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- All Subjects: Educational Psychology
- All Subjects: Statistics
- Genre: Academic theses
- Creators: Levy, Roy
- Status: Published
Description
Dimensionality assessment is an important component of evaluating item response data. Existing approaches to evaluating common assumptions of unidimensionality, such as DIMTEST (Nandakumar & Stout, 1993; Stout, 1987; Stout, Froelich, & Gao, 2001), have been shown to work well under large-scale assessment conditions (e.g., large sample sizes and item pools; see e.g., Froelich & Habing, 2007). It remains to be seen how such procedures perform in the context of small-scale assessments characterized by relatively small sample sizes and/or short tests. The fact that some procedures come with minimum allowable values for characteristics of the data, such as the number of items, may even render them unusable for some small-scale assessments. Other measures designed to assess dimensionality do not come with such limitations and, as such, may perform better under conditions that do not lend themselves to evaluation via statistics that rely on asymptotic theory. The current work aimed to evaluate the performance of one such metric, the standardized generalized dimensionality discrepancy measure (SGDDM; Levy & Svetina, 2011; Levy, Xu, Yel, & Svetina, 2012), under both large- and small-scale testing conditions. A Monte Carlo study was conducted to compare the performance of DIMTEST and the SGDDM statistic in terms of evaluating assumptions of unidimensionality in item response data under a variety of conditions, with an emphasis on the examination of these procedures in small-scale assessments. Similar to previous research, increases in either test length or sample size resulted in increased power. The DIMTEST procedure appeared to be a conservative test of the null hypothesis of unidimensionality. The SGDDM statistic exhibited rejection rates near the nominal rate of .05 under unidimensional conditions, though the reliability of these results may have been less than optimal due to high sampling variability resulting from a relatively limited number of replications. Power values were at or near 1.0 for many of the multidimensional conditions. It was only when the sample size was reduced to N = 100 that the two approaches diverged in performance. Results suggested that both procedures may be appropriate for sample sizes as low as N = 250 and tests as short as J = 12 (SGDDM) or J = 19 (DIMTEST). When used as a diagnostic tool, SGDDM may be appropriate with as few as N = 100 cases combined with J = 12 items. The study was somewhat limited in that it did not include any complex factorial designs, nor were the strength of item discrimination parameters or correlation between factors manipulated. It is recommended that further research be conducted with the inclusion of these factors, as well as an increase in the number of replications when using the SGDDM procedure.
ContributorsReichenberg, Ray E (Author) / Levy, Roy (Thesis advisor) / Thompson, Marilyn S. (Thesis advisor) / Green, Samuel B. (Committee member) / Arizona State University (Publisher)
Created2013
Description
Students with traumatic brain injury (TBI) sometimes experience impairments that can adversely affect educational performance. Consequently, school psychologists may be needed to help determine if a TBI diagnosis is warranted (i.e., in compliance with the Individuals with Disabilities Education Improvement Act, IDEIA) and to suggest accommodations to assist those students. This analogue study investigated whether school psychologists provided with more comprehensive psychoeducational evaluations of a student with TBI succeeded in detecting TBI, in making TBI-related accommodations, and were more confident in their decisions. To test these hypotheses, 76 school psychologists were randomly assigned to one of three groups that received increasingly comprehensive levels of psychoeducational evaluation embedded in a cumulative folder of a hypothetical student whose history included a recent head injury and TBI-compatible school problems. As expected, school psychologists who received a more comprehensive psychoeducational evaluation were more likely to make a TBI educational diagnosis, but the effect size was not strong, and the predictive value came from the variance between the first and third groups. Likewise, school psychologists receiving more comprehensive evaluation data produced more accommodations related to student needs and felt more confidence in those accommodations, but significant differences were not found at all levels of evaluation. Contrary to expectations, however, providing more comprehensive information failed to engender more confidence in decisions about TBI educational diagnoses. Concluding that a TBI is present may itself facilitate accommodations; school psychologists who judged that the student warranted a TBI educational diagnosis produce more TBI-related accommodations. Impact of findings suggest the importance of training school psychologists in the interpretation of neuropsychology test results to aid in educational diagnosis and to increase confidence in their use.
ContributorsHildreth, Lisa Jane (Author) / Hildreth, Lisa J (Thesis advisor) / Wodrich, David (Committee member) / Levy, Roy (Committee member) / Lavoie, Michael (Committee member) / Arizona State University (Publisher)
Created2012
Description
This simulation study compared the utility of various discrepancy measures within a posterior predictive model checking (PPMC) framework for detecting different types of data-model misfit in multidimensional Bayesian network (BN) models. The investigated conditions were motivated by an applied research program utilizing an operational complex performance assessment within a digital-simulation educational context grounded in theories of cognition and learning. BN models were manipulated along two factors: latent variable dependency structure and number of latent classes. Distributions of posterior predicted p-values (PPP-values) served as the primary outcome measure and were summarized in graphical presentations, by median values across replications, and by proportions of replications in which the PPP-values were extreme. An effect size measure for PPMC was introduced as a supplemental numerical summary to the PPP-value. Consistent with previous PPMC research, all investigated fit functions tended to perform conservatively, but Standardized Generalized Dimensionality Discrepancy Measure (SGDDM), Yen's Q3, and Hierarchy Consistency Index (HCI) only mildly so. Adequate power to detect at least some types of misfit was demonstrated by SGDDM, Q3, HCI, Item Consistency Index (ICI), and to a lesser extent Deviance, while proportion correct (PC), a chi-square-type item-fit measure, Ranked Probability Score (RPS), and Good's Logarithmic Scale (GLS) were powerless across all investigated factors. Bivariate SGDDM and Q3 were found to provide powerful and detailed feedback for all investigated types of misfit.
ContributorsCrawford, Aaron (Author) / Levy, Roy (Thesis advisor) / Green, Samuel (Committee member) / Thompson, Marilyn (Committee member) / Arizona State University (Publisher)
Created2014
Description
The study examined how ATFIND, Mantel-Haenszel, SIBTEST, and Crossing SIBTEST function when items in the dataset are modelled to differentially advantage a lower ability focal group over a higher ability reference group. The primary purpose of the study was to examine ATFIND's usefulness as a valid subtest selection tool, but it also explored the influence of DIF items, item difficulty, and presence of multiple examinee populations with different ability distributions on both its selection of the assessment test (AT) and partitioning test (PT) lists and on all three differential item functioning (DIF) analysis procedures. The results of SIBTEST were also combined with those of Crossing SIBTEST, as might be done in practice.
ATFIND was found to be a less-than-effective matching subtest selection tool with DIF items that are modelled unidimensionally. If an item was modelled with uniform DIF or if it had a referent difficulty parameter in the Medium range, it was found to be selected slightly more often for the AT List than the PT List. These trends were seen to increase as sample size increased. All three DIF analyses, and the combined SIBTEST and Crossing SIBTEST, generally were found to perform less well as DIF contaminated the matching subtest, as well as when DIF was modelled less severely or when the focal group ability was skewed. While the combined SIBTEST and Crossing SIBTEST was found to have the highest power among the DIF analyses, it also was found to have Type I error rates that were sometimes extremely high.
ATFIND was found to be a less-than-effective matching subtest selection tool with DIF items that are modelled unidimensionally. If an item was modelled with uniform DIF or if it had a referent difficulty parameter in the Medium range, it was found to be selected slightly more often for the AT List than the PT List. These trends were seen to increase as sample size increased. All three DIF analyses, and the combined SIBTEST and Crossing SIBTEST, generally were found to perform less well as DIF contaminated the matching subtest, as well as when DIF was modelled less severely or when the focal group ability was skewed. While the combined SIBTEST and Crossing SIBTEST was found to have the highest power among the DIF analyses, it also was found to have Type I error rates that were sometimes extremely high.
ContributorsScott, Lietta Marie (Author) / Levy, Roy (Thesis advisor) / Green, Samuel B (Thesis advisor) / Gorin, Joanna S (Committee member) / Williams, Leila E (Committee member) / Arizona State University (Publisher)
Created2014
Description
Missing data are common in psychology research and can lead to bias and reduced power if not properly handled. Multiple imputation is a state-of-the-art missing data method recommended by methodologists. Multiple imputation methods can generally be divided into two broad categories: joint model (JM) imputation and fully conditional specification (FCS) imputation. JM draws missing values simultaneously for all incomplete variables using a multivariate distribution (e.g., multivariate normal). FCS, on the other hand, imputes variables one at a time, drawing missing values from a series of univariate distributions. In the single-level context, these two approaches have been shown to be equivalent with multivariate normal data. However, less is known about the similarities and differences of these two approaches with multilevel data, and the methodological literature provides no insight into the situations under which the approaches would produce identical results. This document examined five multilevel multiple imputation approaches (three JM methods and two FCS methods) that have been proposed in the literature. An analytic section shows that only two of the methods (one JM method and one FCS method) used imputation models equivalent to a two-level joint population model that contained random intercepts and different associations across levels. The other three methods employed imputation models that differed from the population model primarily in their ability to preserve distinct level-1 and level-2 covariances. I verified the analytic work with computer simulations, and the simulation results also showed that imputation models that failed to preserve level-specific covariances produced biased estimates. The studies also highlighted conditions that exacerbated the amount of bias produced (e.g., bias was greater for conditions with small cluster sizes). The analytic work and simulations lead to a number of practical recommendations for researchers.
ContributorsMistler, Stephen (Author) / Enders, Craig K. (Thesis advisor) / Aiken, Leona (Committee member) / Levy, Roy (Committee member) / West, Stephen G. (Committee member) / Arizona State University (Publisher)
Created2015
Description
Many methodological approaches have been utilized to predict student retention and persistence over the years, yet few have utilized a Bayesian framework. It is believed this is due in part to the absence of an established process for guiding educational researchers reared in a frequentist perspective into the realms of Bayesian analysis and educational data mining. The current study aimed to address this by providing a model-building process for developing a Bayesian network (BN) that leveraged educational data mining, Bayesian analysis, and traditional iterative model-building techniques in order to predict whether community college students will stop out at the completion of each of their first six terms. The study utilized exploratory and confirmatory techniques to reduce an initial pool of more than 50 potential predictor variables to a parsimonious final BN with only four predictor variables. The average in-sample classification accuracy rate for the model was 80% (Cohen's κ = 53%). The model was shown to be generalizable across samples with an average out-of-sample classification accuracy rate of 78% (Cohen's κ = 49%). The classification rates for the BN were also found to be superior to the classification rates produced by an analog frequentist discrete-time survival analysis model.
ContributorsArcuria, Philip (Author) / Levy, Roy (Thesis advisor) / Green, Samuel B (Committee member) / Thompson, Marilyn S (Committee member) / Arizona State University (Publisher)
Created2015
Description
The purpose of this study was to investigate the effect of complex structure on dimensionality assessment in compensatory and noncompensatory multidimensional item response models (MIRT) of assessment data using dimensionality assessment procedures based on conditional covariances (i.e., DETECT) and a factor analytical approach (i.e., NOHARM). The DETECT-based methods typically outperformed the NOHARM-based methods in both two- (2D) and three-dimensional (3D) compensatory MIRT conditions. The DETECT-based methods yielded high proportion correct, especially when correlations were .60 or smaller, data exhibited 30% or less complexity, and larger sample size. As the complexity increased and the sample size decreased, the performance typically diminished. As the complexity increased, it also became more difficult to label the resulting sets of items from DETECT in terms of the dimensions. DETECT was consistent in classification of simple items, but less consistent in classification of complex items. Out of the three NOHARM-based methods, χ2G/D and ALR generally outperformed RMSR. χ2G/D was more accurate when N = 500 and complexity levels were 30% or lower. As the number of items increased, ALR performance improved at correlation of .60 and 30% or less complexity. When the data followed a noncompensatory MIRT model, the NOHARM-based methods, specifically χ2G/D and ALR, were the most accurate of all five methods. The marginal proportions for labeling sets of items as dimension-like were typically low, suggesting that the methods generally failed to label two (three) sets of items as dimension-like in 2D (3D) noncompensatory situations. The DETECT-based methods were more consistent in classifying simple items across complexity levels, sample sizes, and correlations. However, as complexity and correlation levels increased the classification rates for all methods decreased. In most conditions, the DETECT-based methods classified complex items equally or more consistent than the NOHARM-based methods. In particular, as complexity, the number of items, and the true dimensionality increased, the DETECT-based methods were notably more consistent than any NOHARM-based method. Despite DETECT's consistency, when data follow a noncompensatory MIRT model, the NOHARM-based method should be preferred over the DETECT-based methods to assess dimensionality due to poor performance of DETECT in identifying the true dimensionality.
ContributorsSvetina, Dubravka (Author) / Levy, Roy (Thesis advisor) / Gorin, Joanna S. (Committee member) / Millsap, Roger (Committee member) / Arizona State University (Publisher)
Created2011
Description
Children with epilepsy represent a unique group of students who may require accommodations in school to be optimally successful. Therefore, it is important for teachers to understand the possible academic consequences epilepsy can have on a child. An important step in providing this information about epilepsy to teachers is understanding where they would prefer to acquire this information. The current study examined differences between teachers of differing ages, school levels and special education teaching status in their preferences for gaining information from parents and the internet. Contrary to expectations, older teachers (those 56 years of age and older) were no less likely that younger teachers to prefer information from the internet. As predicted, elementary school teachers were more likely than high school teachers to prefer information from parents. However, interestingly middle school teachers were also more likely to prefer information from parents than high school teachers. Lastly, contrary to hypothesized results, special education teachers were no more likely to prefer information from parents than non-special education colleagues. Limitations of this study, implications for practice and directions for future research are discussed.
ContributorsGay, Catherine (Author) / Wodrich, David (Thesis advisor) / Levy, Roy (Committee member) / Hart, Juliet (Committee member) / Arizona State University (Publisher)
Created2011
Description
ABSTRACT This study investigated the possibility of item parameter drift (IPD) in a calculus placement examination administered to approximately 3,000 students at a large university in the United States. A single form of the exam was administered continuously for a period of two years, possibly allowing later examinees to have prior knowledge of specific items on the exam. An analysis of IPD was conducted to explore evidence of possible item exposure. Two assumptions concerning items exposure were made: 1) item recall and item exposure are positively correlated, and 2) item exposure results in the items becoming easier over time. Special consideration was given to two contextual item characteristics: 1) item location within the test, specifically items at the beginning and end of the exam, and 2) the use of an associated diagram. The hypotheses stated that these item characteristics would make the items easier to recall and, therefore, more likely to be exposed, resulting in item drift. BILOG-MG 3 was used to calibrate the items and assess for IPD. No evidence was found to support the hypotheses that the items located at the beginning of the test or with an associated diagram drifted as a result of item exposure. Three items among the last ten on the exam drifted significantly and became easier, consistent with item exposure. However, in this study, the possible effects of item exposure could not be separated from the effects of other potential factors such as speededness, curriculum changes, better test preparation on the part of subsequent examinees, or guessing.
ContributorsKrause, Janet (Author) / Levy, Roy (Thesis advisor) / Thompson, Marilyn (Thesis advisor) / Gorin, Joanna (Committee member) / Arizona State University (Publisher)
Created2012
Description
The Culture-Language Interpretive Matrix (C-LIM) is a new tool hypothesized to help practitioners accurately determine whether students who are administered an IQ test are culturally and linguistically different from the normative comparison group (i.e., different) or culturally and linguistically similar to the normative comparison group and possibly have Specific Learning Disabilities (SLD) or other neurocognitive disabilities (i.e., disordered). Diagnostic utility statistics were used to test the ability of the Wechsler Intelligence Scales for Children-Fourth Edition (WISC-IV) C-LIM to accurately identify students from a referred sample of English language learners (Ells) (n = 86) for whom Spanish was the primary language spoken at home and a sample of students from the WISC-IV normative sample (n = 2,033) as either culturally and linguistically different from the WISC-IV normative sample or culturally and linguistically similar to the WISC-IV normative sample. WISC-IV scores from three paired comparison groups were analyzed using the Receiver Operating Characteristic (ROC) curve: (a) Ells with SLD and the WISC-IV normative sample, (b) Ells without SLD and the WISC-IV normative sample, and (c) Ells with SLD and Ells without SLD. Results of the ROC yielded Area Under the Curve (AUC) values that ranged between 0.51 and 0.53 for the comparison between Ells with SLD and the WISC-IV normative sample, AUC values that ranged between 0.48 and 0.53 for the comparison between Ells without SLD and the WISC-IV normative sample, and AUC values that ranged between 0.49 and 0.55 for the comparison between Ells with SLD and Ells without SLD. These values indicate that the C-LIM has low diagnostic accuracy in terms of differentiating between a sample of Ells and the WISC-IV normative sample. Current available evidence does not support use of the C-LIM in applied practice at this time.
ContributorsStyck, Kara M (Author) / Watkins, Marley W. (Thesis advisor) / Levy, Roy (Thesis advisor) / Balles, John (Committee member) / Arizona State University (Publisher)
Created2012