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- All Subjects: Educational tests & measurements
- Creators: Levy, Roy
Description
Institutions of higher education often tout that they are developing students to become lifelong learners. Evaluative efforts in this area have been presumably hindered by the lack of a uniform conceptualization of lifelong learning. Lifelong learning has been defined from institutional, economic, socio-cultural, and pedagogical perspectives, among others. This study presents the existing operational definitions and theories of lifelong learning in the context of higher education and synthesizes them to propose a unified model of college students' orientation toward lifelong learning. The model theorizes that orientation toward lifelong learning is a latent construct which manifests as students' likelihood to engage in four types of learning activities: formal work-related activities, informal work-related activities, formal personal interest activities, and informal personal interest activities. The Postsecondary Orientation toward Lifelong Learning scale (POLL) was developed and the validity of the resulting score interpretations was examined. The instrument was used to compare potential differences in orientation toward lifelong learning between freshmen and seniors. Exploratory factor analyses of the responses of 138 undergraduate college students in the pilot study data provided tentative support for the factor structure within each type of learning activity. Guttman's <λ>λ2 estimates of the learning activity subscales ranged from .78 to .85. Follow-up confirmatory factor analysis using structural equation modeling did not corroborate support for the hypothesized four-factor model using the main student sample data of 405 undergraduate students. Several alternative reflective factor structures were explored. A two-factor model representing factors for Instructing/Presenting and Reading learning activities produced marginal model-data fit and warrants further investigation. The summed POLL total scores had a relatively strong positive correlation with global interest in learning (.58), moderate positive correlations with civic engagement and participation (.38) and life satisfaction (.29), and a small positive correlation with social desirability (.15). The results of the main study do not provide support for the malleability of postsecondary students' orientation toward lifelong learning, as measured by the summed POLL scores. The difference between freshmen and seniors' average total POLL scores was not statistically significant and was negligible in size.
ContributorsArcuria, Phil (Author) / Thompson, Marilyn (Thesis advisor) / Green, Samuel (Committee member) / Levy, Roy (Committee member) / Arizona State University (Publisher)
Created2011
Description
The use of exams for classification purposes has become prevalent across many fields including professional assessment for employment screening and standards based testing in educational settings. Classification exams assign individuals to performance groups based on the comparison of their observed test scores to a pre-selected criterion (e.g. masters vs. nonmasters in dichotomous classification scenarios). The successful use of exams for classification purposes assumes at least minimal levels of accuracy of these classifications. Classification accuracy is an index that reflects the rate of correct classification of individuals into the same category which contains their true ability score. Traditional methods estimate classification accuracy via methods which assume that true scores follow a four-parameter beta-binomial distribution. Recent research suggests that Item Response Theory may be a preferable alternative framework for estimating examinees' true scores and may return more accurate classifications based on these scores. Researchers hypothesized that test length, the location of the cut score, the distribution of items, and the distribution of examinee ability would impact the recovery of accurate estimates of classification accuracy. The current simulation study manipulated these factors to assess their potential influence on classification accuracy. Observed classification as masters vs. nonmasters, true classification accuracy, estimated classification accuracy, BIAS, and RMSE were analyzed. In addition, Analysis of Variance tests were conducted to determine whether an interrelationship existed between levels of the four manipulated factors. Results showed small values of estimated classification accuracy and increased BIAS in accuracy estimates with few items, mismatched distributions of item difficulty and examinee ability, and extreme cut scores. A significant four-way interaction between manipulated variables was observed. In additional to interpretations of these findings and explanation of potential causes for the recovered values, recommendations that inform practice and avenues of future research are provided.
ContributorsKunze, Katie (Author) / Gorin, Joanna (Thesis advisor) / Levy, Roy (Thesis advisor) / Green, Samuel (Committee member) / Arizona State University (Publisher)
Created2013
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
The existing minima for sample size and test length recommendations for DIMTEST (750 examinees and 25 items) are tied to features of the procedure that are no longer in use. The current version of DIMTEST uses a bootstrapping procedure to remove bias from the test statistic and is packaged with a conditional covariance-based procedure called ATFIND for partitioning test items. Key factors such as sample size, test length, test structure, the correlation between dimensions, and strength of dependence were manipulated in a Monte Carlo study to assess the effectiveness of the current version of DIMTEST with fewer examinees and items. In addition, the DETECT program was also used to partition test items; a second feature of this study also compared the structure of test partitions obtained with ATFIND and DETECT in a number of ways. With some exceptions, the performance of DIMTEST was quite conservative in unidimensional conditions. The performance of DIMTEST in multidimensional conditions depended on each of the manipulated factors, and did suggest that the minima of sample size and test length can be made lower for some conditions. In terms of partitioning test items in unidimensional conditions, DETECT tended to produce longer assessment subtests than ATFIND in turn yielding different test partitions. In multidimensional conditions, test partitions became more similar and were more accurate with increased sample size, for factorially simple data, greater strength of dependence, and a decreased correlation between dimensions. Recommendations for sample size and test length minima are provided along with suggestions for future research.
ContributorsFay, Derek (Author) / Levy, Roy (Thesis advisor) / Green, Samuel (Committee member) / Gorin, Joanna (Committee member) / Arizona State University (Publisher)
Created2012
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
Description
Dynamic Bayesian networks (DBNs; Reye, 2004) are a promising tool for modeling student proficiency under rich measurement scenarios (Reichenberg, in press). These scenarios often present assessment conditions far more complex than what is seen with more traditional assessments and require assessment arguments and psychometric models capable of integrating those complexities. Unfortunately, DBNs remain understudied and their psychometric properties relatively unknown. If the apparent strengths of DBNs are to be leveraged, then the body of literature surrounding their properties and use needs to be expanded upon. To this end, the current work aimed at exploring the properties of DBNs under a variety of realistic psychometric conditions. A two-phase Monte Carlo simulation study was conducted in order to evaluate parameter recovery for DBNs using maximum likelihood estimation with the Netica software package. Phase 1 included a limited number of conditions and was exploratory in nature while Phase 2 included a larger and more targeted complement of conditions. Manipulated factors included sample size, measurement quality, test length, the number of measurement occasions. Results suggested that measurement quality has the most prominent impact on estimation quality with more distinct performance categories yielding better estimation. While increasing sample size tended to improve estimation, there were a limited number of conditions under which greater samples size led to more estimation bias. An exploration of this phenomenon is included. From a practical perspective, parameter recovery appeared to be sufficient with samples as low as N = 400 as long as measurement quality was not poor and at least three items were present at each measurement occasion. Tests consisting of only a single item required exceptional measurement quality in order to adequately recover model parameters. The study was somewhat limited due to potentially software-specific issues as well as a non-comprehensive collection of experimental conditions. Further research should replicate and, potentially expand the current work using other software packages including exploring alternate estimation methods (e.g., Markov chain Monte Carlo).
ContributorsReichenberg, Raymond E (Author) / Levy, Roy (Thesis advisor) / Eggum-Wilkens, Natalie (Thesis advisor) / Iida, Masumi (Committee member) / DeLay, Dawn (Committee member) / Arizona State University (Publisher)
Created2018
Description
Investigation of measurement invariance (MI) commonly assumes correct specification of dimensionality across multiple groups. Although research shows that violation of the dimensionality assumption can cause bias in model parameter estimation for single-group analyses, little research on this issue has been conducted for multiple-group analyses. This study explored the effects of mismatch in dimensionality between data and analysis models with multiple-group analyses at the population and sample levels. Datasets were generated using a bifactor model with different factor structures and were analyzed with bifactor and single-factor models to assess misspecification effects on assessments of MI and latent mean differences. As baseline models, the bifactor models fit data well and had minimal bias in latent mean estimation. However, the low convergence rates of fitting bifactor models to data with complex structures and small sample sizes caused concern. On the other hand, effects of fitting the misspecified single-factor models on the assessments of MI and latent means differed by the bifactor structures underlying data. For data following one general factor and one group factor affecting a small set of indicators, the effects of ignoring the group factor in analysis models on the tests of MI and latent mean differences were mild. In contrast, for data following one general factor and several group factors, oversimplifications of analysis models can lead to inaccurate conclusions regarding MI assessment and latent mean estimation.
ContributorsXu, Yuning (Author) / Green, Samuel (Thesis advisor) / Levy, Roy (Committee member) / Thompson, Marilyn (Committee member) / Arizona State University (Publisher)
Created2018
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
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