of the data with the substantive theories underlying the use of the models.Item Assessment of Social Competence and Problem Behavior: The Psychometric Properties of a Social Competency Rating Form(2007-06-26) Nebbergall, Allison Joan; Gottfredson, Gary D.; Counseling and Personnel Services; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Intervention programs commonly target the development of social competencies and the prevention of problem behaviors among children. Practical assessment measures are necessary for evaluating these interventions. Examination of popularly used instruments reveals the need for a brief rating scale that measures both social competencies and problem behaviors. The Social Competency Rating Form (Gottfredson et al., 2002) is a brief 29-item scale designed to be user-friendly and closely aligned with the objectives of cognitive-behavioral social skills training programs for adolescents. It also serves as a research tool in studying social competence and problem behaviors, especially in the context of evaluating intervention programs. This study shows an adaptation of the SCRF to be a reliable and valid measure for use with elementary school children.Item INVESTIGATING DIFFERENTIAL ITEM FUNCTION AMPLIFICATION AND CANCELLATION IN APPLICATION OF ITEM RESPONSE TESTLET MODELS(2007-05-24) Bao, Han; Dayton, Mitchell; Measurement, Statistics and Evaluation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Many educational tests use testlets as a way of providing context, instead of presenting only discrete multiple-choice items, where items are grouped into testlets (Wainer & Kiely, 1987) or item bundles (Rosenbaum, 1988) marked by shared common stimulus materials. One might doubt the usual assumption of standard item response theory of local independence among items in these cases. Plausible causes of local dependence might be test takers' different levels of background knowledge necessary to understand the common passage, as a considerable amount of mental processing may be required to read and understand the stimulus, and different persons' learning experiences. Here, the local dependence can be viewed as additional dimensions other than the latent traits. Furthermore, from the multidimensional differential item functioning (DIF) point of view, different distributions of testlet dimensions among different examinee subpopulations (race, gender, etc) could be the cognitive cause of individual differences in test performance. When testlet effect and item idiosyncratic features of individual items are both considered to be the reasons of DIF, it is interesting to investigate the phenomena of DIF amplification and cancellation resulting from the interactive effects of these two factors. This dissertation presented a study based on a multiple-group testlet item response theory model developed by Li et al. (2006) to examine in detail different situations of DIF amplification and cancellation at the item and testlet level using testlet characteristic curve procedures with signed/ unsigned area indices and logistic regression procedure. The testlet DIF model was estimated using a hierarchical Bayesian framework with the Markov Chain Monte Carlo (MCMC) method implemented in the computer software WINBUGS. The simulation study investigated all of the possible conditions of DIF amplification and cancellation attributed to person-testlet interaction effect and individual item characteristics. Real data analysis indicated the existence of testlet effect and its magnitudes of difference on the means and/or variance of testlet distribution between manifest groups imputed to the different contexts or natures of the passages as well as its interaction with the manifest groups of examinees such as gender or ethnicity.